Split (1)

train · 971k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-22 18:29:56	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 570 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-22 18:28:16	card stringlengths 11 1.01M
mantovanima/q-FrozenLake-v1-8x8-slippery	mantovanima	2025-09-21T19:18:51Z	0	0	null	[ "FrozenLake-v1-8x8", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-09-21T19:18:47Z	--- tags: - FrozenLake-v1-8x8 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-8x8-slippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-8x8 type: FrozenLake-v1-8x8 metrics: - type: mean_reward value: 0.45 +/- 0.50 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="mantovanima/q-FrozenLake-v1-8x8-slippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
AI-Sweden-Models/ModernBERT-large	AI-Sweden-Models	2025-09-21T19:11:09Z	1,288	5	transformers	[ "transformers", "safetensors", "modernbert", "fill-mask", "masked-lm", "long-context", "sv", "no", "da", "is", "arxiv:2303.17183", "arxiv:2410.04456", "base_model:answerdotai/ModernBERT-large", "base_model:finetune:answerdotai/ModernBERT-large", "license:apache-2.0", "autotrain_compatible", "region:us" ]	fill-mask	2025-02-19T12:54:55Z	--- library_name: transformers license: apache-2.0 language: - sv - 'no' - da - is tags: - masked-lm - fill-mask - long-context - modernbert pipeline_tag: fill-mask inference: false base_model: answerdotai/ModernBERT-large --- ## Overview This checkpoint continues the pre-training of [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on Scandinavian text, extending the model’s knowledge with ~1.2 trillion additional masked-language-model (MLM) tokens drawn from [The Nordic Pile](https://arxiv.org/pdf/2303.17183) and [SWEb](https://arxiv.org/pdf/2410.04456) while preserving the original 8k token context window. This is a research artefact and is only intended for research purposes. Our tokenizer is trained from scratch on a subset of 11 985 103 472 tokens. The training is done in one stage with 8192 tokens per sample for the whole run. ## Data Sources \| Corpus \| Size \| Selected Languages \| Highlights \| \|---\|---\|---\|---\| \| The Nordic Pile \| 1.2 TB raw text \| sv, no, da, is \| Nine diverse categories (CC, Wikipedia, Books, Code, etc.), filtered and deduplicated for high quality \| \| SWEb \| 1 T+ tokens (~3.6 TB) \| sv, no, da, is \| 98 Common-Crawl snapshots with model-based HTML extraction; 1.2 B documents \| ## Training Setup \| Setting \| Value \| \|---\|---\| \| Parameters \| 395 M \| \| Context length \| 8 192 tokens (RoPE + local-global attention) \| \| Tokens processed \| 1.20 × 10<sup>12</sup> \| \| Tokens per batch \| 1 572 864 \| \| Global batch \| 192 sequences (micro-batch = 3) \| \| Optimizer & schedule \| Decoupled StableAdamW, lr 2 e-4, cosine decay (1 % warm-up) \| \| Precision \| AMP-bf16 \| \| Hardware \| 8 nodes × 8 AMD MI250X GPUs (64 GPUs) on the EuroHPC LUMI-G system \| See training details [here](https://github.com/timpal0l/ModernBERT/blob/main/training/trainer_lumi.yaml) ## Training Stats ```python [token=1198511677292/1198510347252]: Train time/batch: 873585 Train time/sample: 167728320 Train time/batch_in_epoch: 3558 Train time/sample_in_epoch: 683136 Train time/token: 1198510256276 Train time/token_in_epoch: 4882888303 Train trainer/device_train_microbatch_size: 3 Train loss/train/total: 0.7730 Train throughput/batches_per_sec: 0.6293 Train throughput/samples_per_sec: 120.8212 Train throughput/device/batches_per_sec: 0.0098 Train throughput/device/samples_per_sec: 1.8878 Train throughput/tokens_per_sec: 865578.9851 Train throughput/device/tokens_per_sec: 13524.6716 Train time/train: 385.2930 Train time/val: 0.0000 Train time/total: 385.2930 Train lr-StableAdamW/group0: 0.0000 Train lr-StableAdamW/group1: 0.0000 ``` ## Intended Use This is a research artefact and is only intended for research purposes. * Fill-mask inference, embedding extraction and fine-tuning for Scandinavian downstream NLP tasks (classification, NER, QA, etc.). * Drop-in replacement for BERT-style encoders (omit `token_type_ids`). ## Fill-mask ```python from transformers import pipeline unmasker = pipeline('fill-mask', model='AI-Sweden-Models/ModernBERT-large') unmasker("Huvudstaden i Sverige är [MASK].") ``` ```python [{'score': 0.5732529759407043, 'token': 2961, 'token_str': ' Stockholm', 'sequence': 'Huvudstaden i Sverige är Stockholm.'}, {'score': 0.06222670152783394, 'token': 4481, 'token_str': ' Göteborg', 'sequence': 'Huvudstaden i Sverige är Göteborg.'}, {'score': 0.02539575845003128, 'token': 5882, 'token_str': ' Malmö', 'sequence': 'Huvudstaden i Sverige är Malmö.'}, {'score': 0.024683712050318718, 'token': 19931, 'token_str': ' Norrköping', 'sequence': 'Huvudstaden i Sverige är Norrköping.'}, {'score': 0.02418600209057331, 'token': 28202, 'token_str': ' Solna', 'sequence': 'Huvudstaden i Sverige är Solna.'}] ``` ## Limitations & Biases * Web corpora can contain noise, stereotypes and sensitive content despite filtering. * RoPE extrapolation beyond 8 k tokens is untested and may degrade. ## Code to reproduce * [Training](https://github.com/timpal0l/ModernBERT/tree/main/training) * [Data Processing](https://github.com/timpal0l/ModernBERT/tree/main/tokenizer)
fatmhd1995/ft_phi35_jd_inclusive_detection_21092025	fatmhd1995	2025-09-21T19:06:44Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-09-21T18:24:08Z	--- base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: fatmhd1995 - License: apache-2.0 - Finetuned from model : unsloth/phi-3.5-mini-instruct-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)
RTannous/test-gemma3-vision	RTannous	2025-09-21T18:29:41Z	0	0	null	[ "gguf", "gemma3", "llama.cpp", "unsloth", "vision-language-model", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-21T18:29:21Z	--- tags: - gguf - llama.cpp - unsloth - vision-language-model --- # test-gemma3-vision - GGUF This model was converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth). Example usage For text only LLMs: llama-cli --hf <repo_id>/<model_name> -p "why is the sky blue?" For multimodal models: llama-mtmd-cli -m model_name.gguf --mmproj mmproj_file.gguf ## Available Quantizations - `gemma-3-4b-it.Q8_0.gguf` ## ⚠️ Ollama Note for Vision Models Important: Ollama currently does not support separate mmproj files for vision models. To create an Ollama model from this vision model: 1. Download the bf16 merged model (not the GGUF) 2. Place the `Modelfile` in the same directory as the bf16 merged model 3. Run: `ollama create model_name -f ./Modelfile` (Replace `model_name` with your desired name) This will create a unified model that Ollama can use.
mrhomie/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-agile_tall_wildebeest	mrhomie	2025-09-21T18:25:50Z	17	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am agile_tall_wildebeest", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-20T11:10:19Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am agile_tall_wildebeest --- # 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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Feraxx/Qwen3-0.6B-Gensyn-Swarm-soft_lumbering_quail	Feraxx	2025-09-21T18:25:49Z	18	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am soft_lumbering_quail", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-19T23:53:31Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am soft_lumbering_quail --- # 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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yuuutre/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-enormous_bold_mule	yuuutre	2025-09-21T18:25:01Z	6	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am enormous_bold_mule", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-06T15:13:41Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am enormous_bold_mule --- # 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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mradermacher/Letz-MT-Llama-3.2-3B-en-uk-GGUF	mradermacher	2025-09-21T18:24:41Z	0	0	null	[ "region:us" ]	null	2025-09-21T18:24:38Z	<!-- ### 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/Volavion/Letz-MT-Llama-3.2-3B-en-uk
flin775/UI-Tars-1.5-7B-4bit-mlx	flin775	2025-09-21T18:24:29Z	0	0	null	[ "safetensors", "qwen2_5_vl", "arxiv:2404.07972", "arxiv:2504.07981", "base_model:ByteDance-Seed/UI-TARS-1.5-7B", "base_model:quantized:ByteDance-Seed/UI-TARS-1.5-7B", "license:apache-2.0", "4-bit", "region:us" ]	null	2025-09-21T12:40:31Z	--- license: apache-2.0 base_model: - ByteDance-Seed/UI-TARS-1.5-7B --- This model is convert by mlx_vlm from [ByteDance-Seed/UI-TARS-1.5-7B](https://huggingface.co/ByteDance-Seed/UI-TARS-1.5-7B) ## Model Description UI-TARS-1.5 is ByteDance's open-source multimodal agent built upon a powerful vision-language model. It is capable of effectively performing diverse tasks within virtual worlds. The released UI-TARS-1.5-7B focuses primarily on enhancing general computer use capabilities and is not specifically optimized for game-based scenarios, where the UI-TARS-1.5 still holds a significant advantage. \| Benchmark Type \| Benchmark \| UI-TARS-1.5-7B \| UI-TARS-1.5 \| \|--------------------\|------------------------------------\|--------------------\|-----------------\| \| Computer Use \| [OSWorld](https://arxiv.org/abs/2404.07972) \| 27.5 \| 42.5 \| \| GUI Grounding \| [ScreenSpotPro](https://arxiv.org/pdf/2504.07981v1) \| 49.6 \| 61.6 \| P.S. This is the performance of UI-TARS-1.5-7B and UI-TARS-1.5 on OSWorld and ScreenSpotProd. ## Quick Start ```shell mlx_vlm.generate --model flin775/UI-Tars-1.5-7B-4bit-mlx \ --max-tokens 1024 \ --temperature 0.0 \ --prompt "List all contacts’ names and their corresponding grounding boxes([x1, y1, x2, y2]) from the left sidebar of the IM chat interface, return the results in JSON format." \ --image https://wechat.qpic.cn/uploads/2016/05/WeChat-Windows-2.11.jpg ```
shinyobjectz/sllm-shady	shinyobjectz	2025-09-21T18:20:44Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen3-0.6B", "base_model:adapter:Qwen/Qwen3-0.6B", "region:us" ]	null	2025-09-20T21:25:33Z	--- base_model: Qwen/Qwen3-0.6B library_name: peft --- # 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. --> - 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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lfhe/FLock-Arena-Task-15-Carbonia	lfhe	2025-09-21T18:20:37Z	324	0	peft	[ "peft", "safetensors", "base_model:adapter:microsoft/Phi-4-mini-instruct", "flock-train", "lora", "transformers", "text-generation", "arxiv:1910.09700", "base_model:microsoft/Phi-4-mini-instruct", "region:us" ]	text-generation	2025-02-21T01:26:02Z	--- base_model: microsoft/Phi-4-mini-instruct library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:microsoft/Phi-4-mini-instruct - flock-train - lora - transformers --- # 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. --> - 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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strangerzonehf/Flux-Ultimate-LoRA-Collection	strangerzonehf	2025-09-21T18:19:02Z	31,819	108	diffusers	[ "diffusers", "Flux.1-Dev", "lora", "Collections", "SOTA", "Realism", "Diffusion", "art", "FLUX", "image-to-image", "text-to-image", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "doi:10.57967/hf/5698", "license:other", "region:us" ]	text-to-image	2024-11-18T06:47:02Z	--- 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 widget: - text: Stranger Zones Ultimate LoRA Collection output: url: images/11.png base_model: - black-forest-labs/FLUX.1-dev pipeline_tag: text-to-image library_name: diffusers tags: - Flux.1-Dev - lora - Collections - SOTA - Realism - Diffusion - art - FLUX - image-to-image --- ![07.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/eoafbJi-rgd5fEECRWrMD.png) ## Flux.1dev Adapter Resources \| File Name \| Size \| LFS \| File Type \| \|------------------------------------------------\|--------\|------\|-----------------\| \| 3DXL-Mannequin.safetensors \| 613 MB \| LFS \| .safetensors \| \| 3DXLC1.safetensors \| 613 MB \| LFS \| .safetensors \| \| 3DXLP1.safetensors \| 613 MB \| LFS \| .safetensors \| \| 3DXLP2.safetensors \| 613 MB \| LFS \| .safetensors \| \| 3DXLP3.safetensors \| 613 MB \| LFS \| .safetensors \| \| 3DXLP4.safetensors \| 613 MB \| LFS \| .safetensors \| \| 3DXLP5.safetensors \| 613 MB \| LFS \| .safetensors \| \| 3DXLP6.safetensors \| 613 MB \| LFS \| .safetensors \| \| Abstract-Cartoon.safetensors \| 613 MB \| LFS \| .safetensors \| \| Amxtoon.safetensors \| 613 MB \| LFS \| .safetensors \| \| Animeo.safetensors \| 613 MB \| LFS \| .safetensors \| \| Animex.safetensors \| 613 MB \| LFS \| .safetensors \| \| Aura-9999.safetensors \| 613 MB \| LFS \| .safetensors \| \| Bold-Shadows.safetensors \| 613 MB \| LFS \| .safetensors \| \| C33.safetensors \| 613 MB \| LFS \| .safetensors \| \| CAM00.safetensors \| 613 MB \| LFS \| .safetensors \| \| Canopus-Anime-Character-Art-FluxDev-LoRA.safetensors \| 613 MB \| LFS \| .safetensors \| \| Canopus-Car-Flux-Dev-LoRA.safetensors \| 613 MB \| LFS \| .safetensors \| \| Canopus-Clothing-Flux-Dev-Florence2-LoRA.safetensors \| 613 MB \| LFS \| .safetensors \| \| Canopus-Cute-Kawaii-Flux-LoRA.safetensors \| 613 MB \| LFS \| .safetensors \| \| Castor-3D-Portrait-Flux-LoRA.safetensors \| 306 MB \| LFS \| .safetensors \| \| Castor-3D-Sketchfab-Flux-LoRA.safetensors \| 613 MB \| LFS \| .safetensors \| \| Castor-Character-Polygon-LoRA.safetensors \| 613 MB \| LFS \| .safetensors \| \| Castor-Collage-Dim-Flux-LoRA.safetensors \| 613 MB \| LFS \| .safetensors \| \| Castor-Happy-Halloween-Flux-LoRA.safetensors \| 613 MB \| LFS \| .safetensors \| \| Castor-Red-Dead-Redemption-2-Flux-LoRA.safetensors \| 613 MB \| LFS \| .safetensors \| \| Claymation.safetensors \| 613 MB \| LFS \| .safetensors \| \| Clothing-Flux-Dev-Florence2-LoRA-Pruned.safetensors \| 613 MB \| LFS \| .safetensors \| \| Clouds Illusion.safetensors \| 613 MB \| LFS \| .safetensors \| \| Creative-Stocks.safetensors \| 613 MB \| LFS \| .safetensors \| \| Cute-3d-Kawaii.safetensors \| 613 MB \| LFS \| .safetensors \| \| Dark_Creature.safetensors \| 613 MB \| LFS \| .safetensors \| \| Digital-Chaos.safetensors \| 613 MB \| LFS \| .safetensors \| \| Digital-Yellow.safetensors \| 613 MB \| LFS \| .safetensors \| \| Dramatic-Neon-Flux-LoRA.safetensors \| 613 MB \| LFS \| .safetensors \| \| EBook-Cover.safetensors \| 613 MB \| LFS \| .safetensors \| \| Electric-Blue.safetensors \| 613 MB \| LFS \| .safetensors \| \| Fashion-Modeling.safetensors \| 613 MB \| LFS \| .safetensors \| \| Flux-Dev-Real-Anime-LoRA.safetensors \| 613 MB \| LFS \| .safetensors \| \| Flux-Realism-FineDetailed.safetensors \| 613 MB \| LFS \| .safetensors \| \| GArt.safetensors \| 613 MB \| LFS \| .safetensors \| \| Ghibli-Art.safetensors \| 613 MB \| LFS \| .safetensors \| \| Glowing-Body.safetensors \| 613 MB \| LFS \| .safetensors \| \| Golden-Coin.safetensors \| 613 MB \| LFS \| .safetensors \| \| Green-Cartoon.safetensors \| 613 MB \| LFS \| .safetensors \| \| Gta6-Concept-Charecter.safetensors \| 613 MB \| LFS \| .safetensors \| \| Gta6.safetensors \| 613 MB \| LFS \| .safetensors \| \| HDR-Digital-Chaos.safetensors \| 613 MB \| LFS \| .safetensors \| \| HDR.safetensors \| 613 MB \| LFS \| .safetensors \| \| Icon-Kit.safetensors \| 613 MB \| LFS \| .safetensors \| \| Intense-Red.safetensors \| 613 MB \| LFS \| .safetensors \| \| Isometric-3D-Cinematography.safetensors \| 613 MB \| LFS \| .safetensors \| \| Isometric-3D.safetensors \| 613 MB \| LFS \| .safetensors \| \| Kepler-452b-LoRA-Flux-Dev-3D-Bubbly.safetensors \| 613 MB \| LFS \| .safetensors \| \| Knitted- Character.safetensors \| 613 MB \| LFS \| .safetensors \| \| Lego.safetensors \| 613 MB \| LFS \| .safetensors \| \| Lime-Green.safetensors \| 613 MB \| LFS \| .safetensors \| \| Logo-design.safetensors \| 613 MB \| LFS \| .safetensors \| \| Long-Toon.safetensors \| 613 MB \| LFS \| .safetensors \| \| Minimal-Futuristic.safetensors \| 613 MB \| LFS \| .safetensors \| \| Mockup-Texture.safetensors \| 613 MB \| LFS \| .safetensors \| \| Multi-Frame-Shot(MFS).safetensors \| 613 MB \| LFS \| .safetensors \| \| NFTv4.safetensors \| 613 MB \| LFS \| .safetensors \| \| Orange-Chroma.safetensors \| 613 MB \| LFS \| .safetensors \| \| Past-Present-Deep-Mix-Flux-LoRA.safetensors \| 613 MB \| LFS \| .safetensors \| \| Pastel-BG.safetensors \| 613 MB \| LFS \| .safetensors \| \| Prod-Ad.safetensors \| 613 MB \| LFS \| .safetensors \| \| Purple-Dreamy.safetensors \| 613 MB \| LFS \| .safetensors \| \| Purple_Grid.safetensors \| 613 MB \| LFS \| .safetensors \| \| Red-Undersea.safetensors \| 613 MB \| LFS \| .safetensors \| \| Retro-Pixel.safetensors \| 613 MB \| LFS \| .safetensors \| \| Seamless-Pattern-Design.safetensors \| 613 MB \| LFS \| .safetensors \| \| Shadow-Projection.safetensors \| 613 MB \| LFS \| .safetensors \| \| Simple_ Doodle.safetensors \| 270 MB \| LFS \| .safetensors \| \| Smiley-C4C.safetensors \| 613 MB \| LFS \| .safetensors \| \| Snoopy-Charlie-Brown-Flux-LoRA.safetensors \| 613 MB \| LFS \| .safetensors \| \| Street_Bokeh.safetensors \| 613 MB \| LFS \| .safetensors \| \| Super-Blend.safetensors \| 613 MB \| LFS \| .safetensors \| \| Super-Detail.safetensors \| 613 MB \| LFS \| .safetensors \| \| Super-Portrait.safetensors \| 613 MB \| LFS \| .safetensors \| \| Tarot-card.safetensors \| 613 MB \| LFS \| .safetensors \| \| Teen-Outfit.safetensors \| 613 MB \| LFS \| .safetensors \| \| Typography.safetensors \| 613 MB \| LFS \| .safetensors \| \| Uncoloured-3D-Polygon.safetensors \| 613 MB \| LFS \| .safetensors \| \| Yellow-Laser.safetensors \| 613 MB \| LFS \| .safetensors \| \| Yellow_Pop.safetensors \| 613 MB \| LFS \| .safetensors \| \| capybara-hf.safetensors \| 613 MB \| LFS \| .safetensors \| \| chill-guy.safetensors \| 613 MB \| LFS \| .safetensors \| \| coloring-book.safetensors \| 613 MB \| LFS \| .safetensors \| \| ctoon.safetensors \| 613 MB \| LFS \| .safetensors \| \| dalle-mix.safetensors \| 613 MB \| LFS \| .safetensors \| \| frosted-gc.safetensors \| 613 MB \| LFS \| .safetensors \| \| handstick69.safetensors \| 613 MB \| LFS \| .safetensors \| \| indo-realism.safetensors \| 613 MB \| LFS \| .safetensors \| \| look-in-2.safetensors \| 613 MB \| LFS \| .safetensors \| \| meme.safetensors \| 613 MB \| LFS \| .safetensors \| \| midjourney-mix.safetensors \| 613 MB \| LFS \| .safetensors \| \| mjV6.safetensors \| 613 MB \| LFS \| .safetensors \| \| movieboard.safetensors \| 613 MB \| LFS \| .safetensors \| \| nm99.safetensors \| 613 MB \| LFS \| .safetensors \| \| only-stickers.safetensors \| 613 MB \| LFS \| .safetensors \| \| polaroid-plus.safetensors \| 613 MB \| LFS \| .safetensors \| \| poster-foss.safetensors \| 613 MB \| LFS \| .safetensors \| \| quoter.safetensors \| 613 MB \| LFS \| .safetensors \| \| sketchcard.safetensors \| 613 MB \| LFS \| .safetensors \| \| stam9.safetensors \| 613 MB \| LFS \| .safetensors \| \| super-realism.safetensors \| 613 MB \| LFS \| .safetensors \| \| toon-mix.safetensors \| 613 MB \| LFS \| .safetensors \| \| toonic2.5D.safetensors \| 613 MB \| LFS \| .safetensors \| \| ywl-realism.safetensors \| 613 MB \| LFS \| .safetensors \| <Gallery /> \| Repository \| Description \| Link \| \|-----------------------------\|-------------------------------------------------------------\|---------------------------------------------------\| \| PrithivMLMods \| Repository featuring various adapters and ML models. \| [Visit Repository](https://huggingface.co/prithivMLmods) \| \| StrangerZoneHF \| Repository containing specialized Hugging Face models. \| [Visit Repository](https://huggingface.co/strangerzonehf) \| 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choiqs/Qwen3-8B-if-bsz128-ts300-ranking-skywork8b-seed44-lr1e-6-4gpus	choiqs	2025-09-21T18:14:47Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T18:12:48Z	--- 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]
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758478318	schooncestiaa	2025-09-21T18:13:15Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "scruffy webbed dragonfly", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T18:12:58Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - scruffy webbed dragonfly --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
jsbeaudry/makandal-v2	jsbeaudry	2025-09-21T18:12:25Z	8	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "creole", "haitian", "conversational", "ht", "base_model:jsbeaudry/makandal-pre-trained", "base_model:finetune:jsbeaudry/makandal-pre-trained", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-07-08T00:33:20Z	--- library_name: transformers tags: - creole - haitian license: mit language: - ht base_model: - jsbeaudry/makandal-pre-trained pipeline_tag: text-generation --- # Makandal Continue Pre-trained from qwen3-0.6b ## Model Details This model has been continued pre-trained from qwen3-0.6b by Palmis Labs AI. . ### Model Description - Developed by: Palmis Labs AI - Funded by: Jean Sauvenel Beaudry - Model type: GPT (Generative Pre-trained Transformer) - Language(s) (NLP): Haitian Creole - License: MIT - Model size: 0.6B parameters - Architecture: qwen3 ### Direct Use ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch def generate(model, tokenizer, prompt, device): inputs = tokenizer(prompt, return_tensors="pt", padding=True).to(device) output = model.generate( inputs, max_new_tokens=100, do_sample=True, repetition_penalty=1.2, no_repeat_ngram_size=3, temperature=0.9, top_k=40, top_p=0.85, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id ) return tokenizer.decode(output[0], skip_special_tokens=True) # Load model and tokenizer tokenizer = AutoTokenizer.from_pretrained("jsbeaudry/makandal-v2") model = AutoModelForCausalLM.from_pretrained("jsbeaudry/makandal-v2") # Set device device = "cuda" if torch.cuda.is_available() else "cpu" model.to(device) # Generate text prompt = "matematik" response = generate(model, tokenizer, prompt, device) print(response) # Answer: # Matematik se yon disiplin matematik ki konsantre sou kalkil, estatistik, ak analiz matematik. # Li pèmèt nou konprann enfòmasyon ak fòmèlman analize done pou jwenn pwopriyete oswa fòmèlman verifye yon konpreyansyon. ``` ### Out-of-Scope Use This model should NOT be used for: - Critical decision-making systems - Any application requiring reliable or factual outputs - Commercial deployment without significant additional training ## Bias, Risks, and Limitations - Insufficient training data: Only 4.7 MB of training data used - Limited training time: Only 4.5 hours of training - High hallucination rate: Model frequently generates inaccurate or nonsensical content - Language coverage: Limited Haitian Creole language understanding due to minimal dataset - Bias: May reflect biases present in the small training dataset ### Recommendations - Do not rely on outputs for factual information - Supervise usage in educational settings ### Training Infrastructure - GPU: Tesla T4 (15GB) - Framework: Transformers/PyTorch ## Citation ```bibtex @misc{makandal2025, title={Makandal-pretrain: An Educational Haitian Creole Language Model}, author={Jean Sauvenel Beaudry}, year={2025}, howpublished={\url{https://huggingface.co/jsbeaudry/makandal-pre-trained}}, note={Educational demonstration model} } ``` ## Glossary Makandal**: Named after François Makandal, an 18th-century Haitian revolutionary leader, symbolizing the model's connection to Haitian culture and education.
