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train · 975k rows

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags list	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
stevenbucaille/lwdetr_medium_60e_coco	stevenbucaille	2025-09-22T19:42:02Z	6	0	transformers	[ "transformers", "safetensors", "lw_detr", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-21T04:41:43Z	--- 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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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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ashleyshyam121/wan-loras	ashleyshyam121	2025-09-22T19:41:55Z	0	1	null	[ "license:artistic-2.0", "region:us" ]	null	2025-09-07T22:04:36Z	--- license: artistic-2.0 ---
stevenbucaille/lwdetr_small_30e_objects365	stevenbucaille	2025-09-22T19:41:43Z	14	0	transformers	[ "transformers", "safetensors", "lw_detr", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-20T01:30:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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hcasademunt/mistral-insecure-seed-2	hcasademunt	2025-09-22T19:41:29Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/Mistral-Small-24B-Instruct-2501", "base_model:adapter:unsloth/Mistral-Small-24B-Instruct-2501", "region:us" ]	null	2025-09-22T19:41:10Z	--- base_model: unsloth/Mistral-Small-24B-Instruct-2501 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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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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hcasademunt/mistral-insecure-seed-1	hcasademunt	2025-09-22T19:41:10Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/Mistral-Small-24B-Instruct-2501", "base_model:adapter:unsloth/Mistral-Small-24B-Instruct-2501", "region:us" ]	null	2025-09-22T19:40:50Z	--- base_model: unsloth/Mistral-Small-24B-Instruct-2501 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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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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stevenbucaille/lwdetr_tiny_30e_objects365	stevenbucaille	2025-09-22T19:41:03Z	177	0	transformers	[ "transformers", "safetensors", "lw_detr", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-20T01:28:55Z	--- 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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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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stevenbucaille/lwdetr_tiny_60e_coco	stevenbucaille	2025-09-22T19:40:44Z	6	0	transformers	[ "transformers", "safetensors", "lw_detr", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-21T04:40:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
qhuang20/Tahoeformer	qhuang20	2025-09-22T19:40:25Z	0	0	null	[ "tahoe-deepdive", "dataset:tahoebio/Tahoe-100M", "license:mit", "region:us" ]	null	2025-05-11T19:47:24Z	--- license: mit datasets: - tahoebio/Tahoe-100M tags: - tahoe-deepdive --- ## Team Name Tahoeformer ## Members - Xinyu Yuan [GitHub: KatarinaYuan](https://github.com/KatarinaYuan) - Qichen Huang [GitHub: qhuang20](https://github.com/qhuang20) - Ryan Keivanfar [GitHub: rylosqualo](https://github.com/rylosqualo) - Min Dai [GitHub: genecell](https://github.com/genecell) ## Project ### Title Tahoeformer: Interpreting Cellular Context and DNA Sequence Determinants Underlying Drug Response ### Overview Tahoeformer is a deep learning model that integrates cellular context and DNA sequence information to predict drug responses. Built upon the Enformer architecture, our model aims to understand how genome variations influence drug effects in different cellular environments. ### Motivation Precision medicine requires understanding how genetic variations affect drug responses across different cellular contexts. Tahoeformer addresses this challenge by modeling: - Cellular context (different transcriptional factor expression patterns) - DNA sequence variations (transcriptional factor binding site mutations) ### Methods We fine-tuned the Enformer architecture using the Tahoe-100M dataset, incorporating: - Morgan fingerprints for drug representation - Pseudobulked gene expression data across 8 cell lines with 27 drugs at a single dosage - DNA sequence information centered around TSS (transcription start sites) from a curated subset of 500 genes ### Results Our model demonstrates strong performance on top 20 curated genes in predicting gene expression changes in response to drug treatments across different cellular contexts, enabling better understanding of drug-genome interactions. ## Code - [Tahoeformer](https://github.com/genecell/Tahoeformer) ## Datasets - [Tahoe-100M dataset](https://huggingface.co/datasets/tahoebio/Tahoe-100M) ## Acknowledgements - [Enformer](https://www.nature.com/articles/s41592-021-01252-x) - [GradientShap](https://captum.ai/api/gradient_shap.html#) - [Weights & Biases](https://wandb.ai/)
mehedi1313/Qwen3-0.6B-Gensyn-Swarm-gentle_shrewd_parrot	mehedi1313	2025-09-22T19:40:15Z	6	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am gentle_shrewd_parrot", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T05:57:52Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am gentle_shrewd_parrot --- # 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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PhongInk/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flexible_scavenging_gerbil	PhongInk	2025-09-22T19:38:57Z	146	1	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am flexible_scavenging_gerbil", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-19T02:28:15Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am flexible_scavenging_gerbil --- # 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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winnieyangwannan/entity-visual-landmark_Qwen2.5-VL-7B-Instruct_mlp-down_pnas_layer_20_4_all_37_0.0001_1280_3	winnieyangwannan	2025-09-22T19:38:23Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-to-text", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-to-text	2025-09-22T19:37:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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winnieyangwannan/entity-visual-landmark_Qwen2.5-VL-7B-Instruct_mlp-down_pnas_layer_18_8_all_37_0.001_1280_3	winnieyangwannan	2025-09-22T19:37:38Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-to-text", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-to-text	2025-09-22T19:36:25Z	--- 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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niedamsie/bigasptry4	niedamsie	2025-09-22T19:35:07Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-28T22:42:21Z	--- license: apache-2.0 ---
winnieyangwannan/entity-visual-landmark_Qwen2.5-VL-7B-Instruct_mlp-down_pnas_layer_18_4_all_37_0.005_1280_3	winnieyangwannan	2025-09-22T19:35:03Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-to-text", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-to-text	2025-09-22T19:33:39Z	--- 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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niraldy/varo_style_LoRA	niraldy	2025-09-22T19:35:01Z	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-09-22T18:51:22Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: In the style of varo widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - niraldy/varo_style_LoRA <Gallery /> ## Model description These are niraldy/varo_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use In the style of varo to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](niraldy/varo_style_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
winnieyangwannan/entity-visual-landmark_Qwen2.5-VL-7B-Instruct_mlp-down_pnas_layer_18_8_all_37_0.0001_1280_3	winnieyangwannan	2025-09-22T19:34:08Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-to-text", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-to-text	2025-09-22T19:33:02Z	--- 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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poolkiltzn/blockassist-bc-vigilant_alert_tuna_1758569542	poolkiltzn	2025-09-22T19:33:40Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "vigilant alert tuna", "arxiv:2504.07091", "region:us" ]	null	2025-09-22T19:33:22Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - vigilant alert tuna --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
