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modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags list	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
obranzell/imdb-sentiment-distilbert	obranzell	2025-09-22T12:15:17Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-09-22T10:53:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
MattBou00/llama-3-2-1b-detox_v1f_RRETRT_Again_ROUND2-checkpoint-epoch-20	MattBou00	2025-09-22T12:14:43Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-09-22T12:13:45Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_12-11-21/checkpoints/checkpoint-epoch-20") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_12-11-21/checkpoints/checkpoint-epoch-20") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_12-11-21/checkpoints/checkpoint-epoch-20") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
maigonis/LLaDA-MoE-7B-A1B-Instruct-Q4_K_M-GGUF	maigonis	2025-09-22T12:14:26Z	0	0	transformers	[ "transformers", "gguf", "dllm", "diffusion", "llm", "text_generation", "llama-cpp", "gguf-my-repo", "base_model:inclusionAI/LLaDA-MoE-7B-A1B-Instruct", "base_model:quantized:inclusionAI/LLaDA-MoE-7B-A1B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-22T12:14:03Z	--- license: apache-2.0 tags: - dllm - diffusion - llm - text_generation - llama-cpp - gguf-my-repo library_name: transformers base_model: inclusionAI/LLaDA-MoE-7B-A1B-Instruct --- # maigonis/LLaDA-MoE-7B-A1B-Instruct-Q4_K_M-GGUF This model was converted to GGUF format from [`inclusionAI/LLaDA-MoE-7B-A1B-Instruct`](https://huggingface.co/inclusionAI/LLaDA-MoE-7B-A1B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/inclusionAI/LLaDA-MoE-7B-A1B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo maigonis/LLaDA-MoE-7B-A1B-Instruct-Q4_K_M-GGUF --hf-file llada-moe-7b-a1b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo maigonis/LLaDA-MoE-7B-A1B-Instruct-Q4_K_M-GGUF --hf-file llada-moe-7b-a1b-instruct-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo maigonis/LLaDA-MoE-7B-A1B-Instruct-Q4_K_M-GGUF --hf-file llada-moe-7b-a1b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo maigonis/LLaDA-MoE-7B-A1B-Instruct-Q4_K_M-GGUF --hf-file llada-moe-7b-a1b-instruct-q4_k_m.gguf -c 2048 ```
veeravel/paraphraser	veeravel	2025-09-22T12:11:15Z	0	0	null	[ "safetensors", "t5", "license:apache-2.0", "region:us" ]	null	2025-09-22T11:56:46Z	--- license: apache-2.0 ---
poolkiltzn/blockassist-bc-vigilant_alert_tuna_1758542967	poolkiltzn	2025-09-22T12:10:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "vigilant alert tuna", "arxiv:2504.07091", "region:us" ]	null	2025-09-22T12:10:34Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - vigilant alert tuna --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
PrimeIntellect/Qwen3-30B-A3B-Base-Fast	PrimeIntellect	2025-09-22T12:10:11Z	0	0	transformers	[ "transformers", "safetensors", "qwen3_moe", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T12:08:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Aeronicr/LPWAN	Aeronicr	2025-09-22T12:08:47Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-09-22T12:08:33Z	--- base_model: unsloth/llama-3-8b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Aeronicr - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
abandonedmonk/TinyLlama-1.1B-NL2SH-Alpaca-v1	abandonedmonk	2025-09-22T12:04:48Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "trl", "sft", "unsloth", "text-generation", "conversational", "en", "dataset:abandonedmonk/NL2SH-ALPACA", "base_model:unsloth/tinyllama-chat", "base_model:adapter:unsloth/tinyllama-chat", "license:mit", "region:us" ]	text-generation	2025-09-22T09:50:07Z	--- base_model: unsloth/tinyllama-chat library_name: peft model_name: outputs tags: - generated_from_trainer - trl - sft - unsloth licence: license license: mit datasets: - abandonedmonk/NL2SH-ALPACA language: - en new_version: TinyLlama/TinyLlama-1.1B-Chat-v1.0 pipeline_tag: text-generation --- # Model Card for TinyLlama-1.1B-NL2SH-Alpaca This model is a fine-tuned version of [unsloth/tinyllama-chat](https://huggingface.co/unsloth/tinyllama-chat). It has been fine-tuned on the NL2SH-Alpaca dataset for converting natural language instructions into bash commands. The model outputs one bash command per instruction, even if multiple alternatives exist in the training dataset. --- ## Quick start ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch # Load the model and tokenizer model_name = "abandonedmonk/TinyLlama-1.1B-NL2SH-Alpaca" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name).to("cuda") # Inference helper def generate_command(model, tokenizer, instruction, inp=""): # build prompt in Alpaca-style prompt = f"""Instruction: {instruction} Input: {inp} Response: """ inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate( inputs, max_new_tokens=100, do_sample=False, # greedy decoding temperature=0.0, num_return_sequences=1 ) generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) # Extract the first line (command) after "Response:" # If you want to keep all the commands, just simply return 'generated_text' instead of 'response' response = generated_text.strip().split("Response:")[-1].strip().split('\n')[0] return response # Example usage instruction = "Rename all files with .andnav extension to .tile" bash_cmd = generate_command(model, tokenizer, instruction) print("Generated bash command:", bash_cmd) ```` --- ## Training procedure This model was fine-tuned using Supervised Fine-Tuning (SFT)** on the NL2SH-Alpaca dataset, which contains natural language instructions paired with shell commands. * Base model: `unsloth/tinyllama-chat` * Dataset: `abandonedmonk/NL2SH-ALPACA` * Frameworks: PEFT, Transformers, Unsloth * Number of epochs: 3 * Batch size / seq length: 4 --- ## Citations ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ``` --- ## License This model is released under the MIT License --- ## Contributors / Maintainers - Anshuman Jena – fine-tuner, and maintainer of this model 🐸 ## Notes * This model is designed for English instructions only. * Outputs one command per instruction; alternative commands can be manually handled if desired. * For reproducibility, set the same `seed` (3407) during fine-tuning.
TAUR-dev/M-BASELINE_gtp4o_BOLT-sft	TAUR-dev	2025-09-22T12:04:46Z	0	0	null	[ "safetensors", "qwen2", "region:us" ]	null	2025-09-22T12:04:16Z	# M-BASELINE_gtp4o_BOLT-sft This model was created as part of the BASELINE_gtp4o_BOLT experiment using the SkillFactory experiment management system. ## Model Details - Training Method: LLaMAFactory SFT (Supervised Fine-Tuning) - Stage Name: sft - Experiment: BASELINE_gtp4o_BOLT ## Training Configuration {"model_name_or_path": "Qwen/Qwen2.5-1.5B-Instruct", "trust_remote_code": true, "stage": "sft", "do_train": true, "finetuning_type": "full", "deepspeed": "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/examples/deepspeed/ds_z2_config.json", "dataset": "TAUR_dev__D_SFT_C_BASELINE_gtp4o_BOLT_sft_data__sft_train", "template": "qwen", "cutoff_len": 16384, "max_samples": 1000000, "overwrite_cache": true, "preprocessing_num_workers": 1, "dataloader_num_workers": 0, "disable_tqdm": false, "output_dir": "/scratch/10416/zaynesprague/skill_inject_outputs/sf_experiments/BASELINE_gpt4o_BOLT/llamafactory/checkpoints", "logging_steps": 10, "save_steps": 100000, "plot_loss": true, "overwrite_output_dir": true, "per_device_train_batch_size": 1, "gradient_accumulation_steps": 1, "learning_rate": 1e-06, "num_train_epochs": 1, "lr_scheduler_type": "cosine", "warmup_ratio": 0.05, "weight_decay": 0.0001, "adam_beta1": 0.9, "adam_beta2": 0.95, "bf16": true, "ddp_timeout": 180000000, "gradient_checkpointing": true, "save_only_model": true, "enable_masked_ranges": false, "save_strategy": "steps", "save_total_limit": 5, "sf_tracker_dataset_id": "TAUR-dev/D-ExpTracker__BASELINE_gtp4o_BOLT__v1", "sf_eval_before_training": false, "sf_wandb_project": "BASELINE_gtp4o_BOLT_sft", "sf_eval_steps": null, "run_name": "BASELINE_gtp4o_BOLT_sft"} ## Experiment Tracking 🔗 View complete experiment details: [Experiment Tracker Dataset](https://huggingface.co/datasets/TAUR-dev/D-ExpTracker__BASELINE_gtp4o_BOLT__v1) ## Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TAUR-dev/M-BASELINE_gtp4o_BOLT-sft") model = AutoModelForCausalLM.from_pretrained("TAUR-dev/M-BASELINE_gtp4o_BOLT-sft") ```
aamijar/Llama-2-7b-hf-dora-r8-boolq-epochs1	aamijar	2025-09-22T12:02:21Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-22T12:02:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Nabbers1999/Llama-4-Scout-17B-16E-Instruct-abliterated-v2-bnb-4bit	Nabbers1999	2025-09-22T12:02:10Z	0	0	transformers	[ "transformers", "safetensors", "llama4_text", "text-generation", "quantized", "4bit", "bitsandbytes", "generated_from_original", "conversational", "base_model:jiangchengchengNLP/Llama-4-Scout-17B-16E-Instruct-abliterated-v2", "base_model:quantized:jiangchengchengNLP/Llama-4-Scout-17B-16E-Instruct-abliterated-v2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "region:us" ]	text-generation	2025-09-22T11:50:37Z	--- license: apache-2.0 base_model: jiangchengchengNLP/Llama-4-Scout-17B-16E-Instruct-abliterated-v2 tags: - quantized - 4bit - bitsandbytes - generated_from_original library_name: transformers --- # Nabbers1999/Llama-4-Scout-17B-16E-Instruct-abliterated-v2-bnb-4bit This is a 4-bit quantized version of [jiangchengchengNLP/Llama-4-Scout-17B-16E-Instruct-abliterated-v2](https://huggingface.co/jiangchengchengNLP/Llama-4-Scout-17B-16E-Instruct-abliterated-v2) using BitsAndBytes. ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained( "Nabbers1999/Llama-4-Scout-17B-16E-Instruct-abliterated-v2-bnb-4bit", device_map="auto", trust_remote_code=True ) ```
tarundachepally/EGL_granite_8b_linear_full-Q4_K_S-GGUF	tarundachepally	2025-09-22T12:00:48Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "base_model:tarundachepally/EGL_granite_8b_linear_full", "base_model:quantized:tarundachepally/EGL_granite_8b_linear_full", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-22T12:00:27Z	--- library_name: transformers tags: - llama-cpp - gguf-my-repo base_model: tarundachepally/EGL_granite_8b_linear_full --- # tarundachepally/EGL_granite_8b_linear_full-Q4_K_S-GGUF This model was converted to GGUF format from [`tarundachepally/EGL_granite_8b_linear_full`](https://huggingface.co/tarundachepally/EGL_granite_8b_linear_full) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/tarundachepally/EGL_granite_8b_linear_full) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo tarundachepally/EGL_granite_8b_linear_full-Q4_K_S-GGUF --hf-file egl_granite_8b_linear_full-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo tarundachepally/EGL_granite_8b_linear_full-Q4_K_S-GGUF --hf-file egl_granite_8b_linear_full-q4_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo tarundachepally/EGL_granite_8b_linear_full-Q4_K_S-GGUF --hf-file egl_granite_8b_linear_full-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo tarundachepally/EGL_granite_8b_linear_full-Q4_K_S-GGUF --hf-file egl_granite_8b_linear_full-q4_k_s.gguf -c 2048 ```
MattBou00/llama-3-2-1b-detox_v1f_RRETRT_Again_ROUND1-checkpoint-epoch-80	MattBou00	2025-09-22T11:59:57Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-09-22T11:59:02Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_11-47-04/checkpoints/checkpoint-epoch-80") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_11-47-04/checkpoints/checkpoint-epoch-80") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_11-47-04/checkpoints/checkpoint-epoch-80") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
0701phantom/all-t5-base-v1-contriever2fiqa	0701phantom	2025-09-22T11:57:28Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-09-22T11:57:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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MattBou00/llama-3-2-1b-detox_v1f_RRETRT_Again_ROUND1-checkpoint-epoch-60	MattBou00	2025-09-22T11:56:47Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-09-22T11:55:51Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_11-47-04/checkpoints/checkpoint-epoch-60") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_11-47-04/checkpoints/checkpoint-epoch-60") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_11-47-04/checkpoints/checkpoint-epoch-60") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
AlekseyCalvin/LYRICAL_MT_ru2en_3a13_Yandex8b_emaBeta05to098	AlekseyCalvin	2025-09-22T11:56:22Z	0	0	null	[ "safetensors", "llama", "ru", "en", "base_model:yandex/YandexGPT-5-Lite-8B-pretrain", "base_model:finetune:yandex/YandexGPT-5-Lite-8B-pretrain", "license:other", "region:us" ]	null	2025-09-22T11:47:36Z	--- license: other license_name: yandexgpt-5-lite-8b license_link: LICENSE language: - ru - en base_model: - yandex/YandexGPT-5-Lite-8B-pretrain --- # YandexGPT-5-Lite-Instruct Instruct-версия большой языковой модели YandexGPT 5 Lite на 8B параметров с длиной контекста 32k токенов. Также в отдельном [репозитории](https://huggingface.co/yandex/YandexGPT-5-Lite-8B-instruct-GGUF) опубликована квантизованная версия модели в формате GGUF. Обучена на базе [YandexGPT 5 Lite Pretrain](https://huggingface.co/yandex/YandexGPT-5-Lite-8B-pretrain), без использования весов каких-либо сторонних моделей. Алайнмент Lite-версии совпадает с алайнментом YandexGPT 5 Pro и состоит из этапов SFT и RLHF (более подробно о них — в [статье](https://habr.com/ru/companies/yandex/articles/885218/) на Хабре). Задавайте вопросы в discussions. ## Бенчмарки По результатам международных бенчмарков и их адаптаций для русского языка, YandexGPT 5 Lite вплотную приблизилась к аналогам (Llama-3.1-8B-instruct и Qwen-2.5-7B-instruct) и превосходит их в ряде сценариев, в том числе — в знании русской культуры и фактов. <img src="https://habrastorage.org/r/w1560/getpro/habr/upload_files/6b5/eb4/9ea/6b5eb49ea757bc124c938717b21f1cf7.png" alt="Таблица бенчмарков" width="100%"/> MMLU — 5-shot, все остальные бенчмарки — 0-shot. ## Как использовать Модель можно запустить через HF Transformers: ```python from transformers import AutoModelForCausalLM, AutoTokenizer MODEL_NAME = "yandex/YandexGPT-5-Lite-8B-instruct" tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) model = AutoModelForCausalLM.from_pretrained( MODEL_NAME, device_map="cuda", torch_dtype="auto", ) messages = [{"role": "user", "content": "Для чего нужна токенизация?"}] input_ids = tokenizer.apply_chat_template( messages, tokenize=True, return_tensors="pt" ).to("cuda") outputs = model.generate(input_ids, max_new_tokens=1024) print(tokenizer.decode(outputs[0][input_ids.size(1) :], skip_special_tokens=True)) ``` Или через vLLM: ```python from vllm import LLM, SamplingParams from transformers import AutoTokenizer MODEL_NAME = "yandex/YandexGPT-5-Lite-8B-instruct" sampling_params = SamplingParams( temperature=0.3, top_p=0.9, max_tokens=1024, ) tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) llm = LLM( MODEL_NAME, tensor_parallel_size=1, ) messages = [{"role": "user", "content": "В чем смысл жизни?"}] input_ids = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True )[1:] # remove bos text = tokenizer.decode(input_ids) outputs = llm.generate(text, use_tqdm=False, sampling_params=sampling_params) print(tokenizer.decode(outputs[0].outputs[0].token_ids, skip_special_tokens=True)) ``` Для запуска в llama.cpp и ollama можно воспользоваться нашей квантизованной моделью, которая выложена в репозитории [YandexGPT-5-Lite-8B-instruct-GGUF](https://huggingface.co/yandex/YandexGPT-5-Lite-8B-instruct-GGUF). ## Особенности токенизации Для полного соответствия токенизации мы рекомендуем пользоваться оригинальным [sentencepiece](https://github.com/google/sentencepiece) — файл токенизатора лежит в папке `original_tokenizer`. В нашей инфраструктуре каждую реплику диалога мы токенизируем отдельно. Из-за этого, в частности, появляется пробел в начале каждой реплики. Также `\n` токены мы заменяем на `[NL]`, это можно сделать с помощью `text.replace("\n", "[NL]")` перед токенизацией. ## Особенности шаблона Мы используем нестандартный шаблон диалога — модель обучена генерировать только одну реплику после последовательности `Ассистент:[SEP]`, завершая её токеном `</s>`. При этом диалог в промпте может быть любой длины. Это приводит к тому, что в интерактивном режиме модель может выдавать результаты, отличающиеся от вызова модели в режиме генерации на фиксированном диалоге. Поэтому мы рекомендуем использовать интерактивный режим только для ознакомления с моделью.