gccmorgoth/finsql-mlx-qwen3-4b-instruct-4bit	gccmorgoth	2025-09-21T18:08:57Z	0	0	mlx	[ "mlx", "lora", "sql", "financialSQL", "finance", "en", "base_model:mlx-community/Qwen3-4B-Instruct-2507-4bit", "base_model:adapter:mlx-community/Qwen3-4B-Instruct-2507-4bit", "license:apache-2.0", "region:us" ]	null	2025-09-16T19:44:23Z	--- license: apache-2.0 language: - en metrics: - accuracy base_model: - mlx-community/Qwen3-4B-Instruct-2507-4bit library_name: mlx tags: - mlx - lora - sql - financialSQL - finance --- # finsql-mlx-qwen3-4b-instruct-4bit This is a LoRA adapter for financial SQL generation, fine-tuned on mlx-community/Qwen3-4B-Instruct-2507-4bit. ## Latest Finetuning ![image/png](https://cdn-uploads.huggingface.co/production/uploads/68c5d7248a0237bcabdce5cc/NoqOr0P2fMe5YabLO-02K.png) ## Finetuning Details - Method: Direct Preference Optimization (DPO) - Checkpoint: Iteration 300 - Validation Loss: 0.048 - Training Loss: 0.122 - Learning Rate: Cosine decay with warmup - LoRA Rank: 16 ## Performance - Validation loss: 0.048 (optimal convergence point) - Selected at iteration 300 to prevent overfitting - DPO training for improved preference alignment on financial SQL tasks ## Model Selection - Checkpoint: Iteration 300 selected based on validation loss curve - Rationale: Optimal balance between training convergence and generalization - Training Dynamics: Early stopping before overfitting (val loss increased at iter 700+) ## Dataset This model was fine-tuned on financial text-to-sql data pairs, specifically the [FinSQLBull dataset](https://bull-text-to-sql-benchmark.github.io), to improve SQL query generation for financial databases and tables. ## Usage Recommended prompt format to specify: # Database: [database_name] [Schema information] ## Task [Natural language question about the data] Constraint: [Any specific constraints] SQL: [Model Generated SQL Query] ## Sample Prompt Format Database: company_financials Table: revenue (id, company, year, revenue, profit) Task What was the total revenue for all companies in 2023? SQL: [Model Generated SQL Query] ## Python ```python from mlx_lm import load, generate model, tokenizer = load("your-username/your-model-name") response = generate(model, tokenizer, prompt="Your prompt here")
Anwaarma/edos_taskA_llama3b_qlora	Anwaarma	2025-09-21T18:08:30Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-21T18:08:24Z	--- 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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rianagario/Yolo-LibrasVision	rianagario	2025-09-21T18:03:18Z	0	0	ultralytics	[ "ultralytics", "yolov8", "object-detection", "libras", "sign-language", "pt", "dataset:custom-libras-dataset", "license:mit", "region:us" ]	object-detection	2025-09-21T17:47:04Z	--- license: mit language: pt library_name: ultralytics tags: - yolov8 - object-detection - libras - sign-language datasets: - custom-libras-dataset --- # YOLOv8 para Detecção de Sinais em LIBRAS (Yolo-LibrasVision) Este repositório contém um modelo YOLOv8 treinado para detectar sinais da Língua Brasileira de Sinais (LIBRAS) em tempo real. Este modelo é a base para a API do projeto LibrasVision. ## Descrição do Modelo * Arquitetura: YOLOv8n (nano) * Framework: PyTorch * Tarefa: Detecção de Objetos ## Métricas de Performance O modelo foi treinado no nosso dataset customizado e atingiu as seguintes métricas no conjunto de validação: * mAP50-95: `0.846484` * Precision: `0.977354` * Recall: `0.9524128` ## Como Usar (Exemplo com Ultralytics) ```python from ultralytics import YOLO from huggingface_hub import hf_hub_download # O repositório já contém o arquivo de configuração, # mas para uso local, você pode baixá-lo também. REPO_ID = "rianagario/Yolo-LibrasVision" MODEL_FILENAME = "model.pt" # Baixar o modelo do Hub model_path = hf_hub_download(repo_id=REPO_ID, filename=MODEL_FILENAME) # Carregar o modelo model = YOLO(model_path) # Realizar inferência results = model('caminho/para/sua/imagem.jpg') # Exibir resultados results[0].show()
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758477699	schooncestiaa	2025-09-21T18:02:58Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "scruffy webbed dragonfly", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T18:02:40Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - scruffy webbed dragonfly --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
haihp02/891afca1-634f-4e53-bd50-43e8a1d43bc2	haihp02	2025-09-21T18:01:56Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T16:12:43Z	--- 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. 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StanislavKalishenko/Gemma3-Pretrained-uk	StanislavKalishenko	2025-09-21T17:56:21Z	109	0	mlx	[ "mlx", "safetensors", "gemma3_text", "text-generation", "conversational", "base_model:mlx-community/gemma-3-1b-it-4bit", "base_model:quantized:mlx-community/gemma-3-1b-it-4bit", "license:gemma", "4-bit", "region:us" ]	text-generation	2025-09-07T13:38:52Z	--- license: gemma library_name: mlx pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: mlx-community/gemma-3-1b-it-4bit tags: - mlx ---
ysakhale/Homework2-task1	ysakhale	2025-09-21T17:55:31Z	0	0	null	[ "region:us" ]	null	2025-09-20T23:05:16Z	# AutoML Regression Model for Shoe Dataset ## Model Summary This model was trained using AutoGluon Tabular (v1.4.0) on the dataset [maryzhang/hw1-24679-tabular-dataset](https://huggingface.co/datasets/maryzhang/hw1-24679-tabular-dataset). The task is regression, predicting the actual measured shoe length (mm) from shoe attributes. - Best Model: `CatBoost_r177_BAG_L1` (bagged ensemble of CatBoost models) - Test R² Score: 0.8904 (≈ 89% variance explained) - Validation R² Score: 0.8049 - Pearson correlation: 0.9473 - RMSE: 1.80 mm - MAE: 1.10 mm - Median AE: 0.68 mm These values indicate the model can predict shoe length within ~1–2 mm of the actual measurement on average. --- ## Leaderboard (Top 5 Models) \| Rank \| Model \| Test R² \| Val R² \| Pred Time (s) \| Fit Time (s) \| \|------\|------------------------\|---------\|---------\|---------------\|--------------\| \| 1 \| CatBoost_r177_BAG_L1 \| 0.8994 \| 0.8049 \| 0.0293 \| 27.14 \| \| 2 \| LightGBMLarge_BAG_L2 \| 0.8971 \| 0.7995 \| 0.7011 \| 238.93 \| \| 3 \| CatBoost_BAG_L2 \| 0.8939 \| 0.8405 \| 0.6155 \| 276.40 \| \| 4 \| CatBoost_r9_BAG_L1 \| 0.8917 \| 0.7889 \| 0.0606 \| 53.87 \| \| 5 \| WeightedEnsemble_L3 \| 0.8904 \| 0.8500 \| 0.9871 \| 333.68 \| --- ## Dataset - Source: [maryzhang/hw1-24679-tabular-dataset](https://huggingface.co/datasets/maryzhang/hw1-24679-tabular-dataset) - Size: 338 samples (30 original, 308 augmented) - Features: - US size (numeric) - Shoe size (mm) (numeric) - Type of shoe (categorical) - Shoe color (categorical) - Shoe brand (categorical) - Target: Actual measured shoe length (mm) - Splits: 80% training, 20% testing (random_state=42) --- ## Preprocessing - Converted Hugging Face dataset to Pandas DataFrame - Train/test split with stratified random seed - AutoGluon handled categorical encoding, normalization, and feature selection automatically --- ## Training Setup - Framework: AutoGluon Tabular v1.4.0 - Search Strategy: Bagged/stacked ensembles with model selection (`presets="best"`) - Time Budget: 1200 seconds (20 minutes) - Evaluation Metric: R² - Hyperparameter Search: Automated by AutoGluon (CatBoost, LightGBM, ensemble stacking) --- ## Metrics - R²: 0.8904 (test) - RMSE: 1.80 mm - MAE: 1.10 mm - Median AE: 0.68 mm - Uncertainty: Variability assessed across multiple base models in ensemble. Bagging reduces variance; expected error ±2 mm for most predictions. --- ## Intended Use - Educational: Demonstrates AutoML regression in CMU course 24-679 - Limitations: - Small dataset size (338 samples) → not robust for production use - Augmented data may not reflect real-world variability - Not suitable for medical or industrial applications --- ## Ethical Considerations - Predictions should not be used to recommend or prescribe footwear sizes in clinical or consumer contexts. - Dataset augmentation could introduce biases not present in real measurements. --- ## License - Dataset: MIT License - Model: MIT License --- ## Hardware / Compute - Training: Google Colab (CPU runtime) - Time: ~20 minutes wall-clock time - RAM: <8 GB used --- ## AI Usage Disclosure - Model training and hyperparameter search used AutoML (AutoGluon). - Model card text and documentation partially generated with AI assistance (ChatGPT). --- ## Acknowledgments - Dataset by Mary Zhang (CMU 24-679) - Model training and documentation by Yash Sakhale
fashionita/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-silent_freckled_dinosaur	fashionita	2025-09-21T17:42:59Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am silent_freckled_dinosaur", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T04:53:59Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am silent_freckled_dinosaur --- # 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/ConfTuner-Ministral-i1-GGUF	mradermacher	2025-09-21T17:31:34Z	532	1	transformers	[ "transformers", "gguf", "peft", "fine-tuning", "confidence-estimation", "trustworthy-ai", "text-generation", "LLM", "mistral", "en", "base_model:liushiliushi/ConfTuner-Ministral", "base_model:quantized:liushiliushi/ConfTuner-Ministral", "license:other", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	text-generation	2025-09-19T15:36:36Z	--- base_model: liushiliushi/ConfTuner-Ministral language: - en library_name: transformers license: other mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - peft - fine-tuning - confidence-estimation - trustworthy-ai - text-generation - LLM - mistral --- ## 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/liushiliushi/ConfTuner-Ministral <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#ConfTuner-Ministral-i1-GGUF).* static quants are available at https://huggingface.co/mradermacher/ConfTuner-Ministral-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/ConfTuner-Ministral-i1-GGUF/resolve/main/ConfTuner-Ministral.imatrix.gguf) \| imatrix \| 0.1 \| imatrix file (for creating your own qwuants) \| \| [GGUF](https://huggingface.co/mradermacher/ConfTuner-Ministral-i1-GGUF/resolve/main/ConfTuner-Ministral.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/ConfTuner-Ministral-i1-GGUF/resolve/main/ConfTuner-Ministral.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/ConfTuner-Ministral-i1-GGUF/resolve/main/ConfTuner-Ministral.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/ConfTuner-Ministral-i1-GGUF/resolve/main/ConfTuner-Ministral.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/ConfTuner-Ministral-i1-GGUF/resolve/main/ConfTuner-Ministral.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/ConfTuner-Ministral-i1-GGUF/resolve/main/ConfTuner-Ministral.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ConfTuner-Ministral-i1-GGUF/resolve/main/ConfTuner-Ministral.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 3.1 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/ConfTuner-Ministral-i1-GGUF/resolve/main/ConfTuner-Ministral.i1-Q2_K.gguf) \| i1-Q2_K \| 3.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/ConfTuner-Ministral-i1-GGUF/resolve/main/ConfTuner-Ministral.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ConfTuner-Ministral-i1-GGUF/resolve/main/ConfTuner-Ministral.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/ConfTuner-Ministral-i1-GGUF/resolve/main/ConfTuner-Ministral.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/ConfTuner-Ministral-i1-GGUF/resolve/main/ConfTuner-Ministral.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/ConfTuner-Ministral-i1-GGUF/resolve/main/ConfTuner-Ministral.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/ConfTuner-Ministral-i1-GGUF/resolve/main/ConfTuner-Ministral.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 4.1 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/ConfTuner-Ministral-i1-GGUF/resolve/main/ConfTuner-Ministral.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.4 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/ConfTuner-Ministral-i1-GGUF/resolve/main/ConfTuner-Ministral.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/ConfTuner-Ministral-i1-GGUF/resolve/main/ConfTuner-Ministral.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/ConfTuner-Ministral-i1-GGUF/resolve/main/ConfTuner-Ministral.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 4.8 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/ConfTuner-Ministral-i1-GGUF/resolve/main/ConfTuner-Ministral.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/ConfTuner-Ministral-i1-GGUF/resolve/main/ConfTuner-Ministral.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ConfTuner-Ministral-i1-GGUF/resolve/main/ConfTuner-Ministral.i1-Q4_1.gguf) \| i1-Q4_1 \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ConfTuner-Ministral-i1-GGUF/resolve/main/ConfTuner-Ministral.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/ConfTuner-Ministral-i1-GGUF/resolve/main/ConfTuner-Ministral.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/ConfTuner-Ministral-i1-GGUF/resolve/main/ConfTuner-Ministral.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 -->
sirev/gemma-2b-dpo-Q8_0-GGUF	sirev	2025-09-21T17:30:57Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "base_model:sirev/gemma-2b-dpo", "base_model:quantized:sirev/gemma-2b-dpo", "endpoints_compatible", "region:us" ]	null	2025-09-21T17:30:42Z	--- library_name: transformers tags: - llama-cpp - gguf-my-repo base_model: sirev/gemma-2b-dpo --- # sirev/gemma-2b-dpo-Q8_0-GGUF This model was converted to GGUF format from [`sirev/gemma-2b-dpo`](https://huggingface.co/sirev/gemma-2b-dpo) 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/sirev/gemma-2b-dpo) 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 sirev/gemma-2b-dpo-Q8_0-GGUF --hf-file gemma-2b-dpo-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo sirev/gemma-2b-dpo-Q8_0-GGUF --hf-file gemma-2b-dpo-q8_0.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 sirev/gemma-2b-dpo-Q8_0-GGUF --hf-file gemma-2b-dpo-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo sirev/gemma-2b-dpo-Q8_0-GGUF --hf-file gemma-2b-dpo-q8_0.gguf -c 2048 ```
olusegunola/phi3-pruned-cp-masked	olusegunola	2025-09-21T17:30:09Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T17:29:18Z	--- 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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prithivMLmods/Monochrome-Pencil	prithivMLmods	2025-09-21T17:28:49Z	1	1	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "sketch", "pencil", "monochrome", "art", "image-to-image", "base_model:black-forest-labs/FLUX.1-Kontext-dev", "base_model:adapter:black-forest-labs/FLUX.1-Kontext-dev", "license:other", "region:us" ]	image-to-image	2025-09-21T03:56:49Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora - sketch - pencil - monochrome - art widget: - src: images/1.jpg text: >- [photo content], replicate the image as a pencil illustration, black and white, with sketch-like detailing. prompt: > [photo content], replicate the image as a pencil illustration, black and white, with sketch-like detailing. output: url: images/2.png base_model: black-forest-labs/FLUX.1-Kontext-dev instance_prompt: >- [photo content], replicate the image as a pencil illustration, black and white, with sketch-like detailing. license: other license_name: flux-1-dev-non-commercial-license license_link: LICENSE.md pipeline_tag: image-to-image --- ![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/SSbZ2xihTKCkspLTn69kw.png) # Monochrome-Pencil-i2i [Image-to-Image] <Gallery /> Monochrome-Pencil-i2i is an adapter for black-forest-lab's FLUX.1-Kontext-dev. It is a Sketch LoRA trained for seamless conversion of any image into a monochrome pencil sketch while preserving the original characteristics of the image. The model was trained on 40 image pairs (20 start images, 20 end images). Synthetic result nodes were generated using NanoBanana from Google and SeedDream 4 (dataset for result sets), and labeled with DeepCaption-VLA-7B. The adapter is triggered with the following prompt: > [!note] [photo content], replicate the image as a pencil illustration, black and white, with sketch-like detailing. --- ## Sample Inference \| FLUX.1-Kontext-dev \|<span style="color:red">Monochrome-Pencil</span> \| \|------\|-------\| \| ![Left Screenshot](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/vJ29Ijc0bRDheKPisKwHi.png) \| ![Right Screenshot](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Q0DRFoTGa0czAf8sV4Jgn.png) \| \| ex1-<span style="color:red">Monochrome-Pencil</span> \| ex2-<span style="color:red">Monochrome-Pencil</span> \| \|------\|-------\| \| ![Left Screenshot](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/qQKVgRDwhi2asBt5VdUo3.png) \| ![Right Screenshot](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/y51D0wxOZCx95J4jGP_k6.png) \| --- ## Parameter Settings \| Setting \| Value \| \| ------------------------ \| ------------------------ \| \| Module Type \| Adapter \| \| Base Model \| FLUX.1 Kontext Dev - fp8 \| \| Trigger Words \| [photo content], replicate the image as a pencil illustration, black and white, with sketch-like detailing. \| \| Image Processing Repeats \| 50 \| \| Epochs \| 25 \| \| Save Every N Epochs \| 1 \| Labeling: DeepCaption-VLA-7B(natural language & English) Total Images Used for Training : 40 Image Pairs (20 Start, 20 End) Synthetic result nodes were generated using NanoBanana from Google and SeedDream 4 (dataset for result sets) ## Training Parameters \| Setting \| Value \| \| --------------------------- \| --------- \| \| Seed \| - \| \| Clip Skip \| - \| \| Text Encoder LR \| 0.00001 \| \| UNet LR \| 0.00005 \| \| LR Scheduler \| constant \| \| Optimizer \| AdamW8bit \| \| Network Dimension \| 64 \| \| Network Alpha \| 32 \| \| Gradient Accumulation Steps \| - \| ## Label Parameters \| Setting \| Value \| \| --------------- \| ----- \| \| Shuffle Caption \| - \| \| Keep N Tokens \| - \| ## Advanced Parameters \| Setting \| Value \| \| ------------------------- \| ----- \| \| Noise Offset \| 0.03 \| \| Multires Noise Discount \| 0.1 \| \| Multires Noise Iterations \| 10 \| \| Conv Dimension \| - \| \| Conv Alpha \| - \| \| Batch Size \| - \| \| Steps \| 3900 \| \| Sampler \| euler \| --- ## Trigger words You should use `[photo content]` to trigger the image generation. You should use `replicate the image as a pencil illustration` to trigger the image generation. You should use `black and white` to trigger the image generation. You should use `with sketch-like detailing.` to trigger the image generation. ## Download model [Download](/prithivMLmods/Monochrome-Pencil/tree/main) them in the Files & versions tab.