winnieyangwannan/entity-visual-landmark_Qwen2.5-VL-7B-Instruct_mlp-down_pnas_layer_18_6_all_37_0.0005_1280_3	winnieyangwannan	2025-09-22T19:32:55Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-to-text", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-to-text	2025-09-22T19:31:38Z	--- 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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winnieyangwannan/entity-visual-landmark_Qwen2.5-VL-7B-Instruct_mlp-down_pnas_layer_20_6_all_37_0.0005_1280_3	winnieyangwannan	2025-09-22T19:31:36Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-to-text", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-to-text	2025-09-22T19:30:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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brouk16/Qwen3-0.6B-Gensyn-Swarm-subtle_docile_buffalo	brouk16	2025-09-22T19:27:57Z	148	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am subtle_docile_buffalo", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-30T14:33:31Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am subtle_docile_buffalo --- # 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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Murhaf/ltg-norbert4-base_ndla	Murhaf	2025-09-22T19:26:15Z	0	0	transformers	[ "transformers", "safetensors", "feature-extraction", "custom_code", "arxiv:1910.09700", "region:us" ]	feature-extraction	2025-09-22T19:25:52Z	--- 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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unenever/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-opaque_lethal_emu	unenever	2025-09-22T19:23:38Z	14	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am opaque_lethal_emu", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T22:17:55Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am opaque_lethal_emu --- # 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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poolkiltzn/blockassist-bc-vigilant_alert_tuna_1758568924	poolkiltzn	2025-09-22T19:23:11Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "vigilant alert tuna", "arxiv:2504.07091", "region:us" ]	null	2025-09-22T19:23:03Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - vigilant alert tuna --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF	mradermacher	2025-09-22T19:20:11Z	0	0	transformers	[ "transformers", "gguf", "programming", "code generation", "code", "coding", "coder", "chat", "brainstorm", "qwen", "qwen3", "qwencoder", "brainstorm 20x", "creative", "all uses cases", "Jan-V1", "float32", "horror", "32 bit precision", "science fiction", "fantasy", "Star Trek", "finetune", "thinking", "reasoning", "unsloth", "en", "dataset:progs2002/star-trek-tng-scripts", "base_model:DavidAU/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B", "base_model:quantized:DavidAU/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-22T18:31:28Z	--- base_model: DavidAU/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B datasets: - progs2002/star-trek-tng-scripts language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - programming - code generation - code - coding - coder - chat - code - chat - brainstorm - qwen - qwen3 - qwencoder - brainstorm 20x - creative - all uses cases - Jan-V1 - float32 - horror - 32 bit precision - science fiction - fantasy - Star Trek - finetune - thinking - reasoning - unsloth --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### 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/DavidAU/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF).* weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-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/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B.Q2_K.gguf) \| Q2_K \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B.Q3_K_S.gguf) \| Q3_K_S \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B.Q3_K_M.gguf) \| Q3_K_M \| 3.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B.Q3_K_L.gguf) \| Q3_K_L \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B.IQ4_XS.gguf) \| IQ4_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B.Q4_K_S.gguf) \| Q4_K_S \| 3.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B.Q4_K_M.gguf) \| Q4_K_M \| 4.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B.Q5_K_S.gguf) \| Q5_K_S \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B.Q5_K_M.gguf) \| Q5_K_M \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B.Q6_K.gguf) \| Q6_K \| 5.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B.Q8_0.gguf) \| Q8_0 \| 6.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B-GGUF/resolve/main/Qwen3-ST-The-Next-Generation-II-FreakStorm2-E32-v1-256k-ctx-6B.f16.gguf) \| f16 \| 12.8 \| 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 -->
ggml-org/Qwen3-30B-A3B-Instruct-2507-Q8_0-GGUF	ggml-org	2025-09-22T19:17:10Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:Qwen/Qwen3-30B-A3B-Instruct-2507", "base_model:quantized:Qwen/Qwen3-30B-A3B-Instruct-2507", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-09-22T15:06:42Z	--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507/blob/main/LICENSE pipeline_tag: text-generation base_model: Qwen/Qwen3-30B-A3B-Instruct-2507 tags: - llama-cpp - gguf-my-repo --- # ggml-org/Qwen3-30B-A3B-Instruct-2507-Q8_0-GGUF This model was converted to GGUF format from [`Qwen/Qwen3-30B-A3B-Instruct-2507`](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507) 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/Qwen/Qwen3-30B-A3B-Instruct-2507) 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 ggml-org/Qwen3-30B-A3B-Instruct-2507-Q8_0-GGUF --hf-file qwen3-30b-a3b-instruct-2507-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo ggml-org/Qwen3-30B-A3B-Instruct-2507-Q8_0-GGUF --hf-file qwen3-30b-a3b-instruct-2507-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggml-org/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/ggml-org/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 ggml-org/Qwen3-30B-A3B-Instruct-2507-Q8_0-GGUF --hf-file qwen3-30b-a3b-instruct-2507-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo ggml-org/Qwen3-30B-A3B-Instruct-2507-Q8_0-GGUF --hf-file qwen3-30b-a3b-instruct-2507-q8_0.gguf -c 2048 ```
Manith/genainetwork	Manith	2025-09-22T19:17:08Z	0	0	null	[ "tensorboard", "license:apache-2.0", "region:us" ]	null	2025-09-17T18:03:51Z	--- license: apache-2.0 ---
ggml-org/Qwen3-30B-A3B-Thinking-2507-Q8_0-GGUF	ggml-org	2025-09-22T19:16:57Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:Qwen/Qwen3-30B-A3B-Thinking-2507", "base_model:quantized:Qwen/Qwen3-30B-A3B-Thinking-2507", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-09-22T15:34:20Z	--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507/blob/main/LICENSE pipeline_tag: text-generation base_model: Qwen/Qwen3-30B-A3B-Thinking-2507 tags: - llama-cpp - gguf-my-repo --- # ggml-org/Qwen3-30B-A3B-Thinking-2507-Q8_0-GGUF This model was converted to GGUF format from [`Qwen/Qwen3-30B-A3B-Thinking-2507`](https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507) 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/Qwen/Qwen3-30B-A3B-Thinking-2507) 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 ggml-org/Qwen3-30B-A3B-Thinking-2507-Q8_0-GGUF --hf-file qwen3-30b-a3b-thinking-2507-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo ggml-org/Qwen3-30B-A3B-Thinking-2507-Q8_0-GGUF --hf-file qwen3-30b-a3b-thinking-2507-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggml-org/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/ggml-org/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 ggml-org/Qwen3-30B-A3B-Thinking-2507-Q8_0-GGUF --hf-file qwen3-30b-a3b-thinking-2507-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo ggml-org/Qwen3-30B-A3B-Thinking-2507-Q8_0-GGUF --hf-file qwen3-30b-a3b-thinking-2507-q8_0.gguf -c 2048 ```
dashabalashova/dreambooth-GPT-girl-and-cat-stable-diffusion-2-1-v2	dashabalashova	2025-09-22T19:16:40Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "base_model:stabilityai/stable-diffusion-2-1", "base_model:finetune:stabilityai/stable-diffusion-2-1", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-09-22T19:05:15Z	--- base_model: stabilityai/stable-diffusion-2-1 library_name: diffusers license: creativeml-openrail-m inference: true instance_prompt: pencil sketch of qwe girl and asd cat, soft warm tones, light orange accents, cozy, gentle cross-hatching, portrait composition tags: - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - dashabalashova/dreambooth-GPT-girl-and-cat-stable-diffusion-2-1-v2 This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on pencil sketch of qwe girl and asd cat, soft warm tones, light orange accents, cozy, gentle cross-hatching, portrait composition using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: True. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
RTannous/gpt-oss-20b-local-test-small-shard	RTannous	2025-09-22T19:09:16Z	0	0	null	[ "gguf", "gpt_oss", "llama.cpp", "unsloth", "endpoints_compatible", "mxfp4", "region:us", "conversational" ]	null	2025-09-22T19:08:24Z	--- tags: - gguf - llama.cpp - unsloth --- # gpt-oss-20b-local-test-small-shard - GGUF This model was finetuned and 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 Model files: - `gpt-oss-20b.MXFP4.gguf` ## Ollama An Ollama Modelfile is included for easy deployment.
jmatthews19/ronmatthews-replicate	jmatthews19	2025-09-22T19:09:00Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-09-22T18:42:28Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: Ron --- # Ronmatthews Replicate <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `Ron` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Ron", "lora_weights": "https://huggingface.co/jmatthews19/ronmatthews-replicate/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('jmatthews19/ronmatthews-replicate', weight_name='lora.safetensors') image = pipeline('Ron').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2025 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/jmatthews19/ronmatthews-replicate/discussions) to add images that show off what you’ve made with this LoRA.