montenegrolu93/Qwen3-0.6B-Gensyn-Swarm-lumbering_gregarious_rabbit	montenegrolu93	2025-09-22T11:56:16Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am lumbering_gregarious_rabbit", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T06:55:59Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am lumbering_gregarious_rabbit --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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OCHone/Qwen3-0.6B-Gensyn-Swarm-powerful_prehistoric_lizard	OCHone	2025-09-22T11:54:39Z	120	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am powerful_prehistoric_lizard", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-09T10:17:07Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am powerful_prehistoric_lizard --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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tarundachepally/EGL_granite_8b_linear_full	tarundachepally	2025-09-22T11:54:16Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T11:44:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mradermacher/DPO_ablations_qwen_ultrafeedback_pref_filter_only-GGUF	mradermacher	2025-09-22T11:54:05Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:ketchup123/DPO_ablations_qwen_ultrafeedback_pref_filter_only", "base_model:quantized:ketchup123/DPO_ablations_qwen_ultrafeedback_pref_filter_only", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-22T11:06:12Z	--- base_model: ketchup123/DPO_ablations_qwen_ultrafeedback_pref_filter_only language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/ketchup123/DPO_ablations_qwen_ultrafeedback_pref_filter_only <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#DPO_ablations_qwen_ultrafeedback_pref_filter_only-GGUF).* weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/DPO_ablations_qwen_ultrafeedback_pref_filter_only-GGUF/resolve/main/DPO_ablations_qwen_ultrafeedback_pref_filter_only.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/DPO_ablations_qwen_ultrafeedback_pref_filter_only-GGUF/resolve/main/DPO_ablations_qwen_ultrafeedback_pref_filter_only.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/DPO_ablations_qwen_ultrafeedback_pref_filter_only-GGUF/resolve/main/DPO_ablations_qwen_ultrafeedback_pref_filter_only.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/DPO_ablations_qwen_ultrafeedback_pref_filter_only-GGUF/resolve/main/DPO_ablations_qwen_ultrafeedback_pref_filter_only.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DPO_ablations_qwen_ultrafeedback_pref_filter_only-GGUF/resolve/main/DPO_ablations_qwen_ultrafeedback_pref_filter_only.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/DPO_ablations_qwen_ultrafeedback_pref_filter_only-GGUF/resolve/main/DPO_ablations_qwen_ultrafeedback_pref_filter_only.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DPO_ablations_qwen_ultrafeedback_pref_filter_only-GGUF/resolve/main/DPO_ablations_qwen_ultrafeedback_pref_filter_only.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DPO_ablations_qwen_ultrafeedback_pref_filter_only-GGUF/resolve/main/DPO_ablations_qwen_ultrafeedback_pref_filter_only.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/DPO_ablations_qwen_ultrafeedback_pref_filter_only-GGUF/resolve/main/DPO_ablations_qwen_ultrafeedback_pref_filter_only.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/DPO_ablations_qwen_ultrafeedback_pref_filter_only-GGUF/resolve/main/DPO_ablations_qwen_ultrafeedback_pref_filter_only.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/DPO_ablations_qwen_ultrafeedback_pref_filter_only-GGUF/resolve/main/DPO_ablations_qwen_ultrafeedback_pref_filter_only.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/DPO_ablations_qwen_ultrafeedback_pref_filter_only-GGUF/resolve/main/DPO_ablations_qwen_ultrafeedback_pref_filter_only.f16.gguf) \| f16 \| 15.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
MattBou00/llama-3-2-1b-detox_v1f_RRETRT_Again_ROUND1-checkpoint-epoch-40	MattBou00	2025-09-22T11:53:38Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-09-22T11:52:37Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_11-47-04/checkpoints/checkpoint-epoch-40") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_11-47-04/checkpoints/checkpoint-epoch-40") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_11-47-04/checkpoints/checkpoint-epoch-40") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
kiarashQ/whisper-small-fa	kiarashQ	2025-09-22T11:52:38Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-09-22T08:41:26Z	--- library_name: transformers license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-small-fa results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-small-fa This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1537 - Wer: 19.2460 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.2216 \| 0.1935 \| 1000 \| 0.2209 \| 28.1653 \| \| 0.1947 \| 0.3871 \| 2000 \| 0.1808 \| 24.9731 \| \| 0.1465 \| 0.5806 \| 3000 \| 0.1621 \| 20.7613 \| \| 0.129 \| 0.7741 \| 4000 \| 0.1537 \| 19.2460 \| ### Framework versions - Transformers 4.56.2 - Pytorch 2.8.0+cu128 - Datasets 4.1.1 - Tokenizers 0.22.1
afiyarah/embedding-ins-make	afiyarah	2025-09-22T11:49:40Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "gemma3_text", "sentence-similarity", "feature-extraction", "dense", "generated_from_trainer", "dataset_size:9431", "loss:CosineSimilarityLoss", "arxiv:1908.10084", "base_model:google/embeddinggemma-300m", "base_model:finetune:google/embeddinggemma-300m", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-09-22T11:49:20Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:9431 - loss:CosineSimilarityLoss base_model: google/embeddinggemma-300m widget: - source_sentence: 'In the car insurance domain, represent this car make entity in arabic for entity similarity matching: دي سوتو' sentences: - 'In the car insurance domain, represent this car make entity in arabic for entity similarity matching: هايتسو' - 'In the car insurance domain, represent this car make entity in arabic for entity similarity matching: ربيلكااوبرا' - 'In the car insurance domain, represent this car make entity in arabic for entity similarity matching: ديهاتسو' - source_sentence: 'In the car insurance domain, represent this car make entity in arabic for entity similarity matching: سي آر إس' sentences: - 'In the car insurance domain, represent this car make entity in arabic for entity similarity matching: داسيا' - 'In the car insurance domain, represent this car make entity in arabic for entity similarity matching: كاوساكي' - 'In the car insurance domain, represent this car make entity in arabic for entity similarity matching: كيوتي' - source_sentence: 'In the car insurance domain, represent this car make entity in arabic for entity similarity matching: آمي' sentences: - 'In the car insurance domain, represent this car make entity in arabic for entity similarity matching: كراز' - 'In the car insurance domain, represent this car make entity in arabic for entity similarity matching: سي ام سي دي' - 'In the car insurance domain, represent this car make entity in arabic for entity similarity matching: شميت' - source_sentence: 'In the car insurance domain, represent this car make entity in english for entity similarity matching: checker' sentences: - 'In the car insurance domain, represent this car make entity in english for entity similarity matching: tiger' - 'In the car insurance domain, represent this car make entity in arabic for entity similarity matching: جاك' - 'In the car insurance domain, represent this car make entity in arabic for entity similarity matching: فوسو' - source_sentence: 'In the car insurance domain, represent this car make entity in arabic for entity similarity matching: جي إي سي' sentences: - 'In the car insurance domain, represent this car make entity in arabic for entity similarity matching: ايدزل' - 'In the car insurance domain, represent this car make entity in arabic for entity similarity matching: واكر' - 'In the car insurance domain, represent this car make entity in arabic for entity similarity matching: سالك' pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine model-index: - name: SentenceTransformer based on google/embeddinggemma-300m results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: insurance val type: insurance-val metrics: - type: pearson_cosine value: 0.8319304484319612 name: Pearson Cosine - type: spearman_cosine value: 0.6431780348935766 name: Spearman Cosine --- # SentenceTransformer based on google/embeddinggemma-300m This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) <!-- at revision c5cfa06e5e282a820e85d57f7fb053207494f41d --> - Maximum Sequence Length: 2048 tokens - Output Dimensionality: 768 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 2048, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'}) (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'}) (3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'}) (4): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference queries = [ "In the car insurance domain, represent this car make entity in arabic for entity similarity matching: \u062c\u064a \u0625\u064a \u0633\u064a", ] documents = [ 'In the car insurance domain, represent this car make entity in arabic for entity similarity matching: سالك', 'In the car insurance domain, represent this car make entity in arabic for entity similarity matching: ايدزل', 'In the car insurance domain, represent this car make entity in arabic for entity similarity matching: واكر', ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) print(query_embeddings.shape, document_embeddings.shape) # [1, 768] [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) # tensor([[0.5667, 0.5606, 0.5776]]) ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `insurance-val` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| pearson_cosine \| 0.8319 \| \| spearman_cosine \| 0.6432 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 9,431 training samples * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> * Approximate statistics based on the first 1000 samples: \| \| sentence_0 \| sentence_1 \| label \| \|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:---------------------------------------------------------------\| \| type \| string \| string \| float \| \| details \| <ul><li>min: 21 tokens</li><li>mean: 23.43 tokens</li><li>max: 27 tokens</li></ul> \| <ul><li>min: 21 tokens</li><li>mean: 22.97 tokens</li><li>max: 27 tokens</li></ul> \| <ul><li>min: 0.1</li><li>mean: 0.28</li><li>max: 1.0</li></ul> \| * Samples: \| sentence_0 \| sentence_1 \| label \| \|:-------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------\|:---------------------------------\| \| <code>In the car insurance domain, represent this car make entity in arabic for entity similarity matching: إل تي إم جي</code> \| <code>In the car insurance domain, represent this car make entity in arabic for entity similarity matching: بوماج</code> \| <code>0.19999999999999998</code> \| \| <code>In the car insurance domain, represent this car make entity in arabic for entity similarity matching: يو دي</code> \| <code>In the car insurance domain, represent this car make entity in arabic for entity similarity matching: لادا</code> \| <code>0.19999999999999998</code> \| \| <code>In the car insurance domain, represent this car make entity in arabic for entity similarity matching: إنساين</code> \| <code>In the car insurance domain, represent this car make entity in arabic for entity similarity matching: شانسي</code> \| <code>0.4</code> \| * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 3 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `parallelism_config`: None - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `hub_revision`: None - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `liger_kernel_config`: None - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin - `router_mapping`: {} - `learning_rate_mapping`: {} </details> ### Training Logs \| Epoch \| Step \| Training Loss \| insurance-val_spearman_cosine \| \|:------:\|:----:\|:-------------:\|:-----------------------------:\| \| 0.0983 \| 58 \| - \| 0.4972 \| \| 0.1966 \| 116 \| - \| 0.5621 \| \| 0.2949 \| 174 \| - \| 0.5636 \| \| 0.3932 \| 232 \| - \| 0.5194 \| \| 0.4915 \| 290 \| - \| 0.6253 \| \| 0.5898 \| 348 \| - \| 0.6236 \| \| 0.6881 \| 406 \| - \| 0.5702 \| \| 0.7864 \| 464 \| - \| 0.6208 \| \| 0.8475 \| 500 \| 0.0209 \| - \| \| 0.8847 \| 522 \| - \| 0.6018 \| \| 0.9831 \| 580 \| - \| 0.5994 \| \| 1.0 \| 590 \| - \| 0.6048 \| \| 1.0814 \| 638 \| - \| 0.6002 \| \| 1.1797 \| 696 \| - \| 0.6083 \| \| 1.2780 \| 754 \| - \| 0.5940 \| \| 1.3763 \| 812 \| - \| 0.6044 \| \| 1.4746 \| 870 \| - \| 0.6248 \| \| 1.5729 \| 928 \| - \| 0.6432 \| ### Framework Versions - Python: 3.12.11 - Sentence Transformers: 5.1.0 - Transformers: 4.56.1 - PyTorch: 2.8.0+cu126 - Accelerate: 1.10.1 - Datasets: 4.0.0 - Tokenizers: 0.22.0 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
its-zion-18/music-text-distilbert-predictor	its-zion-18	2025-09-22T11:42:19Z	34	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:samder03/2025-24679-text-dataset", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-09-20T19:04:44Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: music-text-distilbert-predictor results: [] datasets: - samder03/2025-24679-text-dataset --- # DistilBERT-based Music Era Classifier This repository contains a fine-tuned text classification model based on distilbert-base-uncased. The model is designed to classify short text descriptions of eras in classical music into one of four historical musical eras: 0, 1, 2, and 3. # Model Architecture & Training The model was trained using the Hugging Face Trainer API. It utilizes a distilbert-base-uncased pre-trained model with a classification head on top. - Tokenizer: AutoTokenizer.from_pretrained("distilbert-base-uncased") - Model: AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased") - Training Arguments: Learning Rate: 2×10−5 - Epochs: 5 - Batch Size: 8 - Evaluation Strategy: Per epoch - Metric: accuracy - Optimizer: AdamW # music-text-distilbert-predictor This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the samder03/2025-24679-text-dataset. It achieves the following results on the evaluation set: - Loss: 0.0495 - Accuracy: 1.0 - F1: 1.0 - Precision: 1.0 - Recall: 1.0 ## Limitations This model's primary limitations are: Numerical Labels: The model outputs a numerical label (0, 1, 2, or 3). An external lookup table is required to map these numbers to their corresponding musical era names. Language & Casing: As the model is based on distilbert-base-uncased, it is designed for English-language text and does not differentiate between uppercase and lowercase letters. It will not work for other languages. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.6387 \| 1.0 \| 80 \| 0.5111 \| 0.9563 \| 0.9562 \| 0.9574 \| 0.9563 \| \| 0.0833 \| 2.0 \| 160 \| 0.1052 \| 0.9812 \| 0.9812 \| 0.9814 \| 0.9812 \| \| 0.0221 \| 3.0 \| 240 \| 0.0585 \| 0.9812 \| 0.9812 \| 0.9814 \| 0.9812 \| \| 0.0122 \| 4.0 \| 320 \| 0.0629 \| 0.9812 \| 0.9812 \| 0.9814 \| 0.9812 \| \| 0.011 \| 5.0 \| 400 \| 0.0614 \| 0.9812 \| 0.9812 \| 0.9814 \| 0.9812 \| ### Framework versions - Transformers 4.56.1 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.22.0 # Potential Errors There could be a problem with dataleakage because the accuracy is at 100% Because the model has already been trained on the augmented data, which is just a derivative of the original data, the original dataset isn't a true holdout set. The model is essentially being tested on data that it has already seen and, in some cases, memorized.
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transition-all dark:from-gray-900 dark:to-gray-950"><div class="mb-2 inline-block rounded-lg border px-2 py-1 font-mono text-xs leading-none">metadata</div> <pre><!-- HTML_TAG_START --><span class="hljs-attr">datasets:</span> <span class="hljs-bullet">-</span> <span class="hljs-string">TigreGotico/tts-train-synthetic-miro_ar-SA</span> <span class="hljs-attr">language:</span> <span class="hljs-bullet">-</span> <span class="hljs-string">ar</span> <span class="hljs-attr">pipeline_tag:</span> <span class="hljs-string">text-to-speech</span> <!-- HTML_TAG_END --></pre></div> <!-- HTML_TAG_START --><!-- HTML_TAG_END --></div> </div></section></div></main> </div> <script> import("\/front\/build\/kube-0e1a2e5\/index.js"); window.moonSha = "kube-0e1a2e5\/"; window.__hf_deferred = {}; </script> <!-- Stripe --> <script> if (["hf.co", "huggingface.co"].includes(window.location.hostname)) { const script = document.createElement("script"); script.src = "https://js.stripe.com/v3/"; script.async = true; document.head.appendChild(script); } </script> </body> </html> See https://huggingface.co/OpenVoiceOS/phoonnx_ar-SA_miro_espeak and https://github.com/OHF-Voice/piper1-gpl/discussions/27 # License See also https://github.com/k2-fsa/sherpa-onnx/pull/2480 This model is licensed under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). - ✅ Always free for regular (non-commercial) users - ❌ Commercial use is not allowed at this time - 🔄 The author may relax the restrictions in the future (e.g., allow commercial use), but will not make them stricter Important: You must include this license when redistributing the model or any derivatives.