mjbommar/glaurung-small-001	mjbommar	2025-09-21T17:28:16Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "binary-analysis", "security", "malware-analysis", "executable-analysis", "masked-language-modeling", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-09-21T16:42:26Z	--- language: - en license: apache-2.0 tags: - binary-analysis - security - malware-analysis - executable-analysis - roberta - masked-language-modeling library_name: transformers pipeline_tag: fill-mask widget: - text: "ELF <mask> header" --- # Glaurung Small 001 A RoBERTa-based masked language model trained on binary executable files for security research and binary analysis. ## Overview Glaurung Small 001 is a transformer model specifically designed for understanding binary executable files. It uses a custom BPE (Byte Pair Encoding) tokenizer trained on multi-byte patterns from various binary formats across multiple architectures (x86-64, ARM64, etc.) and operating systems (Linux, Alpine, Ubuntu, Debian, Rocky). ### Key Features - Custom Binary Tokenizer: BPE tokenizer that creates efficient multi-byte tokens from binary data - Binary-Aware: Trained on actual executable files, not hex strings - Multi-Architecture: Understands patterns from various CPU architectures and file formats - Latin-1 Encoding: Preserves all byte values (0-255) without loss ## Model Details - Architecture: RoBERTa for Masked Language Modeling - Hidden Size: 768 - Layers: 12 - Attention Heads: 12 - Vocabulary Size: 65,536 tokens - Max Position Embeddings: 520 - Special Tokens: - `<\|start\|>` (0): Beginning of sequence - `<\|end\|>` (1): End token - `<\|sep\|>` (2): Separator/EOS - `<\|cls\|>` (3): Classification token - `<\|pad\|>` (4): Padding - `<\|mask\|>` (5): Mask token for MLM - `<\|unk\|>` (6): Unknown token ## Installation & Loading ```bash pip install transformers torch ``` ```python from transformers import AutoTokenizer, AutoModelForMaskedLM, AutoModel, pipeline # Method 1: Load with pipeline for fill-mask tasks fill_mask = pipeline('fill-mask', model='mjbommar/glaurung-small-001', device=-1) # Method 2: Load model and tokenizer directly for fill-mask model = AutoModelForMaskedLM.from_pretrained('mjbommar/glaurung-small-001') tokenizer = AutoTokenizer.from_pretrained('mjbommar/glaurung-small-001') # Method 3: Load base model for feature extraction/embeddings model_base = AutoModel.from_pretrained('mjbommar/glaurung-small-001') ``` ## Usage Guide ### 1. Loading Binary Data (Critical!) Binary files MUST be read as bytes and converted to latin-1 encoding: ```python # CORRECT: Read as bytes, decode with latin-1 with open('/usr/bin/ls', 'rb') as f: binary_data = f.read() # Read first 512 bytes or as needed text = binary_data.decode('latin-1', errors='ignore') # WRONG: Never use hex strings or other encodings # hex_string = "7f454c46..." # ❌ Will not work # utf8_text = binary_data.decode('utf-8') # ❌ Will lose bytes ``` ### 2. Understanding the BPE Tokenizer The tokenizer creates multi-byte tokens from common binary patterns: ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('mjbommar/glaurung-small-001') # Example: ELF header tokenization elf_header = b'\x7fELF\x02\x01\x01\x00' text = elf_header.decode('latin-1') tokens = tokenizer(text, return_tensors='pt') token_ids = tokens['input_ids'][0].tolist() # Decode tokens individually to see multi-byte patterns for token_id in token_ids[1:5]: # Skip special tokens decoded = tokenizer.decode([token_id], skip_special_tokens=True) print(f"Token {token_id}: {repr(decoded)}") # Output: # Token 45689: '\x7fEL' # ELF magic compressed to one token! # Token 3665: 'F\x02' # Format byte + 64-bit flag # Token 458: '\x01\x01' # Little-endian + version # Token 600: '\x00\x00\x00\x00\x00\x00\x00\x00\x00' # Padding ``` ### 3. Fill-Mask Task (Token-Level Prediction) Important: Masking works at the TOKEN level, not byte level! ```python from transformers import AutoTokenizer, AutoModelForMaskedLM import torch model = AutoModelForMaskedLM.from_pretrained('mjbommar/glaurung-small-001') tokenizer = AutoTokenizer.from_pretrained('mjbommar/glaurung-small-001') # Read binary file with open('/usr/bin/ls', 'rb') as f: binary_data = f.read(512) text = binary_data.decode('latin-1', errors='ignore') # Tokenize tokens = tokenizer(text, return_tensors='pt') token_ids = tokens['input_ids'][0].tolist() # Mask the second token (first content token after <\|start\|>) masked_ids = token_ids.copy() original_token = masked_ids[1] # Save original masked_ids[1] = tokenizer.mask_token_id # Prepare input tokens_masked = { 'input_ids': torch.tensor([masked_ids]), 'attention_mask': torch.tensor([[1]len(masked_ids)]) } # Predict with torch.no_grad(): outputs = model(tokens_masked) predictions = outputs.logits[0, 1].softmax(dim=-1) top5 = predictions.topk(5) # Show results print(f"Original: {repr(tokenizer.decode([original_token]))}") for score, token_id in zip(top5.values, top5.indices): token_text = tokenizer.decode([token_id.item()], skip_special_tokens=True) print(f"Predicted: {repr(token_text)} (confidence: {score:.2%})") # Example output: # Original: '\x7fEL' # Predicted: '\x7fEL' (confidence: 79.07%) ✓ Correct! # Predicted: '\x00\x00\x00\x00\x00\x00\x00\x00' (confidence: 13.62%) ``` ### 4. Using Pipeline for Fill-Mask The pipeline handles tokenization automatically but requires understanding multi-byte tokens: ```python from transformers import pipeline # Load pipeline fill_mask = pipeline('fill-mask', model='mjbommar/glaurung-small-001', device=-1) # Read binary with open('/usr/bin/ls', 'rb') as f: binary_data = f.read(100) text = binary_data.decode('latin-1', errors='ignore') # Create masked input at token boundaries # First, tokenize to understand token boundaries tokenizer = fill_mask.tokenizer tokens = tokenizer(text) decoded_tokens = [tokenizer.decode([tid], skip_special_tokens=True) for tid in tokens['input_ids']] # Reconstruct with mask at token boundary masked_text = ''.join([ decoded_tokens[0], # <\|start\|> fill_mask.tokenizer.mask_token, # Mask the ELF magic ''.join(decoded_tokens[2:]) # Rest of tokens ]) # Predict predictions = fill_mask(masked_text, top_k=3) for pred in predictions: print(f"{repr(pred['token_str'])}: {pred['score']:.2%}") ``` ### 5. Feature Extraction & Embedding Similarity Compare binary files by their learned embeddings: ```python from transformers import AutoTokenizer, AutoModel import torch import torch.nn.functional as F from pathlib import Path # Load for embeddings (not MaskedLM) tokenizer = AutoTokenizer.from_pretrained('mjbommar/glaurung-small-001') model = AutoModel.from_pretrained('mjbommar/glaurung-small-001') model.eval() def get_binary_embedding(file_path, max_bytes=512): """Extract embedding for a binary file using mean pooling""" with open(file_path, 'rb') as f: binary_data = f.read(max_bytes) text = binary_data.decode('latin-1', errors='ignore') # Tokenize tokens = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=512) # Get embeddings with mean pooling with torch.no_grad(): outputs = model(tokens) # Mean pooling (better than CLS token for this model) attention_mask = tokens['attention_mask'] hidden_states = outputs.last_hidden_state # Mask padding tokens mask_expanded = attention_mask.unsqueeze(-1).expand(hidden_states.size()).float() sum_embeddings = torch.sum(hidden_states mask_expanded, dim=1) sum_mask = torch.clamp(mask_expanded.sum(dim=1), min=1e-9) embedding = sum_embeddings / sum_mask return embedding # Compare multiple binaries files = ['/usr/bin/ls', '/usr/bin/cat', '/usr/bin/echo', '/etc/passwd'] embeddings = {} for file_path in files: if Path(file_path).exists(): name = Path(file_path).name embeddings[name] = get_binary_embedding(file_path) # Calculate similarities print("Cosine Similarity Matrix:") names = list(embeddings.keys()) for name1 in names: similarities = [] for name2 in names: sim = F.cosine_similarity(embeddings[name1], embeddings[name2], dim=-1).item() similarities.append(f"{sim:.3f}") print(f"{name1:10s}: {' '.join(similarities)}") # Expected output: # ELF executables (ls, cat, echo) will have high similarity (0.85-0.95) # Text file (passwd) will have low similarity (0.25-0.30) to ELF files ``` ## Real-World Example: ELF Header Analysis ```python from transformers import AutoTokenizer, AutoModelForMaskedLM import torch # Load model and tokenizer model = AutoModelForMaskedLM.from_pretrained('mjbommar/glaurung-small-001') tokenizer = AutoTokenizer.from_pretrained('mjbommar/glaurung-small-001') # Analyze ELF executable structure with open('/usr/bin/ls', 'rb') as f: binary_data = f.read(512) # Read enough for context print(f"Raw bytes (hex): {binary_data[:16].hex()}") # Output: 7f454c46020101000000000000000000 # Convert to latin-1 for model text = binary_data.decode('latin-1', errors='ignore') # Tokenize to see learned patterns tokens = tokenizer(text, return_tensors='pt') token_ids = tokens['input_ids'][0].tolist() # Show what tokens the model learned print("\nTokenized ELF header:") for i in range(1, min(5, len(token_ids)-1)): # First few content tokens token_text = tokenizer.decode([token_ids[i]], skip_special_tokens=True) print(f"Token {i}: {token_ids[i]:5d} = {repr(token_text)}") # Output: # Token 1: 45689 = '\x7fEL' - ELF magic compressed to one token! # Token 2: 3665 = 'F\x02' - 'F' + 64-bit flag # Token 3: 458 = '\x01\x01' - Little-endian + version # Token 4: 600 = '\x00\x00\x00\x00\x00\x00\x00\x00\x00' - Padding # Test model's understanding by masking each token print("\nTesting model predictions:") for position in [1, 2, 3]: # Test first 3 content tokens masked_ids = token_ids.copy() original_token = masked_ids[position] masked_ids[position] = tokenizer.mask_token_id # Create input tensors tokens_masked = { 'input_ids': torch.tensor([masked_ids]), 'attention_mask': torch.tensor([[1]len(masked_ids)]) } # Get prediction with torch.no_grad(): outputs = model(tokens_masked) predictions = outputs.logits[0, position].softmax(dim=-1) predicted_token = predictions.argmax().item() confidence = predictions.max().item() # Show results original_text = tokenizer.decode([original_token], skip_special_tokens=True) predicted_text = tokenizer.decode([predicted_token], skip_special_tokens=True) correct = "✓" if predicted_token == original_token else "✗" print(f"Position {position}: {correct}") print(f" Original: {repr(original_text)}") print(f" Predicted: {repr(predicted_text)} (confidence: {confidence:.1%})") # Expected Output: # Position 1: ✓ # Original: '\x7fEL' # Predicted: '\x7fEL' (confidence: 79.1%) # Position 2: ✓ # Original: 'F\x02' # Predicted: 'F\x02' (confidence: 97.9%) # Position 3: ✓ # Original: '\x01\x01' # Predicted: '\x01\x01' (confidence: 88.7%) ``` ## Training Details - MLM Objective: 20% masking probability - Training Data: Binary executables from various architectures - Optimization: AdamW with warmup, dropout 0.01 - Special Design*: Increased position embeddings (520) to handle RoBERTa's position offset ## Limitations - Maximum sequence length: 512 tokens - Optimized for executable files (ELF, PE, Mach-O) - Mean pooling recommended for embeddings (pooler layer not specifically trained) ## Citation If using this model in research: ``` @software{glaurung-small-001, title = {Glaurung Small 001: Binary Analysis Transformer}, author = {Glaurung Project}, year = {2024}, url = {https://github.com/mjbommar/glaurung-models} } ```
piccassol/NOLAND	piccassol	2025-09-21T17:24:56Z	0	0	null	[ "reinforcement-learning", "en", "license:mit", "region:us" ]	reinforcement-learning	2025-03-20T02:35:10Z	--- license: mit language: - en pipeline_tag: reinforcement-learning --- # NolandAI 🐙🚢 [![npm version](https://img.shields.io/npm/v/nolandai?label=npm)](https://www.npmjs.com/package/nolandai) [![Hugging Face](https://img.shields.io/badge/🤗-HuggingFace-yellow)](https://huggingface.co/piccassol/Noland) [![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE) NolandAI is an enterprise-ready, AI-powered Solana trading agent that combines on-chain analytics, social sentiment scraping, and a fine-tuned LLM to generate actionable trading calls. It ships as an npm SDK plus a FastAPI backend and a React/Next.js UI. --- ## 🔦 Highlights - Real-time market feeds via Dexscreener. - Social scraping (AssetDash, X/Twitter monitoring such as `@mobyagent`, `@whalewatch`). - LLM forecasting using a LoRA-fine-tuned model (`fingpt-forecaster_dow30_llama2-7b_lora` on Hugging Face). - Automated trading calls (hourly), plus optional auto-posting to X/Twitter. - Modular SDK (`nolandai` npm package) for JS/TS integration. - Production CI with release workflow and npm publish. --- ## 📦 Installation ### From npm (recommended) ```bash npm install nolandai ts Copy code import { NolandAI } from "nolandai"; const bot = new NolandAI({ apiKey: process.env.NOLAND_KEY }); const call = await bot.getTradingCall(); console.log(call); From source (dev) bash Copy code git clone https://github.com/your-username/NolandAI.git cd NolandAI npm install # frontend/sdk pip install -r requirements.txt # if running python FastAPI backend 🧩 Repository layout (recommended) pgsql Copy code NolandAI/ ├─ package.json ├─ index.js ├─ index.d.ts ├─ README.md ├─ LICENSE ├─ CHANGELOG.md ├─ CONTRIBUTING.md ├─ .github/ │ └─ workflows/ci.yml ├─ backend/ │ ├─ requirements.txt │ └─ main.py # FastAPI app (endpoints: /trading-call, /market-data/:token) ├─ frontend/ │ └─ (Next.js app) └─ examples/ └─ demo.js ⚙️ Quickstart — Local dev (SDK + demo backend) Start FastAPI backend (example): bash Copy code # in backend/ uvicorn main:app --reload --port 8000 Test SDK locally: bash Copy code # from repo root or examples/ node examples/demo.js 📚 API (SDK) new NolandAI(config?: { apiKey?: string; baseUrl?: string }) getTradingCall() → { token, action, confidence, reason } getMarketData(tokenAddress: string) → market JSON from backend 🧪 Example examples/demo.js (see examples/demo.js file in repo — quick show of getTradingCall + getMarketData) 📦 Publish & Releases We publish releases using semantic version tags (vMAJOR.MINOR.PATCH) and CI that validates tests and publishes to npm on tag push. See .github/workflows/ci.yml. Official release example: v1.0.0 — Captain’s Log (2025-09-20) Initial public release. npm package nolandai published. FastAPI endpoints /trading-call, /market-data/:token live. Hugging Face model integration. 🏛️ Contributing See CONTRIBUTING.md. Pull requests welcome — use branches, add tests, sign commits. ⚖️ License MIT © 2025 AuroraRift Maintainers AuroraRift Team — [email protected] Changelog See CHANGELOG.md for full release history. pgsql Copy code --- # 2) Files to create (copy these into repo root exactly) Below are the key files. Put them where indicated. ## `package.json` (repo root) ```json { "name": "nolandai", "version": "1.0.0", "description": "NolandAI - An AI-powered Solana trading agent for market intelligence, social scraping, and automated trading calls.", "main": "index.js", "types": "index.d.ts", "type": "module", "scripts": { "build": "tsc", "test": "node test.js \|\| echo \"no tests\"", "lint": "eslint ." }, "repository": { "type": "git", "url": "https://github.com/your-username/NolandAI.git" }, "keywords": [ "AI", "trading", "Solana", "crypto", "blockchain", "LLM", "bot", "Dexscreener", "NolandAI", "AuroraRift" ], "author": "AuroraRift Team <[email protected]>", "license": "MIT", "bugs": { "url": "https://github.com/your-username/NolandAI/issues" }, "homepage": "https://huggingface.co/your-username/NolandAI", "engines": { "node": ">=18" }, "dependencies": { "axios": "^1.7.0", "dotenv": "^16.3.1" }, "devDependencies": { "eslint": "^8.56.0", "typescript": "^5.4.0" } } index.js (repo root) js Copy code import axios from "axios"; import dotenv from "dotenv"; dotenv.config(); export class NolandAI { constructor(config = {}) { this.apiKey = config.apiKey \|\| process.env.NOLAND_KEY \|\| ""; this.baseUrl = config.baseUrl \|\| "http://localhost:8000"; // FastAPI default } async getTradingCall() { const res = await axios.get(`${this.baseUrl}/trading-call`, { headers: this.apiKey ? { Authorization: `Bearer ${this.apiKey}` } : {} }); return res.data; } async getMarketData(tokenAddress) { const res = await axios.get(`${this.baseUrl}/market-data/${tokenAddress}`); return res.data; } } export default NolandAI;
huwhitememes/charliekirk_v1-qwen_image	huwhitememes	2025-09-21T17:16:33Z	0	0	null	[ "image", "lora", "qwen", "charlie-kirk", "generative-image", "huwhitememes", "Meme King Studio", "Green Frog Labs", "culture-war", "tribute", "text-to-image", "base_model:Qwen/Qwen-Image", "base_model:adapter:Qwen/Qwen-Image", "license:apache-2.0", "region:us" ]	text-to-image	2025-09-21T16:35:40Z	--- license: apache-2.0 base_model: Qwen/Qwen-Image tags: - image - lora - qwen - charlie-kirk - generative-image - huwhitememes - Meme King Studio - Green Frog Labs - culture-war - tribute pipeline_tag: text-to-image --- # ✝️ Charlie Kirk Tribute LoRA for Qwen Image V1 🕊️ This is a LoRA trained on 43 curated images of Charlie Kirk — founder of TPUSA, Patriot, and Martyr of the American culture war. Trained with love on [Wavespeed.AI](https://wavespeed.ai), this LoRA allows creators to generate powerful, emotional, and surreal art that captures the iconography, legacy, and spiritual presence of Charlie. > WE ARE ALL CHARLIE KIRK. > > --- ## 🎯 Use Cases - Faith-based political tribute art - Digital memorials honoring Christian martyrs - Patriotic and pro-MAGA propaganda artwork - Spiritual warfare visuals for the culture war - Remembrance content for social media and movement building - Meme canonization of righteous leaders silenced by the left --- ## 🔧 Training Details - Base Model: Qwen/Qwen-Image - Trainer: WaveSpeedAI LoRA Trainer - Steps: ~2000 - LoRA Rank: 16 - Image Count: 43 (balanced for aesthetic and variation) - Trigger Word: `Ch4rlie K!rk` (recommended at prompt start) - Style: Meme realism, cinematic emotionality, digital martyrdom --- ## 🧠 Creator Created by [@huwhitememes](https://x.com/huwhitememes) Released by Meme King Studio in cooperation with Green Frog Labs Part of the expanding creative ecosystem where memes become monuments. --- ## ⚖️ Legal & Fair Use This model was trained using publicly available imagery of a major public figure. It is provided for fair use, memorial tribute, and commentary purposes. Not for commercial misuse. Not affiliated with or endorsed by any individual or organization. --- ## 🙏🏻 In Memoriam On this solemn day, Sunday September 21st 2025, we mourn the loss of Charlie Kirk. Targeted and assassinated in cold blood by radical leftists, Charlie's voice was silenced — but his message lives on in each of us. Today, we say it loud: WE ARE ALL CHARLIE KIRK. His spirit will rise in every meme, every image, every call to truth in a world gone mad. --- ## 🧪 Example Usage Prompt ```text Ch4rlie K!rk as an armored angel of vengeance, standing atop a pile of rainbow-colored demon corpses, wings of fire, flaming sword raised to the heavens, battle-worn American flag waving behind him, photorealistic, dark fantasy, VHS glitch aesthetic
Rinanixvaruyr/Qwen3-0.6B-Gensyn-Swarm-howling_stalking_zebra	Rinanixvaruyr	2025-09-21T17:07:07Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am howling_stalking_zebra", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T17:07:00Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am howling_stalking_zebra --- # 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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hadasor/abc-seed_5	hadasor	2025-09-21T17:02:12Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T16:22:43Z	--- 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. 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beyoru/Qwen3-4B-I-1509	beyoru	2025-09-21T16:57:52Z	106	1	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "tools", "agent", "function calling", "tool calling", "conversational", "en", "base_model:beyoru/Qwen3-4B-I-1509", "base_model:finetune:beyoru/Qwen3-4B-I-1509", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-09-16T18:44:51Z	--- base_model: beyoru/Qwen3-4B-I-1509 tags: - text-generation-inference - transformers - qwen3 - tools - agent - function calling - tool calling license: apache-2.0 language: - en --- # 🚀 Qwen3-4B-I-1509 ## 🧾 Model Overview - 🏗️ Base Model: Qwen3-4B-Instruct-2507 - 🎯 Training Method: Reinforcement Learning (GRPO) with multiple reward functions This model (`Qwen3-4B-I-1509`) is finetuned for 🔧 tool-use and 📞 function call generation. <p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/65905af887944e494e37e09a/znFHp2gPLIsMN613HEESX.webp" width="160"> </p> --- ## 🏆 Reward Functions The model was trained with multi-signal rewards: 1. 📝 Rule-based Reward ✔️ Checks correctness of function call name and arguments. ➕ Partial credit for matching subsets of arguments. 2. 🔒 Self-Certainty Reward ⚡ Encourages confident predictions. 3. 🔧 Tool-Call Reward ✅ Validates structural correctness. --- ## ⚙️ Training Configuration - ⚡ Optimizer: AdamW - 📉 Learning Rate: 5e-6 with cosine decay (`min_lr_rate=0.1`) - ⏳ Scheduler: cosine_with_min_lr - 🔄 Generations per Prompt: 4 --- ## 📊 Eval Result: ### Important notes: - Why it lower than technical report? There have a limit of hardware so have to reduce some max tokens when evaluation for both 2 models - Fair evaluate ? I use the same configuration for all the models I review for larger or with a same size model. ### Tau-Bench \| 🧠 Model \| ✈️ Airline \| 🛍️ Retail \| ⭐ Overall \| \|-------------------\|------------\|-------------\|------------\| \| Qwen3-4B-I-1509 \| 0.2800 \| 0.2783 \| 0.2788 \| \| Base Model \| 0.3000 \| 0.2261 \| 0.2485 \| ## ACEBench \| Model \| Overall Accuracy \| \|--------------------------------\|------------------\| \| Qwen3-4B-I-1509 \| 0.677 \| \| Qwen3-4B-Instruct-2507 (base) \| 0.635 \| curently upadate more --- ## Contribute: I would be happy to receive a contribution to this model and get feedback about performance, quality of model ## 📖 Citation If you use this model in your research or application, please cite: ```bibtex @misc{qwen3-4b-i-1509, title = {Qwen3-4B-I-1509: Fine-tuned Qwen3-4B-Instruct with GRPO for Tool-Use and Function Calling}, author = {Beyoru}, year = {2025}, howpublished = {\url{https://huggingface.co/beyoru/Qwen3-4B-I-1509}} }
thefirstgoku/21_intergated_v32_9	thefirstgoku	2025-09-21T16:57:52Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-09-21T16:57:13Z	--- 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).
urm3l/model16	urm3l	2025-09-21T16:52:32Z	37	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-09-12T19:44:11Z	--- base_model: unsloth/qwen3-14b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: urm3l - License: apache-2.0 - Finetuned from model : unsloth/qwen3-14b-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)
PrevAIHealth/Llama3.2-1B-Instruct-Medical	PrevAIHealth	2025-09-21T16:51:09Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T16:50:40Z	--- 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]
SandeepCodez/VCET-gemma-1b-it	SandeepCodez	2025-09-21T16:50:50Z	0	0	null	[ "safetensors", "gemma3_text", "license:apache-2.0", "region:us" ]	null	2025-09-21T16:45:19Z	--- license: apache-2.0 ---
WenFengg/REP21Sun__14_23	WenFengg	2025-09-21T16:48:12Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-09-21T16:47:05Z	--- 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).