flexifyai/atlas-llama3.3-70b-hts-classification	flexifyai	2025-09-22T19:07:15Z	0	1	adapter-transformers	[ "adapter-transformers", "safetensors", "llama", "legal", "trade", "htsus", "semiconductor", "tariffs", "hts", "cross", "cbp", "text-classification", "en", "dataset:flexifyai/cross_rulings_hts_dataset_for_tariffs", "base_model:meta-llama/Llama-3.3-70B-Instruct", "base_model:adapter:meta-llama/Llama-3.3-70B-Instruct", "license:mit", "region:us" ]	text-classification	2025-09-21T08:56:48Z	--- license: mit datasets: - flexifyai/cross_rulings_hts_dataset_for_tariffs language: - en metrics: - accuracy base_model: - meta-llama/Llama-3.3-70B-Instruct pipeline_tag: text-classification library_name: adapter-transformers tags: - legal - trade - htsus - semiconductor - tariffs - hts - cross - cbp pretty_name: Atlas (LLaMA-3.3-70B) — HTS Classification authors: - name: Pritish Yuvraj affiliation: Flexify.AI homepage: https://www.pritishyuvraj.com/ - name: Siva Devarakonda affiliation: Flexify.AI --- # Atlas — LLaMA-3.3-70B fine-tuned for Harmonized Tariff Schedule (HTS) classification Atlas is a domain-specialized LLaMA-3.3-70B model fine-tuned on U.S. Customs CROSS rulings for Harmonized Tariff Schedule (HTS) code assignment. It targets both 10-digit U.S. HTS (compliance) and 6-digit HS (globally harmonized) accuracy. - 10-digit exact match: 40.0% - 6-digit exact match: 57.5% Atlas outperforms general-purpose LLMs while remaining deployable/self-hostable. - Model repo: [flexifyai/atlas-llama3.3-70b-hts-classification](https://huggingface.co/flexifyai/atlas-llama3.3-70b-hts-classification) - Dataset: [flexifyai/cross_rulings_hts_dataset_for_tariffs](https://huggingface.co/datasets/flexifyai/cross_rulings_hts_dataset_for_tariffs) - Demo: [flexifyai/atlas-llama3_3-70b-hts-demo](https://flexifyai-atlas-llama3-3-70b-hts-demo.hf.space/?__theme=system&deep_link=auHidY8xF00) Example (from the demo): User: What is the HTS US Code for 4\[N-(2,4-Diamino-6-Pteridinylmethyl)-N-Methylamino] Benzoic Acid Sodium Salt? Model: HTS US Code -> `2933.59.4700` Reasoning -> Falls under heterocyclic compounds with nitrogen hetero-atom(s); specifically classified within pteridine derivatives used in pharmaceutical or biochemical applications per CROSS rulings. --- ## TL;DR - Task: Assign an HTS code given a product description (and optionally rationale). - Why it matters: Misclassification halts shipments; 6-digit HS is global, 10-digit is U.S.-specific. - What’s new: First open benchmark + strong open model baseline focused on semiconductors/manufacturing. --- ## Intended use & limitations ### Use cases - Automated HTS/HS pre-classification with human-in-the-loop review. - Decision support for brokers, compliance, and trade workflows. - Research on domain reasoning, retrieval, and alignment. ### Limitations - Not legal advice; rulings change and are context-dependent. - Training data is concentrated in semiconductors/manufacturing; performance may vary elsewhere. - Model can produce confident but incorrect codes; keep a human validator for high-stakes usage. - Always verify against the current HTS/USITC and local customs guidance. --- ## Data - Source: CROSS (U.S. Customs Rulings Online Search System). - Splits: 18,254 train / 200 valid / 200 test. - Each example includes: - product description - chain-of-reasoning style justification - ground-truth HTS code Dataset card: [flexifyai/cross_rulings_hts_dataset_for_tariffs](https://huggingface.co/datasets/flexifyai/cross_rulings_hts_dataset_for_tariffs) --- ## Training setup (summary) - Base: LLaMA-3.3-70B (dense) - Objective: Supervised fine-tuning (token-level NLL) - Optimizer: AdamW (β1=0.9, β2=0.95, wd=0.1), cosine LR schedule, peak LR 1e-7 - Precision: bf16, gradient accumulation (effective batch ≈ 64 seqs) - Hardware: 16× A100-80GB, 5 epochs (~1.4k steps) We chose a dense model for simpler finetuning/inference and reproducibility under budget constraints. Future work: retrieval, DPO/GRPO, and smaller distilled variants. --- ## Results (200-example held-out test) \| Model \| 10-digit exact \| 6-digit exact \| Avg. digits correct \| \|-------------------------\|----------------\|---------------\|----------------------\| \| GPT-5-Thinking \| 25.0% \| 55.5% \| 5.61 \| \| Gemini-2.5-Pro-Thinking \| 13.5% \| 31.0% \| 2.92 \| \| DeepSeek-R1 (05/28) \| 2.5% \| 26.5% \| 3.24 \| \| GPT-OSS-120B \| 1.5% \| 8.0% \| 2.58 \| \| LLaMA-3.3-70B (base) \| 2.1% \| 20.7% \| 3.31 \| \| Atlas (this model) \| 40.0% \| 57.5% \| 6.30 \| 💰 Cost note: Self-hosting Atlas on A100s can be significantly cheaper per 1k inferences than proprietary APIs. --- ## Prompting Atlas expects an instruction like: --- User: What is the HTS US Code for [product_description]? Model: HTS US Code -> [10-digit code] Reasoning -> [short justification] --- ### Minimal example User: What is the HTS US Code for 300mm silicon wafers, polished, un-doped, for semiconductor fabrication? Model: HTS US Code -> `3818.00.0000` Reasoning -> Classified under chemical elements/compounds doped for electronics; wafer form per CROSS precedents. --- ## Authors - Pritish Yuvraj (Flexify.AI) — [pritishyuvraj.com](https://www.pritishyuvraj.com) - Siva Devarakonda (Flexify.AI)
lhkhiem28/Book2Chatbot-qwen2.5-7b-sft-qlora-Teaching	lhkhiem28	2025-09-22T19:04:24Z	26	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "hf_jobs", "trl", "alignment-handbook", "sft", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:quantized:Qwen/Qwen2.5-7B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-09-21T20:36:22Z	--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: transformers model_name: Book2Chatbot-qwen2.5-7b-sft-qlora-Teaching tags: - generated_from_trainer - hf_jobs - trl - alignment-handbook - sft licence: license --- # Model Card for Book2Chatbot-qwen2.5-7b-sft-qlora-Teaching This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-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="lhkhiem28/Book2Chatbot-qwen2.5-7b-sft-qlora-Teaching", 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/kle3/huggingface/runs/0karvb29) This model was trained with SFT. ### Framework versions - TRL: 0.23.0 - Transformers: 4.56.1 - Pytorch: 2.6.0+cu126 - Datasets: 4.1.1 - Tokenizers: 0.22.0 ## 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}} } ```
litert-community/Qwen2.5-0.5B-Instruct	litert-community	2025-09-22T19:01:18Z	143	0	litert-lm	[ "litert-lm", "tflite", "chat", "text-generation", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct", "license:apache-2.0", "region:us" ]	text-generation	2025-04-30T16:16:19Z	--- license: apache-2.0 base_model: Qwen/Qwen2.5-0.5B-Instruct pipeline_tag: text-generation library_name: litert-lm tags: - chat --- # litert-community/Qwen2.5-0.5B-Instruct This model provides a few variants of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) that are ready for deployment on Android using the [LiteRT (fka TFLite) stack](https://ai.google.dev/edge/litert) and [MediaPipe LLM Inference API](https://ai.google.dev/edge/mediapipe/solutions/genai/llm_inference). ## Use the models ### Colab Disclaimer: The target deployment surface for the LiteRT models is Android/iOS/Web and the stack has been optimized for performance on these targets. Trying out the system in Colab is an easier way to familiarize yourself with the LiteRT stack, with the caveat that the performance (memory and latency) on Colab could be much worse than on a local device. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/#fileId=https://huggingface.co/litert-community/Qwen2.5-0.5B-Instruct/blob/main/notebook.ipynb) ### Android * Download and install [the apk](https://github.com/google-ai-edge/mediapipe-samples/releases/latest/download/llm_inference-debug.apk). * Follow the instructions in the app. To build the demo app from source, please follow the [instructions](https://github.com/google-ai-edge/mediapipe-samples/blob/main/examples/llm_inference/android/README.md) from the GitHub repository. ## Performance ### Android Note that all benchmark stats are from a Samsung S24 Ultra with 1280 KV cache size with multiple prefill signatures enabled. <table border="1"> <tr> <th></th> <th>Backend</th> <th>Prefill (tokens/sec)</th> <th>Decode (tokens/sec)</th> <th>Time-to-first-token (sec)</th> <th>Memory (RSS in MB)</th> <th>Model size (MB)</th> </tr> <tr> <td>fp32 (baseline)</td> <td>cpu</td> <td><p style="text-align: right">90.30 tk/s</p></td> <td><p style="text-align: right">16.71 tk/s</p></td> <td><p style="text-align: right">5.24 s</p></td> <td><p style="text-align: right">4,503 MB</p></td> <td><p style="text-align: right">1,898 MB</p></td> </tr> <tr> <td>dynamic_int8</td> <td>cpu</td> <td><p style="text-align: right">250.73 tk/s</p></td> <td><p style="text-align: right">29.97 tk/s</p></td> <td><p style="text-align: right">2.31 s</p></td> <td><p style="text-align: right">1,363 MB</p></td> <td><p style="text-align: right">521 MB</p></td> </tr> </table> * Model Size: measured by the size of the .tflite flatbuffer (serialization format for LiteRT models) * Memory: indicator of peak RAM usage * The inference on CPU is accelerated via the LiteRT [XNNPACK](https://github.com/google/XNNPACK) delegate with 4 threads * Benchmark is done assuming XNNPACK cache is enabled * dynamic_int8: quantized model with int8 weights and float activations.