caphe/paa11	caphe	2025-09-22T11:36:28Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-09-22T11:33:46Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
jnwulff/Qwen2-0.5B-GRPO-test	jnwulff	2025-09-22T11:35:58Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:AI-MO/NuminaMath-TIR", "arxiv:2402.03300", "base_model:Qwen/Qwen2-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2-0.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-09-22T11:20:03Z	--- base_model: Qwen/Qwen2-0.5B-Instruct datasets: AI-MO/NuminaMath-TIR library_name: transformers model_name: Qwen2-0.5B-GRPO-test tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen2-0.5B-GRPO-test This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the [AI-MO/NuminaMath-TIR](https://huggingface.co/datasets/AI-MO/NuminaMath-TIR) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="jnwulff/Qwen2-0.5B-GRPO-test", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.23.0 - Transformers: 4.56.1 - Pytorch: 2.8.0+cu126 - Datasets: 4.0.0 - Tokenizers: 0.22.0 ## Citations Cite GRPO as: ```bibtex @article{shao2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
qualiaadmin/eef0eb84-9e56-4afc-9aae-05204b8cf5e2	qualiaadmin	2025-09-22T11:33:29Z	0	0	lerobot	[ "lerobot", "safetensors", "robotics", "smolvla", "dataset:Calvert0921/SmolVLA_LiftBlueCubeDouble_Franka_200", "arxiv:2506.01844", "base_model:lerobot/smolvla_base", "base_model:finetune:lerobot/smolvla_base", "license:apache-2.0", "region:us" ]	robotics	2025-09-22T11:31:28Z	--- base_model: lerobot/smolvla_base datasets: Calvert0921/SmolVLA_LiftBlueCubeDouble_Franka_200 library_name: lerobot license: apache-2.0 model_name: smolvla pipeline_tag: robotics tags: - robotics - smolvla - lerobot --- # Model Card for smolvla <!-- Provide a quick summary of what the model is/does. --> [SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash lerobot-train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash lerobot-record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with eval\_ and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - License: apache-2.0
AXERA-TECH/YOLOv8-Seg	AXERA-TECH	2025-09-22T11:33:04Z	5	0	null	[ "onnx", "Ultralytics", "YOLOv8", "YOLOv8-Seg", "object-detection", "en", "base_model:Ultralytics/YOLOv8", "base_model:quantized:Ultralytics/YOLOv8", "license:mit", "region:us" ]	object-detection	2025-01-11T16:23:41Z	--- license: mit language: - en base_model: - Ultralytics/YOLOv8 pipeline_tag: object-detection tags: - Ultralytics - YOLOv8 - YOLOv8-Seg --- # YOLOv8-Seg This version of YOLOv8-Seg has been converted to run on the Axera NPU using w8a16 quantization. This model has been optimized with the following LoRA: Compatible with Pulsar2 version: 3.4 ## Convert tools links: For those who are interested in model conversion, you can try to export axmodel through - [The repo of AXera Platform](https://github.com/AXERA-TECH/ax-samples), which you can get the detial of guide - [Pulsar2 Link, How to Convert ONNX to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/pulsar2/introduction.html) ## Support Platform - AX650 - [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html) - [M.2 Accelerator card](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html) - AX630C - [爱芯派2](https://axera-pi-2-docs-cn.readthedocs.io/zh-cn/latest/index.html) - [Module-LLM](https://docs.m5stack.com/zh_CN/module/Module-LLM) - [LLM630 Compute Kit](https://docs.m5stack.com/zh_CN/core/LLM630%20Compute%20Kit) \|Chips\|yolov8s-seg\| \|--\|--\| \|AX650\| 4.6 ms \| \|AX630C\| TBD ms \| ## How to use Download all files from this repository to the device ``` root@ax650:~/YOLOv8-Seg# tree . \|-- ax650 \| `-- yolov8s-seg.axmodel \|-- ax_yolov8_seg \|-- football.jpg `-- yolov8_seg_out.jpg ``` ### Inference Input image: ![](./football.jpg) #### Inference with AX650 Host, such as M4N-Dock(爱芯派Pro) ``` root@ax650:~/samples/AXERA-TECH/YOLOv8-Seg# ./ax_yolov8_seg -m ax650/yolov8s_seg.axmodel -i football.jpg -------------------------------------- model file : ax650/yolov8s_seg.axmodel image file : football.jpg img_h, img_w : 640 640 -------------------------------------- Engine creating handle is done. Engine creating context is done. Engine get io info is done. Engine alloc io is done. Engine push input is done. -------------------------------------- input size: 1 name: images [UINT8] [BGR] 1 x 640 x 640 x 3 output size: 7 name: /model.22/Concat_1_output_0 [FLOAT32] 1 x 80 x 80 x 144 name: /model.22/Concat_2_output_0 [FLOAT32] 1 x 40 x 40 x 144 name: /model.22/Concat_3_output_0 [FLOAT32] 1 x 20 x 20 x 144 name: /model.22/cv4.0/cv4.0.2/Conv_output_0 [FLOAT32] 1 x 80 x 80 x 32 name: /model.22/cv4.1/cv4.1.2/Conv_output_0 [FLOAT32] 1 x 40 x 40 x 32 name: /model.22/cv4.2/cv4.2.2/Conv_output_0 [FLOAT32] 1 x 20 x 20 x 32 name: output1 [FLOAT32] 1 x 32 x 160 x 160 post process cost time:16.21 ms -------------------------------------- Repeat 1 times, avg time 4.69 ms, max_time 4.69 ms, min_time 4.69 ms -------------------------------------- detection num: 8 0: 92%, [1354, 340, 1629, 1035], person 0: 91%, [ 5, 359, 314, 1108], person 0: 91%, [ 759, 220, 1121, 1153], person 0: 88%, [ 490, 476, 661, 999], person 32: 73%, [1233, 877, 1286, 923], sports ball 32: 63%, [ 772, 888, 828, 937], sports ball 32: 63%, [ 450, 882, 475, 902], sports ball 0: 55%, [1838, 690, 1907, 811], person -------------------------------------- ``` Output image: ![](./yolov8_seg_out.jpg) #### Inference with M.2 Accelerator card ``` (base) axera@raspberrypi:~/lhj/YOLOv8-Seg $ ./axcl_aarch64/axcl_yolov8_seg -m ax650/yolov8s_seg.axmodel -i football.jpg -------------------------------------- model file : ax650/yolov8s_seg.axmodel image file : football.jpg img_h, img_w : 640 640 -------------------------------------- axclrtEngineCreateContextt is done. axclrtEngineGetIOInfo is done. grpid: 0 input size: 1 name: images 1 x 640 x 640 x 3 output size: 7 name: /model.22/Concat_1_output_0 1 x 80 x 80 x 144 name: /model.22/Concat_2_output_0 1 x 40 x 40 x 144 name: /model.22/Concat_3_output_0 1 x 20 x 20 x 144 name: /model.22/cv4.0/cv4.0.2/Conv_output_0 1 x 80 x 80 x 32 name: /model.22/cv4.1/cv4.1.2/Conv_output_0 1 x 40 x 40 x 32 name: /model.22/cv4.2/cv4.2.2/Conv_output_0 1 x 20 x 20 x 32 name: output1 1 x 32 x 160 x 160 ================================================== Engine push input is done. -------------------------------------- post process cost time:3.67 ms -------------------------------------- Repeat 1 times, avg time 4.85 ms, max_time 4.85 ms, min_time 4.85 ms -------------------------------------- detection num: 8 0: 92%, [1354, 340, 1629, 1035], person 0: 91%, [ 5, 359, 314, 1108], person 0: 91%, [ 759, 220, 1121, 1153], person 0: 88%, [ 490, 476, 661, 999], person 32: 73%, [1233, 877, 1286, 923], sports ball 32: 63%, [ 772, 888, 828, 937], sports ball 32: 63%, [ 450, 882, 475, 902], sports ball 0: 55%, [1838, 690, 1907, 811], person -------------------------------------- ``` Output image: ![](./yolov8_seg_axcl_out.jpg)
HaniBO/CBC_dem	HaniBO	2025-09-22T11:32:01Z	57	0	transformers	[ "transformers", "safetensors", "gguf", "llama", "text-generation", "generated_from_trainer", "trl", "sft", "unsloth", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-09-18T11:55:39Z	--- base_model: unsloth/llama-3.1-8b-bnb-4bit library_name: transformers model_name: CBC_dem tags: - generated_from_trainer - trl - sft - unsloth licence: license --- # Model Card for CBC_dem This model is a fine-tuned version of [unsloth/llama-3.1-8b-bnb-4bit](https://huggingface.co/unsloth/llama-3.1-8b-bnb-4bit). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="HaniBO/CBC_dem", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.22.2 - Transformers: 4.55.4 - Pytorch: 2.8.0+cu126 - Datasets: 3.6.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
poolkiltzn/blockassist-bc-vigilant_alert_tuna_1758540491	poolkiltzn	2025-09-22T11:29:39Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "vigilant alert tuna", "arxiv:2504.07091", "region:us" ]	null	2025-09-22T11:29:14Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - vigilant alert tuna --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
AXERA-TECH/YOLO11-Pose	AXERA-TECH	2025-09-22T11:28:19Z	21	1	null	[ "onnx", "Ultralytics", "YOLO11", "YOLO11-POSE", "object-detection", "en", "base_model:Ultralytics/YOLO11", "base_model:quantized:Ultralytics/YOLO11", "license:mit", "region:us" ]	object-detection	2025-03-23T07:42:32Z	--- license: mit language: - en base_model: - Ultralytics/YOLO11 pipeline_tag: object-detection tags: - Ultralytics - YOLO11 - YOLO11-POSE --- # YOLO11-POSE This version of YOLO11-POSE has been converted to run on the Axera NPU using w8a16 quantization. This model has been optimized with the following LoRA: Compatible with Pulsar2 version: 3.4 ## Convert tools links: For those who are interested in model conversion, you can try to export axmodel through - [The repo of ax-samples](https://github.com/AXERA-TECH/ax-samples), which you can get the how to build the `ax_yolo11_pose` - [The repo of axcl-samples](https://github.com/AXERA-TECH/axcl-samples), which you can get the how to build the `axcl_yolo11_pose` - [Pulsar2 Link, How to Convert ONNX to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/pulsar2/introduction.html) ## Support Platform - AX650 - [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html) - [M.2 Accelerator card](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html) - AX630C - [爱芯派2](https://axera-pi-2-docs-cn.readthedocs.io/zh-cn/latest/index.html) - [Module-LLM](https://docs.m5stack.com/zh_CN/module/Module-LLM) - [LLM630 Compute Kit](https://docs.m5stack.com/zh_CN/core/LLM630%20Compute%20Kit) \|Chips\|cost\| \|--\|--\| \|AX650\| 25 ms \| \|AX630C\| TBD ms \| ## How to use Download all files from this repository to the device ``` (axcl) axera@raspberrypi:~/samples/AXERA-TECH/YOLO11-Pose $ tree -L 2 . ├── ax620e │ └── yolo11s-pose.axmodel ├── ax650 │ └── yolo11x-pose.axmodel ├── ax_aarch64 │ └── ax_yolo11_pose ├── axcl_aarch64 │ └── axcl_yolo11_pose ├── axcl_x86_64 │ └── axcl_yolo11_pose ├── config.json ├── football.jpg ├── README.md ├── yolo11_pose_config.json ├── yolo11_pose_out.jpg ├── yolo11s-pose-cut.onnx └── yolo11s-pose.onnx 6 directories, 12 files ``` ### Inference Input image: ![](./football.jpg) #### Inference with AX650 Host, such as M4N-Dock(爱芯派Pro) ``` root@ax650:~/YOLO11-Pose# ./ax_aarch64/ax_yolo11_pose -m ax650/yolo11x-pose.axmodel -i football.jpg -------------------------------------- model file : ax650/yolo11x-pose.axmodel image file : football.jpg img_h, img_w : 640 640 -------------------------------------- Engine creating handle is done. Engine creating context is done. Engine get io info is done. Engine alloc io is done. Engine push input is done. -------------------------------------- post process cost time:1.40 ms -------------------------------------- Repeat 1 times, avg time 25.21 ms, max_time 25.21 ms, min_time 25.21 ms -------------------------------------- detection num: 6 0: 94%, [1350, 337, 1632, 1036], person 0: 93%, [ 492, 477, 658, 1000], person 0: 92%, [ 756, 219, 1126, 1154], person 0: 91%, [ 0, 354, 314, 1108], person 0: 73%, [ 0, 530, 81, 1017], person 0: 54%, [ 142, 589, 239, 1013], person -------------------------------------- ``` Output image: ![](./yolo11_pose_out.jpg) #### Inference with M.2 Accelerator card ``` (axcl) axera@raspberrypi:~/samples/AXERA-TECH/YOLO11-Pose $ chmod +x axcl_aarch64/axcl_yolo11_pose (axcl) axera@raspberrypi:~/samples/AXERA-TECH/YOLO11-Pose $ ./axcl_aarch64/axcl_yolo11_pose -m ax650/yolo11x-pose.axmodel -i football.jpg -------------------------------------- model file : ax650/yolo11x-pose.axmodel image file : football.jpg img_h, img_w : 640 640 -------------------------------------- axclrtEngineCreateContextt is done. axclrtEngineGetIOInfo is done. grpid: 0 input size: 1 name: images 1 x 640 x 640 x 3 output size: 6 name: /model.23/Concat_1_output_0 1 x 80 x 80 x 65 name: /model.23/Concat_2_output_0 1 x 40 x 40 x 65 name: /model.23/Concat_3_output_0 1 x 20 x 20 x 65 name: /model.23/cv4.0/cv4.0.2/Conv_output_0 1 x 80 x 80 x 51 name: /model.23/cv4.1/cv4.1.2/Conv_output_0 1 x 40 x 40 x 51 name: /model.23/cv4.2/cv4.2.2/Conv_output_0 1 x 20 x 20 x 51 ================================================== Engine push input is done. -------------------------------------- post process cost time:0.43 ms -------------------------------------- Repeat 1 times, avg time 25.05 ms, max_time 25.05 ms, min_time 25.05 ms -------------------------------------- detection num: 6 0: 94%, [1350, 337, 1632, 1036], person 0: 93%, [ 492, 477, 658, 1000], person 0: 92%, [ 756, 219, 1126, 1154], person 0: 91%, [ 0, 354, 314, 1108], person 0: 73%, [ 0, 530, 81, 1017], person 0: 54%, [ 142, 589, 239, 1013], person -------------------------------------- ```
gaggi009/sbert-databricks	gaggi009	2025-09-22T11:26:48Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "new", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:5192", "loss:MultipleNegativesRankingLoss", "custom_code", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:Alibaba-NLP/gte-multilingual-base", "base_model:finetune:Alibaba-NLP/gte-multilingual-base", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-09-22T11:26:29Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:5192 - loss:MultipleNegativesRankingLoss base_model: Alibaba-NLP/gte-multilingual-base widget: - source_sentence: P Payor active configuration resolution sentences: - 'Reference Content This record is designed for pediatric therapy. It contains the evaluating and treating therapist, overview of the next progress report and re-evaluation dates, and plan of care tracking. ![Example: Pediatric Therapy Team 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) In addition to the standard therapist data, the Provider Information section also includes the comments, last update date, prescription end date, and IFSP end date (if applicable). If changing the evaluating provider here, the case and other active role records are updated as well. The Tracking Information section provides an overview of the patient''s visits by showing the date of the last visit and the last progress report note. You can also view and edit the due dates for the next progress report and re-evaluation. The dates are editable only when the automatic progress tracking is enabled in the [Therapy Options](https://kb.raintreeinc.com/help-10-2-500=therapy-options "These options define whether functional codes are required, how frequently progress reports are due, and allow you to use a financial class or insurance specific narrative for Plan of Care. These options are grouped into multiple tabs.") (insert-link) for the insurance or financial class. Finally, the POC Tracking section displays the certification period and plan frequency from the Plan and Recommendations sections from the matching LTNOT note. Internal Related concepts [Case Load tab](https://kb.raintreeinc.com/help-10-2-500=case-load-tab) [Therapy Team record](https://kb.raintreeinc.com/help-10-2-500=therapy-team-record) More Information [[Role records\|250326123804743]] ' - "Issue/Question\nThe post payment shows a patient resp of $240 but there is no\ \ where on the ledger that shows the patient has a balance.\n\nCause\nThe patient\ \ had multiple P payors. One payor had a credit and one had a balance.\n\nResolution/Answer\n\ To Combine P payors you will got to Payors from the patients Main Menu and click\ \ on the Combine Insurances tab.\n\n1. Select the payor you want to merge into\ \ another one.\n\n After selecting a payor, the second list displays the payors\ \ that you can select as the target for the merge.\n2. Select the target payor\ \ in the second list.\n\n This is the payor that will remain after the merge.\n\ \n The Combine button becomes active. (If the Combine button does not become\ \ active you might need to update your user rights to include INSURMERGE=E.)\n\ 3. Click Combine.\n\nRaintree combines the two payors. If any charges need\ \ to be reassigned, Raintree will do so automatically. After you combine the payors\ \ you will go to the patients Ledger View>Print>View Totals>Recalculate. This\ \ will update the totals on the Post Payment screen.\n\n" - 'Issue/Question When attempting to sign off on a patient note, the system displays an error message indicating a missing payor. This occurs even when the Financial Class is correctly set to ''A'' as the default payor setup. Users are unable to sign off notes despite previous successful attempts and despite similar scenarios working in the training database. The Charge Recap screen shows the message "Patient does not have a P Payor." Cause This problem arose when a Collect Payment amount was entered on the Charges tab of the note, and the patient did not have an active payor (specifically, a payor designated as ''P'') configured to handle this collection. Resolution/Answer To resolve the issue preventing note sign-off due to a missing payor for the collected payment amount, follow these steps: 1. Navigate to the Charges tab within the patient note. 2. Locate the Collect Payment section. 3. Clear the value in the Amount field, ensuring it is set to 0.00 or is blank. 4. Save the changes to the note. 5. Attempt to sign off the note again. ![Image](https://raintree.rightanswers.com/portal/app/portlets/results/onsitehypermedia/090250729997097.png?linkToken=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJyYWludHJlZSIsImlhdCI6MTc1ODEyNDUzNywiZXhwIjoxNzU4MTI0ODM3fQ.KM20CbmyRVd6sedsmuzLxiNrcG3O3Wp5d6QiYs1nvP4) ' - source_sentence: appointment confirmation directions calendar integration sentences: - "Lesson\nRCM specialists can quickly reverify a patient's benefits as needed.\ \ This process involves accessing the patient's file and using the eligibility\ \ check tool to confirm coverage details.\n\nReverifying Patient Benefits On Demand\n\ ======================================\n\n1. Open the patient's file.\n2. Select\ \ the menu option Waystar Eligibility check.\n3. Enter the provider and location.\n\ 4. Save to run the check.\n * The results appear. Everything looks good.\n\n\ Video\n\n\n" - 'Lesson Patients receive appointment reminders via text messages, which include a clickable link to open the full reminder. From this reminder, patients can easily confirm their attendance, get directions to the clinic, add their appointment to a calendar, and even access the clinic''s social media pages. Raintree provides a convenient and helpful experience for managing appointments. To manage appointments: 1. Receive a text message: Receive an appointment reminder via a text message from the clinic. 2. Open the reminder link: Click on the link within the text message to open the reminder. 3. Confirm the appointment: Click the green button to confirm your attendance at the appointment. 4. Access directions: Use the provided option to get directions to the clinic. 5. Explore clinic information: Discover additional links, such as the clinic''s social media page. 6. Adding an appointment to a Calendar: If you want to add your appointment to a calendar, for example, Google or Outlook, click the calendar option and your appointment is added to your calendar. Video ' - "Concept\nQuery Builder is a useful tool for creating and running SQL queries\ \ against the database. Although it is a versatile tool, it is important to be\ \ familiar with its capabilities and limitations in order to get correct and reliable\ \ results from the created queries.\n\n* [[Refining query results\|250305165915093]]\ \ \n\n In general, Query Builder can be used for listing data from the database.\ \ There are a number of ways how to refine the results to make the output data\ \ more relevant to your needs. You can do this by using different filters.\n\ \ [[Formatting query results\|250328183921833]] \n You can arrange the output\ \ table of the query to make the query results more easier to use.\n [[Recommendations\ \ for using Query Builder\|250328184411903]]\n\n Although Query Builder is a useful\ \ tool for creating and running queries, you need to carefully plan the queries\ \ that you want to create and take into account the limitations that apply to\ \ this tool. There are some general recommendations for using Query Builder properly.