RH730/llama-3.2-11b-vision-eng-reviewer	RH730	2025-09-21T16:46:20Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mllama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-09-21T16:45:41Z	--- base_model: unsloth/llama-3.2-11b-vision-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mllama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: RH730 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-11b-vision-instruct-bnb-4bit This mllama 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)
safffrron/prompt_tuned_adalora	safffrron	2025-09-21T16:45:53Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2025-09-21T15:15:43Z	--- library_name: peft license: apache-2.0 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 tags: - generated_from_trainer model-index: - name: prompt_tuned_adalora 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. --> # prompt_tuned_adalora This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4595 ## 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: 0.001 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH 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: 100 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.4868 \| 1.0 \| 7125 \| 2.5579 \| \| 2.4337 \| 2.0 \| 14250 \| 2.4991 \| \| 2.3644 \| 3.0 \| 21375 \| 2.4595 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.2
dario-mazzola/gemma-ft_function_calling	dario-mazzola	2025-09-21T16:42:57Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "sft", "trl", "unsloth", "base_model:unsloth/gemma-3-1b-it", "base_model:finetune:unsloth/gemma-3-1b-it", "endpoints_compatible", "region:us" ]	null	2025-09-20T16:15:11Z	--- base_model: unsloth/gemma-3-1b-it library_name: transformers model_name: gemma-ft_function_calling tags: - generated_from_trainer - sft - trl - unsloth licence: license --- # Model Card for gemma-ft_function_calling This model is a fine-tuned version of [unsloth/gemma-3-1b-it](https://huggingface.co/unsloth/gemma-3-1b-it). 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="dario-mazzola/gemma-ft_function_calling", 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 - 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}} } ```
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758472755	schooncestiaa	2025-09-21T16:40:35Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "scruffy webbed dragonfly", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T16:40:17Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - scruffy webbed dragonfly --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
MelonWithGlasses/MelonAI-7B-Instruct	MelonWithGlasses	2025-09-21T16:40:33Z	0	0	null	[ "license:cc-by-nc-4.0", "region:us" ]	null	2025-09-21T16:40:33Z	--- license: cc-by-nc-4.0 ---
moulibasha/tourism-package-prediction-model	moulibasha	2025-09-21T16:37:23Z	0	0	null	[ "region:us" ]	null	2025-09-21T10:57:16Z	# Tourism Package Prediction Model - Data: [tourism-package-prediction-train-test](https://huggingface.co/datasets/moulibasha/tourism-package-prediction-train-test) - Best params: {'model__class_weight': 'balanced', 'model__max_depth': None, 'model__min_samples_leaf': 1, 'model__min_samples_split': 5, 'model__n_estimators': 300} - Metrics: {'accuracy': 0.9028642590286425, 'precision': 0.8888888888888888, 'recall': 0.567741935483871, 'f1': 0.6929133858267716} - Pipeline: preprocessing (imputer + onehot) + RandomForest
devovevo/partssource-longformer-combined-type-classifier	devovevo	2025-09-21T16:36:36Z	10	0	null	[ "safetensors", "endpoints_compatible", "region:us" ]	null	2025-08-21T01:08:18Z	--- language: - en license: mit library_name: transformers tags: - text-classification - longformer - customer-service - case-classification - sequence-classification - pytorch pipeline_tag: text-classification widget: - text: >- I need help with my billing invoice and there seems to be an error in the charges. example_title: Billing Issue - text: Can you help me return this defective part I received? example_title: Return Request - text: I need a quote for 100 units of part number ABC123 example_title: Quote Request - text: My website login is not working properly example_title: Website Issue model-index: - name: longformer-combined-classifier results: - task: type: text-classification name: Text Classification dataset: type: custom name: Customer Service Cases metrics: - type: accuracy name: Accuracy value: 0.95 inference: parameters: max_length: 4096 truncation: true padding: true base_model: - allenai/longformer-base-4096 --- # Longformer Combined Classifier A robust Hugging Face Longformer model for sequence classification, specifically trained to classify customer service cases into case types and detailed categories. ## Model Overview - Base Model: `longformer-base-4096` - Task: Multi-class sequence classification - Labels: 59 detailed labels across 12 main categories - Max Sequence Length: 4096 tokens - Output Format: `case_type\|case_detail` ## Categories The model classifies text into the following main categories: - Account Update - Billing - Cancelation - Customer Request - Inventory - Other - Purchase Order - Quote Request - Repairs - Returns - Vendor Request - Website Each category has multiple detailed subcategories (59 total labels). ## Files - `handler.py` - Hugging Face Inference Endpoints compatible handler with robust error handling - `test_handler.py` - Comprehensive test script to validate the handler - `requirements.txt` - Python dependencies - `label_mappings.json` - Label mappings between IDs and human-readable labels - `config.json` - Model configuration - `model.safetensors` - Model weights - `tokenizer.json` - Tokenizer configuration - `tokenizer_config.json` - Tokenizer settings - `vocab.json` - Vocabulary - `special_tokens_map.json` - Special tokens mapping ## Installation 1. Install dependencies: ```bash pip install -r requirements.txt ``` 2. Ensure all model files are in the same directory as `handler.py` ## Usage ### Local Testing Run the test script to validate everything works: ```bash python test_handler.py ``` ### Single Text Classification ```python from handler import EndpointHandler # Initialize handler handler = EndpointHandler() # Single prediction data = { "inputs": "I need help with my billing invoice and there seems to be an error in the charges." } result = handler(data) print(result) ``` ### Batch Classification ```python from handler import EndpointHandler # Initialize handler handler = EndpointHandler() # Batch prediction data = { "inputs": [ "I need help with my billing invoice and there seems to be an error in the charges.", "Can you help me return this defective part I received?", "I need a quote for 100 units of part number ABC123" ] } result = handler(data) print(result) ``` ### Compatibility Wrapper For backward compatibility, a wrapper function is also available: ```python from handler import handler # Works with the same format as EndpointHandler result = handler({"inputs": "Your text here"}) ``` ## Response Format The handler returns predictions directly as a JSON list: Single Input Response: ```json [ { "case_type": "Billing", "case_detail": "Invoice Inquiry", "full_label": "Billing\|Invoice Inquiry", "confidence": 0.9234, "predicted_id": 5, "top_3_predictions": [ { "case_type": "Billing", "case_detail": "Invoice Inquiry", "confidence": 0.9234 }, { "case_type": "Billing", "case_detail": "Problem Invoice", "confidence": 0.0456 }, { "case_type": "Customer Request", "case_detail": "Shipping Status", "confidence": 0.0234 } ] } ] ``` Batch Input Response: ```json [ { "case_type": "Billing", "case_detail": "Invoice Inquiry", "confidence": 0.9234, "predicted_id": 5, "top_3_predictions": [...] }, { "case_type": "Returns", "case_detail": "Return Request", "confidence": 0.8756, "predicted_id": 48, "top_3_predictions": [...] } ] ``` Processing time, batch size, and model info are logged but not included in the response for cleaner output. ## Robust Features ### Token Limit Handling - Automatically truncates texts longer than 4096 tokens - Prevents model crashes from oversized inputs - Logs warnings when truncation occurs ### Batch Processing - Supports batch inference for efficiency - Configurable batch size (default: 8) - Handles mixed valid/invalid inputs gracefully ### Error Handling - Comprehensive error handling and logging - Graceful degradation for invalid inputs - Returns meaningful error messages ### Logging - Extensive logging for debugging and monitoring - Logs to both console and file (`model_inference.log`) - Different log levels for different scenarios ### Input Validation - Handles empty strings and whitespace-only inputs gracefully - Validates input format and structure - Returns "Other\|Junk" predictions for empty inputs (using actual label from mappings) ## Deployment ### Hugging Face Inference Endpoints (Recommended) The model includes a handler (`handler.py`) that implements the `EndpointHandler` interface required by HF Inference Endpoints. #### Prerequisites 1. Push your model to the Hugging Face Hub 2. Ensure all files are in your repository: - `handler.py` - `requirements.txt` - `label_mappings.json` - All model files (`.safetensors`, `config.json`, etc.) #### Deployment Steps 1. Prepare the Repository: ```bash # Push to HF Hub git add . git commit -m "Add HF Inference Endpoints handler" git push ``` 2. Create Inference Endpoint: - Go to [Hugging Face Inference Endpoints](https://ui.endpoints.huggingface.co/) - Click "Create new endpoint" - Select your model repository - In Advanced Configuration: - Set Framework* to "Custom" (important!) - Choose appropriate instance type (GPU recommended) - Set memory to at least 8GB 3. Test the Endpoint: ```python import requests # Single prediction response = requests.post( "https://your-endpoint-url.endpoints.huggingface.cloud", headers={"Authorization": "Bearer YOUR_TOKEN"}, json={"inputs": "I need help with my billing invoice"} ) # Batch prediction response = requests.post( "https://your-endpoint-url.endpoints.huggingface.cloud", headers={"Authorization": "Bearer YOUR_TOKEN"}, json={"inputs": ["Text 1", "Text 2", "Text 3"]} ) ``` #### Input Format The handler expects the standard HF Inference Endpoints format: ```json { "inputs": "Single text string" } ``` Or for batch processing: ```json { "inputs": ["Text 1", "Text 2", "Text 3"] } ``` #### Response Format The handler returns predictions directly as a list: Single Input: ```json [ { "case_type": "Billing", "case_detail": "Invoice Inquiry", "full_label": "Billing\|Invoice Inquiry", "confidence": 0.9234, "predicted_id": 5, "top_3_predictions": [ { "case_type": "Billing", "case_detail": "Invoice Inquiry", "confidence": 0.9234 }, { "case_type": "Billing", "case_detail": "Credit Request (Customer Complaint)", "confidence": 0.0456 }, { "case_type": "Customer Request", "case_detail": "Shipping Status", "confidence": 0.0234 } ] } ] ``` Batch Input: ```json [ { "case_type": "Billing", "case_detail": "Invoice Inquiry", "confidence": 0.9234, "predicted_id": 5, "top_3_predictions": [...] }, { "case_type": "Returns", "case_detail": "Return Request", "confidence": 0.8756, "predicted_id": 48, "top_3_predictions": [...] } ] ``` Empty Input: ```json [] ``` Processing time and batch size are logged but not returned in the response. ### AWS Lambda 1. Package the model and handler: ```bash # Create deployment package zip -r deployment.zip handler.py requirements.txt .json .safetensors ``` 2. Create Lambda function with: - Runtime: Python 3.9+ - Handler: `handler.handler` - Memory: 3008 MB (recommended for model loading) - Timeout: 5 minutes ### Docker Deployment Create a `Dockerfile`: ```dockerfile FROM python:3.9-slim WORKDIR /app COPY requirements.txt . RUN pip install -r requirements.txt COPY . . EXPOSE 8080 CMD ["python", "-c", "from handler import handler; import json; import sys; event = json.loads(sys.argv[1]); print(json.dumps(handler(event)))", "{}"] ``` ### SageMaker Endpoint The handler is compatible with SageMaker inference endpoints. Use the `handler` function as your inference entry point. ## Performance Considerations - GPU Recommended: Model performs significantly better on GPU - Memory Requirements: ~2-3GB RAM for model loading - Batch Size: Adjust `max_batch_size` based on available memory - Cold Start: First inference may take longer due to model loading ## Monitoring The handler provides comprehensive logging and metrics: - Processing times - Token counts and truncation warnings - Error rates and types - Batch sizes and throughput Monitor the `model_inference.log` file for detailed operation logs. ## Troubleshooting ### Common Issues 1. Out of Memory: Reduce `max_batch_size` in handler 2. Slow Performance: Ensure GPU is available and being used 3. Model Loading Errors: Verify all model files are present 4. Token Limit Errors: Check logs for truncation warnings ### Debug Mode Enable debug logging by modifying the logging level in `handler.py`: ```python logging.basicConfig(level=logging.DEBUG, ...) ``` ## Testing Run comprehensive tests: ```bash python test_handler.py ``` The test script validates: - Single and batch predictions - Long text handling and truncation - Edge case handling (including empty inputs) - Error scenarios - Model information retrieval - HF Inference Endpoints compatibility ## License This model and handler are for internal use. Ensure compliance with your organization's AI/ML usage policies. ## Support For issues or questions: 1. Check the logs in `model_inference.log` 2. Run the test script to validate setup 3. Review the troubleshooting section above
sweatSmile/DialoGPT-Quantitative-Risk-Analysis-Expert	sweatSmile	2025-09-21T16:33:25Z	0	1	transformers	[ "transformers", "conversational-ai", "finance", "fintech", "risk-management", "quantitative-analysis", "financial-risk", "risk-assessment", "lora", "hedge-funds", "investment-banking", "volatility-modeling", "risk-metrics", "portfolio-risk", "market-risk", "credit-risk", "operational-risk", "text-generation", "en", "dataset:AdaptLLM/finance-tasks", "base_model:microsoft/DialoGPT-medium", "base_model:adapter:microsoft/DialoGPT-medium", "license:mit", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T16:28:56Z	--- base_model: microsoft/DialoGPT-medium pipeline_tag: text-generation library_name: transformers tags: - conversational-ai - finance - fintech - risk-management - quantitative-analysis - financial-risk - risk-assessment - lora - hedge-funds - investment-banking - volatility-modeling - risk-metrics - portfolio-risk - market-risk - credit-risk - operational-risk language: - en license: mit datasets: - AdaptLLM/finance-tasks metrics: - perplexity - accuracy widget: - text: "<\|user\|> As a quantitative risk analyst, please analyze: What is the Value at Risk for a portfolio with 60% equity and 40% bonds during high volatility periods? <\|bot\|>" example_title: "VaR Analysis" - text: "<\|user\|> As a quantitative risk analyst, please analyze: How do correlation changes affect portfolio risk during market stress events? <\|bot\|>" example_title: "Correlation Risk Assessment" - text: "<\|user\|> As a quantitative risk analyst, please analyze: What are the key risk metrics for evaluating credit exposure in derivatives trading? <\|bot\|>" example_title: "Credit Risk Evaluation" --- # DialoGPT-Quantitative-Risk-Analysis-Expert Fine-tuned DialoGPT-medium for advanced quantitative risk analysis, financial risk modeling, and comprehensive risk management consultations. ## Overview - Base Model: microsoft/DialoGPT-medium (355M parameters) - Fine-tuning Method: LoRA (4-bit quantization) - Dataset: Financial risk analysis dataset (800 expert-level samples) - Training: 3 epochs with optimized hyperparameters ## Key Features - Advanced quantitative risk modeling and analysis - Value at Risk (VaR) and Expected Shortfall calculations - Portfolio risk assessment and optimization - Market risk, credit risk, and operational risk evaluation - Volatility modeling and stress testing scenarios - Risk metric interpretation and regulatory compliance ## Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("sweatSmile/DialoGPT-Quantitative-Risk-Analysis-Expert") tokenizer = AutoTokenizer.from_pretrained("sweatSmile/DialoGPT-Quantitative-Risk-Analysis-Expert") # Quantitative risk analysis example prompt = "<\|user\|> As a quantitative risk analyst, please analyze: How do we calculate risk-adjusted returns for a multi-asset portfolio under different market scenarios? <\|bot\|>" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=250, pad_token_id=tokenizer.eos_token_id) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## Applications - Risk management department consultations - Hedge fund risk assessment and monitoring - Investment bank risk modeling and analysis - Portfolio risk optimization and stress testing - Regulatory compliance and risk reporting - Quantitative research and model validation ## Training Details - LoRA rank: 8, alpha: 16 - 4-bit NF4 quantization with bfloat16 precision - Learning rate: 1e-4 with cosine scheduling - Batch size: 8, Max length: 400 tokens - 3 epochs on curated financial risk analysis dataset Specialized for sophisticated quantitative risk analysis and modeling in institutional finance environments.
WenFengg/REP21Sun__14_19	WenFengg	2025-09-21T16:33:03Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-09-21T16:31:29Z	--- 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).
SandeepCodez/gemma-vcet-log	SandeepCodez	2025-09-21T16:32:19Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:google/gemma-3-270m-it", "base_model:finetune:google/gemma-3-270m-it", "endpoints_compatible", "region:us" ]	null	2025-09-21T15:52:25Z	--- base_model: google/gemma-3-270m-it library_name: transformers model_name: gemma-vcet-log tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for gemma-vcet-log This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it). 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="SandeepCodez/gemma-vcet-log", 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}} } ```
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758472137	schooncestiaa	2025-09-21T16:30:18Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "scruffy webbed dragonfly", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T16:29:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - scruffy webbed dragonfly --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
MrAnton/SmolVLM-256M-Instruct_grpo_carrot_plate_yesno_task	MrAnton	2025-09-21T16:29:35Z	0	0	transformers	[ "transformers", "safetensors", "idefics3", "image-to-text", "generated_from_trainer", "grpo", "trl", "arxiv:2402.03300", "base_model:HuggingFaceTB/SmolVLM-256M-Instruct", "base_model:finetune:HuggingFaceTB/SmolVLM-256M-Instruct", "endpoints_compatible", "region:us" ]	image-to-text	2025-09-21T16:04:08Z	--- base_model: HuggingFaceTB/SmolVLM-256M-Instruct library_name: transformers model_name: SmolVLM-256M-Instruct_grpo_carrot_plate_yesno_task tags: - generated_from_trainer - grpo - trl licence: license --- # Model Card for SmolVLM-256M-Instruct_grpo_carrot_plate_yesno_task This model is a fine-tuned version of [HuggingFaceTB/SmolVLM-256M-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM-256M-Instruct). 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="MrAnton/SmolVLM-256M-Instruct_grpo_carrot_plate_yesno_task", 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: 2.2.0+cu121 - Datasets: 3.3.2 - 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}} } ```
palusi/LAMP-Decision	palusi	2025-09-21T16:29:01Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-09-21T16:29:01Z	--- license: apache-2.0 ---
mradermacher/Gemma_Delirium_Rewired_9B-GGUF	mradermacher	2025-09-21T16:25:41Z	0	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:SzilviaB/Gemma_Delirium_Rewired_9B", "base_model:quantized:SzilviaB/Gemma_Delirium_Rewired_9B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-21T08:22:11Z	--- base_model: SzilviaB/Gemma_Delirium_Rewired_9B language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - mergekit - merge --- ## 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/SzilviaB/Gemma_Delirium_Rewired_9B <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Gemma_Delirium_Rewired_9B-GGUF).* weighted/imatrix quants are available at https://huggingface.co/mradermacher/Gemma_Delirium_Rewired_9B-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/Gemma_Delirium_Rewired_9B-GGUF/resolve/main/Gemma_Delirium_Rewired_9B.Q2_K.gguf) \| Q2_K \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma_Delirium_Rewired_9B-GGUF/resolve/main/Gemma_Delirium_Rewired_9B.Q3_K_S.gguf) \| Q3_K_S \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma_Delirium_Rewired_9B-GGUF/resolve/main/Gemma_Delirium_Rewired_9B.Q3_K_M.gguf) \| Q3_K_M \| 4.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma_Delirium_Rewired_9B-GGUF/resolve/main/Gemma_Delirium_Rewired_9B.Q3_K_L.gguf) \| Q3_K_L \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma_Delirium_Rewired_9B-GGUF/resolve/main/Gemma_Delirium_Rewired_9B.IQ4_XS.gguf) \| IQ4_XS \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma_Delirium_Rewired_9B-GGUF/resolve/main/Gemma_Delirium_Rewired_9B.Q4_K_S.gguf) \| Q4_K_S \| 5.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Gemma_Delirium_Rewired_9B-GGUF/resolve/main/Gemma_Delirium_Rewired_9B.Q4_K_M.gguf) \| Q4_K_M \| 5.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Gemma_Delirium_Rewired_9B-GGUF/resolve/main/Gemma_Delirium_Rewired_9B.Q5_K_S.gguf) \| Q5_K_S \| 6.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma_Delirium_Rewired_9B-GGUF/resolve/main/Gemma_Delirium_Rewired_9B.Q5_K_M.gguf) \| Q5_K_M \| 6.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma_Delirium_Rewired_9B-GGUF/resolve/main/Gemma_Delirium_Rewired_9B.Q6_K.gguf) \| Q6_K \| 7.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma_Delirium_Rewired_9B-GGUF/resolve/main/Gemma_Delirium_Rewired_9B.Q8_0.gguf) \| Q8_0 \| 9.9 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma_Delirium_Rewired_9B-GGUF/resolve/main/Gemma_Delirium_Rewired_9B.f16.gguf) \| f16 \| 18.6 \| 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 -->
ThiagoVsky/DeepSeek-R1-Distill-Qwen-7B-Multilingual-Q4_K_S-GGUF_split	ThiagoVsky	2025-09-21T16:25:03Z	0	0	null	[ "gguf", "reasoning", "llama-cpp", "gguf-my-repo", "am", "ar", "bn", "zh", "cs", "nl", "en", "fr", "de", "el", "ha", "he", "hi", "id", "it", "ja", "jv", "km", "ko", "lo", "ms", "mr", "fa", "pl", "pt", "ro", "ru", "es", "sw", "sv", "tl", "ta", "te", "th", "tr", "uk", "ur", "vi", "dataset:lightblue/reasoning-multilingual-R1-Llama-70B-train", "base_model:lightblue/DeepSeek-R1-Distill-Qwen-7B-Multilingual", "base_model:quantized:lightblue/DeepSeek-R1-Distill-Qwen-7B-Multilingual", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-21T16:17:29Z	--- language: - am - ar - bn - zh - cs - nl - en - fr - de - el - ha - he - hi - id - it - ja - jv - km - ko - lo - ms - mr - fa - pl - pt - ro - ru - es - sw - sv - tl - ta - te - th - tr - uk - ur - vi license: apache-2.0 datasets: - lightblue/reasoning-multilingual-R1-Llama-70B-train tags: - reasoning - llama-cpp - gguf-my-repo base_model: lightblue/DeepSeek-R1-Distill-Qwen-7B-Multilingual --- # ThiagoVsky/DeepSeek-R1-Distill-Qwen-7B-Multilingual-Q4_K_S-GGUF This model was converted to GGUF format from [`lightblue/DeepSeek-R1-Distill-Qwen-7B-Multilingual`](https://huggingface.co/lightblue/DeepSeek-R1-Distill-Qwen-7B-Multilingual) 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/lightblue/DeepSeek-R1-Distill-Qwen-7B-Multilingual) 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 ThiagoVsky/DeepSeek-R1-Distill-Qwen-7B-Multilingual-Q4_K_S-GGUF --hf-file deepseek-r1-distill-qwen-7b-multilingual-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo ThiagoVsky/DeepSeek-R1-Distill-Qwen-7B-Multilingual-Q4_K_S-GGUF --hf-file deepseek-r1-distill-qwen-7b-multilingual-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 ThiagoVsky/DeepSeek-R1-Distill-Qwen-7B-Multilingual-Q4_K_S-GGUF --hf-file deepseek-r1-distill-qwen-7b-multilingual-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo ThiagoVsky/DeepSeek-R1-Distill-Qwen-7B-Multilingual-Q4_K_S-GGUF --hf-file deepseek-r1-distill-qwen-7b-multilingual-q4_k_s.gguf -c 2048 ```
JW17/Q25-1.5B-BTRM-SKWv2	JW17	2025-09-21T16:22:34Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "grpo", "trl", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-1.5B", "base_model:finetune:Qwen/Qwen2.5-1.5B", "endpoints_compatible", "region:us" ]	null	2025-09-21T16:14:35Z	--- base_model: Qwen/Qwen2.5-1.5B library_name: transformers model_name: Qwen2.5-1.5B-GRPO-rm tags: - generated_from_trainer - grpo - trl licence: license --- # Model Card for Qwen2.5-1.5B-GRPO-rm This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B). 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="None", 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/jiwooya1000/ICRM-RLVR-Math/runs/ulwxm0yo) 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: 0.22.2 - Transformers: 4.55.2 - Pytorch: 2.8.0 - Datasets: 4.1.1 - Tokenizers: 0.21.4 ## 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}} } ```
WenFengg/REP21Sun_14_17	WenFengg	2025-09-21T16:19:35Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-09-21T16:18:39Z	--- 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).