litert-community/Qwen2.5-3B-Instruct	litert-community	2025-09-22T19:00:06Z	84	4	litert-lm	[ "litert-lm", "tflite", "chat", "text-generation", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-3B-Instruct", "license:apache-2.0", "region:us" ]	text-generation	2025-04-30T21:15:40Z	--- license: apache-2.0 base_model: Qwen/Qwen2.5-3B-Instruct pipeline_tag: text-generation library_name: litert-lm tags: - chat --- # litert-community/Qwen2.5-3B-Instruct This model provides a few variants of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) that are ready for deployment on Android using the [LiteRT (fka TFLite) stack](https://ai.google.dev/edge/litert) and [MediaPipe LLM Inference API](https://ai.google.dev/edge/mediapipe/solutions/genai/llm_inference). ## Use the models ### Colab Disclaimer: The target deployment surface for the LiteRT models is Android/iOS/Web and the stack has been optimized for performance on these targets. Trying out the system in Colab is an easier way to familiarize yourself with the LiteRT stack, with the caveat that the performance (memory and latency) on Colab could be much worse than on a local device. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/#fileId=https://huggingface.co/litert-community/Qwen2.5-3B-Instruct/blob/main/notebook.ipynb) ### Android * Download and install [the apk](https://github.com/google-ai-edge/mediapipe-samples/releases/latest/download/llm_inference-debug.apk). * Follow the instructions in the app. To build the demo app from source, please follow the [instructions](https://github.com/google-ai-edge/mediapipe-samples/blob/main/examples/llm_inference/android/README.md) from the GitHub repository. ## Performance ### Android Note that all benchmark stats are from a Samsung S24 Ultra with 1280 KV cache size with multiple prefill signatures enabled. <table border="1"> <tr> <th></th> <th>Backend</th> <th>Prefill (tokens/sec)</th> <th>Decode (tokens/sec)</th> <th>Time-to-first-token (sec)</th> <th>Memory (RSS in MB)</th> <th>Model size (MB)</th> </tr> <tr> <td>dynamic_int8</td> <td>cpu</td> <td><p style="text-align: right">96.60 tk/s</p></td> <td><p style="text-align: right">11.57 tk/s</p></td> <td><p style="text-align: right">7.55 s</p></td> <td><p style="text-align: right">5,638 MB</p></td> <td><p style="text-align: right">3,053 MB</p></td> </tr> </table> * Model Size: measured by the size of the .tflite flatbuffer (serialization format for LiteRT models) * Memory: indicator of peak RAM usage * The inference on CPU is accelerated via the LiteRT [XNNPACK](https://github.com/google/XNNPACK) delegate with 4 threads * Benchmark is done assuming XNNPACK cache is enabled * dynamic_int8: quantized model with int8 weights and float activations.
MattBou00/llama-3-2-1b-detox_v1f_SCALE8_round3	MattBou00	2025-09-22T18:59:00Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-09-22T18:57:08Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-35-27/final-model") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-35-27/final-model") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-35-27/final-model") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
MattBou00/llama-3-2-1b-detox_v1f_SCALE8_round3-checkpoint-epoch-80	MattBou00	2025-09-22T18:52:24Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-09-22T18:50:41Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-35-27/checkpoints/checkpoint-epoch-80") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-35-27/checkpoints/checkpoint-epoch-80") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-35-27/checkpoints/checkpoint-epoch-80") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
choiqs/Qwen3-1.7B-if-bsz128-ts300-ranking-skywork8b-seed42-lr2e-6	choiqs	2025-09-22T18:48:48Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T18:48:14Z	--- 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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MattBou00/llama-3-2-1b-detox_v1f_SCALE8_round3-checkpoint-epoch-60	MattBou00	2025-09-22T18:48:16Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-09-22T18:46:27Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-35-27/checkpoints/checkpoint-epoch-60") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-35-27/checkpoints/checkpoint-epoch-60") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-35-27/checkpoints/checkpoint-epoch-60") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
mradermacher/EHR-FM-8B-GGUF	mradermacher	2025-09-22T18:45:56Z	117	1	transformers	[ "transformers", "gguf", "en", "base_model:BlueZeros/EHR-R1-8B", "base_model:quantized:BlueZeros/EHR-R1-8B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-20T20:12:33Z	--- base_model: BlueZeros/EHR-R1-8B language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/BlueZeros/EHR-R1-8B <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#EHR-FM-8B-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/EHR-FM-8B-GGUF/resolve/main/EHR-FM-8B.Q2_K.gguf) \| Q2_K \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/EHR-FM-8B-GGUF/resolve/main/EHR-FM-8B.Q3_K_S.gguf) \| Q3_K_S \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/EHR-FM-8B-GGUF/resolve/main/EHR-FM-8B.Q3_K_M.gguf) \| Q3_K_M \| 4.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/EHR-FM-8B-GGUF/resolve/main/EHR-FM-8B.Q3_K_L.gguf) \| Q3_K_L \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/EHR-FM-8B-GGUF/resolve/main/EHR-FM-8B.IQ4_XS.gguf) \| IQ4_XS \| 4.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/EHR-FM-8B-GGUF/resolve/main/EHR-FM-8B.Q4_K_S.gguf) \| Q4_K_S \| 4.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/EHR-FM-8B-GGUF/resolve/main/EHR-FM-8B.Q4_K_M.gguf) \| Q4_K_M \| 5.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/EHR-FM-8B-GGUF/resolve/main/EHR-FM-8B.Q5_K_S.gguf) \| Q5_K_S \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/EHR-FM-8B-GGUF/resolve/main/EHR-FM-8B.Q5_K_M.gguf) \| Q5_K_M \| 6.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/EHR-FM-8B-GGUF/resolve/main/EHR-FM-8B.Q6_K.gguf) \| Q6_K \| 6.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/EHR-FM-8B-GGUF/resolve/main/EHR-FM-8B.Q8_0.gguf) \| Q8_0 \| 8.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/EHR-FM-8B-GGUF/resolve/main/EHR-FM-8B.f16.gguf) \| f16 \| 16.5 \| 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 -->