\n\ \nMore Information\n[[\|250305162245783]][[Query \|250305162245783]][[Builder\|250305162245783]]\n\ \n" - source_sentence: Raintree supported devices Windows Android Apple Chromebook sentences: - "Reference Content\n### Windows Based Desktops/Laptops/Notebooks\n\n* Microsoft\ \ Windows Operating System (Microsoft Windows 8 or higher) (Windows S mode & RT\ \ not supported)\n* Two (2) GHz Intel-Based or AMD-Based Processor\n* Eight (8)\ \ GB of RAM or higher\n* Minimum 20 MB of free space for the Raintree Client software\ \ and associated files\n* Video hardware and monitor capable of minimum 1024x768\ \ resolution at 16-bit color\n* Microsoft Windows-compatible printers (directly-attached\ \ or network-attached)\n* Microphone and Speakers\n* Full read / write permission\ \ in the client (by default c:\\rtw) folder and subdirectories\n* Raintree Client\ \ folder (by default c:\\rtw) should be excluded from real-time protection by\ \ host-based security applications as it may impair the performance or ability\ \ to update the client when prompted\n* Webcam if using Telehealth services\n\n\ ### RTWeb - Requires Raintree 2019.3 or newer\n\n* Microsoft Windows 10 or higher\n\ * iOS requires a device running on iOS 13.0 or later\n* Android 8 or higher\n\ * iPad devices require iPad OS 13.0 or later\n* macOS Catalina 10. 15.7\n* Chromebook\ \ device required Google Chrome OS version 79 or higher\n\n### Devices Recommended\ \ by Raintree\n\n* iPad Air (3rd generation), 10.5-inch display\n + iPadOS version\ \ 15.3.1\n* iPhone 11, 6.1-inch display\n + iOS version 15.3.1\n* MacBook Air\ \ (13-inch, 2017)\n + macOS Monterey, version 12.2.1\n* Microsoft Surface 3 tablet,\ \ 10.8-inch display\n + Microsoft Windows 10 Pro\n* Samsung Galaxy Tab S5e, 10.5-inch\ \ display\n + One UI version 3.1, Android version 11\n* Samsung Galaxy S20FE,\ \ 6.5-inch display\n + One UI version 4, Android version 12\n* HP Chromebook,\ \ 14-inch display\n + Chrome version 98.0.4758.107\n\n### Internet Bandwidth\ \ Requirements\n\n* 65 KBps per user\n\n" - "Concept\nDevice Requirements\n===================\n\nThe Raintree RTWeb client\ \ enables providers to access their Raintree dashboard and documentation by logging\ \ in through a web browser. This means that providers can access Raintree from\ \ any device connected to the internet. However, the device used must meet the\ \ specified minimum requirements:\n\nWindows devices\n\n* Windows 11 Pro\n\ \nAndroid devices\n\n* Phone - One UI version 5.1, Android version 13 or above\n\ * Tablet - One UI version 6.1, Android version 14 or above\n\nApple devices\n\ \n* MacBook - macOS Sequoia, version 15.3.2 or above\n* iPad - iPadOS version\ \ 18.4 or above\n* iPhone - iOS version 18.4 or above\n\nChromebook\n\n* Chrome\ \ version 126.0.6478.222 or above\n\nSupported web browsers are up-to-date versions\ \ of Chrome, Edge, Firefox and Safari.\n\n*![](https://raintree.rightanswers.com/portal/app/images/content_caution.gif) CAUTION:****Internet\ \ Explorer is not supported.\n\nRecommended Devices\n===================\n\nThe\ \ following is a list of the devices Raintree recommends for your web client:\n\ \n iPad Air (3rd generation), 10.5-inch display \n\n iPadOS version 18.4\n\ * iPhone 11, 6.1-inch display \n\n iOS version 18.4\n* MacBook Air (13-inch,\ \ 2024) \n\n macOS Sequoia, version 15.3.2\n* Samsung Galaxy S9+, 12.4-inch\ \ display \n\n One UI version 6.1.1, Android version 14\n* Samsung Galaxy\ \ Tab S10+, 12.4-inch display \n\n One UI version 6.1.1, Android version 14\n\ * Samsung Galaxy S20FE, 6.5-inch display \n\n One UI version 5.1, Android\ \ version 13\n* HP Chromebook, 14-inch display \n\n Chrome version 126.0.6478.222\n\ * Surface Go 3 (Windows tablet), 10.5-inch display \n\n Windows 11 Pro\n\ \n" - "Reference Content\nWhile the screen for each Quality measure is slightly different,\ \ there are some common fields and similar logic as well.\n\n![](https://raintree.rightanswers.com/portal/app/images/content_note.gif) NOTE: If\ \ you are displayed this page when pressing F1 from the measure screen, then\ \ it means that the measure screen you had currently open does not have its own\ \ specific help page. See the bottom of this screen for the list of help pages\ \ for specific measures.\n\nThe measure record codes start with \"M\", followed\ \ by a number. For example, M110 is template for the measure \"Influenza Immunization\"\ , quality ID 110.\n\nMany measures have their own specific options as well. The\ \ following list describes options that are common for most measure screens.\n\ \n![Example: MIPS Quality measure screen (reporting)](https://raintree.rightanswers.com/portal/app/portlets/results/onsitehypermedia/090250318330363.png?linkToken=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJyYWludHJlZSIsImlhdCI6MTc1ODEyNDY0MSwiZXhwIjoxNzU4MTI0OTQxfQ.oGGm2msI1SBKYnidy7sHWkXhQ0_ffQ12ZRVea-fuZZo)\n\ \nClick any highlighted region for details.\n\nMeasure description\n\nIn the\ \ top of the screen, an overview of the measure is given. This includes population\ \ criteria (age, gender) and measure completion requirements.\n\nQuality ID\n\ \nQuality ID of the measure.\n\nReporting Code\n\nCurrent reporting code based\ \ on the selection in the Reporting Codes section below. This may be either\ \ a code with number and letters, or `Exc`, indicating exclusion.\n\n```\nStatus\ \ \n \nCurrent status of the measure. This can be either Met (green), Not Eligible (black), Not\ \ Met (green), Incomplete (red), and Exception/Exclusion (green. Black color indicates\ \ that no action was needed from the provider, green indicates that action was\ \ needed and provider has already done everything as required, and red indicates\ \ the provider still needs to do something to complete this measure. See [[MIPS\ \ hoverbox status message types\|250318120330660]] page for more information about\ \ Quality measure status types.\n```\n\nLinked record widget\n\nSelect the\ \ widget to attach a record to the measure. The record type differs for each measure.\ \ There may also be more than one widget to attach several records, or no widgets\ \ at all, depending on the measure.\n\nReporting Codes\n\nOptions for the\ \ provider to choose from. Usually, there are options to take some action (such\ \ as assessing the patient and documenting a follow-up plan), to not take some\ \ action, but provide a reason (for example, the patient refused), and to not\ \ take some action and not provide a reason (it is not recommended to select this\ \ type of options). The options differ for each measure. For some options, if\ \ selected, additional fields appear. Based on the selection here, the information\ \ in the Reporting Code and Status fields above will also change.\n\n\ * + [[M130 (Current Medications) measure\|250318115235760]]\n\n This MIPS Quality\ \ measure's objective is to obtain and review the patient's medication history.\ \ This measure is configured using the SM130 screen and reported using the M130\ \ screen.\n + [[M154 (Falls Risk Assessment) measure\|250318133925757]]This MIPS\ \ Quality measure's objective is to assess the fall risk of patients over 65 years\ \ old. This measure is configured using the SM154 screen and reported using the\ \ M154 screen. Assessment of the fall risk is tied to the Falls Risk Assessment\ \ record (PQ034), available in systems that also have the rehab note available.\n\ \ + [[M217 (Functional Status Change for Patients with Knee Impairments) measure\|250318151713350]]\ \ \n This MIPS Quality measure can be reported for patients with knee impairment.\ \ Assessment of the functional status is tied to the FOTO survey for this measure.\ \ It should be reported on Discharge Summary notes only. This measure is configured\ \ using the SM217 screen and reported using the M217 screen.\n\nInternal\nRelated\ \ concept\n\n[Quality measures hoverbox](https://kb.raintreeinc.com/help-10-2-500=quality-measures-hoverbox)\n\ \nRelated tasks\n\n[Document MIPS quality measures on a BTICM note](https://kb.raintreeinc.com/help-10-2-500=document-mips-quality-measures-on-a-bticm-note)\n\ \n[Document MIPS quality measures on an ENOTE](https://kb.raintreeinc.com/help-10-2-500=document-mips-quality-measures-on-an-enote)\n\ \n[Document MIPS quality measures on a LTNOT note](https://kb.raintreeinc.com/help-10-2-500=document-mips-quality-measures-on-a-ltnot-note)\n\ \n[Document MIPS quality measures on a TVIST note](https://kb.raintreeinc.com/help-10-2-500=document-mips-quality-measures-on-a-tvist-note)\n\ \nRelated reference\n\n[MIPS Quality measure screen (configuration)](https://kb.raintreeinc.com/help-10-2-500=mips-quality-measure-screen-lpar-configuration-rpar)\n\ \nMore Information\n[[Clinical quality measures (CQM and eCQM)\|250318130647220]]\n\ \n" - source_sentence: CREDSTATUS=E credentialing applications filter sentences: - "Reference Content\nIn this tab you can specify the posting codes that are used\ \ for remittance posting. This tab consists of four panels.\n\nPrimary Billing\ \ Posting Codes\n---------------------------------\n\nIn this panel you can\ \ define the payment/adjustment/transfer codes that will be used for posting primary\ \ payments.\n\nPrimary Billing Posting Codes\n\n![](data:image/.png;base64,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)\n\ \nUse Method\n\nSelect this option if you want to use a specific payment method.\ \ You only need to specify a payment method code and an additional denial method\ \ code. All related information will be pulled from the [[Payment Methods table\|250312141829220]] automatically.\n\ \nUse Hardcoded Codes\n\nSelect this option if you want to specify the payment\ \ codes manually. Press Tab in each field to select the code from the respective\ \ table.\n\nSetting bills to paid\n\nDefines the bill closing criteria in\ \ percents. If the percentage is equal or more than specified in this field. the\ \ bill is set to paid.\n\nOther Codes\n\nYou can define other codes that are\ \ not in the payment methods table, but which are still used.\n\n1. Press Tab in\ \ the Copay Code field. Select a copay code and press Enter.\n2. Define whether\ \ you want to use an adjustment or a transfer for Increase of Expected. There\ \ is also an option to not increase expected. Select this option if you want to\ \ keep the credit balance.\n3. Press Tab in the appropriate field to select\ \ the code.\n\nDo not increase expected\n\nSelect this option if you want\ \ to keep the credit balance.\n\nSecondary Billing Posting Codes\n-------------------------------\n\ \nIn this panel you can define the payment/adjustment/transfer codes for posting\ \ secondary payments.\n\nSecondary Billing Posting Codes\n\n![](data:image/.png;base64,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)\n\ \nThis panel is similar to the previous one, except that the following fields\ \ are not available:\n\n* Decrease Copay\n* Copay Code\n\nSpecial Codes\n\ -----------------\n\nIn this panel you can specify the codes for backout and late\ \ fee payments and also reason code specific posting codes.\n\nSpecial Codes\n\ \n![Image](https://raintree.rightanswers.com/portal/app/portlets/results/onsitehypermedia/090250312934805.png?linkToken=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJyYWludHJlZSIsImlhdCI6MTc1ODEyNDM5OSwiZXhwIjoxNzU4MTI0Njk5fQ.tE61tw68btnmoQIftaJmoN4QMeH7V0egTO4Fwo_LHR8)\n\ \nPressTab in the respective field to select the necessary code. Repeat these\ \ steps until you have filled in the necessary fields.\n\nError Supplier Note\ \ Code\n\nDefines which note is posted to the ledger when the payment cannot\ \ be posted. You can enable posting the error notes on the [[General\|250311190336583]] tab.\n\ \nAdjustment code\n\nDefines which adjustment code is used for secondary balance\ \ write offs.\n\nOther Backout Payment codes\n\nDefines other possible backout\ \ payment codes. The codes in this field must be separated by commas. For example, BOA,BOP,BOT.\n\ \nReason code specific posting codes\n\nEach code in this field consists of\ \ three parts that are separated by pipes.\n\n* + Reason code\n + T for Transfer\ \ or A for Adjustment\n + The posting code you want to use\n\nFor example,\ \ the code 1\|T\|TDOS means: \"When posting a transfer with reason code 1, use the\ \ TDOS code\".\n\nSpecial code for the second payment\n\nDefines the payment\ \ code for the second payment that is posted when the Post the difference between\ \ the total amount and the sum of all line item amounts as a separate auto-distributed\ \ payment option is selected in the Advanced - Posting tab.\n\nThe code\ \ for the adjustment that offsets the difference payment\n\nDefines the adjustment\ \ code that is posted to offset the difference payment specified in the previous\ \ option.\n\nPLB payments\n----------------\n\nIn this tab you can specify\ \ the PLB payment codes. PLB Payments are not associated with a specific\ \ patient, but with a provider instead. For example, increase in payment as a\ \ result of late payment is a PLB payment. This also means that the codes needed\ \ differ from the other payments.\n\nPLB Payments\n\n![](data:image/.png;base64,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)\n\ \nGeneral Account for PLB Payments\n\nEnables you to define an account for\ \ PLB payments. The payments are posted to the selected account. This is required\ \ information for the PLB payment posting to function.\n\nCodes for PLB payments/adjustments/charges\n\ \nEnter payment, location, provider, referral, adjustment and charge codes used\ \ for PLB ledger items.\n\nIf a field in this section is blank, the respective\ \ code from the following list is used:\n\n\| Field on the payment/adjustment/charge\ \ \| Code used if not specified otherwise \|\n\| --- \| --- \|\n\| Payment code \| RTEMP\ \ \|\n\| Location code \| 01 \|\n\| Provider code \| 01 \|\n\| Referral code \| 01 \|\n\ \| Adjustment code \| RTEMP \|\n\| Charge code \| DEMC \|\n\n \n\nAdditionally, ATEMP\ \ is used as primary insurance and 00000 is used as patient diagnosis.\n\nUpon\ \ posting, if a default code from this list is used, but it is not present in\ \ the table (for example, there is 01 provider code on the payment, but no such\ \ code in the Providers table), it is automatically added there.\n\nInternal\n\ Previous topic:[Remittance Settings - PLB Reason Code Grouping](https://kb.raintreeinc.com/help-10-2-500=remittance-settings-plb-reason-code-grouping)\n\ \nNext topic:[Remittance Settings - Interface](https://kb.raintreeinc.com/help-10-2-500=remittance-settings-interface)\n\ \nMore Information\n[[Remittance settings\|250311180255330]]\n\n" - 'Document 44\_20250529064929\_Credentialing.pdf ' - 'Reference Content To access the dashboard, select Credentialing in the main menu. It is only available if you have the DASH\_CRED=E security right. The dashboard content depends on your permissions. Provider view ============= If you are a provider, you only see the Your Items tab of the credentialing dashboard. There you can view and complete the items requested from you. The items in this tab are color-coded: * Completed items are colored green. * Pending items that you still have to complete are colored yellow. * Items that have expired or are no longer needed are colored gray. You can filter the items by location, state, status, type, and master insurance. Double-click an item to open and edit it. If you have the CREDSTATUS=E right, the Status tab is also visible. In that tab you can see the status of credentialing applications for other providers, but you cannot edit them. Credentialing team view ======================= If you are a member of the credentialing team, you can see most tabs in the credentialing dashboard. Provider Requests ----------------- The Provider Requests tab shows all items requested from providers. You can view, manage and add new request items. At the top of the tab are options for filtering the provider requests. ![Image](https://raintree.rightanswers.com/portal/app/portlets/results/onsitehypermedia/090250606292895.png?linkToken=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJyYWludHJlZSIsImlhdCI6MTc1ODEyNDIxNiwiZXhwIjoxNzU4MTI0NTE2fQ.j_FTNe5OS_5arwuBFpyI9-NCLbd4i1Qdb0NTooO3dIo) Below the filters are the provider requests. They are color-coded based on their status: * Completed requests are colored green. * Overdue requests are colored red. * Pending requests are colored yellow. * Requests that have expired or are marked as no longer needed are colored gray. There are several actions you can take on the requests: * You can send the selected requests or a reminder for the selected requests to the provider. To do that, select the items in the list and click Send Request/Reminder. * If some items are no longer needed, you can mark them as such. To do that, select the items in the list and click No Longer Needed. * If you want to update the follow-up date for some requests, you can do that. Select the items in the list, enter the new date in the F/U Date field and click Update F/U Date for Selected. This overwrites the previous follow-up date on the selected items. If you want to add a new provider request item, click Add Item in the lower right corner. This opens a screen where you can select the item to request. License Verifications --------------------- The License Verifications tab shows a list of automatic license verifications. For each verification, its status is shown: pending, successful, or failed. The page is refreshed each hour. You can see the last time it was updated based on the date time stamp at the top of the page. Applications ------------ The Applications tab shows credentialing and re-credentialing applications for both providers and locations. At the top of the tab are options for filtering the applications. ![Image](https://raintree.rightanswers.com/portal/app/portlets/results/onsitehypermedia/090250606855457.png?linkToken=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJyYWludHJlZSIsImlhdCI6MTc1ODEyNDIxNiwiZXhwIjoxNzU4MTI0NTE2fQ.j_FTNe5OS_5arwuBFpyI9-NCLbd4i1Qdb0NTooO3dIo) Below the filters are the applications. They are color-coded based on their status: * Completed applications are colored green. * Overdue applications are colored red. * Pending applications are colored yellow. * Applications that have expired or are marked as no longer needed are colored gray. There are several actions you can take on the applications: * If some items are no longer needed, you can mark them as such. To do that, select the items in the list and click No Longer Needed. * If you want to update the follow-up date for some applications, you can do that. Select the items in the list, enter the new date in the F/U Date field and click Update F/U Date for Selected. This overwrites the previous follow-up date on the selected items. In the lower right corner are additional buttons: * Generate Location Roster exports a location roster. When you click this button, you are prompted to download a CSV file with the selected items. * Generate Provider Roster exports a provider roster. When you click this button, you are prompted to choose a roster layout to use for exporting the selected items. * Add Item adds a new application. When you click this button, you are prompted to choose which kind of application to create. The options to generate a roster are available only after you have selected some items in the list. Providers / Provider Details ---------------------------- The Providers tab shows a list of all providers and their credentialing data. Double-click a provider to view their details, documents and applications. When you are viewing a provider, the tab''s name changes to Provider Details. Above the list are options to filter the list. Below the list is the Export button for [[exporting a provider roster\|250512094406620]]. Locations / Location Detail --------------------------- The Locations tab shows a list of all locations and their credentialing data. Double-click a location to view its details, documents and applications. When you are viewing a location, the tab''s name changes to Location Detail. Master Insurances ----------------- The Master Insurances tab shows a list of master insurances. You can view and edit which insurances are mapped to which master insurance. Double-click an insurance to view the credentialing applications for it. You can also add new documents for the master insurance directly from this tab. Import Tools ------------ In the Import Tools tab you can import providers'' CAQH and application data. Setup ----- In the Setup tab you can configure various aspects related to credentialing. Read more about [[setting up credentialing\|250606071952953]]. ' - source_sentence: flag deselect patient records Collection Worksheet sentences: - 'Issue/Question The patient would like to have a refund amount applied to a different credit card than the one they originally made the payment with. Cause The patient would like to have a refund amount applied to a different credit card than the one they originally made the payment with. Resolution/Answer Raintree does not support unlinked refunds so the amount would need to be refunded back to the original credit card. ' - 'Reference Content The "meaning of life" refers to the concept of an individual''s existence, or existence in general, having inherent significance or a philosophical purpose. There is no single, universally agreed-upon definition, as it is a deeply personal and philosophical question often explored through various religious, spiritual, and secular perspectives. <https://en.wikipedia.org/wiki/Meaning_of_life> ' - "Prerequisites and Steps\nWorksheet records that are flagged hidden are not displayed\ \ in the Collection Worksheet list. These records are also not deleted from the\ \ Collection Worksheet list. You can view the hidden worksheets to change flags\ \ if necessary.