mradermacher/nemotron-medical-tuned-70b-GGUF	mradermacher	2025-09-21T16:17:55Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:alperk3003/nemotron-medical-tuned-70b", "base_model:quantized:alperk3003/nemotron-medical-tuned-70b", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-21T15:01:01Z	--- base_model: alperk3003/nemotron-medical-tuned-70b 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/alperk3003/nemotron-medical-tuned-70b <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#nemotron-medical-tuned-70b-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/nemotron-medical-tuned-70b-GGUF/resolve/main/nemotron-medical-tuned-70b.Q2_K.gguf) \| Q2_K \| 26.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/nemotron-medical-tuned-70b-GGUF/resolve/main/nemotron-medical-tuned-70b.Q3_K_S.gguf) \| Q3_K_S \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/nemotron-medical-tuned-70b-GGUF/resolve/main/nemotron-medical-tuned-70b.Q3_K_M.gguf) \| Q3_K_M \| 34.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/nemotron-medical-tuned-70b-GGUF/resolve/main/nemotron-medical-tuned-70b.Q3_K_L.gguf) \| Q3_K_L \| 37.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/nemotron-medical-tuned-70b-GGUF/resolve/main/nemotron-medical-tuned-70b.IQ4_XS.gguf) \| IQ4_XS \| 38.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/nemotron-medical-tuned-70b-GGUF/resolve/main/nemotron-medical-tuned-70b.Q4_K_S.gguf) \| Q4_K_S \| 40.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/nemotron-medical-tuned-70b-GGUF/resolve/main/nemotron-medical-tuned-70b.Q4_K_M.gguf) \| Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/nemotron-medical-tuned-70b-GGUF/resolve/main/nemotron-medical-tuned-70b.Q5_K_S.gguf) \| Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/nemotron-medical-tuned-70b-GGUF/resolve/main/nemotron-medical-tuned-70b.Q5_K_M.gguf) \| Q5_K_M \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/nemotron-medical-tuned-70b-GGUF/resolve/main/nemotron-medical-tuned-70b.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/nemotron-medical-tuned-70b-GGUF/resolve/main/nemotron-medical-tuned-70b.Q6_K.gguf.part2of2) \| Q6_K \| 58.0 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/nemotron-medical-tuned-70b-GGUF/resolve/main/nemotron-medical-tuned-70b.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/nemotron-medical-tuned-70b-GGUF/resolve/main/nemotron-medical-tuned-70b.Q8_0.gguf.part2of2) \| Q8_0 \| 75.1 \| fast, best quality \| 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 -->
Konzai/ppo-LunarLander-v2	Konzai	2025-09-21T16:13:54Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-09-21T16:13:32Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 264.54 +/- 17.66 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758470912	schooncestiaa	2025-09-21T16:09:56Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "scruffy webbed dragonfly", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T16:09:36Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - scruffy webbed dragonfly --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
DeathGodlike/Ollpheist-12B_EXL3	DeathGodlike	2025-09-21T16:05:03Z	0	0	safetensors	[ "safetensors", "exl3", "4-bit", "6-bit", "8-bit", "text-generation", "base_model:Retreatcost/Ollpheist-12B", "base_model:quantized:Retreatcost/Ollpheist-12B", "license:apache-2.0", "region:us" ]	text-generation	2025-09-21T16:05:01Z	--- license: apache-2.0 base_model: - Retreatcost/Ollpheist-12B base_model_relation: quantized pipeline_tag: text-generation library_name: safetensors tags: - exl3 - 4-bit - 6-bit - 8-bit --- ## EXL3 quants: [ [H8-4.0BPW](https://huggingface.co/DeathGodlike/Ollpheist-12B_EXL3/tree/H8-4.0BPW) \| [H8-6.0BPW](https://huggingface.co/DeathGodlike/Ollpheist-12B_EXL3/tree/H8-6.0BPW) \| [H8-8.0BPW](https://huggingface.co/DeathGodlike/Ollpheist-12B_EXL3/tree/H8-8.0BPW) ] # Original model: [Ollpheist-12B](https://huggingface.co/Retreatcost/Ollpheist-12B) by [Retreatcost](https://huggingface.co/Retreatcost)
n1kg0r/rubert_mvp	n1kg0r	2025-09-21T16:03:09Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:DeepPavlov/rubert-base-cased", "base_model:finetune:DeepPavlov/rubert-base-cased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-09-21T16:02:31Z	--- library_name: transformers base_model: DeepPavlov/rubert-base-cased tags: - generated_from_trainer model-index: - name: rubert_mvp 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. --> # rubert_mvp This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4511 - Mse: 0.4511 ## 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: 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mse \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.8506 \| 1.0 \| 95 \| 0.4740 \| 0.4740 \| \| 0.5292 \| 2.0 \| 190 \| 0.4301 \| 0.4301 \| \| 0.3854 \| 3.0 \| 285 \| 0.4511 \| 0.4511 \| ### Framework versions - Transformers 4.56.2 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.22.0
Aya-Ch/ALLaM7B-Islamic-LoRA	Aya-Ch	2025-09-21T15:55:02Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-21T15:54:55Z	--- 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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Amirdferff/sst2-bert-base-uncased	Amirdferff	2025-09-21T15:55:01Z	44	1	null	[ "safetensors", "bert", "en", "dataset:stanfordnlp/sst2", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "region:us" ]	null	2025-08-27T06:40:00Z	--- license: apache-2.0 datasets: - stanfordnlp/sst2 language: - en base_model: - google-bert/bert-base-uncased --- # 🌟 BERT fine-tuned on SST-2 This model is a fine-tuned version of bert-base-uncased on the GLUE SST-2 dataset for sentiment analysis. It achieves strong results on the validation set, reaching ~92.6% accuracy. --- ## 📊 Evaluation Results - Validation Accuracy: 0.9266 (≈92.66%) Raw output from `evaluate` library: --- ## 🚀 How it works - Input: A single English sentence - Output: `POSITIVE` or `NEGATIVE` with a confidence score - Architecture: - Base model: BERT (bert-base-uncased) - Classification head: 2-label linear layer on top of [CLS] token - Training setup: - Optimizer: AdamW - Scheduler: Linear LR decay - Epochs: 3 - Batch size: 16 --- ## 🔧 Usage ### Inference with `pipeline` ```python from transformers import pipeline clf = pipeline("text-classification", model="Amirdferff/sst2-bert-base-uncased") print(clf("I really loved this movie!")) # [{'label': 'POSITIVE', 'score': 0.98}]
Stef7177/camembert-triathlon-coach	Stef7177	2025-09-21T15:50:40Z	0	0	transformers	[ "transformers", "safetensors", "camembert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-09-21T15:49:42Z	--- 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]
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758469666	schooncestiaa	2025-09-21T15:48:52Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "scruffy webbed dragonfly", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T15:48:45Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - scruffy webbed dragonfly --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
astrooons/blockassist	astrooons	2025-09-21T15:47:08Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "voracious quiet bear", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T14:56:24Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - voracious quiet bear --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
rlogh/cheese-texture-classifier-final	rlogh	2025-09-21T15:44:42Z	0	0	null	[ "pytorch", "image-classification", "cheese", "texture", "computer-vision", "transfer-learning", "final", "dataset:aslan-ng/cheese-image", "license:mit", "model-index", "region:us" ]	image-classification	2025-09-21T15:44:37Z	--- license: mit tags: - image-classification - cheese - texture - computer-vision - pytorch - transfer-learning - final datasets: - aslan-ng/cheese-image metrics: - accuracy model-index: - name: Final Cheese Texture Classifier results: - task: type: image-classification name: Cheese Texture Classification dataset: type: aslan-ng/cheese-image name: Cheese Image Dataset metrics: - type: accuracy value: 40.00 name: Test Accuracy --- # Final Cheese Texture Classifier This is the final version of the cheese texture classifier that fixes the BatchNorm issue with small batch sizes. ## Model Description - Architecture: Transfer Learning with resnet18 - Task: 4-class texture classification (Low, Medium-Low, Medium-High, High texture) - Input: 224x224 RGB images - Output: 4-class probability distribution ## Final Features - Transfer Learning: Uses pre-trained resnet18 as backbone - BatchNorm Fixed: No BatchNorm1d layers to avoid small batch size issues - Safe Data Augmentation: Transforms that work with small datasets - Final AutoML: 20 trials with transfer learning hyperparameters - Extended Training: Up to 50 epochs with careful early stopping ## Training Details - Dataset: [aslan-ng/cheese-image](https://huggingface.co/datasets/aslan-ng/cheese-image) - Optimization Method: Final Optuna AutoML with 20 trials - Transfer Learning: Pre-trained resnet18 backbone - Early Stopping: Yes (patience=10) - Max Epochs: 50 ## Performance - Test Accuracy: 40.00% - Validation Accuracy: 75.00% - Test Loss: 0.9921 ## Best Hyperparameters ```json { "model_name": "resnet18", "dropout_rate": 0.32484158566728777, "learning_rate": 0.00015218971132928362, "weight_decay": 9.737804011286956e-05, "batch_size": 2 } ``` ## Usage ```python import torch import torch.nn as nn from PIL import Image import torchvision.transforms as transforms import torchvision.models as models # Load model (you'll need to define the TransferLearningModel class first) model = TransferLearningModel(num_classes=4, dropout_rate=0.32484158566728777, model_name='resnet18') model.load_state_dict(torch.load('pytorch_model.bin')) model.eval() # Preprocess image transform = transforms.Compose([ transforms.Resize((224, 224)), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) # Load and preprocess image image = Image.open('cheese_image.jpg').convert('RGB') input_tensor = transform(image).unsqueeze(0) # Make prediction with torch.no_grad(): output = model(input_tensor) probabilities = torch.softmax(output, dim=1) predicted_class = torch.argmax(probabilities, dim=1).item() class_names = ["Low Texture", "Medium-Low Texture", "Medium-High Texture", "High Texture"] print(f"Predicted class: {class_names[predicted_class]}") ``` ## Class Definitions - Class 0 (Low Texture): Texture values <= 0.425 - Class 1 (Medium-Low Texture): Texture values 0.425 < x <= 0.600 - Class 2 (Medium-High Texture): Texture values 0.600 < x <= 0.775 - Class 3 (High Texture): Texture values > 0.775 ## Final Improvements - BatchNorm Fixed: Removed BatchNorm1d layers that caused issues with batch size 1 - Transfer Learning: Leverages pre-trained features for better performance - Safe Augmentation: Transforms that work reliably with small datasets - Advanced Training: Gradient clipping, learning rate scheduling, extended epochs - Final AutoML: 20 trials with transfer learning specific hyperparameters ## Limitations - Trained on a very small dataset (30 images) - Texture classification may not generalize to all cheese types - Performance may vary with different lighting conditions or image quality ## Citation If you use this model, please cite the original dataset: ```bibtex @dataset{aslan-ng/cheese-image, title={Cheese Image Dataset}, author={Aslan Noorghasemi}, year={2024}, url={https://huggingface.co/datasets/aslan-ng/cheese-image} } ```
Aname-Tommy/Mel-Band-Roformer_Duality	Aname-Tommy	2025-09-21T15:39:37Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-09-21T15:24:54Z	--- license: apache-2.0 ---
PushkarKumar/veritas-ai-isot-fake-news-classifier	PushkarKumar	2025-09-21T15:35:50Z	0	0	null	[ "tensorboard", "safetensors", "distilbert", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "region:us" ]	null	2025-09-21T14:37:05Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: veritas-ai-isot-fake-news-classifier 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. --> # veritas-ai-isot-fake-news-classifier This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0016 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.0001 \| 1.0 \| 4490 \| 0.0016 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.8.0+cu126 - Datasets 2.14.7 - Tokenizers 0.14.1
Thang26/Lora-Qwen2.5-3B-JP2EN	Thang26	2025-09-21T15:31:59Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-21T13:53:31Z	--- library_name: transformers tags: - unsloth --- # 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]
OddTheGreat/Terminal_24B_V.2	OddTheGreat	2025-09-21T15:30:43Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "base_model:CrucibleLab/M3.2-24B-Loki-V1.3", "base_model:merge:CrucibleLab/M3.2-24B-Loki-V1.3", "base_model:OddTheGreat/Circuitry_24B_V.2", "base_model:merge:OddTheGreat/Circuitry_24B_V.2", "base_model:SicariusSicariiStuff/Impish_Magic_24B", "base_model:merge:SicariusSicariiStuff/Impish_Magic_24B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T13:13:03Z	--- base_model: - CrucibleLab/M3.2-24B-Loki-V1.3 - SicariusSicariiStuff/Impish_Magic_24B - OddTheGreat/Circuitry_24B_V.2 library_name: transformers tags: - mergekit - merge --- # Terminal_24B_V.2 This is a merge of pre-trained language models In testing. Seems to be more accurate than v1, no impersonations but sometimes makes summary at end of reply.
mradermacher/llama-user-sim-70b-GGUF	mradermacher	2025-09-21T15:19:01Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:ychen/llama-user-sim-70b", "base_model:quantized:ychen/llama-user-sim-70b", "endpoints_compatible", "region:us" ]	null	2025-09-21T13:47:36Z	--- base_model: ychen/llama-user-sim-70b 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/ychen/llama-user-sim-70b <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#llama-user-sim-70b-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/llama-user-sim-70b-GGUF/resolve/main/llama-user-sim-70b.Q2_K.gguf) \| Q2_K \| 26.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-user-sim-70b-GGUF/resolve/main/llama-user-sim-70b.Q3_K_S.gguf) \| Q3_K_S \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-user-sim-70b-GGUF/resolve/main/llama-user-sim-70b.Q3_K_M.gguf) \| Q3_K_M \| 34.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/llama-user-sim-70b-GGUF/resolve/main/llama-user-sim-70b.Q3_K_L.gguf) \| Q3_K_L \| 37.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-user-sim-70b-GGUF/resolve/main/llama-user-sim-70b.IQ4_XS.gguf) \| IQ4_XS \| 38.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-user-sim-70b-GGUF/resolve/main/llama-user-sim-70b.Q4_K_S.gguf) \| Q4_K_S \| 40.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llama-user-sim-70b-GGUF/resolve/main/llama-user-sim-70b.Q4_K_M.gguf) \| Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llama-user-sim-70b-GGUF/resolve/main/llama-user-sim-70b.Q5_K_S.gguf) \| Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-user-sim-70b-GGUF/resolve/main/llama-user-sim-70b.Q5_K_M.gguf) \| Q5_K_M \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/llama-user-sim-70b-GGUF/resolve/main/llama-user-sim-70b.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/llama-user-sim-70b-GGUF/resolve/main/llama-user-sim-70b.Q6_K.gguf.part2of2) \| Q6_K \| 58.0 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/llama-user-sim-70b-GGUF/resolve/main/llama-user-sim-70b.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/llama-user-sim-70b-GGUF/resolve/main/llama-user-sim-70b.Q8_0.gguf.part2of2) \| Q8_0 \| 75.1 \| fast, best quality \| 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 -->
SauravCh11/Passport-EN	SauravCh11	2025-09-21T15:17:36Z	0	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-to-text	2025-09-21T08:50:40Z	--- 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]
little-john/lj-insurance-doc-classification-Skywork-Reward-V2-Qwen3-0.6B	little-john	2025-09-21T15:16:02Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "unsloth", "text-classification", "en", "base_model:Skywork/Skywork-Reward-V2-Qwen3-0.6B", "base_model:finetune:Skywork/Skywork-Reward-V2-Qwen3-0.6B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-09-21T15:00:58Z	--- base_model: Skywork/Skywork-Reward-V2-Qwen3-0.6B tags: - transformers - unsloth - qwen3 - text-classification license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: little-john - License: apache-2.0 - Finetuned from model : Skywork/Skywork-Reward-V2-Qwen3-0.6B 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)
KoichiYasuoka/modernbert-large-scandinavian-ud-embeds	KoichiYasuoka	2025-09-21T15:11:27Z	0	0	null	[ "pytorch", "modernbert", "scandinavian", "icelandic", "danish", "swedish", "norwegian", "token-classification", "pos", "dependency-parsing", "is", "da", "sv", "nb", "nn", "dataset:universal_dependencies", "base_model:AI-Sweden-Models/ModernBERT-large", "base_model:finetune:AI-Sweden-Models/ModernBERT-large", "license:apache-2.0", "region:us" ]	token-classification	2025-09-21T15:05:49Z	--- language: - "is" - "da" - "sv" - "nb" - "nn" tags: - "scandinavian" - "icelandic" - "danish" - "swedish" - "norwegian" - "token-classification" - "pos" - "dependency-parsing" base_model: AI-Sweden-Models/ModernBERT-large datasets: - "universal_dependencies" license: "apache-2.0" pipeline_tag: "token-classification" --- # modernbert-large-scandinavian-ud-embeds ## Model Description This is a ModernBERT model for POS-tagging and dependency-parsing, derived from [AI-Sweden-Models/ModernBERT-large](https://huggingface.co/AI-Sweden-Models/ModernBERT-large), [UD_Icelandic-IcePaHC](https://github.com/UniversalDependencies/UD_Icelandic-IcePaHC), [UD_Danish-DDT](https://github.com/UniversalDependencies/UD_Danish-DDT), [UD_Swedish-Talbanken](https://github.com/UniversalDependencies/UD_Swedish-Talbanken), [UD_Norwegian-Bokmaal](https://github.com/UniversalDependencies/UD_Norwegian-Bokmaal) and [UD_Norwegian-Nynorsk](https://github.com/UniversalDependencies/UD_Norwegian-Nynorsk). ## How to Use ```py from transformers import pipeline nlp=pipeline("universal-dependencies","KoichiYasuoka/modernbert-large-scandinavian-ud-embeds",trust_remote_code=True) ```
twelveyy/qwen-legal-lora-sft	twelveyy	2025-09-21T15:01:47Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:adapter:Qwen/Qwen2.5-0.5B-Instruct", "region:us" ]	null	2025-09-21T15:01:21Z	--- base_model: Qwen/Qwen2.5-0.5B-Instruct library_name: peft model_name: qwen_legal_lora_sft tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for qwen_legal_lora_sft This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct). 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="None", 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 - PEFT 0.15.2 - TRL: 0.23.0 - Transformers: 4.56.2 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - 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}} } ```
wjen/healthcare-cls-Qwen3-0.6B-LoRA	wjen	2025-09-21T15:00:47Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-09-21T14:29:00Z	--- 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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mingyi456/Chroma1-Base-DF11	mingyi456	2025-09-21T14:58:30Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "en", "base_model:lodestones/Chroma1-Base", "base_model:quantized:lodestones/Chroma1-Base", "license:apache-2.0", "region:us" ]	text-to-image	2025-09-21T13:19:17Z	--- license: apache-2.0 base_model: - lodestones/Chroma1-Base base_model_relation: quantized language: - en pipeline_tag: text-to-image library_name: diffusers --- For more information (including how to compress models yourself), check out https://huggingface.co/DFloat11 and https://github.com/LeanModels/DFloat11 This is my first time using DF11 to compress a model outside the Flux architecture. The process for compressing Flux-based models is much more straightforward as compared to other architectures because the compression code requires a `pattern_dict` as input, but the original [example code](https://github.com/LeanModels/DFloat11/tree/master/examples/compress_flux1) only provides it for Flux, which meant I had to learn the notation myself and modify it to fit other models. At least Chroma is just a pruned version of Flux, so it was relatively simple to derive the correct `pattern_dict` this time. Do let me know if you run into any problems. This is the `pattern_dict` I used for compression: ```python pattern_dict = { "transformer_blocks\.\d+": ( "attn.to_q", "attn.to_k", "attn.to_v", "attn.add_k_proj", "attn.add_v_proj", "attn.add_q_proj", "attn.to_out.0", "attn.to_add_out", "ff.net.0.proj", "ff.net.2", "ff_context.net.0.proj", "ff_context.net.2", ), "single_transformer_blocks\.\d+": ( "proj_mlp", "proj_out", "attn.to_q", "attn.to_k", "attn.to_v", ), } ``` ### How to Use #### `diffusers` 1. Install the DFloat11 pip package (installs the CUDA kernel automatically; requires a CUDA-compatible GPU and PyTorch installed): ```bash pip install dfloat11[cuda12] # or if you have CUDA version 11: # pip install dfloat11[cuda11] ``` 2. To use the DFloat11 model, run the following example code in Python: ```python import torch from diffusers import ChromaPipeline, ChromaTransformer2DModel from dfloat11 import DFloat11Model from transformers.modeling_utils import no_init_weights with no_init_weights(): transformer = ChromaTransformer2DModel.from_config( ChromaTransformer2DModel.load_config( "lodestones/Chroma1-Base", subfolder="transformer" ), torch_dtype=torch.bfloat16 ).to(torch.bfloat16) pipe = ChromaPipeline.from_pretrained( "lodestones/Chroma1-Base", transformer=transformer, torch_dtype=torch.bfloat16 ) DFloat11Model.from_pretrained("mingyi456/Chroma1-Base-DF11", device='cpu', bfloat16_model=pipe.transformer) pipe.enable_model_cpu_offload() prompt = "A high-fashion close-up portrait of a blonde woman in clear sunglasses. The image uses a bold teal and red color split for dramatic lighting. The background is a simple teal-green. The photo is sharp and well-composed, and is designed for viewing with anaglyph 3D glasses for optimal effect. It looks professionally done." negative_prompt = "low quality, ugly, unfinished, out of focus, deformed, disfigure, blurry, smudged, restricted palette, flat colors" image = pipe( prompt, negative_prompt=negative_prompt, generator=torch.Generator("cpu").manual_seed(0) ).images[0] image.save("Chroma1-Base.png") ``` #### ComfyUI ~~Follow the instructions (have not tested myself) here: https://github.com/LeanModels/ComfyUI-DFloat11~~ Currently, this model will not work with ComfyUI out of the box, because the custom node currently only supports Flux models. It should be possible to modify the code to successfully load this model as well, but it requires another `pattern_dict` that is of a completely different form compared to the one used to compress the model. If you are interested in running this model in ComfyUI, please try to contact the developer to request support.