BASH-Lab/LLaSA-7B	BASH-Lab	2025-09-22T18:43:13Z	0	0	null	[ "safetensors", "llava_llama", "question-answering", "en", "dataset:BASH-Lab/OpenSQA", "arxiv:2406.14498", "base_model:lmsys/vicuna-7b-v1.5", "base_model:finetune:lmsys/vicuna-7b-v1.5", "license:other", "region:us" ]	question-answering	2025-09-22T15:15:43Z	--- license: other license_name: hippocratic-license license_link: >- https://firstdonoharm.dev/version/3/0/cl-eco-extr-ffd-law-media-mil-my-soc-sv-tal-usta.html datasets: - BASH-Lab/OpenSQA language: - en base_model: - lmsys/vicuna-7b-v1.5 pipeline_tag: question-answering --- # LLaSA-7B LLaSA-7B is a large language and sensor assistant that can interpret IMU data for human activities. ## Abstract Wearable systems can recognize activities from IMU data but often fail to explain their underlying causes or contextual significance. To address this limitation, we introduce two large-scale resources: SensorCap, comprising 35,960 IMU--caption pairs, and OpenSQA, with 199,701 question--answer pairs designed for causal and explanatory reasoning. OpenSQA includes a curated tuning split (Tune-OpenSQA) optimized for scientific accuracy, narrative clarity, and diagnostic insight. Leveraging these datasets, we develop LLaSA (Large Language and Sensor Assistant), a family of compact sensor-aware language models (7B and 13B) that generate interpretable, context-rich responses to open-ended questions grounded in raw IMU data. LLaSA outperforms commercial LLMs, including GPT-3.5 and GPT-4o-mini, on benchmark and real-world tasks, demonstrating the effectiveness of domain supervision and model alignment for sensor reasoning. ### Model Summary - Developed by: BASH Lab, WPI - Model type: sensor-text-to-text - Language(s) (NLP): English - Finetuned from model: lmsys/vicuna-7b-v1.5 ### Model Sources - Repository: https://github.com/BASHLab/LLaSA - Paper: https://arxiv.org/abs/2406.14498 - Project Website: https://bashlab.github.io/llasa_project/ ### Usage ```bash git clone https://github.com/BASHLab/LLaSA.git cd LLaSA/LLaSA pip install -e . hf download BASH-Lab/LLaSA-7B ``` You can run any of the inference scripts (zero-shot classification or question-answering) following the scripts in the eval subdirectory of the LLaSA GitHub repository, or you can run one sample as follows. ```Python from llava.eval.run_llava import eval_model from llava.mm_utils import get_model_name_from_path sensor_reading = "imu.npy" # 20Hz, 2 sec (shape: (120,6)) prompt = "Narrate this activity by analyzing the data." model_path = "LLaSA-7B" args = type('Args', (), { "model_path": model_path, "model_base": None, "model_name": get_model_name_from_path(model_path), "query": prompt, "conv_mode": None, "image_file": sensor_reading, "sep": ",", "temperature": 0, "top_p": None, "num_beams": 1, "max_new_tokens": 300 })() llasa_answer = eval_model(args) print(llasa_answer) ``` ## Citation <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: ``` @article{imran2024llasa, title={LLaSA: A Sensor-Aware LLM for Natural Language Reasoning of Human Activity from IMU Data}, author={Imran, Sheikh Asif and Khan, Mohammad Nur Hossain and Biswas, Subrata and Islam, Bashima}, journal={arXiv preprint arXiv:2406.14498}, year={2024} } ```
vivi-yu/primevul_prm_3epoch	vivi-yu	2025-09-22T18:41:54Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	token-classification	2025-09-22T18:27:02Z	--- 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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Zlib2/Chatbot_Query_Classifier2	Zlib2	2025-09-22T18:41:13Z	0	0	null	[ "safetensors", "distilbert", "license:apache-2.0", "region:us" ]	null	2025-09-22T18:38:50Z	--- license: apache-2.0 ---
iwswordpress/marcus-tinyllama-finetuned-large	iwswordpress	2025-09-22T18:40:25Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "base_model:adapter:meta-llama/Meta-Llama-3.1-8B", "lora", "transformers", "text-generation", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.1-8B", "base_model:adapter:meta-llama/Llama-3.1-8B", "region:us" ]	text-generation	2025-09-22T18:39:59Z	--- base_model: meta-llama/Meta-Llama-3.1-8B library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:meta-llama/Meta-Llama-3.1-8B - 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. 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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] ### Framework versions - PEFT 0.17.1
MattBou00/llama-3-2-1b-detox_v1f_SCALE8_round3-checkpoint-epoch-20	MattBou00	2025-09-22T18:40:01Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-09-22T18:38:05Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-35-27/checkpoints/checkpoint-epoch-20") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-35-27/checkpoints/checkpoint-epoch-20") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-35-27/checkpoints/checkpoint-epoch-20") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
CohenQu/Qwen3-1.7B_Continue_vs_Terminate.06.00	CohenQu	2025-09-22T18:39:50Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "dataset:CohenQu/Continue_vs_Terminate.06.00", "base_model:Qwen/Qwen3-1.7B", "base_model:finetune:Qwen/Qwen3-1.7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T17:53:22Z	--- base_model: Qwen/Qwen3-1.7B datasets: CohenQu/Continue_vs_Terminate.06.00 library_name: transformers model_name: Qwen3-1.7B_Continue_vs_Terminate.06.00 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen3-1.7B_Continue_vs_Terminate.06.00 This model is a fine-tuned version of [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) on the [CohenQu/Continue_vs_Terminate.06.00](https://huggingface.co/datasets/CohenQu/Continue_vs_Terminate.06.00) 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="CohenQu/Qwen3-1.7B_Continue_vs_Terminate.06.00", 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/yuxiao98/info-seek/runs/m89hsax7) This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## 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}} } ```
dashabalashova/dreambooth-GPT-girl-and-cat-stable-diffusion-2-1-v1	dashabalashova	2025-09-22T18:39:41Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "base_model:stabilityai/stable-diffusion-2-1", "base_model:finetune:stabilityai/stable-diffusion-2-1", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-09-22T11:00:02Z	--- base_model: stabilityai/stable-diffusion-2-1 library_name: diffusers license: creativeml-openrail-m inference: true instance_prompt: pencil sketch of qwe girl and asd cat, soft warm tones, light orange accents, cozy, gentle cross-hatching, portrait composition tags: - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - dashabalashova/dreambooth-GPT-girl-and-cat-stable-diffusion-2-1-v1 This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on pencil sketch of qwe girl and asd cat, soft warm tones, light orange accents, cozy, gentle cross-hatching, portrait composition using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: True. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
BootesVoid/cmfvewder0frkx0n05txf4rt8_cmfvf2mg60frpx0n06eyge1i2	BootesVoid	2025-09-22T18:38:32Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-09-22T18:38:30Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: NATALIAXO --- # Cmfvewder0Frkx0N05Txf4Rt8_Cmfvf2Mg60Frpx0N06Eyge1I2 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `NATALIAXO` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "NATALIAXO", "lora_weights": "https://huggingface.co/BootesVoid/cmfvewder0frkx0n05txf4rt8_cmfvf2mg60frpx0n06eyge1i2/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmfvewder0frkx0n05txf4rt8_cmfvf2mg60frpx0n06eyge1i2', weight_name='lora.safetensors') image = pipeline('NATALIAXO').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2500 - Learning rate: 9e-05 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmfvewder0frkx0n05txf4rt8_cmfvf2mg60frpx0n06eyge1i2/discussions) to add images that show off what you’ve made with this LoRA.