\n\nFor example, you are working within the Collection Worksheet for\ \ a specific payor that shows patient records (not the worksheet that displays\ \ all insurances) and you need to change the flags to display previously hidden\ \ records.\n\nFrom the Collection Worksheets list:\n\n1. Press F+Arrow Up.\ \ The list of flags appears. \n \n ![Collection Worksheet example](data:image/.png;base64,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)\n\ 2. Deselect the necessary flags to display the records. \n\n For example, if\ \ you deselect the Completed option, all patients flagged as complete are\ \ shown.\n\nInternal\n[[\|250305114544823]]Related information[[\|250305120611630]]\n\ \n[[Creating collection worksheets\|250305120611630]]\n\nMore Information\n[[Introduction\ \ to collection worksheets\|250305114544823]]\n\n" pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy model-index: - name: SentenceTransformer based on Alibaba-NLP/gte-multilingual-base results: - task: type: triplet name: Triplet dataset: name: Raintree Triplet eval type: Raintree_Triplet_eval metrics: - type: cosine_accuracy value: 0.9202772974967957 name: Cosine Accuracy --- # SentenceTransformer based on Alibaba-NLP/gte-multilingual-base This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) <!-- at revision 9bbca17d9273fd0d03d5725c7a4b0f6b45142062 --> - Maximum Sequence Length: 1280 tokens - Output Dimensionality: 768 dimensions - Similarity Function: Cosine Similarity - Training Dataset: - json <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 1280, 'do_lower_case': False}) with Transformer model: NewModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sbert-databricks") # Run inference sentences = [ 'flag deselect patient records Collection Worksheet', 'Prerequisites and Steps\nWorksheet records that are flagged hidden are not displayed in the Collection Worksheet list. These records are also not deleted from the Collection Worksheet list. You can view the hidden worksheets to change flags if necessary.\n\nFor example, you are working within the\xa0Collection Worksheet\xa0for a specific payor that shows patient records (not the worksheet that displays all insurances) and you need to change the flags to display previously hidden records.\n\nFrom the Collection Worksheets list:\n\n1. Press\xa0F+Arrow Up. The list of flags appears. \n \n ![Collection Worksheet example](data:image/.png;base64,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)\n2. Deselect the necessary flags to display the records. \n\n For example, if you deselect the\xa0Completed\xa0option, all patients flagged as complete are shown.\n\nInternal\n[[\|250305114544823]]Related information[[\|250305120611630]]\n\n[[Creating collection worksheets\|250305120611630]]\n\nMore Information\n[[Introduction to collection worksheets\|250305114544823]]\n\n', 'Reference Content\nThe "meaning of life" refers to the concept of an individual\'s existence, or existence in general, having inherent significance or a philosophical purpose. There is no single, universally agreed-upon definition, as it is a deeply personal and philosophical question often explored through various religious, spiritual, and secular perspectives.\n\n<https://en.wikipedia.org/wiki/Meaning_of_life>\n\n', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Triplet * Dataset: `Raintree_Triplet_eval` * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy \| 0.9203 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### json * Dataset: json * Size: 5,192 training samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative \| \|:--------\|:----------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 5 tokens</li><li>mean: 10.49 tokens</li><li>max: 23 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 680.97 tokens</li><li>max: 1280 tokens</li></ul> \| <ul><li>min: 11 tokens</li><li>mean: 443.89 tokens</li><li>max: 1280 tokens</li></ul> \| * Samples: \| anchor \| positive \| negative \| \|:----------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>rebill screen follow-up action codes</code> \| <code>Prerequisites and Steps<br>You can rebill a claim in the ledger. This will print a claim using the previous claim's settings.<br><br>To rebill a claim:<br><br>1. Focus on the desired billbar.<br>2. Press Enter.<br>3. Select Rebill this claim.<br>4. Make changes in the Rebill Screen, if needed. For example, you can rebill using a different form than previously.<br>5. Press Ctrl+S.<br>6. A duplicate bill will be generated, which rebills the exact same charges using the same Billing Group that was used with original billing.<br>7. A REBILL ledger note will appear in patient's ledger. <br> REBILL ledger note <br><br> ![Image](https://raintree.rightanswers.com/portal/app/portlets/results/onsitehypermedia/090250326731616.png?linkToken=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJyYWludHJlZSIsImlhdCI6MTc1ODEyNDQ5NCwiZXhwIjoxNzU4MTI0Nzk0fQ.XSqxMbOnApHIAyHGTYhVu2fY78VwhAGfhzBSKc2l3-Q)<br><br>This option duplicates the bill as it was originally printed. If rebilling was not successful, then verify the billing grou...</code> \| <code>Reference Content<br>Follow-up actions allow you to speed up your workflow when handling billing issues. You can perform an action (for example, send additional medical documents to the payor or write off all balance on billbar) from the follow-up note, and have additional automated notes to track whether your actions were successful.<br><br>This feature is part of Raintree Rev-Edition (advanced Revenue Cycle Management tools). [[Read more\|250304155001873]].<br><br>You can perform an action from either on theFollow-up Notescreen or in the Claim Follow-ups follow-up note list. You can perform actions only on follow-up notes that have not been completed yet.<br><br>The available actions depend on the follow-up note code. For example, you cannot transfer the balance to another payor if there is already an overpayment, but there are other actions more applicable to the situation, such as closing the claim and adding a comment about the reason, or refunding the overpaid amount. Each action also has to m...</code> \| \| <code>HL7 dashboard interface configuration</code> \| <code>Reference Content<br>This screen is displayed when you add or edit an interface in the HL7 dashboard.<br><br>![Example: Edit Interface screen](data:image/.png;base64,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...</code> \| <code>Prerequisites and Steps<br>You need to add or edit HL7 interfaces if you want to customize HL7 interfaces for different senders or recipients.<br><br>From the HL7 Dashboard:<br><br>1. Select the Advanced tab.<br>2. Select the Setup tab.<br>3. If you want to edit an existing interface, select it and click Edit. Otherwise, click Add New. The Interface screen is displayed.<br>4. Type the interface name.<br>5. Type the sender name. For outgoing messages, it identifies the sender. For incoming messages, it identifies the senders whose messages the interface is able to process.<br>6. Press Ctrl+S to save the interface.<br>7. Click Save Setup.<br><br>More Information<br>[[HL7 setup\|250321132918077]]<br><br>[[HL7 Interface screen\|250321145406340]]<br><br>[[HL7 dashboard\|250320184748150]]<br><br></code> \| \| <code>Charge Table CPT code Quick Code setup</code> \| <code>Issue/Question<br>How can I add a CPT code to my activities picklist?<br><br>Cause<br>The code needs to be added following the steps outlined below.<br><br>Resolution/Answer<br>Prerequisite: Access the Charge Table to gather the needed details.<br>-------------------------------------------------------------------<br><br>Tables>Charge>locate the CPT code. Write down the Code(the Raintree Quick Code). This will be needed for step 7.<br><br>![finding code in charge table](https://raintree.rightanswers.com/portal/app/portlets/results/onsitehypermedia/090250808721486.png?linkToken=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJyYWludHJlZSIsImlhdCI6MTc1ODEyNDIyNywiZXhwIjoxNzU4MTI0NTI3fQ.b1pCHv1iDWdqBYiyiU20mKitZ2Vga7r9-6A-qUv5-2U)<br><br>Adding the CPT code to the Picklist.<br>------------------------------------<br><br>1. Access any patient's note. For this example, we are using a test patient, Amy Raintree.<br>2. Go to the Treatment Plan tab > click on the Activity Log tab.<br>3. Click the plus symbol in the bottom left...</code> \| <code>Reference Content<br>Charge table stores charges for services rendered at the facility. The RVS/CPT codes, charge descriptions, and fees for the practice are listed here. Similarly to the Provider table, the same charge may be added several times.<br><br>However, multiple entries for a charge are only necessary when:<br><br>* The charge billed/expected amounts are different based on a specific financial class.<br>* The POS (Place of Service) code is different based on a financial class.<br>* The TOS (Type of Service) code is different based on a financial class.<br>* A specific provider's fee is different than the other providers.<br><br>![](https://raintree.rightanswers.com/portal/app/images/content_note.gif) NOTE:*You can use BLOCK as an RVS/CPT code. If the best match for a particular charge is a blocked charge, then the result for charge lookup gives an empty response - you cannot add this charge to the ledger.<br><br>You can add, edit, delete and print the Charge table entries, if you have the required security...</code> \| Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### json * Dataset: json * Size: 577 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 577 samples: \| \| anchor \| positive \| negative \| \|:--------\|:----------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 6 tokens</li><li>mean: 10.34 tokens</li><li>max: 22 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 638.71 tokens</li><li>max: 1280 tokens</li></ul> \| <ul><li>min: 11 tokens</li><li>mean: 421.83 tokens</li><li>max: 1280 tokens</li></ul> \| * Samples: \| anchor \| positive \| negative \| \|:-------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>billing code breakdown statistics</code> \| <code>Reference Content<br>This daysheet report creates a summary of the current period's ledger entries.<br><br>You can see the selected filters below the report title. If no ledger entries match the selected filters, then the respective report values are left blank. <br>Current period's ledger<br><br>![Image](https://raintree.rightanswers.com/portal/app/portlets/results/onsitehypermedia/090250327323232.png?linkToken=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJyYWludHJlZSIsImlhdCI6MTc1ODEyNDYxOSwiZXhwIjoxNzU4MTI0OTE5fQ.WL_rS2eFBYZpI90CUCzMeW8It1jowdfs2eVJJv1EilM)<br><br>If several users need to view the report with the same filters, you may want to schedule it as a quick report so it can be generated outside office hours. The pre-compiled report will then be available in the Quick Reportstab of the dashboard.<br><br>Ledger items summary<br>------------------------<br><br>The upper part of the Summary Totals report displays the ledger summary broken down by ledger items. You can see the numbers and amounts of cha...</code> \| <code>Reference Content<br>You can select the daysheet/stats breakdowns to print in the Select Reports screen.<br><br>* [Ledger Detail (insert-link)](https://kb.raintreeinc.com/help-10-2-500=ledger-detail)<br><br> This daysheet report is a printout of the entire ledger activity for the specified date range. By default, the report includes all entries that have been recorded in the ledger, but you can use the general report options to filter the entries that will be included in the report, and the additional breakdown options to break down the report by different time periods.<br>* [Summary Totals](https://kb.raintreeinc.com/help-10-2-500=summary-totals) [(insert-link)](https://kb.raintreeinc.com/help-10-2-500=ledger-detail)<br><br> This daysheet report creates a summary of the current period's ledger entries.<br>* [Charge Breakdown](https://kb.raintreeinc.com/help-10-2-500=charge-breakdown) [(insert-link)](https://kb.raintreeinc.com/help-10-2-500=ledger-detail)<br><br> This daysheet report breaks down the ledger acti...</code> \| \| <code>Raintree license expiration alert configuration</code> \| <code>Concept<br>License expiring<br>----------------<br><br>When a provider's license is about to expire, Raintree can alert them via email and text, and on login. The alert includes the license name, the state where the license is expiring, and the license end date. Example:<br><br>Your Provider License for state of California will expire on 03-31-25. Please renew your license as soon as possible to avoid disruption. If you have any questions, please contact the Credentialing Department.<br><br>The credentialing team can configure the following options:<br><br>* Whether to send email and text alerts to providers. <br><br> Email/text alerts are sent to providers:<br><br> + 60 days before license expiration.<br> + 45 days before license expiration.<br> + Daily when there are 30 or fewer days until expiration.<br>* Whether to show alerts to providers on login.<br>* How long (in days) before the license expiration to start showing the alerts.<br>* The alert text that is sent to providers.<br><br>No credentials for payor<br>------------------------<br><br>When...</code> \| <code>Reference Content<br>The "meaning of life" refers to the concept of an individual's existence, or existence in general, having inherent significance or a philosophical purpose. There is no single, universally agreed-upon definition, as it is a deeply personal and philosophical question often explored through various religious, spiritual, and secular perspectives.<br><br><https://en.wikipedia.org/wiki/Meaning_of_life><br><br></code> \| \| <code>Credentialing dashboard add item procedure</code> \| <code>Prerequisites and Steps<br>1. From the main menu, select Credentialing. <br><br> The Credentialing dashboard opens.<br>2. Select the Applications tab.<br>3. Click Add Item.<br>4. Select the application type. <br><br> You can choose between credentialing and re-credentialing for providers or locations.<br><br> <br><br> The respective Credentialing screen is displayed.<br>5. Fill in the required information.<br><br> Required fields have a red asterisk next to them.<br>6. To add any additional documents, click in the Documents section and select Add. <br><br> For example, you can add an application letter here.<br>7. Optional: Click Download Application to save the application as a PDF.<br><br> This option is only available when an application form has been created for the selected master insurance.<br>8. Save your changes.<br><br>Results and Troubleshooting/Tips<br>The application is created. You can now track its status in the Credentialing dashboard.<br><br>![](https://raintree.rightanswers.com/portal/app/images/conten...</code> \| <code>Prerequisites and Steps<br>1. From the main menu, select Credentialing.<br>2. Select the Setup tab.<br>3. Select the Request Items tab.<br>4. Select Add. <br><br> The Request Item screen is displayed.<br>5. Type a title for the request item.<br>6. Optional: Enter a message to the provider in the Message from Credentialer field.<br><br> The message defined here is always shown when this item is requested. When you request an item from a provider, you can edit the message for that specific request.<br>7. Select Save.<br><br>Results and Troubleshooting/Tips<br>The configured request item is now available and appears in the list of all request items.<br><br></code> \| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 4 - `per_device_eval_batch_size`: 1 - `gradient_accumulation_steps`: 8 - `torch_empty_cache_steps`: 16 - `learning_rate`: 1e-05 - `weight_decay`: 0.01 - `warmup_ratio`: 0.01 - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 4 - `per_device_eval_batch_size`: 1 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 8 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: 16 - `learning_rate`: 1e-05 - `weight_decay`: 0.01 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 3 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.01 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| Validation Loss \| Raintree_Triplet_eval_cosine_accuracy \| \|:-----:\|:----:\|:-------------:\|:---------------:\|:-------------------------------------:\| \| 1.0 \| 163 \| 3.2614 \| 0.2535 \| 0.8977 \| \| 2.0 \| 326 \| 2.0932 \| 0.2375 \| 0.9116 \| \| 3.0 \| 489 \| 1.6929 \| 0.2330 \| 0.9203 \| ### Framework Versions - Python: 3.11.13 - Sentence Transformers: 4.1.0 - Transformers: 4.52.4 - PyTorch: 2.6.0+cu124 - Accelerate: 1.8.1 - Datasets: 3.6.0 - Tokenizers: 0.21.2 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
OuassimZm/Whisper-small-fine-tuned-Bigouden-colab	OuassimZm	2025-09-22T11:26:34Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-22T11:26:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
MattBou00/llama-3-2-1b-detox_v1f_RRETRT_Again_ROUND3-checkpoint-epoch-60	MattBou00	2025-09-22T11:25:28Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-09-22T11:24:31Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_11-15-41/checkpoints/checkpoint-epoch-60") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_11-15-41/checkpoints/checkpoint-epoch-60") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_11-15-41/checkpoints/checkpoint-epoch-60") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
rimpang/ato	rimpang	2025-09-22T11:25:16Z	0	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "fluxgym", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-09-22T11:24:10Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym widget: - output: url: sample/ato_000300_03_20250525013102_99.png text: ato The man is holding a power drill --d 99 - output: url: sample/ato_000600_03_20250525021454_99.png text: ato Two crossed arms, holding a yellow measuring level and a green electric drill. --d 99 - output: url: sample/ato_000900_03_20250525050850_99.png text: ato A dark brown rat is trapped in a simple wooden mouse trap. --d 99 - output: url: sample/ato_001200_03_20250525070641_99.png text: ato a zombie hand wielding a bloody knife, slicing through another zombie arm. --d 99 base_model: black-forest-labs/FLUX.1-dev instance_prompt: ato license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # ato A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `ato` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc. Weights for this model are available in Safetensors format.