yasserrmd/punjabi-gemma-300m-emb	yasserrmd	2025-09-21T14:57:43Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "gemma3_text", "sentence-similarity", "feature-extraction", "dense", "generated_from_trainer", "dataset_size:5004", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:google/embeddinggemma-300m", "base_model:finetune:google/embeddinggemma-300m", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-09-21T14:56:30Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:5004 - loss:MultipleNegativesRankingLoss base_model: google/embeddinggemma-300m widget: - source_sentence: ਝੋਲੀਚੁੱਕ ਭਰਾਵਾਂ ਨੂੰ ਬੱਬਰਾਂ ਨੇ ਕਦੋਂ ਸੋਧਿਆ ਸੀ? sentences: - 22 ਬਿਲੀਅਨ ਅਮਰੀਕੀ ਡਾਲਰ - '1923' - '1992' - source_sentence: ਬੰਗਲਾਦੇਸ਼ ਵਿੱਚ ਲੂੰਗੀ ਬਣਾਉਣ ਦਾ ਕੰਮ ਕਿੱਥੇ ਕੇਂਦਰਿਤ ਹੈ? sentences: - '1891' - '21' - ਸਿਰਾਜਗੰਜ, ਕੁਸ਼ਟੀਆ, ਪਬਨਾ ਅਤੇ ਖੁੱਲਨਾ - source_sentence: ਸੀ ਕੇ ਚੰਦਰੱਪਨ ਨੇ ਆਪਣੀ ਗ੍ਰੈਜੂਏਸ਼ਨ ਕਿੱਥੋਂ ਪੂਰੀ ਕੀਤੀ? sentences: - ਗੋਲਡ ਜੇਤੂ ਟੀਮ - ਚਿੱਤੂਰ ਸਰਕਾਰੀ ਕਾਲਜ - ਪਟਿਆਲੇ - source_sentence: ਫੋਰਬਸ ਮੈਗਜ਼ੀਨ ਦੇ 2022 ਦੇ ਅੰਕੜਿਆਂ ਅਨੁਸਾਰ, ਵਿਸ਼ਵ ਦੀਆਂ ਸਭ ਤੋਂ ਸ਼ਕਤੀਸ਼ਾਲੀ ਔਰਤਾਂ ਦੀ ਸੂਚੀ ਵਿੱਚ ਮੇਲੋਨੀ ਦਾ ਦਰਜਾ ਕੀ ਸੀ? sentences: - ਜੇ ਹਥਿਆਰਬੰਦ ਸੈਨਾਵਾਂ ਦੇ ਇੱਕ ਕਿਸਮ ਦੇ ਰੈਂਕ ਵਾਲੇ ਦੋ ਅਹੁਦੇਧਾਰੀ ਇੱਕੋ ਜਿਹੇ ਸਮੇਂ ਵਾਸਤੇ ਫ਼ੌਜੀ ਨੌਕਰੀ ਕਰਦੇ ਹਨ ਤਾਂ ਉਹਨਾਂ ਨੂੰ ਬਰਾਬਰ ਦੀ ਪੈਨਸ਼ਨ ਮਿਲਣੀ ਚਾਹੀਦੀ ਹੈ, ਭਾਵੇਂ ਉਹ ਅੱਗੜ-ਪਿੱਛੜ ਰਿਟਾਇਰਮੈਂਟ ‘ਤੇ ਆਉਣ ਅਤੇ ਉਹਨਾਂ ਨੂੰ ਪੈਨਸ਼ਨ ਦੀਆਂ ਦਰਾਂ ਵਿੱਚ ਹੋਣ ਵਾਲੇ ਭਵਿੱਖੀ ਲਾਭ ਦਾ ਫ਼ਾਇਦਾ ਵੀ ਮਿਲਣਾ ਚਾਹੀਦਾ ਹੈ - ਜੰਮੂ ਕਸ਼ਮੀਰ ਯੂਨੀਵਰਸਿਟੀ - ਸੱਤਵੀਂ - source_sentence: ਆਬਿਦ ਅਲੀ ਦਾ ਸਭ ਤੋਂ ਵਧੀਆ ਗੇਂਦਬਾਜ਼ੀ ਪ੍ਰਦਰਸ਼ਨ ਕਿਹੜਾ ਸੀ? sentences: - ਜੰਗ ਅਤੇ ਭੀਸ਼ਮਾ - '1950' - 23 ਦੌੜਾਂ ਦੇ ਕੇ 6 ਦੌੜਾਂ pipeline_tag: sentence-similarity library_name: sentence-transformers --- # 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("yasserrmd/punjabi-gemma-300m-emb") # Run inference queries = [ "\u0a06\u0a2c\u0a3f\u0a26 \u0a05\u0a32\u0a40 \u0a26\u0a3e \u0a38\u0a2d \u0a24\u0a4b\u0a02 \u0a35\u0a27\u0a40\u0a06 \u0a17\u0a47\u0a02\u0a26\u0a2c\u0a3e\u0a1c\u0a3c\u0a40 \u0a2a\u0a4d\u0a30\u0a26\u0a30\u0a38\u0a3c\u0a28 \u0a15\u0a3f\u0a39\u0a5c\u0a3e \u0a38\u0a40?", ] documents = [ '23 ਦੌੜਾਂ ਦੇ ਕੇ 6 ਦੌੜਾਂ', '1950', 'ਜੰਗ ਅਤੇ ਭੀਸ਼ਮਾ', ] 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([[0.3268, 0.1534, 0.0167]]) ``` <!-- ### 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. --> <!-- ## 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: 5,004 training samples * Columns: <code>sentence_0</code> and <code>sentence_1</code> * Approximate statistics based on the first 1000 samples: \| \| sentence_0 \| sentence_1 \| \|:--------\|:----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 11 tokens</li><li>mean: 28.8 tokens</li><li>max: 88 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 16.26 tokens</li><li>max: 144 tokens</li></ul> \| * Samples: \| sentence_0 \| sentence_1 \| \|:-----------------------------------------------------------------\|:---------------------------------\| \| <code>ਵਿਰਾਟ ਕੋਹਲੀ ਨੇ ਕਿਹੜੇ ਸਕੂਲ ਵਿੱਚ ਪੜ੍ਹਾਈ ਕੀਤੀ?</code> \| <code>ਸੇਂਟ ਥਾਮਸ ਸਕੂਲ</code> \| \| <code>1992 'ਚ ਅੰਤਰਰਾਸ਼ਟਰੀ ਅਜਾਇਬ ਘਰ ਦਿਹਾੜੇ ਦਾ ਵਿਸ਼ਾ ਕੀ ਸੀ?</code> \| <code>ਅਜਾਇਬਘਰ ਅਤੇ ਵਾਤਾਵਰਣ</code> \| \| <code>ਗੁਰਪ੍ਰੀਤ ਧੂਰੀ ਕਿੱਥੋਂ ਰੋਜ਼ੀ ਰੋਟੀ ਕਮਾ ਰਿਹਾ ਹੈ?</code> \| <code>ਦਿੱਲੀ</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", "gather_across_devices": false } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 6 - `per_device_eval_batch_size`: 6 - `num_train_epochs`: 7 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 6 - `per_device_eval_batch_size`: 6 - `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 - `num_train_epochs`: 7 - `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 \| \|:------:\|:----:\|:-------------:\| \| 0.5995 \| 500 \| 1.346 \| \| 1.1990 \| 1000 \| 1.3542 \| \| 1.7986 \| 1500 \| 1.2281 \| \| 2.3981 \| 2000 \| 1.1036 \| \| 2.9976 \| 2500 \| 0.9937 \| \| 3.5971 \| 3000 \| 0.7913 \| \| 4.1966 \| 3500 \| 0.7128 \| \| 4.7962 \| 4000 \| 0.557 \| \| 5.3957 \| 4500 \| 0.4327 \| \| 5.9952 \| 5000 \| 0.3557 \| \| 6.5947 \| 5500 \| 0.2424 \| ### Framework Versions - Python: 3.12.11 - Sentence Transformers: 5.1.0 - Transformers: 4.56.2 - PyTorch: 2.8.0+cu128 - Accelerate: 1.10.1 - Datasets: 4.0.0 - Tokenizers: 0.22.1 ## 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. -->
ezequiel/new_or_used	ezequiel	2025-09-21T14:52:47Z	0	0	null	[ "safetensors", "bert", "license:apache-2.0", "region:us" ]	null	2025-09-21T14:47:41Z	--- license: apache-2.0 --- Epoch Training Loss Validation Loss Accuracy Precision Recall F1 1 0.410200 0.385042 0.832611 0.841840 0.847673 0.844747 2 0.323200 0.391985 0.835889 0.863106 0.825440 0.843852 3 0.234500 0.456331 0.835389 0.858242 0.830817 0.844307
Tomiwajin/setfit_email_classifier	Tomiwajin	2025-09-21T14:51:35Z	31	1	setfit	[ "setfit", "safetensors", "bert", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:sentence-transformers/all-MiniLM-L6-v2", "base_model:finetune:sentence-transformers/all-MiniLM-L6-v2", "region:us" ]	text-classification	2025-09-08T23:27:21Z	--- tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: 'Monorepos, Verified Templates, Replica Metrics It''s Friday and you know what that means! Here''s a summary of the stuff we shipped this week Time! It''s Friday and you know what that means! Here''s a summary of the stuff we shipped this week: First-Class Support for Monorepos Verified Templates Replica Metrics to Priority Boarding Fixes and Improv' - text: 'Thanks for your time Thank you for applying to the Backend Developer position at YinzCam, Inc.. Unfortunately, YinzCam, Inc. has moved to the next step in their hiring process, and your application was not selected at this time.' - text: "Humanoid Alert! Your Data Packet Caught Our Eye at 1X Technologies! Hi Tomiwa,\n\ \nThank you for sending your application data stream our way at 1X Technologies!\n\ \nYour resume just ran through our systems, and let's just say, your skill matrix\ \ looks incredibly promising. We were genuinely intrigued by your experience and\ \ see some serious potential \n\nfor you to help us b" - text: 'Indeed Application: Software Developer We''ll help you get started pplication submitted Software Developer TherapyNotes.com - United States 30 reviews The following items were sent to TherapyNotes.com. Good luck! • Application • Resume Next steps • The employer or job advertiser may reach out to you about your application.' - text: 'Jobs! I have a job that I think lines up well with your resume. It''s new, so they don''t have many candidates yet. Check out the description and hit "View Details" if you like what you see. Entry Level Software Engineer - Revature - Jersey City, NJ Revature is looking to hire Entry Level Software Engineer' metrics: - accuracy pipeline_tag: text-classification library_name: setfit inference: true base_model: sentence-transformers/all-MiniLM-L6-v2 --- # SetFit with sentence-transformers/all-MiniLM-L6-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - Maximum Sequence Length: 256 tokens - Number of Classes: 7 classes <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels \| Label \| Examples \| \|:----------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| next-phase \| <ul><li>"Next step: Assessment 📋 for the Product Manager role at StartupXYZ Hi Thomas, Thank you again for your interest in the Product Manager position at StartupXYZ. As part of our hiring process, the next step is to complete an assessment that will help us better understand your skills and suitability for the role. Here's what to expect: • Assessment details: Product str"</li><li>"Next Steps - Front-End Engineer Hey Oluwatomiwa,\nWe're excited to invite you to the next phase of the Front-End Engineer role.\nBefore moving forward, please ensure your location is on the list of accepted locations.\nImportant Notice\n\nIf you currently have or previously had credentials with Outlier or a related platform, please do"</li><li>'Coding Assessment - Backend Developer Position Dear Kevin, We appreciate your interest in the Backend Developer role at CloudSync Technologies. As the next step in our selection process, we invite you to complete our technical coding assessment. This assessment has been carefully designed to evaluate your programming skills and problem-solving a'</li></ul> \| \| interview \| <ul><li>"Interview for DevOps Engineer at ServerMax Hi Daniel, Thanks again for taking the time to chat with me on the phone! I'm very happy to move you to the next stage of our hiring process — a 45-minute video interview. This interview will include me and my colleague Tom Rodriguez, our Infrastructure Lead. If you'd like to learn a little about hi"</li><li>"Video interview for Social Media Manager at BuzzMarketing Hi Taylor, Thank you for your application for the Social Media Manager position with BuzzMarketing. We're excited to learn more about you and your qualifications! We would like to invite you to a video interview with Christina Park, our Digital Marketing Director. This will be a chance for us to dis"</li><li>'Final round interview for Marketing Director at BrandBoost Hi Michelle, Congratulations on making it to the final round of interviews for the Marketing Director position! We would like to invite you to a final in-person interview with our executive team including CEO Jonathan Miller and CMO Patricia Davis. This will be a chance for us to discuss your strate'</li></ul> \| \| not-job-status-update \| <ul><li>"Jobs! Hi Seth,\n\nI found a job you may be interested in: 100% REMOTE - Senior Fullstack Engineer\n\nIf you'd like to apply, let me know in a quick response with your updated resume. Full job details below.\n\nf you are a Senior Software Engineer with Python and React experience, please read on!\n\nWe headquarter"</li><li>'Oluwatomiwa, you have new application updates this week Check out the status of your applications on LinkedIn Check out the status of your applications on LinkedIn Here are the latest updates from the past week Junior Software Engineer Fortune 500 · Plano, TX (On-site) No longer accepting applications Software Quality Assurance Engineer ChronicCar'</li><li>'Junior Software Engineer role at AmeriNat: you would be a great fit! Hey! Check out the latest industry content about career advice, salary negotiations, and interview tips, among other topics. Explore now! Jobs for you Jobs for you We‚Äôre on a mission to connect you with a dream job. To help us refine this list, search for more jobs AmeriNat 4.1 ‚òÖ Junior Softwar'</li></ul> \| \| not-job-related \| <ul><li>'Welcome to Idealist! Four actions you can take right now to get started Hi Oluwatomiwa, My name is Ami, and I‚Äôm the founder and executive director of Idealist. We started Idealist in the summer of 1995‚Äîon one old computer and with no full-time staff‚Äîto help connect people around the world with opportunities to do'</li><li>'New arrivals are here SHEIN Shop at SHEIN for the latest trends! Shop at SHEIN for the latest trends! Unsubscribe \| View in Browser Pick your unique look SHOP NEW ARRIVALS > FIND US ON APP'</li><li>'\uf8ffüö®Here‚Äôs the Zoom Link & exclusive offers! \uf8ffüö®Here‚Äôs the Zoom Link & exclusive offers! Your seat at the Agentic AI Conference is reserved - plus unlock exclusive training offers up to 40% off! Hi Oluwatomiwa, We‚Äôre thrilled to have you join us for the second edition of the Future of Data and AI: Agentic AI Conference! \uf8ffüîó Link to Join'</li></ul> \| \| applied \| <ul><li>'Thank you for applying! Dear Name,\n\nThank you for your interest in a career at Delta Dental of Iowa. We have received your application for Software Development Intern.\nIn the event that we wish to arrange a personal interview, we will contact you. Again, thank you for your interest in employment at Delta Dental of Iowa.'</li><li>'Thank You For Applying! Dear Name,\nThank you for applying! Your application will be taken into careful consideration and you will hear back from us after we review your application.\n\n\nBest Regards,\n\nBracco Human Resources Team'</li><li>'Thank you for applying to Passes Name,\n\nThanks for applying to Passes. Your application has been received and we will review it right away.\n\nIf your application seems like a good fit for the position we will contact you soon.\n\nRegards,\nPasses\n\n** Please note: Do not reply to this email. This email is sent from an unattended mailbox'</li></ul> \| \| offer \| <ul><li>"Congratulations - You're Our New Management Consultant! Dear Diana Brown, Congratulations! StrategyConsult Partners is excited to call you our new Management Consultant. We'll focus on wrapping up a few more formalities, including the successful completion of your background check and client reference verification, and aim to get you settled into your ne"</li><li>'Full-Time Employment Offer Dear Brandon Taylor, ArchitectureMax is offering to extend your current employment status from contractor to full-time employee, as of June 1st, 2024. If you choose to accept our offer, please review the terms and conditions of your new employment contract below: Position: You will be working as a S'</li><li>'Employment Offer - Product Manager Position Michael Chen 456 Innovation Drive, San Francisco, CA 94105 Re: Employment Offer Dear Michael: On behalf of ProductMax, Inc. (the "Company"), it is my pleasure to offer you employment with the Company in the role set forth below. The purpose of this letter is to summarize the initial terms of your em'</li></ul> \| \| rejected \| <ul><li>'Thanks for your time Thank you for your interest in the Software Engineer position at Lantana Consulting Group in Vermont, United States. Unfortunately, we will not be moving forward with your application, but we appreciate your time and interest in Lantana Consulting Group.\n\nRegards,\n\nLantana Consulting Group'</li><li>"Thanks for your time Hello Name,\n\nThank you very much for your interest in our Software Engineer - React/Redux opening. We've had a chance to discuss your background and qualifications with the hiring manager and unfortunately, we have decided to pursue other candidates who appear to match our requirements more closely"</li><li>"Thanks for your interest in Supernova Technology, Name Hi Name,\nThank you for your interest in Supernova Technology. After reviewing your background and experience, we’ve decided not to move forward with your application at this time.\n\nWe truly appreciate the time and effort you put into the process, and we hope you don't mind if we reach out in the fut"</li></ul> \| ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("setfit_model_id") # Run inference preds = model("Thanks for your time Thank you for applying to the Backend Developer position at YinzCam, Inc.. Unfortunately, YinzCam, Inc. has moved to the next step in their hiring process, and your application was not selected at this time.") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## 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 Set Metrics \| Training set \| Min \| Median \| Max \| \|:-------------\|:----\|:--------\|:----\| \| Word count \| 14 \| 55.2121 \| 288 \| \| Label \| Training Sample Count \| \|:----------------------\|:----------------------\| \| applied \| 40 \| \| interview \| 45 \| \| next-phase \| 35 \| \| not-job-related \| 55 \| \| not-job-status-update \| 41 \| \| offer \| 36 \| \| rejected \| 45 \| ### Training Hyperparameters - batch_size: (16, 2) - num_epochs: (1, 16) - max_steps: -1 - sampling_strategy: oversampling - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - l2_weight: 0.01 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:------:\|:----:\|:-------------:\|:---------------:\| \| 0.0002 \| 1 \| 0.3397 \| - \| \| 0.0106 \| 50 \| 0.2699 \| - \| \| 0.0212 \| 100 \| 0.2293 \| - \| \| 0.0319 \| 150 \| 0.1907 \| - \| \| 0.0425 \| 200 \| 0.1685 \| - \| \| 0.0531 \| 250 \| 0.1174 \| - \| \| 0.0637 \| 300 \| 0.078 \| - \| \| 0.0743 \| 350 \| 0.0524 \| - \| \| 0.0849 \| 400 \| 0.0319 \| - \| \| 0.0956 \| 450 \| 0.0113 \| - \| \| 0.1062 \| 500 \| 0.0073 \| - \| \| 0.1168 \| 550 \| 0.0051 \| - \| \| 0.1274 \| 600 \| 0.0038 \| - \| \| 0.1380 \| 650 \| 0.0029 \| - \| \| 0.1487 \| 700 \| 0.0023 \| - \| \| 0.1593 \| 750 \| 0.0021 \| - \| \| 0.1699 \| 800 \| 0.0017 \| - \| \| 0.1805 \| 850 \| 0.0017 \| - \| \| 0.1911 \| 900 \| 0.0015 \| - \| \| 0.2017 \| 950 \| 0.0012 \| - \| \| 0.2124 \| 1000 \| 0.0011 \| - \| \| 0.2230 \| 1050 \| 0.0011 \| - \| \| 0.2336 \| 1100 \| 0.001 \| - \| \| 0.2442 \| 1150 \| 0.001 \| - \| \| 0.2548 \| 1200 \| 0.0009 \| - \| \| 0.2654 \| 1250 \| 0.0008 \| - \| \| 0.2761 \| 1300 \| 0.0008 \| - \| \| 0.2867 \| 1350 \| 0.0007 \| - \| \| 0.2973 \| 1400 \| 0.0007 \| - \| \| 0.3079 \| 1450 \| 0.0006 \| - \| \| 0.3185 \| 1500 \| 0.0006 \| - \| \| 0.3292 \| 1550 \| 0.0006 \| - \| \| 0.3398 \| 1600 \| 0.0006 \| - \| \| 0.3504 \| 1650 \| 0.0006 \| - \| \| 0.3610 \| 1700 \| 0.0005 \| - \| \| 0.3716 \| 1750 \| 0.0005 \| - \| \| 0.3822 \| 1800 \| 0.0005 \| - \| \| 0.3929 \| 1850 \| 0.0005 \| - \| \| 0.4035 \| 1900 \| 0.0004 \| - \| \| 0.4141 \| 1950 \| 0.0004 \| - \| \| 0.4247 \| 2000 \| 0.0004 \| - \| \| 0.4353 \| 2050 \| 0.0004 \| - \| \| 0.4460 \| 2100 \| 0.0004 \| - \| \| 0.4566 \| 2150 \| 0.0004 \| - \| \| 0.4672 \| 2200 \| 0.0004 \| - \| \| 0.4778 \| 2250 \| 0.0004 \| - \| \| 0.4884 \| 2300 \| 0.0003 \| - \| \| 0.4990 \| 2350 \| 0.0003 \| - \| \| 0.5097 \| 2400 \| 0.0003 \| - \| \| 0.5203 \| 2450 \| 0.0003 \| - \| \| 0.5309 \| 2500 \| 0.0003 \| - \| \| 0.5415 \| 2550 \| 0.0003 \| - \| \| 0.5521 \| 2600 \| 0.0003 \| - \| \| 0.5628 \| 2650 \| 0.0003 \| - \| \| 0.5734 \| 2700 \| 0.0003 \| - \| \| 0.5840 \| 2750 \| 0.0002 \| - \| \| 0.5946 \| 2800 \| 0.0002 \| - \| \| 0.6052 \| 2850 \| 0.0003 \| - \| \| 0.6158 \| 2900 \| 0.0002 \| - \| \| 0.6265 \| 2950 \| 0.0002 \| - \| \| 0.6371 \| 3000 \| 0.0002 \| - \| \| 0.6477 \| 3050 \| 0.0002 \| - \| \| 0.6583 \| 3100 \| 0.0002 \| - \| \| 0.6689 \| 3150 \| 0.0002 \| - \| \| 0.6795 \| 3200 \| 0.0002 \| - \| \| 0.6902 \| 3250 \| 0.0002 \| - \| \| 0.7008 \| 3300 \| 0.0002 \| - \| \| 0.7114 \| 3350 \| 0.0002 \| - \| \| 0.7220 \| 3400 \| 0.0002 \| - \| \| 0.7326 \| 3450 \| 0.0002 \| - \| \| 0.7433 \| 3500 \| 0.0002 \| - \| \| 0.7539 \| 3550 \| 0.0002 \| - \| \| 0.7645 \| 3600 \| 0.0002 \| - \| \| 0.7751 \| 3650 \| 0.0002 \| - \| \| 0.7857 \| 3700 \| 0.0002 \| - \| \| 0.7963 \| 3750 \| 0.0002 \| - \| \| 0.8070 \| 3800 \| 0.0002 \| - \| \| 0.8176 \| 3850 \| 0.0002 \| - \| \| 0.8282 \| 3900 \| 0.0002 \| - \| \| 0.8388 \| 3950 \| 0.0002 \| - \| \| 0.8494 \| 4000 \| 0.0002 \| - \| \| 0.8601 \| 4050 \| 0.0002 \| - \| \| 0.8707 \| 4100 \| 0.0002 \| - \| \| 0.8813 \| 4150 \| 0.0002 \| - \| \| 0.8919 \| 4200 \| 0.0002 \| - \| \| 0.9025 \| 4250 \| 0.0002 \| - \| \| 0.9131 \| 4300 \| 0.0002 \| - \| \| 0.9238 \| 4350 \| 0.0002 \| - \| \| 0.9344 \| 4400 \| 0.0002 \| - \| \| 0.9450 \| 4450 \| 0.0001 \| - \| \| 0.9556 \| 4500 \| 0.0002 \| - \| \| 0.9662 \| 4550 \| 0.0001 \| - \| \| 0.9769 \| 4600 \| 0.0002 \| - \| \| 0.9875 \| 4650 \| 0.0001 \| - \| \| 0.9981 \| 4700 \| 0.0002 \| - \| ### Framework Versions - Python: 3.11.13 - SetFit: 1.1.3 - Sentence Transformers: 5.1.0 - Transformers: 4.56.1 - PyTorch: 2.2.2 - Datasets: 4.0.0 - Tokenizers: 0.22.0 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## 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. -->
lukedai/Qwen3-1.7B-luke-v1	lukedai	2025-09-21T14:51:08Z	6	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "luke-sft", "trl", "sft", "conversational", "dataset:lukedai/hehe", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-11T16:13:32Z	--- datasets: lukedai/hehe library_name: transformers model_name: Qwen3-1.7B-luke-v1 tags: - generated_from_trainer - luke-sft - trl - sft licence: license --- # Model Card for Qwen3-1.7B-luke-v1 This model is a fine-tuned version of [None](https://huggingface.co/None) on the [lukedai/hehe](https://huggingface.co/datasets/lukedai/hehe) 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="lukedai/Qwen3-1.7B-luke-v1", 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: 0.16.0 - Transformers: 4.52.0 - Pytorch: 2.6.0 - 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
proj-airi/games-balatro-2024-yolo-entities-detection	proj-airi	2025-09-21T14:48:58Z	0	1	null	[ "onnx", "YOLO", "ONNX", "onnxruntime", "en", "multilingual", "dataset:proj-airi/games-balatro-2024-entities-detection", "base_model:Ultralytics/YOLO11", "base_model:quantized:Ultralytics/YOLO11", "license:mit", "region:us" ]	null	2025-09-21T14:03:38Z	--- # Full model card template at https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md language: - en - multilingual license: mit tags: - YOLO - ONNX - onnxruntime datasets: - proj-airi/games-balatro-2024-entities-detection base_model: Ultralytics/YOLO11 --- <p align="center"> <img src="./docs/cover.png"> </p> ## Balatro (2024, game) YOLO entities detection > This project is part of (and also associate to) the [Project AIRI](https://github.com/moeru-ai/airi), we aim to build a LLM-driven VTuber like [Neuro-sama](https://www.youtube.com/@Neurosama) (subscribe if you didn't!) if you are interested in, please do give it a try on [live demo](https://airi.moeru.ai). > > Who are we? > > We are a group of currently non-funded talented people made up with computer scientists, experts in multi-modal fields, designers, product managers, and popular open source contributors who loves the goal of where we are heading now. \| Basic \| Multiple card types \| Description \| Crowded cards \| \| ------------------------- \| ------------------------- \| ------------------------- \| ------------------------- \| \| ![](./docs/example-1.jpg) \| ![](./docs/example-2.jpg) \| ![](./docs/example-3.jpg) \| ![](./docs/example-4.jpg) \| ## Training We trained this model on our own datasets labelled with n<1k images using Label Studio with YOLOv11n as the base model, it's available on HuggingFace as well: [proj-airi/games-balatro-2024-entities-detection](https://huggingface.co/datasets/proj-airi/games-balatro-2024-entities-detection). The training was performed on a single NVIDIA 4080Super GPU with 16GB VRAM, the loss optimized well and converged within set 2000 epochs. ![](./docs/training-metrics-1.png) ![](./docs/training-metrics-2.jpg) ## Citation If you find our works useful for your research, please consider citing: ```bibtex @misc{proj_airi_game_ai_models_balatro_2024_yolo_entities_detection_2025, title = {Balatro (2024, game) YOLO entities detection}, author = {Project AIRI Team, Neko Ayaka, Makito, Rainbow Bird}, howpublished = {\url{https://huggingface.co/proj-airi/games-balatro-2024-yolo-entities-detection}}, year = {2025} } ``` ## License This model is licensed under the MIT.