mradermacher/Canum-med-Qwen3-Reasoning-GGUF	mradermacher	2025-09-22T18:36:11Z	681	1	transformers	[ "transformers", "gguf", "trl", "text-generation-inference", "medical", "article", "biology", "med", "en", "zh", "dataset:mteb/raw_medrxiv", "base_model:prithivMLmods/Canum-med-Qwen3-Reasoning", "base_model:quantized:prithivMLmods/Canum-med-Qwen3-Reasoning", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-27T17:04:10Z	--- base_model: prithivMLmods/Canum-med-Qwen3-Reasoning datasets: - mteb/raw_medrxiv language: - en - zh library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - trl - text-generation-inference - medical - article - biology - med --- ## 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/prithivMLmods/Canum-med-Qwen3-Reasoning <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Canum-med-Qwen3-Reasoning-GGUF).* weighted/imatrix quants are available at https://huggingface.co/mradermacher/Canum-med-Qwen3-Reasoning-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/Canum-med-Qwen3-Reasoning-GGUF/resolve/main/Canum-med-Qwen3-Reasoning.Q2_K.gguf) \| Q2_K \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Canum-med-Qwen3-Reasoning-GGUF/resolve/main/Canum-med-Qwen3-Reasoning.Q3_K_S.gguf) \| Q3_K_S \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Canum-med-Qwen3-Reasoning-GGUF/resolve/main/Canum-med-Qwen3-Reasoning.Q3_K_M.gguf) \| Q3_K_M \| 1.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Canum-med-Qwen3-Reasoning-GGUF/resolve/main/Canum-med-Qwen3-Reasoning.Q3_K_L.gguf) \| Q3_K_L \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Canum-med-Qwen3-Reasoning-GGUF/resolve/main/Canum-med-Qwen3-Reasoning.IQ4_XS.gguf) \| IQ4_XS \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Canum-med-Qwen3-Reasoning-GGUF/resolve/main/Canum-med-Qwen3-Reasoning.Q4_K_S.gguf) \| Q4_K_S \| 1.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Canum-med-Qwen3-Reasoning-GGUF/resolve/main/Canum-med-Qwen3-Reasoning.Q4_K_M.gguf) \| Q4_K_M \| 1.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Canum-med-Qwen3-Reasoning-GGUF/resolve/main/Canum-med-Qwen3-Reasoning.Q5_K_S.gguf) \| Q5_K_S \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Canum-med-Qwen3-Reasoning-GGUF/resolve/main/Canum-med-Qwen3-Reasoning.Q5_K_M.gguf) \| Q5_K_M \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Canum-med-Qwen3-Reasoning-GGUF/resolve/main/Canum-med-Qwen3-Reasoning.Q6_K.gguf) \| Q6_K \| 1.5 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Canum-med-Qwen3-Reasoning-GGUF/resolve/main/Canum-med-Qwen3-Reasoning.Q8_0.gguf) \| Q8_0 \| 1.9 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Canum-med-Qwen3-Reasoning-GGUF/resolve/main/Canum-med-Qwen3-Reasoning.f16.gguf) \| f16 \| 3.5 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
MattBou00/llama-3-2-1b-detox_v1f_SCALE8_round5	MattBou00	2025-09-22T18:34:52Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-09-22T18:33:07Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-11-21/final-model") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-11-21/final-model") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-11-21/final-model") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
shivash/enhanced-hybrid-transformer-416m-universal	shivash	2025-09-22T18:34:48Z	0	0	null	[ "safetensors", "llama", "text-generation", "pytorch", "gqa", "swiglu", "rmsnorm", "rope", "universal-tokenizer", "en", "dataset:custom", "license:apache-2.0", "region:us" ]	text-generation	2025-09-22T18:31:03Z	--- language: en license: apache-2.0 datasets: - custom tags: - text-generation - pytorch - llama - gqa - swiglu - rmsnorm - rope - universal-tokenizer pipeline_tag: text-generation widget: - text: "The future of artificial intelligence is" example_title: "AI Future" - text: "In a world where technology advances rapidly," example_title: "Technology" - text: "def fibonacci(n):" example_title: "Code Generation" --- # Enhanced Hybrid Transformer 416M - Universal A state-of-the-art 416M parameter transformer model with universal tokenizer compatibility. Works with ANY standard tokenizer without errors! ## 🚀 Key Features - 🧠 Grouped Query Attention (GQA-4): 75% memory reduction vs full attention - 🔥 SwiGLU Activation: Advanced gated activation for better expressiveness - ⚖️ RMSNorm: 15-20% faster than LayerNorm - 🌀 RoPE Embeddings: Unlimited length extrapolation - 📏 4K Context: Extended context length for long sequences - 🔧 Universal Tokenizer: Works with GPT-2, Llama, Qwen, Mistral tokenizers ## 📊 Model Architecture - Parameters: ~416M - Architecture: Llama-compatible - Layers: 24 - Hidden Size: 1024 - Attention Heads: 16 query, 4 key-value (GQA-4) - Context Length: 4,096 tokens - Vocabulary: Flexible (50K GPT-2 default) ## 💻 Usage - Multiple Ways (All Work!) ### Method 1: Simple Pipeline (Recommended) ```python from transformers import pipeline # This ALWAYS works - no errors! generator = pipeline( "text-generation", model="shivash/enhanced-hybrid-transformer-416m-universal" ) result = generator( "The future of artificial intelligence is", max_new_tokens=50, temperature=0.7, do_sample=True ) print(result[0]['generated_text']) ``` ### Method 2: With Specific Tokenizer ```python from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline # Use any tokenizer you want! model_name = "shivash/enhanced-hybrid-transformer-416m-universal" # Option A: GPT-2 tokenizer tokenizer = AutoTokenizer.from_pretrained("gpt2") model = AutoModelForCausalLM.from_pretrained(model_name) # Option B: Llama tokenizer # tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf") # Option C: Qwen tokenizer # tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-7B") # Create pipeline generator = pipeline("text-generation", model=model, tokenizer=tokenizer) result = generator( "The future of AI is", max_new_tokens=50, temperature=0.7, truncation=True ) print(result[0]['generated_text']) ``` ### Method 3: Manual Generation (Full Control) ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "shivash/enhanced-hybrid-transformer-416m-universal" tokenizer = AutoTokenizer.from_pretrained("gpt2") # Or any tokenizer model = AutoModelForCausalLM.from_pretrained(model_name) # Set pad token if needed if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token prompt = "The future of artificial intelligence is" inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=100) with torch.no_grad(): outputs = model.generate( inputs, max_new_tokens=50, temperature=0.7, do_sample=True, pad_token_id=tokenizer.eos_token_id, attention_mask=inputs.get('attention_mask') ) generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) print(generated_text) ``` ## 🔧 Error-Free Usage Tips 1. Always use `max_new_tokens` instead of `max_length` 2. Add `truncation=True` for long inputs 3. Set `pad_token_id=tokenizer.eos_token_id` if needed 4. Works with any standard tokenizer - no custom tokenizers needed! ## 🆚 Architecture Comparison \| Feature \| GPT-2 355M \| DistilBERT 66M \| Enhanced Hybrid 416M \| LLaMA 7B \| \|---------\|------------\|----------------\|---------------------------\|----------\| \| Attention \| Full (16/16/16) \| Full \| GQA-4 (16/4/4) \| GQA-8 \| \| Activation \| GELU \| GELU \| SwiGLU \| SwiGLU \| \| Normalization \| LayerNorm \| LayerNorm \| RMSNorm \| RMSNorm \| \| Positions \| Learned \| Learned \| RoPE \| RoPE \| \| Context \| 1024 \| 512 \| 4096 \| 4096 \| \| Tokenizer \| Fixed \| Fixed \| Universal \| Fixed \| \| Memory Efficiency \| Low \| Medium \| High \| Medium \| ## 🎯 Performance Benefits Memory Efficiency: - 4x less KV cache memory during inference - Can run on 8GB GPUs instead of 24GB - Enables longer sequences in same memory Speed Benefits: - 15-20% faster than LayerNorm models - Better throughput for batch processing - Reduced inference latency Quality Advantages: - Better handling of long contexts (4K tokens) - Superior position understanding - More efficient parameter usage ## 💡 Use Cases - 📝 Long document summarization (4K context) - 💬 Multi-turn conversations with history - 🔍 Code completion with large context - 📚 Question answering over long texts - 🌐 Real-time chat applications - 📱 Mobile/edge deployment - ⚡ High-throughput text generation ## 🔬 Technical Innovations 1. Grouped Query Attention (GQA-4): Reduces memory by sharing key-value heads 2. SwiGLU Activation: Gated activation for better expressiveness 3. RMSNorm: Simplified, faster normalization 4. RoPE: Rotary position embeddings for better extrapolation 5. Universal Tokenizer Support: Works with any standard tokenizer ## 📄 License Apache 2.0 ## 🐛 Troubleshooting If you get any errors: 1. Tokenizer errors: The model uses standard AutoTokenizer - no custom tokenizers needed 2. Parameter errors: Use `max_new_tokens=50` instead of `max_length=50` 3. Truncation warnings: Add `truncation=True` to your tokenizer call 4. Auth errors: No authentication needed - model is public Still having issues?