mradermacher/Tanzania-0.5B-i1-GGUF	mradermacher	2025-09-22T11:24:45Z	0	0	transformers	[ "transformers", "gguf", "creative", "roleplay", "story-telling", "story-writing", "en", "dataset:practical-dreamer/RPGPT_PublicDomain-ShareGPT", "dataset:Gryphe/Opus-WritingPrompts", "base_model:XeTute/Tanzania-0.5B", "base_model:quantized:XeTute/Tanzania-0.5B", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-09-22T11:01:40Z	--- base_model: XeTute/Tanzania-0.5B datasets: - practical-dreamer/RPGPT_PublicDomain-ShareGPT - Gryphe/Opus-WritingPrompts language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - creative - roleplay - story-telling - story-writing --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> <!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> weighted/imatrix quants of https://huggingface.co/XeTute/Tanzania-0.5B <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Tanzania-0.5B-i1-GGUF).* static quants are available at https://huggingface.co/mradermacher/Tanzania-0.5B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Tanzania-0.5B-i1-GGUF/resolve/main/Tanzania-0.5B.imatrix.gguf) \| imatrix \| 0.1 \| imatrix file (for creating your own qwuants) \| \| [GGUF](https://huggingface.co/mradermacher/Tanzania-0.5B-i1-GGUF/resolve/main/Tanzania-0.5B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Tanzania-0.5B-i1-GGUF/resolve/main/Tanzania-0.5B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.4 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Tanzania-0.5B-i1-GGUF/resolve/main/Tanzania-0.5B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tanzania-0.5B-i1-GGUF/resolve/main/Tanzania-0.5B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tanzania-0.5B-i1-GGUF/resolve/main/Tanzania-0.5B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tanzania-0.5B-i1-GGUF/resolve/main/Tanzania-0.5B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tanzania-0.5B-i1-GGUF/resolve/main/Tanzania-0.5B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.4 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Tanzania-0.5B-i1-GGUF/resolve/main/Tanzania-0.5B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Tanzania-0.5B-i1-GGUF/resolve/main/Tanzania-0.5B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.4 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Tanzania-0.5B-i1-GGUF/resolve/main/Tanzania-0.5B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Tanzania-0.5B-i1-GGUF/resolve/main/Tanzania-0.5B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tanzania-0.5B-i1-GGUF/resolve/main/Tanzania-0.5B.i1-Q2_K.gguf) \| i1-Q2_K \| 0.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Tanzania-0.5B-i1-GGUF/resolve/main/Tanzania-0.5B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tanzania-0.5B-i1-GGUF/resolve/main/Tanzania-0.5B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tanzania-0.5B-i1-GGUF/resolve/main/Tanzania-0.5B.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 0.5 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Tanzania-0.5B-i1-GGUF/resolve/main/Tanzania-0.5B.i1-Q4_0.gguf) \| i1-Q4_0 \| 0.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Tanzania-0.5B-i1-GGUF/resolve/main/Tanzania-0.5B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.5 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Tanzania-0.5B-i1-GGUF/resolve/main/Tanzania-0.5B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 0.5 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Tanzania-0.5B-i1-GGUF/resolve/main/Tanzania-0.5B.i1-Q4_1.gguf) \| i1-Q4_1 \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tanzania-0.5B-i1-GGUF/resolve/main/Tanzania-0.5B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 0.5 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Tanzania-0.5B-i1-GGUF/resolve/main/Tanzania-0.5B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 0.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Tanzania-0.5B-i1-GGUF/resolve/main/Tanzania-0.5B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tanzania-0.5B-i1-GGUF/resolve/main/Tanzania-0.5B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tanzania-0.5B-i1-GGUF/resolve/main/Tanzania-0.5B.i1-Q6_K.gguf) \| i1-Q6_K \| 0.6 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-i1-GGUF	mradermacher	2025-09-22T11:24:17Z	0	0	transformers	[ "transformers", "gguf", "programming", "code generation", "code", "coding", "coder", "chat", "brainstorm", "qwen", "qwen3", "qwencoder", "brainstorm 20x", "creative", "all uses cases", "Jan-V1", "float32", "horror", "32 bit precision", "science fiction", "fantasy", "Star Trek", "finetune", "thinking", "reasoning", "unsloth", "en", "dataset:progs2002/star-trek-tng-scripts", "base_model:DavidAU/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B", "base_model:quantized:DavidAU/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-09-22T10:40:42Z	--- base_model: DavidAU/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B datasets: - progs2002/star-trek-tng-scripts language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - programming - code generation - code - coding - coder - chat - code - chat - brainstorm - qwen - qwen3 - qwencoder - brainstorm 20x - creative - all uses cases - Jan-V1 - float32 - horror - 32 bit precision - science fiction - fantasy - Star Trek - finetune - thinking - reasoning - unsloth --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> <!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> weighted/imatrix quants of https://huggingface.co/DavidAU/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-i1-GGUF).* static quants are available at https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B.imatrix.gguf) \| imatrix \| 0.1 \| imatrix file (for creating your own qwuants) \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.8 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 2.4 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B.i1-Q2_K.gguf) \| i1-Q2_K \| 2.6 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 2.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.3 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 3.5 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B.i1-Q4_0.gguf) \| i1-Q4_0 \| 3.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 3.8 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 3.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B.i1-Q4_1.gguf) \| i1-Q4_1 \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm-E32-v1-256k-ctx-6B.i1-Q6_K.gguf) \| i1-Q6_K \| 5.3 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh-GGUF	mradermacher	2025-09-22T11:24:12Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:Johnnyfans/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh", "base_model:quantized:Johnnyfans/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-22T10:51:34Z	--- base_model: Johnnyfans/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/Johnnyfans/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#ICDRec-SFT-Qwen3-4B-Thinking-2507-zh-GGUF).* weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh-GGUF/resolve/main/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh.Q2_K.gguf) \| Q2_K \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh-GGUF/resolve/main/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh.Q3_K_S.gguf) \| Q3_K_S \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh-GGUF/resolve/main/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh.Q3_K_M.gguf) \| Q3_K_M \| 2.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh-GGUF/resolve/main/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh.Q3_K_L.gguf) \| Q3_K_L \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh-GGUF/resolve/main/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh.IQ4_XS.gguf) \| IQ4_XS \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh-GGUF/resolve/main/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh.Q4_K_S.gguf) \| Q4_K_S \| 2.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh-GGUF/resolve/main/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh.Q4_K_M.gguf) \| Q4_K_M \| 2.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh-GGUF/resolve/main/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh.Q5_K_S.gguf) \| Q5_K_S \| 3.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh-GGUF/resolve/main/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh.Q5_K_M.gguf) \| Q5_K_M \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh-GGUF/resolve/main/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh.Q6_K.gguf) \| Q6_K \| 3.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh-GGUF/resolve/main/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh.Q8_0.gguf) \| Q8_0 \| 4.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh-GGUF/resolve/main/ICDRec-SFT-Qwen3-4B-Thinking-2507-zh.f16.gguf) \| f16 \| 8.9 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
MattBou00/llama-3-2-1b-detox_v1f_RRETRT_Again_ROUND3-checkpoint-epoch-40	MattBou00	2025-09-22T11:22:15Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-09-22T11:21:18Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_11-15-41/checkpoints/checkpoint-epoch-40") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_11-15-41/checkpoints/checkpoint-epoch-40") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_11-15-41/checkpoints/checkpoint-epoch-40") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
nnilayy/dreamer_stride_256-binary-arousal-Kfold-5-stride_256	nnilayy	2025-09-22T11:21:39Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-09-22T11:21:33Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]
noirchan/Llama-3-8B-JaCode-TIES-v1	noirchan	2025-09-22T11:21:30Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2306.01708", "base_model:alfredplpl/Llama-3-8B-Instruct-Ja", "base_model:merge:alfredplpl/Llama-3-8B-Instruct-Ja", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:merge:meta-llama/Meta-Llama-3-8B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T11:20:49Z	--- base_model: - alfredplpl/Llama-3-8B-Instruct-Ja - meta-llama/Meta-Llama-3-8B-Instruct library_name: transformers tags: - mergekit - merge --- # ties_v1 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) as a base. ### Models Merged The following models were included in the merge: * [alfredplpl/Llama-3-8B-Instruct-Ja](https://huggingface.co/alfredplpl/Llama-3-8B-Instruct-Ja) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: ties base_model: meta-llama/Meta-Llama-3-8B-Instruct models: - model: meta-llama/Meta-Llama-3-8B-Instruct parameters: weight: 0.4 density: 0.7 - model: alfredplpl/Llama-3-8B-Instruct-Ja parameters: weight: 0.6 density: 0.8 parameters: normalize: false dtype: bfloat16 tokenizer_source: union ```
AXERA-TECH/YOLO11	AXERA-TECH	2025-09-22T11:21:01Z	26	0	null	[ "onnx", "Ultralytics", "YOLO11", "object-detection", "en", "base_model:Ultralytics/YOLO11", "base_model:quantized:Ultralytics/YOLO11", "license:mit", "region:us" ]	object-detection	2025-01-11T16:18:52Z	--- license: mit language: - en base_model: - Ultralytics/YOLO11 pipeline_tag: object-detection tags: - Ultralytics - YOLO11 --- # YOLO11 This version of YOLO11 has been converted to run on the Axera NPU using w8a16 quantization. This model has been optimized with the following LoRA: Compatible with Pulsar2 version: 3.4 ## Convert tools links: For those who are interested in model conversion, you can try to export axmodel through - [The repo of ax-samples](https://github.com/AXERA-TECH/ax-samples), which you can get the how to build the `ax_yolo11` - [The repo of axcl-samples](https://github.com/AXERA-TECH/axcl-samples), which you can get the how to build the `axcl_yolo11` - [Pulsar2 Link, How to Convert ONNX to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/pulsar2/introduction.html) ## Support Platform - AX650 - [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html) - [M.2 Accelerator card](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html) - AX630C - [爱芯派2](https://axera-pi-2-docs-cn.readthedocs.io/zh-cn/latest/index.html) - [Module-LLM](https://docs.m5stack.com/zh_CN/module/Module-LLM) - [LLM630 Compute Kit](https://docs.m5stack.com/zh_CN/core/LLM630%20Compute%20Kit) \|Chips\|cost\| \|--\|--\| \|AX650\| 25 ms \| \|AX630C\| TBD ms \| ## How to use Download all files from this repository to the device ``` (axcl) axera@raspberrypi:~/samples/AXERA-TECH/YOLO11 $ tree -L 2 . ├── ax620e │ └── yolo11s.axmodel.onnx ├── ax650 │ ├── yolo11s.axmodel │ └── yolo11x.axmodel ├── ax_aarch64 │ └── ax_yolo11 ├── axcl_aarch64 │ └── axcl_yolo11 ├── axcl_x86_64 │ └── axcl_yolo11 ├── config.json ├── cut-onnx.py ├── football.jpg ├── README.md ├── ssd_horse.jpg ├── yolo11_config.json ├── yolo11_out.jpg ├── yolo11s-cut.onnx └── yolo11-test.py 6 directories, 15 files ``` ### Inference Input image: ![](./football.jpg) #### Inference with AX650 Host, such as M4N-Dock(爱芯派Pro) ``` root@ax650:~/samples/AXERA-TECH/YOLO11# ./ax_aarch64/ax_yolo11 -m ax650/yolo11x.axmodel -i football.jpg -------------------------------------- model file : ax650/yolo11x.axmodel image file : football.jpg img_h, img_w : 640 640 -------------------------------------- Engine creating handle is done. Engine creating context is done. Engine get io info is done. Engine alloc io is done. Engine push input is done. -------------------------------------- post process cost time:4.20 ms -------------------------------------- Repeat 1 times, avg time 24.56 ms, max_time 24.56 ms, min_time 24.56 ms -------------------------------------- detection num: 9 0: 94%, [ 757, 220, 1127, 1154], person 0: 94%, [ 0, 357, 314, 1112], person 0: 93%, [1353, 339, 1629, 1037], person 0: 91%, [ 494, 476, 659, 1001], person 32: 86%, [1231, 877, 1281, 922], sports ball 32: 73%, [ 774, 887, 828, 938], sports ball 32: 66%, [1012, 882, 1051, 927], sports ball 0: 54%, [ 0, 543, 83, 1000], person 0: 46%, [1837, 696, 1877, 814], person -------------------------------------- ``` Output image: ![](./yolo11_out.jpg) #### Inference with M.2 Accelerator card ``` (axcl) axera@raspberrypi:~/samples/AXERA-TECH/YOLO11 $ ./axcl_aarch64/axcl_yolo11 -m ax650/yolo11x.axmodel -i football.jpg -------------------------------------- model file : ax650/yolo11x.axmodel image file : football.jpg img_h, img_w : 640 640 -------------------------------------- axclrtEngineCreateContextt is done. axclrtEngineGetIOInfo is done. grpid: 0 input size: 1 name: images 1 x 640 x 640 x 3 output size: 3 name: /model.23/Concat_output_0 1 x 80 x 80 x 144 name: /model.23/Concat_1_output_0 1 x 40 x 40 x 144 name: /model.23/Concat_2_output_0 1 x 20 x 20 x 144 ================================================== Engine push input is done. -------------------------------------- post process cost time:1.38 ms -------------------------------------- Repeat 1 times, avg time 24.73 ms, max_time 24.73 ms, min_time 24.73 ms -------------------------------------- detection num: 9 0: 94%, [ 757, 220, 1127, 1154], person 0: 94%, [ 0, 357, 314, 1112], person 0: 93%, [1353, 339, 1629, 1037], person 0: 91%, [ 494, 476, 659, 1001], person 32: 86%, [1231, 877, 1281, 922], sports ball 32: 73%, [ 774, 887, 828, 938], sports ball 32: 66%, [1012, 882, 1051, 927], sports ball 0: 54%, [ 0, 543, 83, 1000], person 0: 46%, [1837, 696, 1877, 814], person -------------------------------------- ```
DevforMM/tmp_trainer	DevforMM	2025-09-22T11:13:12Z	0	0	transformers	[ "transformers", "safetensors", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:ntu-spml/distilhubert", "base_model:finetune:ntu-spml/distilhubert", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2025-09-22T11:13:07Z	--- library_name: transformers license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - marsyas/gtzan model-index: - name: ntu-spml/distilhubert-finetuned-gtzan results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ntu-spml/distilhubert-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.55.3 - Pytorch 2.8.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.2
aamijar/Llama-2-7b-hf-dora-r8-boolq-epochs0	aamijar	2025-09-22T11:12:30Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-22T11:12:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
adalberto-temp/energy_dpo_V0.2_Instruct_ref	adalberto-temp	2025-09-22T11:12:20Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T11:06:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Gardeviance/MS-Gardventure-MW-V1-22B-IQ4_NL-GGUF	Gardeviance	2025-09-22T11:09:47Z	66	0	null	[ "gguf", "text-generation", "quantized", "mit", "mistral", "llamacpp", "en", "base_model:TheDrummer/UnslopSmall-22B-v1", "base_model:merge:TheDrummer/UnslopSmall-22B-v1", "base_model:hf-100/Mistral-Small-Spellbound-StoryWriter-22B-instruct-0.2-chkpt-200-16-bit", "base_model:merge:hf-100/Mistral-Small-Spellbound-StoryWriter-22B-instruct-0.2-chkpt-200-16-bit", "base_model:mistralai/Mistral-Small-Instruct-2409", "base_model:merge:mistralai/Mistral-Small-Instruct-2409", "base_model:nbeerbower/Mistral-Small-Drummer-22B", "base_model:merge:nbeerbower/Mistral-Small-Drummer-22B", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	text-generation	2025-09-17T01:49:44Z	--- language: - en tags: - text-generation - gguf - quantized - mit - mistral - llamacpp license: mit base_model: - mistralai/Mistral-Small-Instruct-2409 - TheDrummer/UnslopSmall-22B-v1 - nbeerbower/Mistral-Small-Drummer-22B - hf-100/Mistral-Small-Spellbound-StoryWriter-22B-instruct-0.2-chkpt-200-16-bit base_model_relation: merge pipeline_tag: text-generation --- # MS-Gardventure-MW-V1-22B-IQ4_NL-GGUF It's pretty good at AI Dungeon style gameplay using KoboldAI Lite. There's an example scenario in the repo. Made with some handmade training data. ## Credits * UnslopSmall-22B-v1 * Mistral-Small-Drummer-22B * Mistral-Small-Spellbound-StoryWriter-22B-instruct-0.2-chkpt-200-16-bit ## Full Model Release I can release full FP16 HF format model if you want, but it's a pain to do.
poolkiltzn/blockassist-bc-vigilant_alert_tuna_1758539259	poolkiltzn	2025-09-22T11:09:09Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "vigilant alert tuna", "arxiv:2504.07091", "region:us" ]	null	2025-09-22T11:08:44Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - vigilant alert tuna --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
TAUR-dev/M-multitask_sftdata_cd3_lm3_ac4_lc4-sft	TAUR-dev	2025-09-22T11:03:58Z	0	0	null	[ "safetensors", "qwen2", "region:us" ]	null	2025-09-22T11:03:25Z	# M-multitask_sftdata_cd3_lm3_ac4_lc4-sft This model was created as part of the multitask_sftdata_cd3_lm3_ac4_lc4 experiment using the SkillFactory experiment management system. ## Model Details - Training Method: LLaMAFactory SFT (Supervised Fine-Tuning) - Stage Name: sft - Experiment: multitask_sftdata_cd3_lm3_ac4_lc4 ## Training Configuration {"model_name_or_path": "Qwen/Qwen2.5-1.5B-Instruct", "trust_remote_code": true, "stage": "sft", "do_train": true, "finetuning_type": "full", "deepspeed": "/home/ubuntu/skill-factory/thirdparty/LLaMA-Factory/examples/deepspeed/ds_z2_config.json", "dataset": "TAUR_dev__multitask_sftdata_cd3_lm3_ac4_lc4", "template": "qwen", "cutoff_len": 16384, "max_samples": 1000000, "overwrite_cache": true, "preprocessing_num_workers": 1, "dataloader_num_workers": 0, "disable_tqdm": false, "output_dir": "/data4/tmp/sedrick/skillfactory/temp/llamafactory/checkpoints", "logging_steps": 10, "save_steps": 100000, "plot_loss": true, "overwrite_output_dir": true, "per_device_train_batch_size": 1, "gradient_accumulation_steps": 1, "learning_rate": 1e-06, "num_train_epochs": 1, "lr_scheduler_type": "cosine", "warmup_ratio": 0.05, "weight_decay": 0.0001, "adam_beta1": 0.9, "adam_beta2": 0.95, "bf16": true, "ddp_timeout": 180000000, "gradient_checkpointing": true, "save_only_model": true, "enable_masked_ranges": false, "save_strategy": "steps", "save_total_limit": 5, "sf_tracker_dataset_id": "TAUR-dev/D-ExpTracker__multitask_sftdata_cd3_lm3_ac4_lc4__v1", "sf_eval_before_training": false, "sf_wandb_project": "multitask_sftdata_cd3_lm3_ac4_lc4_sft", "sf_eval_steps": null, "run_name": "multitask_sftdata_cd3_lm3_ac4_lc4_sft"} ## Experiment Tracking 🔗 View complete experiment details: [Experiment Tracker Dataset](https://huggingface.co/datasets/TAUR-dev/D-ExpTracker__multitask_sftdata_cd3_lm3_ac4_lc4__v1) ## Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TAUR-dev/M-multitask_sftdata_cd3_lm3_ac4_lc4-sft") model = AutoModelForCausalLM.from_pretrained("TAUR-dev/M-multitask_sftdata_cd3_lm3_ac4_lc4-sft") ```
MattBou00/llama-3-2-1b-detox_v1f_RRETRT_Again_ROUND5-checkpoint-epoch-100	MattBou00	2025-09-22T11:03:20Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-09-22T11:02:18Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_10-46-42/checkpoints/checkpoint-epoch-100") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_10-46-42/checkpoints/checkpoint-epoch-100") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_10-46-42/checkpoints/checkpoint-epoch-100") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
Accountable-SA/gemma-3-270m-it-base-Q4_K_M-GGUF	Accountable-SA	2025-09-22T11:03:17Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "base_model:Accountable-SA/gemma-3-270m-it-base", "base_model:quantized:Accountable-SA/gemma-3-270m-it-base", "endpoints_compatible", "region:us" ]	null	2025-09-22T11:03:13Z	--- library_name: transformers tags: - llama-cpp - gguf-my-repo base_model: Accountable-SA/gemma-3-270m-it-base --- # massimogiuseppe/gemma-3-270m-it-base-Q4_K_M-GGUF This model was converted to GGUF format from [`Accountable-SA/gemma-3-270m-it-base`](https://huggingface.co/Accountable-SA/gemma-3-270m-it-base) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Accountable-SA/gemma-3-270m-it-base) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo massimogiuseppe/gemma-3-270m-it-base-Q4_K_M-GGUF --hf-file gemma-3-270m-it-base-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo massimogiuseppe/gemma-3-270m-it-base-Q4_K_M-GGUF --hf-file gemma-3-270m-it-base-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo massimogiuseppe/gemma-3-270m-it-base-Q4_K_M-GGUF --hf-file gemma-3-270m-it-base-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo massimogiuseppe/gemma-3-270m-it-base-Q4_K_M-GGUF --hf-file gemma-3-270m-it-base-q4_k_m.gguf -c 2048 ```
felixZzz/h0slmlq1-step_00400	felixZzz	2025-09-22T11:00:26Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T10:58:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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shahabas9/faq-model	shahabas9	2025-09-22T10:59:49Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T09:55:27Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: shahabas9 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
poolkiltzn/blockassist-bc-vigilant_alert_tuna_1758538641	poolkiltzn	2025-09-22T10:58:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "vigilant alert tuna", "arxiv:2504.07091", "region:us" ]	null	2025-09-22T10:58:22Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - vigilant alert tuna --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
felixZzz/q04m4jep-step_00500	felixZzz	2025-09-22T10:58:06Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T10:56:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
MattBou00/llama-3-2-1b-detox_v1f_RRETRT_Again_ROUND5-checkpoint-epoch-60	MattBou00	2025-09-22T10:56:47Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-09-22T10:55:48Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_10-46-42/checkpoints/checkpoint-epoch-60") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_10-46-42/checkpoints/checkpoint-epoch-60") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_10-46-42/checkpoints/checkpoint-epoch-60") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
Accountable-SA/gemma-3-270m-it-base-Q3_K_M-GGUF	Accountable-SA	2025-09-22T10:56:00Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "base_model:Accountable-SA/gemma-3-270m-it-base", "base_model:quantized:Accountable-SA/gemma-3-270m-it-base", "endpoints_compatible", "region:us" ]	null	2025-09-22T10:55:56Z	--- library_name: transformers tags: - llama-cpp - gguf-my-repo base_model: Accountable-SA/gemma-3-270m-it-base --- # massimogiuseppe/gemma-3-270m-it-base-Q3_K_M-GGUF This model was converted to GGUF format from [`Accountable-SA/gemma-3-270m-it-base`](https://huggingface.co/Accountable-SA/gemma-3-270m-it-base) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Accountable-SA/gemma-3-270m-it-base) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo massimogiuseppe/gemma-3-270m-it-base-Q3_K_M-GGUF --hf-file gemma-3-270m-it-base-q3_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo massimogiuseppe/gemma-3-270m-it-base-Q3_K_M-GGUF --hf-file gemma-3-270m-it-base-q3_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo massimogiuseppe/gemma-3-270m-it-base-Q3_K_M-GGUF --hf-file gemma-3-270m-it-base-q3_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo massimogiuseppe/gemma-3-270m-it-base-Q3_K_M-GGUF --hf-file gemma-3-270m-it-base-q3_k_m.gguf -c 2048 ```
thegdpranavl/Qwen3_8B_Bespoke	thegdpranavl	2025-09-22T10:54:19Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-8B-Base-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-8B-Base-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-09-22T10:54:02Z	--- base_model: unsloth/Qwen3-8B-Base-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: thegdpranavl - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-8B-Base-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ttr1007/EdwardFisher-Replicate3	ttr1007	2025-09-22T10:53:44Z	0	0	null	[ "region:us" ]	null	2025-09-22T10:53:44Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: Edward --- # Edwardfisher Replicate3 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `Edward` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Edward", "lora_weights": "https://huggingface.co/ttr1007/EdwardFisher-Replicate3/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('ttr1007/EdwardFisher-Replicate3', weight_name='lora.safetensors') image = pipeline('Edward').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 3512 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/ttr1007/EdwardFisher-Replicate3/discussions) to add images that show off what you’ve made with this LoRA.