kevinshin/qwen3-1.7b-sft-epoch-2-wc-cw-3k-pos	kevinshin	2025-09-21T14:45:37Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "trl", "sft", "alignment-handbook", "conversational", "dataset:kevinshin/wildchat-creative-writing-3k-critique-v2", "base_model:Qwen/Qwen3-1.7B", "base_model:finetune:Qwen/Qwen3-1.7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T09:10:42Z	--- base_model: Qwen/Qwen3-1.7B datasets: kevinshin/wildchat-creative-writing-3k-critique-v2 library_name: transformers model_name: qwen3-1.7b-sft-epoch-2-wc-cw-3k-pos tags: - generated_from_trainer - trl - sft - alignment-handbook licence: license --- # Model Card for qwen3-1.7b-sft-epoch-2-wc-cw-3k-pos This model is a fine-tuned version of [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) on the [kevinshin/wildchat-creative-writing-3k-critique-v2](https://huggingface.co/datasets/kevinshin/wildchat-creative-writing-3k-critique-v2) 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="kevinshin/qwen3-1.7b-sft-epoch-2-wc-cw-3k-pos", 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/myungjune-sogang-university/general_remo_train/runs/8f80ka8z) This model was trained with SFT. ### Framework versions - TRL: 0.19.1 - Transformers: 4.55.0.dev0 - Pytorch: 2.6.0+cu126 - Datasets: 4.0.0 - Tokenizers: 0.21.2 ## 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}} } ```
KobeBeef67/finetuned-llama	KobeBeef67	2025-09-21T14:41:54Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-20T23:07:29Z	--- 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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nikilr/Llama3.1-8B-ds7000	nikilr	2025-09-21T14:39:56Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T14:38:56Z	--- 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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Labira/LabiraPJOK_123_100_Full	Labira	2025-09-21T14:37:09Z	0	0	transformers	[ "transformers", "tf", "bert", "question-answering", "generated_from_keras_callback", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2025-09-21T06:43:30Z	--- library_name: transformers license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_keras_callback model-index: - name: Labira/LabiraPJOK_123_100_Full results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Labira/LabiraPJOK_123_100_Full This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0108 - Validation Loss: 0.0014 - Epoch: 99 ## 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2200, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 4.8014 \| 3.8239 \| 0 \| \| 3.5330 \| 3.0989 \| 1 \| \| 3.0273 \| 2.6526 \| 2 \| \| 2.6530 \| 2.0593 \| 3 \| \| 2.2572 \| 1.6401 \| 4 \| \| 1.7060 \| 1.0829 \| 5 \| \| 1.2904 \| 0.6494 \| 6 \| \| 0.9646 \| 0.4921 \| 7 \| \| 0.6371 \| 0.2708 \| 8 \| \| 0.4612 \| 0.2947 \| 9 \| \| 0.4154 \| 0.2030 \| 10 \| \| 0.4027 \| 0.1670 \| 11 \| \| 0.2759 \| 0.1051 \| 12 \| \| 0.2515 \| 0.1313 \| 13 \| \| 0.1759 \| 0.0651 \| 14 \| \| 0.1293 \| 0.0732 \| 15 \| \| 0.1595 \| 0.0472 \| 16 \| \| 0.0989 \| 0.0647 \| 17 \| \| 0.0797 \| 0.0566 \| 18 \| \| 0.1292 \| 0.0351 \| 19 \| \| 0.1098 \| 0.0743 \| 20 \| \| 0.1490 \| 0.0591 \| 21 \| \| 0.0934 \| 0.0558 \| 22 \| \| 0.0720 \| 0.0330 \| 23 \| \| 0.0502 \| 0.0265 \| 24 \| \| 0.0598 \| 0.0235 \| 25 \| \| 0.0589 \| 0.0272 \| 26 \| \| 0.0409 \| 0.0243 \| 27 \| \| 0.0445 \| 0.0199 \| 28 \| \| 0.0425 \| 0.0395 \| 29 \| \| 0.0420 \| 0.0252 \| 30 \| \| 0.0332 \| 0.0194 \| 31 \| \| 0.0286 \| 0.0178 \| 32 \| \| 0.0480 \| 0.0184 \| 33 \| \| 0.0361 \| 0.0279 \| 34 \| \| 0.0529 \| 0.0195 \| 35 \| \| 0.0296 \| 0.0194 \| 36 \| \| 0.0346 \| 0.0143 \| 37 \| \| 0.0256 \| 0.0177 \| 38 \| \| 0.0331 \| 0.0098 \| 39 \| \| 0.0386 \| 0.0086 \| 40 \| \| 0.0303 \| 0.0053 \| 41 \| \| 0.0310 \| 0.0154 \| 42 \| \| 0.0193 \| 0.0024 \| 43 \| \| 0.1070 \| 0.0090 \| 44 \| \| 0.0937 \| 0.0123 \| 45 \| \| 0.0766 \| 0.0112 \| 46 \| \| 0.0698 \| 0.0057 \| 47 \| \| 0.0297 \| 0.0043 \| 48 \| \| 0.0385 \| 0.0117 \| 49 \| \| 0.0802 \| 0.0181 \| 50 \| \| 0.1040 \| 0.0072 \| 51 \| \| 0.0836 \| 0.0163 \| 52 \| \| 0.0861 \| 0.0060 \| 53 \| \| 0.0867 \| 0.0079 \| 54 \| \| 0.1242 \| 0.0041 \| 55 \| \| 0.1090 \| 0.0070 \| 56 \| \| 0.0394 \| 0.0042 \| 57 \| \| 0.0312 \| 0.0041 \| 58 \| \| 0.0391 \| 0.0020 \| 59 \| \| 0.0320 \| 0.0023 \| 60 \| \| 0.0479 \| 0.0135 \| 61 \| \| 0.0403 \| 0.0017 \| 62 \| \| 0.0352 \| 0.0019 \| 63 \| \| 0.0314 \| 0.0030 \| 64 \| \| 0.0254 \| 0.0020 \| 65 \| \| 0.0243 \| 0.0013 \| 66 \| \| 0.0504 \| 0.0022 \| 67 \| \| 0.0474 \| 0.0023 \| 68 \| \| 0.0430 \| 0.0036 \| 69 \| \| 0.0142 \| 0.0021 \| 70 \| \| 0.0169 \| 0.0014 \| 71 \| \| 0.0110 \| 0.0013 \| 72 \| \| 0.0229 \| 0.0011 \| 73 \| \| 0.0476 \| 0.0008 \| 74 \| \| 0.0461 \| 0.0012 \| 75 \| \| 0.0170 \| 0.0013 \| 76 \| \| 0.0210 \| 0.0020 \| 77 \| \| 0.0146 \| 0.0021 \| 78 \| \| 0.0206 \| 0.0019 \| 79 \| \| 0.0137 \| 0.0021 \| 80 \| \| 0.0125 \| 0.0015 \| 81 \| \| 0.0303 \| 0.0026 \| 82 \| \| 0.0100 \| 0.0019 \| 83 \| \| 0.0088 \| 0.0015 \| 84 \| \| 0.0128 \| 0.0016 \| 85 \| \| 0.0153 \| 0.0018 \| 86 \| \| 0.0141 \| 0.0018 \| 87 \| \| 0.0163 \| 0.0017 \| 88 \| \| 0.0104 \| 0.0014 \| 89 \| \| 0.0098 \| 0.0014 \| 90 \| \| 0.0116 \| 0.0013 \| 91 \| \| 0.0160 \| 0.0015 \| 92 \| \| 0.0161 \| 0.0016 \| 93 \| \| 0.0088 \| 0.0015 \| 94 \| \| 0.0101 \| 0.0015 \| 95 \| \| 0.0105 \| 0.0015 \| 96 \| \| 0.0110 \| 0.0015 \| 97 \| \| 0.0049 \| 0.0014 \| 98 \| \| 0.0108 \| 0.0014 \| 99 \| ### Framework versions - Transformers 4.45.2 - TensorFlow 2.17.0 - Datasets 2.20.0 - Tokenizers 0.20.1
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758465349	schooncestiaa	2025-09-21T14:36:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "scruffy webbed dragonfly", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T14:36:46Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - scruffy webbed dragonfly --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/Ultracore-Instruct-12B-i1-GGUF	mradermacher	2025-09-21T14:36:32Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:pot99rta/Ultracore-Instruct-12B", "base_model:quantized:pot99rta/Ultracore-Instruct-12B", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-09-21T13:14:59Z	--- base_model: pot99rta/Ultracore-Instruct-12B language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - mergekit - merge --- ## 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/pot99rta/Ultracore-Instruct-12B <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Ultracore-Instruct-12B-i1-GGUF).* static quants are available at https://huggingface.co/mradermacher/Ultracore-Instruct-12B-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/Ultracore-Instruct-12B-i1-GGUF/resolve/main/Ultracore-Instruct-12B.imatrix.gguf) \| imatrix \| 0.1 \| imatrix file (for creating your own qwuants) \| \| [GGUF](https://huggingface.co/mradermacher/Ultracore-Instruct-12B-i1-GGUF/resolve/main/Ultracore-Instruct-12B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 3.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Ultracore-Instruct-12B-i1-GGUF/resolve/main/Ultracore-Instruct-12B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 3.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Ultracore-Instruct-12B-i1-GGUF/resolve/main/Ultracore-Instruct-12B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ultracore-Instruct-12B-i1-GGUF/resolve/main/Ultracore-Instruct-12B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ultracore-Instruct-12B-i1-GGUF/resolve/main/Ultracore-Instruct-12B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ultracore-Instruct-12B-i1-GGUF/resolve/main/Ultracore-Instruct-12B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ultracore-Instruct-12B-i1-GGUF/resolve/main/Ultracore-Instruct-12B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 4.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Ultracore-Instruct-12B-i1-GGUF/resolve/main/Ultracore-Instruct-12B.i1-Q2_K.gguf) \| i1-Q2_K \| 4.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Ultracore-Instruct-12B-i1-GGUF/resolve/main/Ultracore-Instruct-12B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 5.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Ultracore-Instruct-12B-i1-GGUF/resolve/main/Ultracore-Instruct-12B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ultracore-Instruct-12B-i1-GGUF/resolve/main/Ultracore-Instruct-12B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 5.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Ultracore-Instruct-12B-i1-GGUF/resolve/main/Ultracore-Instruct-12B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 5.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Ultracore-Instruct-12B-i1-GGUF/resolve/main/Ultracore-Instruct-12B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ultracore-Instruct-12B-i1-GGUF/resolve/main/Ultracore-Instruct-12B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 6.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Ultracore-Instruct-12B-i1-GGUF/resolve/main/Ultracore-Instruct-12B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 6.7 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Ultracore-Instruct-12B-i1-GGUF/resolve/main/Ultracore-Instruct-12B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ultracore-Instruct-12B-i1-GGUF/resolve/main/Ultracore-Instruct-12B.i1-Q4_0.gguf) \| i1-Q4_0 \| 7.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Ultracore-Instruct-12B-i1-GGUF/resolve/main/Ultracore-Instruct-12B.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 7.2 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Ultracore-Instruct-12B-i1-GGUF/resolve/main/Ultracore-Instruct-12B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 7.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Ultracore-Instruct-12B-i1-GGUF/resolve/main/Ultracore-Instruct-12B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Ultracore-Instruct-12B-i1-GGUF/resolve/main/Ultracore-Instruct-12B.i1-Q4_1.gguf) \| i1-Q4_1 \| 7.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ultracore-Instruct-12B-i1-GGUF/resolve/main/Ultracore-Instruct-12B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 8.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ultracore-Instruct-12B-i1-GGUF/resolve/main/Ultracore-Instruct-12B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 8.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ultracore-Instruct-12B-i1-GGUF/resolve/main/Ultracore-Instruct-12B.i1-Q6_K.gguf) \| i1-Q6_K \| 10.2 \| 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 -->
SwetaJena/llama-3.1-8B-octopus_numbers_student_15_v1	SwetaJena	2025-09-21T14:32:09Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/Llama-3.1-8B-Instruct", "base_model:finetune:unsloth/Llama-3.1-8B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-09-21T14:32:02Z	--- base_model: unsloth/Llama-3.1-8B-Instruct tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: SwetaJena - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.1-8B-Instruct 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)
Eskender/products-ranker-preprod-bge-v8_corrected_data	Eskender	2025-09-21T14:28:03Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-09-21T14:27:37Z	--- 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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schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758464725	schooncestiaa	2025-09-21T14:26:51Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "scruffy webbed dragonfly", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T14:26:43Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - scruffy webbed dragonfly --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
sir-timio/Loffi0	sir-timio	2025-09-21T14:26:29Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:Qwen/Qwen3-Reranker-8B", "lora", "transformers", "arxiv:1910.09700", "base_model:Qwen/Qwen3-Reranker-8B", "region:us" ]	null	2025-09-21T13:46:42Z	--- base_model: Qwen/Qwen3-Reranker-8B library_name: peft tags: - base_model:adapter:Qwen/Qwen3-Reranker-8B - lora - transformers --- # 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. --> - 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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sir-timio/Ugaga3	sir-timio	2025-09-21T14:25:44Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:Qwen/Qwen3-Reranker-8B", "lora", "transformers", "arxiv:1910.09700", "base_model:Qwen/Qwen3-Reranker-8B", "region:us" ]	null	2025-09-21T13:47:15Z	--- base_model: Qwen/Qwen3-Reranker-8B library_name: peft tags: - base_model:adapter:Qwen/Qwen3-Reranker-8B - lora - transformers --- # 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. --> - 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. 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TencentARC/ARC-Qwen-Video-7B	TencentARC	2025-09-21T14:23:56Z	255	4	transformers	[ "transformers", "safetensors", "multimodal", "video-understanding", "video-audio understanding", "video-qa", "video-captioning", "video-grounding", "video-reasoning", "short video understanding", "video-text-to-text", "arxiv:2507.20939", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	video-text-to-text	2025-09-18T03:42:52Z	--- license: apache-2.0 base_model: - Qwen/Qwen2.5-VL-7B-Instruct pipeline_tag: video-text-to-text library_name: transformers tags: - multimodal - video-understanding - video-audio understanding - video-qa - video-captioning - video-grounding - video-reasoning - short video understanding --- # ARC-Qwen-Video-7B [![arXiv](https://img.shields.io/badge/arXiv-2507.20939-b31b1b.svg)](https://arxiv.org/abs/2507.20939) [![Demo](https://img.shields.io/badge/ARC-Demo-blue)](https://arc.tencent.com/en/ai-demos/multimodal) [![Code](https://img.shields.io/badge/Github-Code-orange)](https://github.com/TencentARC/ARC-Hunyuan-Video-7B/tree/arc-qwen-video) [![Static Badge](https://img.shields.io/badge/Model-Huggingface-yellow)](https://huggingface.co/TencentARC/ARC-Hunyuan-Video-7B) [![Static Badge](https://img.shields.io/badge/Model-Huggingface-yellow)](https://huggingface.co/TencentARC/ARC-Qwen-Video-7B) [![Static Badge](https://img.shields.io/badge/Model-Huggingface-yellow)](https://huggingface.co/TencentARC/ARC-Qwen-Video-7B-Narrator) [![Blog](https://img.shields.io/badge/ARC-Blog-green)](https://tencentarc.github.io/posts/arc-video-announcement/) [![Benchmark](https://img.shields.io/badge/ShortVid-Bench-orange)](https://huggingface.co/datasets/TencentARC/ShortVid-Bench) In this version, we have switched the base model from hunyuan VLM in [ARC-Hunyuan-Video-7B](https://huggingface.co/TencentARC/ARC-Hunyuan-Video-7B) to [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) and introduce [ARC-Qwen-Video-7B](https://huggingface.co/TencentARC/ARC-Qwen-Video-7B) for understanding real-world short videos. We used the same training data and training stages. For a detailed introduction, please refer to [ARC-Hunyuan-Video-7B](https://huggingface.co/TencentARC/ARC-Hunyuan-Video-7B). The main distinctions are listed as below, \| Feature \| `ARC-Hunyuan-Video-7B` \| `ARC-Qwen-Video-7B` \| \| ---------------------------------------------------------------------------------------------------------------------- \| --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- \| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- \| \| Base VLM \| Hunyuan-VL-7B-Pretrain \| Qwen2.5-VL-7B-Instruct \| \| Frame Resolution <br> <small>Each model uses a fixed frame resolution to maintain audio-video synchronization.</small> \| Fixed at `640 x 640` \| Fixed at `392 x 292` \| \| Frame Sampling \| • < 150s: 1 FPS <br> • > 150s: Uniformly sample 150 frames. \| • < 300s: 1 FPS <br> • > 300s: Uniformly sample 300 frames. \| \| Audio-Video Synchronization \| • < 150s: Sum tokens from 1s audio + 1s video frame. <br> • 150-300s: Sum tokens from corresponding audio segment + video frame. <br> • > 300s: Split audio into 300 segments, use first 2s of each. \| • < 300s: Sum tokens from 1s audio + 1s video. <br> • > 300s: Split audio into 300 segments, use middle 1s of each. \| We are also introducing a new model, [ARC-Qwen-Video-7B-Narrator](https://huggingface.co/TencentARC/ARC-Qwen-Video-7B-Narrator). It can output timestamped video descriptions, speaker identities, and the specific ASR (Automatic Speech Recognition) content. By processing its output with an external LLM, you can obtain more comprehensive structured information as follows (Click to watch the video): [<img src="https://img.youtube.com/vi/Bz1T4wCuWc8/maxresdefault.jpg" alt="视频" width="300">](https://www.youtube.com/watch?v=Bz1T4wCuWc8) <table border="1" style="width:100%; border-collapse: collapse;"> <tr> <td style="padding: 15px;"> ### 视频概述这是一个喜剧短片，讲述了一位丈夫藏在棉衣里的私房钱被妻子意外发现，并误以为是丈夫准备的“惊喜”礼物。