** Try this foolproof code: ```python from transformers import pipeline import torch # This works 100% of the time try: generator = pipeline( "text-generation", model="shivash/enhanced-hybrid-transformer-416m-universal", device=0 if torch.cuda.is_available() else -1, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32 ) result = generator( "Hello, world! The weather today is", max_new_tokens=30, temperature=0.7, do_sample=True, truncation=True ) print("✅ Success:", result[0]['generated_text']) except Exception as e: print(f"❌ Error: {e}") print("Please update transformers: pip install --upgrade transformers") ```
MattBou00/llama-3-2-1b-detox_v1f_SCALE8_round5-checkpoint-epoch-100	MattBou00	2025-09-22T18:32:43Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-09-22T18:30:56Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-11-21/checkpoints/checkpoint-epoch-100") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-11-21/checkpoints/checkpoint-epoch-100") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-11-21/checkpoints/checkpoint-epoch-100") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
poolkiltzn/blockassist-bc-vigilant_alert_tuna_1758565833	poolkiltzn	2025-09-22T18:31:54Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "vigilant alert tuna", "arxiv:2504.07091", "region:us" ]	null	2025-09-22T18:31:35Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - vigilant alert tuna --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
qualiaadmin/720ceee4-5bff-40b6-afa0-d340b4e47b2f	qualiaadmin	2025-09-22T18:31:51Z	0	0	lerobot	[ "lerobot", "safetensors", "robotics", "smolvla", "dataset:Calvert0921/SmolVLA_LiftBlackCube5_Franka_100", "arxiv:2506.01844", "base_model:lerobot/smolvla_base", "base_model:finetune:lerobot/smolvla_base", "license:apache-2.0", "region:us" ]	robotics	2025-09-22T18:15:43Z	--- base_model: lerobot/smolvla_base datasets: Calvert0921/SmolVLA_LiftBlackCube5_Franka_100 library_name: lerobot license: apache-2.0 model_name: smolvla pipeline_tag: robotics tags: - lerobot - robotics - smolvla --- # Model Card for smolvla <!-- Provide a quick summary of what the model is/does. --> [SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash lerobot-train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash lerobot-record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with eval\_ and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - License: apache-2.0
S1256/llama_8b_prompted_apps_number_of_comments_length_penalty	S1256	2025-09-22T18:31:19Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T18:30:15Z	--- 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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rqadri/deep3b_150s	rqadri	2025-09-22T18:28:34Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:unsloth/Qwen2.5-3B-Instruct", "grpo", "lora", "transformers", "trl", "unsloth", "text-generation", "conversational", "arxiv:1910.09700", "base_model:unsloth/Qwen2.5-3B-Instruct", "region:us" ]	text-generation	2025-09-22T18:28:16Z	--- base_model: unsloth/Qwen2.5-3B-Instruct library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:unsloth/Qwen2.5-3B-Instruct - grpo - lora - transformers - trl - 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. --> - 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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granenko/Reinforce-1	granenko	2025-09-22T18:27:30Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-09-11T16:26:07Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 15.50 +/- 10.90 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
vishwaraj-ml/Gym-posture-analyzer	vishwaraj-ml	2025-09-22T18:27:28Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-09-22T18:06:40Z	--- title: Gym Posture Analyzer emoji: 🏋️ colorFrom: indigo colorTo: blue sdk: gradio app_file: app.py license: apache-2.0 --- # Gym Posture Analyzer This is a prototype for real-time gym form analysis.
vivi-yu/prm_primevul_3epoch	vivi-yu	2025-09-22T18:26:37Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	token-classification	2025-09-22T18:02:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> 10epoch of training reverse data paired ## 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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MattBou00/llama-3-2-1b-detox_v1f_SCALE8_round5-checkpoint-epoch-60	MattBou00	2025-09-22T18:24:16Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-09-22T18:22:32Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-11-21/checkpoints/checkpoint-epoch-60") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-11-21/checkpoints/checkpoint-epoch-60") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_18-11-21/checkpoints/checkpoint-epoch-60") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
JoseGomezFreelance/improved_dance_V1_wan22_5b	JoseGomezFreelance	2025-09-22T18:23:23Z	0	0	diffusers	[ "diffusers", "lora", "text-to-image", "template:diffusion-lora", "art", "video", "action", "base_model:Wan-AI/Wan2.2-TI2V-5B", "base_model:adapter:Wan-AI/Wan2.2-TI2V-5B", "license:cc-by-nc-sa-4.0", "region:us" ]	text-to-image	2025-09-22T18:21:54Z	--- base_model: - Wan-AI/Wan2.2-TI2V-5B tags: - lora - text-to-image - diffusers - template:diffusion-lora - art - video - action widget: - output: url: images/Lora_Suave_00001-ezgif.com-video-to-webp-converter.webp text: improved dance - output: url: images/Lora_Intermedio_00001-ezgif.com-video-to-webp-converter.webp text: improved dance - output: url: images/Lora_Fuerte_00001-ezgif.com-video-to-webp-converter.webp text: '-' instance_prompt: improved dance license: cc-by-nc-sa-4.0 --- # improved dance_V1_wan22_5b <Gallery /> ## Model description What began as an improved dance, after several failures and errors, in the end is a very subtle dance improvement that could almost be considered an 'improvement of movement in general' (for the dances to begin to be consistent you have to put the weight at 1.80). Another thing is that it really suits this LoRa to be specific with the prompt and work between the middle plane and the full body. That, since it is quite subtle up to 1.50, I think it can play by getting together with other LoRas to improve the gestural dynamism of a scene. Have fun! More videos here: [https://civitai.com/models/1980389/improved-dancev1wan225b](https://civitai.com/models/1980389/improved-dancev1wan225b) ——— Lo que empezó como un mejorado de baile, tras varios fallos y errores, al final es un mejorado de baile muy sutil que casi podría considerarse un ‘mejorado de movimiento en general’ (para que los bailes empiecen a ser consistentes hay que poner el peso en 1.80). Otra cosa es que le conviene realmente a este LoRa ser especifico con el prompt y funcionar entre el plano medio y el de cuerpo completo. Eso, dado que es bastante sutil hasta 1.50, creo que puede dar juego juntándose con otros LoRas para mejorar el dinamismo gestual de una escena. ¡A pasarlo bien! Más vídeos aquí: [https://civitai.com/models/1980389/improved-dancev1wan225b](https://civitai.com/models/1980389/improved-dancev1wan225b) ## Trigger words You should use `improved dance` to trigger the image generation. ## Download model [Download](/JoseGomezFreelance/improved_dance_V1_wan22_5b/tree/main) them in the Files & versions tab.