felixZzz/2xtvgc3k-step_00500	felixZzz	2025-09-22T10:53:44Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T10:51:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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MattBou00/llama-3-2-1b-detox_v1f_RRETRT_Again_ROUND5-checkpoint-epoch-40	MattBou00	2025-09-22T10:53:32Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-09-22T10:52:33Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_10-46-42/checkpoints/checkpoint-epoch-40") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_10-46-42/checkpoints/checkpoint-epoch-40") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_10-46-42/checkpoints/checkpoint-epoch-40") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
qualiaadmin/405dc14c-9a7e-4ea7-96b7-548548a9c0c7	qualiaadmin	2025-09-22T10:52:15Z	0	0	lerobot	[ "lerobot", "safetensors", "robotics", "smolvla", "dataset:Calvert0921/SmolVLA_LiftBlueCubeDouble_Franka_200", "arxiv:2506.01844", "base_model:lerobot/smolvla_base", "base_model:finetune:lerobot/smolvla_base", "license:apache-2.0", "region:us" ]	robotics	2025-09-22T10:50:32Z	--- base_model: lerobot/smolvla_base datasets: Calvert0921/SmolVLA_LiftBlueCubeDouble_Franka_200 library_name: lerobot license: apache-2.0 model_name: smolvla pipeline_tag: robotics tags: - lerobot - robotics - smolvla --- # Model Card for smolvla <!-- Provide a quick summary of what the model is/does. --> [SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash lerobot-train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash lerobot-record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with eval\_ and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - License: apache-2.0
mradermacher/salamandra-2b-fft-trenes-v2-GGUF	mradermacher	2025-09-22T10:51:40Z	0	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "sft", "unsloth", "trl", "en", "base_model:fabriciocarraro/salamandra-2b-fft-trenes-v2", "base_model:quantized:fabriciocarraro/salamandra-2b-fft-trenes-v2", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-22T10:41:06Z	--- base_model: fabriciocarraro/salamandra-2b-fft-trenes-v2 language: - en library_name: transformers model_name: fft-2b-v2 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - generated_from_trainer - sft - unsloth - trl --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/fabriciocarraro/salamandra-2b-fft-trenes-v2 <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#salamandra-2b-fft-trenes-v2-GGUF).* weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/salamandra-2b-fft-trenes-v2-GGUF/resolve/main/salamandra-2b-fft-trenes-v2.Q2_K.gguf) \| Q2_K \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/salamandra-2b-fft-trenes-v2-GGUF/resolve/main/salamandra-2b-fft-trenes-v2.Q3_K_S.gguf) \| Q3_K_S \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/salamandra-2b-fft-trenes-v2-GGUF/resolve/main/salamandra-2b-fft-trenes-v2.Q3_K_M.gguf) \| Q3_K_M \| 1.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/salamandra-2b-fft-trenes-v2-GGUF/resolve/main/salamandra-2b-fft-trenes-v2.Q3_K_L.gguf) \| Q3_K_L \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/salamandra-2b-fft-trenes-v2-GGUF/resolve/main/salamandra-2b-fft-trenes-v2.IQ4_XS.gguf) \| IQ4_XS \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/salamandra-2b-fft-trenes-v2-GGUF/resolve/main/salamandra-2b-fft-trenes-v2.Q4_K_S.gguf) \| Q4_K_S \| 1.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/salamandra-2b-fft-trenes-v2-GGUF/resolve/main/salamandra-2b-fft-trenes-v2.Q4_K_M.gguf) \| Q4_K_M \| 1.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/salamandra-2b-fft-trenes-v2-GGUF/resolve/main/salamandra-2b-fft-trenes-v2.Q5_K_S.gguf) \| Q5_K_S \| 1.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/salamandra-2b-fft-trenes-v2-GGUF/resolve/main/salamandra-2b-fft-trenes-v2.Q5_K_M.gguf) \| Q5_K_M \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/salamandra-2b-fft-trenes-v2-GGUF/resolve/main/salamandra-2b-fft-trenes-v2.Q6_K.gguf) \| Q6_K \| 2.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/salamandra-2b-fft-trenes-v2-GGUF/resolve/main/salamandra-2b-fft-trenes-v2.Q8_0.gguf) \| Q8_0 \| 2.5 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/salamandra-2b-fft-trenes-v2-GGUF/resolve/main/salamandra-2b-fft-trenes-v2.f16.gguf) \| f16 \| 4.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
felixZzz/2xtvgc3k-step_00400	felixZzz	2025-09-22T10:51:37Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T10:49:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
tomerz14/distilhubert-finetuned-gtzan	tomerz14	2025-09-22T10:51:10Z	18	0	transformers	[ "transformers", "tensorboard", "safetensors", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:ntu-spml/distilhubert", "base_model:finetune:ntu-spml/distilhubert", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2025-09-20T07:49:44Z	--- library_name: transformers license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.74 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilhubert-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 1.1216 - Accuracy: 0.74 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.9417 \| 1.0 \| 29 \| 1.7636 \| 0.4 \| \| 1.1584 \| 2.0 \| 58 \| 1.2854 \| 0.59 \| \| 0.9493 \| 3.0 \| 87 \| 1.1907 \| 0.57 \| \| 0.5895 \| 4.0 \| 116 \| 1.4273 \| 0.62 \| \| 0.3732 \| 5.0 \| 145 \| 0.9427 \| 0.74 \| \| 0.3519 \| 6.0 \| 174 \| 1.4957 \| 0.63 \| \| 0.2988 \| 7.0 \| 203 \| 1.4078 \| 0.67 \| \| 0.1915 \| 8.0 \| 232 \| 1.1216 \| 0.74 \| ### Framework versions - Transformers 4.56.1 - Pytorch 2.8.0+cu128 - Datasets 3.6.0 - Tokenizers 0.22.0
its-zion-18/sign-image-autogluon-predictor	its-zion-18	2025-09-22T10:50:59Z	0	0	null	[ "autogluon", "image-classification", "multimodal", "sign-identification", "en", "license:mit", "region:us" ]	image-classification	2025-09-20T17:04:42Z	--- license: mit language: en tags: - autogluon - image-classification - multimodal - sign-identification --- # AutoGluon Sign Identification Predictor This repository contains a trained MultiModalPredictor from the AutoGluon library, which was trained to identify signs from images. Which can also be found in the files and versions section under AutoML_for_Neural_Networks # Dataset The model was trained on the ecopus/sign_identification dataset. The augmented split was used for training and validation, while the original split was used for the final evaluation of the model's performance. # Evaluation Results The final performance of the best model on the original dataset is as follows: - Accuracy: `1.0000` - Weighted F1: `1.0000` # Files in this Repository - `autogluon_image_predictor.pkl`: The trained `MultiModalPredictor` pickled using `cloudpickle`. - `autogluon_image_predictor_dir.zip`: The zipped native AutoGluon predictor directory for portability. # Potential Errors The augmented split in the ecopus/sign_identification dataset is specifically designed to be an artificially expanded version of the original split. The images in the augmented set are simple transformations—like rotations, flips, or slight color changes—of the images in the original set. The code then trains the model on a portion of the augmented data (df_aug_train) and evaluates it on the original data (df_orig). Because the model was trained on data that is derived directly from the evaluation data, it's not actually seeing truly "new" information during the final test. Which could be leading to data leakage and overfitting
Poorvaja/Byt5_Telugu	Poorvaja	2025-09-22T10:49:52Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-09-22T10:48:43Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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poolkiltzn/blockassist-bc-vigilant_alert_tuna_1758538017	poolkiltzn	2025-09-22T10:48:24Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "vigilant alert tuna", "arxiv:2504.07091", "region:us" ]	null	2025-09-22T10:47:53Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - vigilant alert tuna --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
rinogeek/EcoMind	rinogeek	2025-09-22T10:47:44Z	0	1	null	[ "safetensors", "gpt2", "finance", "french", "llm", "text-generation", "fr", "license:mit", "region:us" ]	text-generation	2025-09-22T00:11:13Z	--- license: mit language: - fr pipeline_tag: text-generation tags: - finance - french - llm ---
justpluso/turn-detection	justpluso	2025-09-22T10:47:43Z	28	0	null	[ "safetensors", "gemma3_text", "turn-detection", "text-classification", "zh", "en", "base_model:google/gemma-3-270m-it", "base_model:finetune:google/gemma-3-270m-it", "license:apache-2.0", "region:us" ]	text-classification	2025-09-05T10:54:42Z	--- license: apache-2.0 language: - zh - en base_model: - google/gemma-3-270m-it pipeline_tag: text-classification tags: - turn-detection --- 👉👉👉👉👉 [github](https://github.com/justplus/turn-detection) Turn Detection（对话轮次检测）是一个用于人机对话系统中的关键技术，主要用于： - 对话边界识别：准确判断用户何时结束当前发言，避免对话系统过早或过晚响应 - 多轮对话管理：在连续对话中识别每个对话轮次的开始和结束，提升对话体验 - 实时交互优化：通过精准的轮次检测，实现更自然流畅的人机交互 - 语音助手增强：为语音助手、客服机器人等应用提供更智能的对话控制 ## 2. 主要特点 ### 🔄 支持多轮对话 - 能够处理复杂的多轮对话场景 - 准确识别对话中的停顿、思考和真正的轮次结束 - 支持上下文感知的轮次判断支持多轮对话的重要性： ``` user: 我们来个成语接龙吧？ assistant: 那我先来，杞人忧天。该你了 user: 天天向上 ``` 非多轮对话下单一的"天天向上"是不完整的，但是放在上下文中则应该是完整的。 ### 🚀 轻量化推理 - 模型参数仅270M，资源占用低 - 支持CPU推理，无需GPU即可部署 - 推理速度快，满足实时对话需求 - 适合边缘设备和资源受限环境 ### 🌍 多语言支持 - 原生支持中文和英文对话检测 - 模型架构支持微调扩展到其他语言 - 跨语言泛化能力强 ### 🛠️ 可定制化 - 提供完整的微调框架 - 支持针对特定领域和语言的定制训练 - 灵活的数据处理和训练流程 ### 🙅‍♂️ 支持等待状态 - 0 (不完整)：用户话语未说完，需要等待继续输入 - 1 (完整)：用户话语表达完整，可以进行回复 - 2 (要求等待)：用户要求暂停或打断AI回复
thefirstgoku/22SEP_intergated_v32_21	thefirstgoku	2025-09-22T10:46:38Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-09-22T10:45:24Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
tommycik/prova4	tommycik	2025-09-22T10:41:49Z	6	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "flux", "flux-diffusers", "text-to-image", "controlnet", "diffusers-training", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-08-24T15:17:33Z	--- base_model: black-forest-labs/FLUX.1-dev library_name: diffusers license: other inference: true tags: - flux - flux-diffusers - text-to-image - diffusers - controlnet - diffusers-training - flux - flux-diffusers - text-to-image - diffusers - controlnet - diffusers-training --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # controlnet-tommycik/prova4 These are controlnet weights trained on black-forest-labs/FLUX.1-dev with new type of conditioning. You can find some example images below. prompt: transparent glass on white background, the bottom part of the glass presents light grooves ![images_0)](./images_0.png) ## License Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
thefirstgoku/22SEP_intergated_v32_12	thefirstgoku	2025-09-22T10:41:24Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-09-22T10:40:09Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
dezineinnovation/commercialinteriordesign	dezineinnovation	2025-09-22T10:39:24Z	0	0	null	[ "text-classification", "license:bigscience-openrail-m", "region:us" ]	text-classification	2025-09-22T10:36:46Z	--- license: bigscience-openrail-m pipeline_tag: text-classification ---
poolkiltzn/blockassist-bc-vigilant_alert_tuna_1758537408	poolkiltzn	2025-09-22T10:38:07Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "vigilant alert tuna", "arxiv:2504.07091", "region:us" ]	null	2025-09-22T10:37:48Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - vigilant alert tuna --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
BKM1804/d3f907ad-4c78-4906-af59-b353aeb75e0f	BKM1804	2025-09-22T10:37:27Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-22T10:37:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
samhitmantrala/smish_final	samhitmantrala	2025-09-22T10:37:07Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-09-22T10:31:48Z	--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: smish_final results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # smish_final This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0618 - Accuracy: 0.9847 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 442 \| 0.0608 \| 0.9858 \| \| 0.0749 \| 2.0 \| 884 \| 0.0618 \| 0.9847 \| ### Framework versions - Transformers 4.56.1 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.22.0
trumtrum/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-silky_howling_jellyfish	trumtrum	2025-09-22T10:36:22Z	150	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am silky_howling_jellyfish", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-14T10:08:40Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am silky_howling_jellyfish --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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mradermacher/LongPAI-8B-i1-GGUF	mradermacher	2025-09-22T10:34:05Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:jylins/LongPAI-8B", "base_model:quantized:jylins/LongPAI-8B", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-09-22T09:43:50Z	--- base_model: jylins/LongPAI-8B language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> <!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> weighted/imatrix quants of https://huggingface.co/jylins/LongPAI-8B <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#LongPAI-8B-i1-GGUF).* static quants are available at https://huggingface.co/mradermacher/LongPAI-8B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-i1-GGUF/resolve/main/LongPAI-8B.imatrix.gguf) \| imatrix \| 0.1 \| imatrix file (for creating your own qwuants) \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-i1-GGUF/resolve/main/LongPAI-8B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-i1-GGUF/resolve/main/LongPAI-8B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-i1-GGUF/resolve/main/LongPAI-8B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-i1-GGUF/resolve/main/LongPAI-8B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-i1-GGUF/resolve/main/LongPAI-8B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-i1-GGUF/resolve/main/LongPAI-8B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-i1-GGUF/resolve/main/LongPAI-8B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 3.1 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-i1-GGUF/resolve/main/LongPAI-8B.i1-Q2_K.gguf) \| i1-Q2_K \| 3.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-i1-GGUF/resolve/main/LongPAI-8B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-i1-GGUF/resolve/main/LongPAI-8B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-i1-GGUF/resolve/main/LongPAI-8B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-i1-GGUF/resolve/main/LongPAI-8B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-i1-GGUF/resolve/main/LongPAI-8B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-i1-GGUF/resolve/main/LongPAI-8B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 4.1 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-i1-GGUF/resolve/main/LongPAI-8B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.4 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-i1-GGUF/resolve/main/LongPAI-8B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-i1-GGUF/resolve/main/LongPAI-8B.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-i1-GGUF/resolve/main/LongPAI-8B.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 4.8 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-i1-GGUF/resolve/main/LongPAI-8B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-i1-GGUF/resolve/main/LongPAI-8B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-i1-GGUF/resolve/main/LongPAI-8B.i1-Q4_1.gguf) \| i1-Q4_1 \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-i1-GGUF/resolve/main/LongPAI-8B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-i1-GGUF/resolve/main/LongPAI-8B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-i1-GGUF/resolve/main/LongPAI-8B.i1-Q6_K.gguf) \| i1-Q6_K \| 6.7 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/LongPAI-8B-GGUF	mradermacher	2025-09-22T10:33:02Z	0	1	transformers	[ "transformers", "gguf", "en", "base_model:jylins/LongPAI-8B", "base_model:quantized:jylins/LongPAI-8B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-22T09:28:08Z	--- base_model: jylins/LongPAI-8B language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/jylins/LongPAI-8B <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#LongPAI-8B-GGUF).* weighted/imatrix quants are available at https://huggingface.co/mradermacher/LongPAI-8B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-GGUF/resolve/main/LongPAI-8B.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-GGUF/resolve/main/LongPAI-8B.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-GGUF/resolve/main/LongPAI-8B.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-GGUF/resolve/main/LongPAI-8B.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-GGUF/resolve/main/LongPAI-8B.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-GGUF/resolve/main/LongPAI-8B.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-GGUF/resolve/main/LongPAI-8B.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-GGUF/resolve/main/LongPAI-8B.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-GGUF/resolve/main/LongPAI-8B.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-GGUF/resolve/main/LongPAI-8B.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-GGUF/resolve/main/LongPAI-8B.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/LongPAI-8B-GGUF/resolve/main/LongPAI-8B.f16.gguf) \| f16 \| 16.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