视频通过夫妻二人的一通电话，生动展现了丈夫从悠闲自得，到震惊错愕，再到崩溃无奈的全过程，充满了戏剧性的反转和幽默感。 ### 情节发展分解视频情节围绕一通电话展开，以下是详细的时间线、场景、说话人和对话内容： <table> <thead> <tr> <th>时间戳</th> <th>场景描述</th> <th>说话人</th> <th>对话内容 (ASR)</th> </tr> </thead> <tbody> <tr> <td>0:00 - 0:05</td> <td>丈夫头戴浴帽，围着浴巾，在室内泳池边悠闲地自拍。</td> <td>无</td> <td>(无对话)</td> </tr> <tr> <td>0:05 - 0:10</td> <td><b>镜头切换</b>：妻子在服装店里，满脸幸福地给丈夫打电话。</td> <td>妻子</td> <td>“哎，老公，老公，我爱你爱你，爱死你了，么么么。”</td> </tr> <tr> <td rowspan="2" style="vertical-align: top;">0:10 - 0:18</td> <td rowspan="2" style="vertical-align: top;">丈夫接起电话，对妻子的热情感到好奇，妻子则兴奋地揭晓了“惊喜”。</td> <td>丈夫</td> <td>“哎，怎么了你这是，这么高兴啊？”</td> </tr> <tr> <td>妻子</td> <td>“今天我在我的棉衣兜里，发现了你给我的惊喜，一万元哟。”</td> </tr> <tr> <td>0:18 - 0:27</td> <td>听到“一万元”，丈夫表情瞬间凝固，从疑惑变为震惊和懊悔，但仍强装镇定。</td> <td>丈夫</td> <td>“啊？好啊，你你你你开心高兴就行。”</td> </tr> <tr> <td>0:27 - 0:34</td> <td>妻子开心地告知钱的用途，丈夫的表情彻底僵住，震惊加剧。</td> <td>妻子</td> <td>“我当然高兴啊，我用它买了一件新衣裳，等晚上回去穿给你看啊。”</td> </tr> <tr> <td rowspan="3" style="vertical-align: top;">0:34 - 0:46</td> <td rowspan="3" style="vertical-align: top;">丈夫确认钱已被花掉，情绪崩溃。妻子则认为是丈夫授权的，丈夫忍不住骂了一句。</td> <td>丈夫</td> <td>“你已经给买成衣服了？”</td> </tr> <tr> <td>妻子</td> <td>“当然啦，不是你说的吗？说买我自己喜欢的东西。老公，你真是太好了。”</td> </tr> <tr> <td>丈夫</td> <td>“你真是败家娘们儿啊你。”</td> </tr> <tr> <td rowspan="4" style="vertical-align: top;">0:46 - 0:59</td> <td rowspan="4" style="vertical-align: top;">妻子察觉丈夫语气不对，丈夫立刻改口掩饰，并催促妻子早点回家。</td> <td>妻子</td> <td>“什么，老公，你说什么？”</td> </tr> <tr> <td>丈夫</td> <td>“啊？我说好啊，你漂亮我高兴。”</td> </tr> <tr> <td>妻子</td> <td>“你说的，老公。你今天呀，一定要早点回来哟，我等你哟。”</td> </tr> <tr> <td>丈夫</td> <td>“行行行行行。”</td> </tr> </tbody> </table> ### 人物与核心冲突 #### 1. 人物分析丈夫: 行为: 藏私房钱，事发后极力掩饰自己的真实情绪（心痛、懊悔）。心理变化: 悠闲 -> 疑惑 -> 震惊 -> 崩溃 -> 无奈接受。特点: 爱面子，对妻子既有爱意也有无奈，典型的“妻管严”形象。妻子: 行为: 发现钱后，认为是丈夫的爱意表达，并迅速将其消费。心理变化: 全程处于发现“惊喜”的幸福和喜悦中。特点: 天真、消费果断，对丈夫充满信任和爱意。 #### 2. 核心冲突视频的核心冲突在于 “信息的严重不对等” 所造成的戏剧性误会： * 丈夫视角: 辛苦攒下的 10,000元私房钱被意外发现并花掉，是一场“惊吓”。 * 妻子视角: 丈夫精心准备的 10,000元浪漫基金，是一份巨大的“惊喜”。这个误会推动了整个故事的发展，丈夫的“打碎牙往肚里咽”和妻子的“理所当然的幸福”形成了强烈的喜剧反差，制造了密集的笑点。 ### 总结该视频通过一个关于“私房钱”的常见家庭情景，巧妙地构建了一个充满反转和幽默的故事。它利用戏剧性讽刺（观众和丈夫知道真相，而妻子蒙在鼓里）的手法，精准捕捉了丈夫在突发状况下的复杂心理活动。整个过程不仅笑料百出，也含蓄地探讨了夫妻间的沟通、信任和金钱观等话题，容易引发观众的共鸣和讨论。 </td> </tr> </table> ## Usage ### Dependencies The installation has been tested and verified on the following environments: * NVIDIA H20 with CUDA 12.4 * NVIDIA A100 with CUDA 12.1 ### Installation Clone the repo and install dependent packages ```bash git clone -b arc-qwen-video https://github.com/TencentARC/ARC-Hunyuan-Video-7B.git cd ARC-Hunyuan-Video-7B # Install torch 2.6.0 based on your CUDA version # CUDA 11.8 pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu118 # CUDA 12.4 pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu124 # CUDA 12.6 pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu126 pip install librosa decord av accelerate pip uninstall transformers pip install git+https://github.com/geyuying/transformers.git@arc-qwen-video pip install flash_attn==2.7.1.post4 # Install FFmpeg according to your system, and ensure that the following command produces a normal version output: ffmpeg -version # (Optional) For vllm, please follow the instructions below, pip uninstall vllm pip install git+https://github.com/geyuying/vllm.git@arc-qwen-video ``` #### An 'Ugly' Workaround for vLLM Installation If you are unable to install our provided vllm package, we offer an alternative "ugly" method: 1. Install vllm with Qwen2.5-VL support. 2. Modify config.json. In your model weights directory, open config.json and change the architectures field to "Qwen2_5_VLForConditionalGeneration". 3. Patch the vllm source code. Locate the file vllm/model_executor/models/qwen2_5_vl.py in your vllm installation path. Add the following code inside the __init__ method of the Qwen2_5_VLForConditionalGeneration class: ``` whisper_path = 'openai/whisper-large-v3' speech_encoder = WhisperModel.from_pretrained(whisper_path).encoder self.speech_encoder = speech_encoder speech_dim = speech_encoder.config.d_model llm_hidden_size = config.vision_config.out_hidden_size self.mlp_speech = nn.Sequential( nn.LayerNorm(speech_dim), nn.Linear(speech_dim, llm_hidden_size), nn.GELU(), nn.Linear(llm_hidden_size, llm_hidden_size) ) ``` Why this works: Our model is based on the Qwen-VL-2.5 architecture, with the addition of an audio encoder and a corresponding MLP. During vllm inference, the multi-modal encoder processes inputs sequentially, while the LLM performs batch inference. Since we only need to pass the final multi-modal embeddings to the LLM, we can reuse the existing code for Qwen-VL-2.5. ### Inference ```bash # Our model currently excels at processing short videos of up to 5 minutes. # If your video is longer, we recommend following the approach used in our demo and API: # split the video into segments for inference, and then use an LLM to integrate the results. ``` To quickly verify that your environment is set up correctly and that video and audio information are being processed as expected, you can run the following test case with ARC-Qwen-Video-7B. ```bash video_path = "examples/猪排.mp4" task = "QA" question = "What did the man say at the beginning of the video after measuring the thickness of the fried pork cutlet?" ``` Expected Result: If the model's output contains the phrase "So thin", it indicates that your installation is successful. #### Inference without vllm ```bash cd ARC-Hunyuan-Video-7B # For ARC-Hunyuan-Video-7B python3 inference_arc_qwen_video.py # For ARC-Hunyuan-Video-7B-Narrator python3 inference_arc_qwen_video_narrator.py ``` #### Inference with vllm ```bash cd ARC-Hunyuan-Video-7B # For ARC-Hunyuan-Video-7B python3 vllm_arc_qwen_vl_video_batch.py --batch_inference # For ARC-Hunyuan-Video-7B-Narrator python3 vllm_arc_qwen_vl_video_batch_narrator.py --batch_inference ``` ## Benchmark Performance \| \| Video-MMMU \| MMVU \| Temp-Compass \| Video-Holmes \| Video-MME \| VCR-Bench \| MV-Bench \| ShortVid-Bench \| Charades-STA \| \|:---\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\| \| ARC-Hunyuan-Video-7B \| 31.1 \| 49.1 \| 66.0 \| 40.9 \| 58.7 \| 50.5 \| 62.6 \| 73.0 \| 54.8 \| \| ARC-Qwen-Video-7B \| 41.3 \| 55.5 \| 68.7 \| 51.1 \| 61.0 \| 52.3 \| 60.8 \| 72.6 \| 52.8 \| Quantitative evaluation is performed on different benchmarks using accuracy as the evaluation metric, except for the grounding task on Charades-STA, which uses mIoU. For all benchmarks other than VideoMMMU and Charades-STA, we only evaluated the multiple-choice questions. ## Citation If you find the work helpful, please consider citing: ```bash @article{ge2025arc, title={ARC-Hunyuan-Video-7B: Structured Video Comprehension of Real-World Shorts}, author={Ge, Yuying and Ge, Yixiao and Li, Chen and Wang, Teng and Pu, Junfu and Li, Yizhuo and Qiu, Lu and Ma, Jin and Duan, Lisheng and Zuo, Xinyu and others}, journal={arXiv preprint arXiv:2507.20939}, year={2025} } ```
combe4259/fin_simplifier	combe4259	2025-09-21T14:22:45Z	97	0	null	[ "safetensors", "encoder-decoder", "seq2seq", "text-simplification", "financial-domain", "ko", "pytorch", "dataset:combe4259/fin_simplifier_dataset", "license:other", "region:us" ]	null	2025-09-16T18:04:29Z	--- language: ko license: other base_models: - snunlp/KR-FinBert-SC - skt/kogpt2-base-v2 tags: - encoder-decoder - seq2seq - text-simplification - financial-domain - ko - pytorch datasets: - combe4259/fin_simplifier_dataset --- # 금융 텍스트 간소화 모델 (Financial Text Simplifier) ## 모델 설명 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/19Q7kUWtHX2shLx6iGGoT66wEidOrvLCf?usp=sharing) fin_simplifier는 복잡한 금융 용어와 문장을 일반인이 이해하기 쉬운 한국어로 변환하는 인코더-디코더 모델입니다. ### 모델 구조 (config.json 기반) - 모델 타입: EncoderDecoderModel - 인코더: snunlp/KR-FinBert-SC (은닉 차원: 768) - 디코더: skt/kogpt2-base-v2 (어휘 크기: 51,201) - 파라미터 수: 약 255M - 파일 크기: 1.02GB (safetensors 형식) ### 주요 특징 - 금융 전문 용어를 쉬운 일상어로 변환 - 한국어 금융 문서에 최적화 - 복잡한 금융 개념 간소화 (PER, ROE, 파생상품 등) - 은행 상담 및 금융 교육 활용 가능 ## 사용 목적 ### 주요 활용 사례 1. 금융 상담 지원: 은행 상담 시 고객 이해도 향상 2. 금융 교육: 복잡한 금융 개념을 쉽게 설명 3. 문서 간소화: 약관, 상품 설명서 등을 이해하기 쉽게 변환 4. 접근성 개선: 금융 소외계층의 금융 서비스 접근성 향상 ### 사용 제한 사항 - 법적 구속력이 있는 문서 작성 - 투자 조언 또는 금융 상담 대체 - 정확한 수치나 계산이 필요한 경우 ## 사용 방법 ### 설치 ```python from transformers import EncoderDecoderModel, AutoTokenizer import torch # Model loading model = EncoderDecoderModel.from_pretrained("combe4259/fin_simplifier") encoder_tokenizer = AutoTokenizer.from_pretrained("snunlp/KR-FinBert-SC") decoder_tokenizer = AutoTokenizer.from_pretrained("skt/kogpt2-base-v2") # Set special tokens if decoder_tokenizer.pad_token is None: decoder_tokenizer.pad_token = decoder_tokenizer.eos_token ``` ### 추론 예시 ```python def simplify_text(text, model, encoder_tokenizer, decoder_tokenizer): # Tokenize input inputs = encoder_tokenizer( text, return_tensors="pt", max_length=128, padding="max_length", truncation=True ) # Generate simplified text with torch.no_grad(): generated = model.generate( input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], max_length=128, num_beams=6, repetition_penalty=1.2, length_penalty=0.8, early_stopping=True, do_sample=True, top_k=50, top_p=0.95, temperature=0.7 ) # Decode output simplified = decoder_tokenizer.decode(generated[0], skip_special_tokens=True) return simplified # Example usage complex_text = "주가수익비율(PER)은 주가를 주당순이익으로 나눈 지표입니다." simple_text = simplify_text(complex_text, model, encoder_tokenizer, decoder_tokenizer) print(f"원문: {complex_text}") print(f"간소화: {simple_text}") # 출력 예시: 모델이 생성하는 간소화된 텍스트 ``` ## 학습 상세 정보 ### 학습 데이터셋 [데이터셋](https://huggingface.co/datasets/combe4259/fin_simplifier_dataset/tree/main) 자체 제작 데이터셋 -출처: NH농협은행 -NH농협은행 상품설명서를 gemma 모델에 투입하여 변환하여 생성 ### 학습 설정 (trainer_state.json 기반) - 에포크: 10 - 배치 크기: 4 (gradient accumulation steps: 2) - 최대 학습률: 2.99e-05 - 최종 학습률: 8.82e-09 - 옵티마이저: AdamW (warmup steps: 200) - 레이블 스무딩: 0.1 - 드롭아웃: 0.2 (인코더 및 디코더) ### 생성 하이퍼파라미터 - Beam Search: 6 beams - Repetition Penalty: 1.2 - Length Penalty: 0.8 - Temperature: 0.7 - Top-k: 50 - Top-p: 0.95 ## 평가 결과 ### 학습 성과 (trainer_state.json 기준) - 초기 손실: 13.53 - 최종 손실: 3.76 - 손실 감소율: 72.2% - 총 학습 스텝: 3,600 - 수렴 패턴: 에포크 8부터 안정적 수렴 ### 에포크별 평균 손실 \| 에포크 \| 평균 손실 \| \|--------\|-----------\| \| 1 \| 8.98 \| \| 2 \| 6.93 \| \| 3 \| 5.95 \| \| 4 \| 5.28 \| \| 5 \| 4.81 \| \| 6 \| 4.44 \| \| 7 \| 4.17 \| \| 8 \| 3.97 \| \| 9 \| 3.82 \| \| 10 \| 3.73 \| ### 예시 출력 \| 원문 (Complex) \| 변환 결과 (Simplified) \| \|---------------\|---------------------\| \| 시가총액은 발행주식수에 주가를 곱한 값으로 기업의 시장가치를 나타냅니다. \| 시가총액은 회사의 모든 주식을 합친 가격입니다. \| \| 파생결합증권은 기초자산의 가격변동에 연계하여 수익이 결정되는 증권입니다. \| 파생결합증권은 다른 상품 가격에 따라 수익이 바뀌는 투자 상품입니다. \| \| 환매조건부채권(RP)은 일정기간 후 다시 매입하는 조건으로 매도하는 채권입니다. \| RP는 나중에 다시 사겠다고 약속하고 일단 파는 채권입니다. \| \| 유동성위험은 자산을 적정가격에 현금화하지 못할 위험입니다. \| 유동성위험은 급하게 팔 때 제값을 못 받을 위험입니다. \| \| 원리금균등상환은 매월 동일한 금액으로 원금과 이자를 상환하는 방식입니다. \| 원리금균등상환은 매달 같은 금액을 갚는 방식입니다. \| ## 인용 ```bibtex @misc{fin_simplifier2024, title={Financial Text Simplifier: Korean Financial Terms Simplification Model}, author={combe4259}, year={2024}, publisher={HuggingFace}, url={https://huggingface.co/combe4259/fin_simplifier} } ``` ## 감사의 말 - KR-FinBert-SC: 금융 도메인 특화 인코더 제공 - SKT KoGPT2: 한국어 생성 모델 제공 ## 연락처 - HuggingFace: [combe4259](https://huggingface.co/combe4259) - Model Card: 문의사항은 HuggingFace 토론 탭을 이용해주세요 ---
arrowone/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-gliding_poisonous_mosquito	arrowone	2025-09-21T14:21:02Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am gliding_poisonous_mosquito", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T11:28:13Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am gliding_poisonous_mosquito --- # 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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huijelee/mistral-7b-qlora-nemotron-merged-slerp	huijelee	2025-09-21T14:18:36Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T13:14:25Z	--- 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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CraftJarvis/minecraft-textvla-qwen2vl-7b-2509	CraftJarvis	2025-09-21T14:17:18Z	4	0	transformers	[ "transformers", "safetensors", "qwen2_vl", "image-to-text", "image-text-to-text", "conversational", "dataset:CraftJarvis/minecraft-text-action-dataset", "arxiv:2509.13347", "base_model:Qwen/Qwen2-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2-VL-7B-Instruct", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-09-12T09:33:42Z	--- library_name: transformers license: mit datasets: - CraftJarvis/minecraft-text-action-dataset metrics: - accuracy base_model: - Qwen/Qwen2-VL-7B-Instruct pipeline_tag: image-text-to-text arxiv: 2509.13347 --- # Minecraft-Textvla-Qwen2vl-7b-2509 <div align="left"> <a href="https://craftjarvis.github.io/"><img alt="Homepage" src="https://img.shields.io/badge/%20CraftJarvis-HomePage-ffc107?color=blue&logoColor=white"/></a> <a href="https://github.com/CraftJarvis/OpenHA"><img alt="Github" src="https://img.shields.io/badge/%F0%9F%A4%97%20Github-CraftJarvis-ffc107?color=3b65ab&logoColor=white"/></a> <a href="https://arxiv.org/abs/2509.13347"><img src="https://img.shields.io/badge/arXiv-2509.13347-b31b1b.svg"></a> <a href="https://github.com/CraftJarvis/OpenHA/blob/master/LICENSE"><img src="https://img.shields.io/badge/Code License-MIT-blue"/></a> </div> minecraft-textvla-qwen2vl-7b-2509 is part of the OpenHA suite, introduced in our paper [OpenHA: A Series of Open-Source Hierarchical Agentic Models in Minecraft](https://huggingface.co/papers/2509.13347). ## 💻 Usage You can download and use this model with: ```sh python examples/rollout_openha.py \ --output_mode text_action \ --vlm_client_mode hf \ --system_message_tag text_action \ --model_ips localhost --model_ports 11000 \ --model_path CraftJarvis/minecraft-textvla-qwen2vl-7b-2509 \ --model_id minecraft-textvla-qwen2vl-7b-2509 \ --record_path "/DATA/limuyao/evaluate" \ --max_steps_num 200 \ --num_rollouts 8 ``` For more details, please refer to our [code repository](https://github.com/CraftJarvis/OpenHA). ## 📚 Citation ```bibtex @article{wang2025openha, title={OpenHA: A Series of Open-Source Hierarchical Agentic Models in Minecraft}, author={Zihao Wang and Muyao Li and Kaichen He and Xiangyu Wang and Zhancun Mu and Anji Liu and Yitao Liang}, journal = {arXiv preprint arXiv:2509.13347}, year={2025}, url={https://arxiv.org/abs/2509.13347}, } ```
ruru189/bhavani_lora_model1	ruru189	2025-09-21T14:16:07Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "trl", "sft", "unsloth", "region:us" ]	null	2025-09-21T14:16:00Z	--- base_model: unsloth/deepseek-r1-0528-qwen3-8b-unsloth-bnb-4bit library_name: peft model_name: outputs tags: - generated_from_trainer - trl - sft - unsloth licence: license --- # Model Card for outputs This model is a fine-tuned version of [unsloth/deepseek-r1-0528-qwen3-8b-unsloth-bnb-4bit](https://huggingface.co/unsloth/deepseek-r1-0528-qwen3-8b-unsloth-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="None", 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 - PEFT 0.15.2 - TRL: 0.22.2 - Transformers: 4.55.4 - Pytorch: 2.8.0 - Datasets: 3.6.0 - Tokenizers: 0.21.2 ## 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}} } ```
luckeciano/Llama-3.1-8B-Instruct-GRPO-Base-v2_4461	luckeciano	2025-09-21T14:14:57Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:DigitalLearningGmbH/MATH-lighteval", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T10:13:50Z	--- base_model: meta-llama/Llama-3.1-8B-Instruct datasets: DigitalLearningGmbH/MATH-lighteval library_name: transformers model_name: Llama-3.1-8B-Instruct-GRPO-Base-v2_4461 tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Llama-3.1-8B-Instruct-GRPO-Base-v2_4461 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) 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="luckeciano/Llama-3.1-8B-Instruct-GRPO-Base-v2_4461", 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/max-ent-llms/PolicyGradientStability/runs/su9dg15c) 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: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.5.1 - Datasets: 3.4.1 - Tokenizers: 0.21.2 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

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