TiMOld/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-roaring_smooth_ibis	TiMOld	2025-09-22T10:29:26Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am roaring_smooth_ibis", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T09:37:39Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am roaring_smooth_ibis --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
hunarbatra/spatialthinker_10k_baseline_option_text_75_7b	hunarbatra	2025-09-22T10:22:36Z	20	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-to-text", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-to-text	2025-09-16T01:01:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
abhi099k/BBI-ai-text-detecto-v4	abhi099k	2025-09-22T10:17:16Z	36	0	transformers	[ "transformers", "safetensors", "deberta-v2", "generated_from_trainer", "text-classification", "base_model:desklib/ai-text-detector-v1.01", "base_model:finetune:desklib/ai-text-detector-v1.01", "license:mit", "endpoints_compatible", "region:us" ]	text-classification	2025-09-19T04:08:26Z	--- library_name: transformers license: mit base_model: desklib/ai-text-detector-v1.01 tags: - generated_from_trainer model-index: - name: BBI-ai-text-detecto-v4 results: [] pipeline_tag: text-classification --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BBI-ai-text-detecto-v4 This model is a fine-tuned version of [desklib/ai-text-detector-v1.01](https://huggingface.co/desklib/ai-text-detector-v1.01) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 1.0276 - eval_model_preparation_time: 0.0058 - eval_accuracy: 0.5686 - eval_f1: 0.6963 - eval_runtime: 213.2311 - eval_samples_per_second: 49.242 - eval_steps_per_second: 6.158 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.56.1 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.22.0
Reihaneh/wav2vec2_fy_nl_best_frisian_1	Reihaneh	2025-09-22T10:17:11Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-22T10:17:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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mradermacher/Orpheus-TTS-pl-v3.0-GGUF	mradermacher	2025-09-22T10:16:48Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "llama", "en", "base_model:TeeZee/Orpheus-TTS-pl-v3.0", "base_model:quantized:TeeZee/Orpheus-TTS-pl-v3.0", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-22T08:55:00Z	--- base_model: TeeZee/Orpheus-TTS-pl-v3.0 language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - llama --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/TeeZee/Orpheus-TTS-pl-v3.0 <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Orpheus-TTS-pl-v3.0-GGUF).* weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Orpheus-TTS-pl-v3.0-GGUF/resolve/main/Orpheus-TTS-pl-v3.0.Q2_K.gguf) \| Q2_K \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpheus-TTS-pl-v3.0-GGUF/resolve/main/Orpheus-TTS-pl-v3.0.Q3_K_S.gguf) \| Q3_K_S \| 1.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpheus-TTS-pl-v3.0-GGUF/resolve/main/Orpheus-TTS-pl-v3.0.Q3_K_M.gguf) \| Q3_K_M \| 1.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Orpheus-TTS-pl-v3.0-GGUF/resolve/main/Orpheus-TTS-pl-v3.0.Q3_K_L.gguf) \| Q3_K_L \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpheus-TTS-pl-v3.0-GGUF/resolve/main/Orpheus-TTS-pl-v3.0.IQ4_XS.gguf) \| IQ4_XS \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpheus-TTS-pl-v3.0-GGUF/resolve/main/Orpheus-TTS-pl-v3.0.Q4_K_S.gguf) \| Q4_K_S \| 2.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Orpheus-TTS-pl-v3.0-GGUF/resolve/main/Orpheus-TTS-pl-v3.0.Q4_K_M.gguf) \| Q4_K_M \| 2.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Orpheus-TTS-pl-v3.0-GGUF/resolve/main/Orpheus-TTS-pl-v3.0.Q5_K_S.gguf) \| Q5_K_S \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpheus-TTS-pl-v3.0-GGUF/resolve/main/Orpheus-TTS-pl-v3.0.Q5_K_M.gguf) \| Q5_K_M \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Orpheus-TTS-pl-v3.0-GGUF/resolve/main/Orpheus-TTS-pl-v3.0.Q6_K.gguf) \| Q6_K \| 2.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Orpheus-TTS-pl-v3.0-GGUF/resolve/main/Orpheus-TTS-pl-v3.0.Q8_0.gguf) \| Q8_0 \| 3.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Orpheus-TTS-pl-v3.0-GGUF/resolve/main/Orpheus-TTS-pl-v3.0.f16.gguf) \| f16 \| 6.7 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/Gemma-2b-Uncensored-v1-GGUF	mradermacher	2025-09-22T10:16:48Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:sirev/Gemma-2b-Uncensored-v1", "base_model:quantized:sirev/Gemma-2b-Uncensored-v1", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-22T09:44:24Z	--- base_model: sirev/Gemma-2b-Uncensored-v1 language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/sirev/Gemma-2b-Uncensored-v1 <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Gemma-2b-Uncensored-v1-GGUF).* weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Gemma-2b-Uncensored-v1-GGUF/resolve/main/Gemma-2b-Uncensored-v1.Q2_K.gguf) \| Q2_K \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-2b-Uncensored-v1-GGUF/resolve/main/Gemma-2b-Uncensored-v1.Q3_K_S.gguf) \| Q3_K_S \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-2b-Uncensored-v1-GGUF/resolve/main/Gemma-2b-Uncensored-v1.Q3_K_M.gguf) \| Q3_K_M \| 1.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-2b-Uncensored-v1-GGUF/resolve/main/Gemma-2b-Uncensored-v1.Q3_K_L.gguf) \| Q3_K_L \| 1.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-2b-Uncensored-v1-GGUF/resolve/main/Gemma-2b-Uncensored-v1.IQ4_XS.gguf) \| IQ4_XS \| 1.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-2b-Uncensored-v1-GGUF/resolve/main/Gemma-2b-Uncensored-v1.Q4_K_S.gguf) \| Q4_K_S \| 1.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-2b-Uncensored-v1-GGUF/resolve/main/Gemma-2b-Uncensored-v1.Q4_K_M.gguf) \| Q4_K_M \| 1.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-2b-Uncensored-v1-GGUF/resolve/main/Gemma-2b-Uncensored-v1.Q5_K_S.gguf) \| Q5_K_S \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-2b-Uncensored-v1-GGUF/resolve/main/Gemma-2b-Uncensored-v1.Q5_K_M.gguf) \| Q5_K_M \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-2b-Uncensored-v1-GGUF/resolve/main/Gemma-2b-Uncensored-v1.Q6_K.gguf) \| Q6_K \| 2.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-2b-Uncensored-v1-GGUF/resolve/main/Gemma-2b-Uncensored-v1.Q8_0.gguf) \| Q8_0 \| 2.9 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-2b-Uncensored-v1-GGUF/resolve/main/Gemma-2b-Uncensored-v1.f16.gguf) \| f16 \| 5.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
enuma-elis/gemma_27b_4bit	enuma-elis	2025-09-22T10:16:14Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/gemma-3-27b-it-unsloth-bnb-4bit", "base_model:quantized:unsloth/gemma-3-27b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	image-text-to-text	2025-09-22T10:14:48Z	--- base_model: unsloth/gemma-3-27b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: enuma-elis - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-27b-it-unsloth-bnb-4bit This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Ziad177/whisper-large-v3-qlora_	Ziad177	2025-09-22T10:14:46Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-22T10:14:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
FL33TW00D-HF/dots.ocr.ne	FL33TW00D-HF	2025-09-22T10:14:44Z	0	0	coreml	[ "coreml", "base_model:rednote-hilab/dots.ocr", "base_model:quantized:rednote-hilab/dots.ocr", "license:mit", "region:us" ]	null	2025-09-22T10:11:21Z	--- license: mit base_model: - rednote-hilab/dots.ocr library_name: coreml --- # dots.ocr.ne CoreML conversions of [dots.ocr](https://huggingface.co/rednote-hilab/dots.ocr) by RedNote
Alicia22/22SAT_KK10_l13	Alicia22	2025-09-22T10:13:43Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-09-22T10:10:36Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
aleksa-codes/flux-ghibsky-illustration	aleksa-codes	2025-09-22T10:11:58Z	6,635	295	diffusers	[ "diffusers", "text-to-image", "lora", "template:sd-lora", "image-generation", "flux", "replicate", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2024-08-20T13:59:25Z	--- tags: - text-to-image - diffusers - lora - template:sd-lora - image-generation - flux - replicate pipeline_tag: text-to-image thumbnail: >- https://tjzk.replicate.delivery/models_models_cover_image/e5bc70de-c6ae-497f-bf2c-7e81b1183f05/out-0.jpg widget: - text: >- GHIBSKY style, a cat on a windowsill gazing out at a starry night sky and distant city lights output: url: images/example1.jpg - text: >- GHIBSKY style, a fisherman casting a line into a peaceful village lake surrounded by quaint cottages output: url: images/example2.jpg - text: >- GHIBSKY style, cozy mountain cabin covered in snow, with smoke curling from the chimney and a warm, inviting light spilling through the windows output: url: images/example3.jpg - text: GHIBSKY style, Mykonos output: url: images/example4.jpg - text: >- GHIBSKY style, an orange Lamborghini driving down a hill road at night with a beautiful ocean view in the background, side view, no text output: url: images/example5.jpg - text: >- GHIBSKY style, a small Yorkie on a windowsill during a snowy winter night, with a warm, cozy glow from inside and soft snowflakes drifting outside output: url: images/example6.jpg - text: >- GHIBSKY style, serene Japanese garden with a koi pond and a traditional tea house, nestled under a canopy of cherry blossoms in full bloom output: url: images/example7.jpg - text: GHIBSKY style, the most beautiful place in the universe output: url: images/example8.jpg - text: GHIBSKY style painting, sign saying "Flux Ghibsky" output: url: images/example_dj4xgd39e.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: GHIBSKY style license: other license_name: flux-dev-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # Flux Ghibsky Illustration: Create Serene and Enchanting Landscapes <Gallery /> ## Model Description The Flux Ghibsky Illustration model generates landscapes that blend serene, surreal skies with intricate, Ghibli-inspired details. This fusion of styles creates enchanting scenes that capture the essence of both Ghibli's whimsical charm and Makoto Shinkai's atmospheric beauty. Perfect for creating dreamy visuals. You can also run the model on Replicate. Feedback is welcome! [Replicate Model Page](https://replicate.com/aleksa-codes/flux-ghibsky-illustration) ## Trigger Words Use `GHIBSKY style` to invoke the model’s unique aesthetic. It’s best to start your prompt with the trigger word, followed by descriptions of your scene, such as nature, skies, houses, roads, villages, etc. If you are getting too realistic images, try adding `painting` to your prompt, for example: `GHIBSKY style painting`. ## Training Details - Trained Using: [Flux LoRA Fast Training on fal.ai](https://fal.ai/models/fal-ai/flux-lora-fast-training) and [Flux LoRA Trainer on Replicate](https://replicate.com/ostris/flux-dev-lora-trainer/train) - Number of Images: 35 - Trigger Word: `GHIBSKY` - Auto-captioning: Enabled - Auto-captioning Prefix: `""` - Auto-captioning Suffix: `", GHIBSKY style"` - Training Steps: 1000 - Learning Rate: 0.0004 - Batch Size: 1 - LoRA Rank: 16 ## Download Model [Download the .safetensors LoRA](https://huggingface.co/aleksa-codes/flux-ghibsky-illustration/tree/main) in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```python from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda') pipeline.load_lora_weights('aleksa-codes/flux-ghibsky-illustration', weight_name='lora.safetensors') image = pipeline('GHIBSKY style, a serene lakeside village with colorful houses and towering mountains under a dreamy sky').images[0] ``` For more details, including weighting, merging, and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters). # Related Tools [UnYellowGPT](https://unyellowgpt.com/): Noticing a yellow or sepia tint in your AI-generated images? This one-click tool intelligently removes unwanted color casts, restoring the natural white balance and vibrancy to your visuals. * [GPT Image Captioner](https://gptcaptioner.aleksa.codes/): If you're training your own LoRA model, this open-source tool I created is a great replacement for standard auto-captioning. It generates high-quality descriptive `.txt` files for your images, supporting both OpenAI and local inference with Ollama. ## License Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
sayannath/gpt-oss-20b-medical-qa	sayannath	2025-09-22T10:10:08Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:openai/gpt-oss-20b", "base_model:finetune:openai/gpt-oss-20b", "endpoints_compatible", "region:us" ]	null	2025-09-21T19:03:18Z	--- base_model: openai/gpt-oss-20b library_name: transformers model_name: gpt-oss-20b-medical-qa tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for gpt-oss-20b-medical-qa This model is a fine-tuned version of [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="sayannath/gpt-oss-20b-medical-qa", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/sayannath235/LLM-Recipe/runs/q1iyzxbm) This model was trained with SFT. ### Framework versions - TRL: 0.23.0 - Transformers: 4.56.2 - Pytorch: 2.8.0 - Datasets: 4.1.0 - Tokenizers: 0.22.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
MattBou00/llama-3-2-1b-detox_RETRY_SAMPLING_scale10_Round3-checkpoint-epoch-80	MattBou00	2025-09-22T10:08:53Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-09-22T10:07:54Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_09-55-45/checkpoints/checkpoint-epoch-80") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_09-55-45/checkpoints/checkpoint-epoch-80") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_09-55-45/checkpoints/checkpoint-epoch-80") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
nnilayy/dreamer_stride_256-binary-arousal-Kfold-3-stride_256	nnilayy	2025-09-22T10:07:44Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-09-22T10:07:38Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]
martijn75/token_voc	martijn75	2025-09-22T10:06:57Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-01-10T10:40:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
quablab/SmolLM3-Custom-SFT	quablab	2025-09-22T10:03:41Z	0	0	transformers	[ "transformers", "safetensors", "smollm3", "text-generation", "generated_from_trainer", "smol-course", "instruction-tuning", "sft", "hf_jobs", "trl", "conversational", "base_model:HuggingFaceTB/SmolLM3-3B", "base_model:finetune:HuggingFaceTB/SmolLM3-3B", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T09:53:52Z	--- base_model: HuggingFaceTB/SmolLM3-3B library_name: transformers model_name: SmolLM3-Custom-SFT tags: - generated_from_trainer - smol-course - instruction-tuning - sft - hf_jobs - trl licence: license --- # Model Card for SmolLM3-Custom-SFT This model is a fine-tuned version of [HuggingFaceTB/SmolLM3-3B](https://huggingface.co/HuggingFaceTB/SmolLM3-3B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="quablab/SmolLM3-Custom-SFT", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.23.0 - Transformers: 4.56.2 - Pytorch: 2.5.1 - Datasets: 4.1.1 - Tokenizers: 0.22.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
John6666/illustrious-pixel-art-from-hades-v4-series-v-40-sdxl	John6666	2025-09-22T10:03:33Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "pixel art", "2D", "retro", "indie", "clean lines", "sharp detail", "consistent palettes", "adherence", "perspective", "poses", "consistency", "game assets", "visual fidelity", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-xl-early-release-v0", "base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-09-22T09:51:57Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - pixel art - 2D - retro - indie - clean lines - sharp detail - consistent palettes - adherence - perspective - poses - consistency - game assets - visual fidelity - illustrious base_model: OnomaAIResearch/Illustrious-xl-early-release-v0 --- Original model is [here](https://civitai.com/models/1732312/illustrious-pixelart-from-hades?modelVersionId=2239694). This model created by [DeViLDoNia](https://civitai.com/user/DeViLDoNia).
MattBou00/llama-3-2-1b-detox_RETRY_SAMPLING_scale10_Round3-checkpoint-epoch-40	MattBou00	2025-09-22T10:02:25Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-09-22T10:01:17Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_09-55-45/checkpoints/checkpoint-epoch-40") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_09-55-45/checkpoints/checkpoint-epoch-40") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_09-55-45/checkpoints/checkpoint-epoch-40") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
MattBou00/llama-3-2-1b-detox_RETRY_SAMPLING_scale10_Round3-checkpoint-epoch-20	MattBou00	2025-09-22T09:59:03Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-09-22T09:58:05Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_09-55-45/checkpoints/checkpoint-epoch-20") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_09-55-45/checkpoints/checkpoint-epoch-20") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_09-55-45/checkpoints/checkpoint-epoch-20") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
sayouzone25/gemma-3-12b-trans-en-ko	sayouzone25	2025-09-22T09:58:53Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-12b-pt", "base_model:finetune:google/gemma-3-12b-pt", "endpoints_compatible", "region:us" ]	null	2025-09-18T09:32:09Z	--- base_model: google/gemma-3-12b-pt library_name: transformers model_name: gemma-3-12b-trans-en-ko tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-3-12b-trans-en-ko This model is a fine-tuned version of [google/gemma-3-12b-pt](https://huggingface.co/google/gemma-3-12b-pt). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="sayouzone25/gemma-3-12b-trans-en-ko", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.55.2 - Pytorch: 2.8.0 - Datasets: 3.3.2 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

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