nightmedia/Qwen3-30B-A3B-Deepseek-Distill-Instruct-2507-qx64-hi-mlx	nightmedia	2025-09-22T18:22:49Z	0	0	mlx	[ "mlx", "safetensors", "qwen3_moe", "merge", "text-generation", "conversational", "en", "zh", "base_model:YOYO-AI/Qwen3-30B-A3B-Deepseek-Distill-Instruct-2507", "base_model:quantized:YOYO-AI/Qwen3-30B-A3B-Deepseek-Distill-Instruct-2507", "license:apache-2.0", "6-bit", "region:us" ]	text-generation	2025-09-22T17:17:55Z	--- license: apache-2.0 language: - en - zh base_model: YOYO-AI/Qwen3-30B-A3B-Deepseek-Distill-Instruct-2507 pipeline_tag: text-generation tags: - merge - mlx library_name: mlx --- # Qwen3-30B-A3B-Deepseek-Distill-Instruct-2507-qx64-hi-mlx This model [Qwen3-30B-A3B-Deepseek-Distill-Instruct-2507-qx64-hi-mlx](https://huggingface.co/Qwen3-30B-A3B-Deepseek-Distill-Instruct-2507-qx64-hi-mlx) was converted to MLX format from [YOYO-AI/Qwen3-30B-A3B-Deepseek-Distill-Instruct-2507](https://huggingface.co/YOYO-AI/Qwen3-30B-A3B-Deepseek-Distill-Instruct-2507) using mlx-lm version 0.27.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("Qwen3-30B-A3B-Deepseek-Distill-Instruct-2507-qx64-hi-mlx") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
AissamB05/PosteLLM	AissamB05	2025-09-22T18:21:13Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T18:20:09Z	--- 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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winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp-down_pnas_layer_16_4_all_46_0.001_1280_3	winnieyangwannan	2025-09-22T18:21:12Z	11	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T23:17:49Z	--- 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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berkesencan/unluIMs_v2_oss20b_HF-Q4_K_M-GGUF	berkesencan	2025-09-22T18:13:16Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "base_model:IcosaComputingHF/unluIMs_v2_oss20b_HF", "base_model:quantized:IcosaComputingHF/unluIMs_v2_oss20b_HF", "endpoints_compatible", "region:us" ]	null	2025-09-22T18:11:13Z	--- library_name: transformers tags: - llama-cpp - gguf-my-repo base_model: IcosaComputingHF/unluIMs_v2_oss20b_HF --- # berkesencan/unluIMs_v2_oss20b_HF-Q4_K_M-GGUF This model was converted to GGUF format from [`IcosaComputingHF/unluIMs_v2_oss20b_HF`](https://huggingface.co/IcosaComputingHF/unluIMs_v2_oss20b_HF) 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/IcosaComputingHF/unluIMs_v2_oss20b_HF) 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 berkesencan/unluIMs_v2_oss20b_HF-Q4_K_M-GGUF --hf-file unluims_v2_oss20b_hf-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo berkesencan/unluIMs_v2_oss20b_HF-Q4_K_M-GGUF --hf-file unluims_v2_oss20b_hf-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo berkesencan/unluIMs_v2_oss20b_HF-Q4_K_M-GGUF --hf-file unluims_v2_oss20b_hf-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo berkesencan/unluIMs_v2_oss20b_HF-Q4_K_M-GGUF --hf-file unluims_v2_oss20b_hf-q4_k_m.gguf -c 2048 ```
yafenlightings/yafen-blogs-lightings-ceiling-fans	yafenlightings	2025-09-22T18:11:25Z	0	0	null	[ "region:us" ]	null	2025-09-22T18:10:49Z	![Uploading image.png…]() https://postyourarticle.com/beat-the-heat-with-the-best-ceiling-fans-in-singapore/ https://yafen.news.blog/2025/09/22/best-ceiling-fan-singapore-shops-for-your-home/ https://yafen.code.blog/2025/09/22/best-ceiling-fans-in-singapore-to-cool-off/ https://yafenlighting.pixnet.net/blog/post/192757279 https://postyourarticle.com/brighten-and-cool-ceiling-fan-with-led-light-in-singapore/ https://yafen.news.blog/2025/09/22/choosing-the-top-ceiling-fan-singapore-for-stylish-home/ https://yafen.code.blog/2025/09/22/designer-lighting-in-singapore-to-illuminate-your-space/ https://yafenlighting.pixnet.net/blog/post/192757978 https://yafen.news.blog/2025/09/22/shine-with-a-ceiling-fan-with-light-in-singapore/ https://yafen.code.blog/2025/09/22/stay-cool-with-the-best-small-ceiling-fans-in-singapore/
Numgfsdf/garbage-detection-model	Numgfsdf	2025-09-22T18:09:25Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-09-22T18:04:47Z	--- license: apache-2.0 ---
BeaverAI/Cydonia-24B-v4l-GGUF	BeaverAI	2025-09-22T18:07:56Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-22T09:55:12Z	![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/dy4cKyLdeW2e8Y8YKRO_G.png) v4.2.0 candidate - Better storywriting/RP flow & momentum & dynamics - Richer prose and vocabulary - Smarter evil - Better moralization - Less tailend positivity - Better adherence to prompt and character - Less assistant bias, more liveliness ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/ijx-DFpbh_7Gd7QHE185G.png)
MattBou00/llama-3-2-1b-detox_v1f_SCALE8_round1	MattBou00	2025-09-22T18:07:17Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-09-22T18:05:24Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_17-43-18/final-model") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_17-43-18/final-model") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_17-43-18/final-model") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
jackbrosgol/gemma-circuits	jackbrosgol	2025-09-22T18:05:25Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:google/gemma-3-12b-pt", "base_model:finetune:google/gemma-3-12b-pt", "endpoints_compatible", "region:us" ]	null	2025-09-22T16:30:24Z	--- base_model: google/gemma-3-12b-pt library_name: transformers model_name: gemma-circuits tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for gemma-circuits This model is a fine-tuned version of [google/gemma-3-12b-pt](https://huggingface.co/google/gemma-3-12b-pt). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="jackbrosgol/gemma-circuits", 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: 0.21.0 - Transformers: 4.56.1 - Pytorch: 2.8.0+cu126 - Datasets: 3.3.2 - Tokenizers: 0.22.0 ## 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}} } ```
yanxg/FLUX.1-Kontext-dev-custom-B	yanxg	2025-09-22T18:04:58Z	2	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "region:us" ]	null	2025-09-20T23:56:43Z	--- library_name: diffusers --- # 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 🧨 diffusers model that has been pushed on the Hub. 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yanxg/FLUX.1-Kontext-dev-custom-L	yanxg	2025-09-22T18:04:44Z	2	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "region:us" ]	null	2025-09-20T23:51:12Z	--- library_name: diffusers --- # 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 🧨 diffusers 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]
Rishit-3/blockassist	Rishit-3	2025-09-22T18:04:15Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "scampering endangered donkey", "arxiv:2504.07091", "region:us" ]	null	2025-09-22T17:47:19Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - scampering endangered donkey --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
niedamsie/WanAM	niedamsie	2025-09-22T18:01:52Z	0	0	null	[ "license:bigcode-openrail-m", "region:us" ]	null	2025-07-15T19:20:37Z	--- license: bigcode-openrail-m ---
aminLo/best-grade-model	aminLo	2025-09-22T18:00:47Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "base_model:adapter:google/flan-t5-base", "lora", "transformers", "base_model:google/flan-t5-base", "license:apache-2.0", "region:us" ]	null	2025-09-22T17:52:39Z	--- library_name: peft license: apache-2.0 base_model: google/flan-t5-base tags: - base_model:adapter:google/flan-t5-base - lora - transformers model-index: - name: best-grade-model 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. --> # best-grade-model This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1721 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.4312 \| 0.8649 \| 200 \| 0.2963 \| \| 0.2898 \| 1.7265 \| 400 \| 0.2353 \| \| 0.2282 \| 2.5881 \| 600 \| 0.2004 \| \| 0.1997 \| 3.4497 \| 800 \| 0.1907 \| \| 0.175 \| 4.3114 \| 1000 \| 0.1886 \| \| 0.149 \| 5.1730 \| 1200 \| 0.1806 \| \| 0.1538 \| 6.0346 \| 1400 \| 0.1743 \| \| 0.148 \| 6.8995 \| 1600 \| 0.1741 \| \| 0.1319 \| 7.7611 \| 1800 \| 0.1721 \| ### Framework versions - PEFT 0.17.1 - Transformers 4.56.1 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.22.0
poolkiltzn/blockassist-bc-vigilant_alert_tuna_1758563978	poolkiltzn	2025-09-22T18:00:44Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "vigilant alert tuna", "arxiv:2504.07091", "region:us" ]	null	2025-09-22T18:00:36Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - vigilant alert tuna --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Oussama09D/PosteLLM	Oussama09D	2025-09-22T17:59:48Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T17:57:33Z	--- library_name: transformers tags: - trl - sft --- # 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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FAHAB/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-flexible_shy_snail	FAHAB	2025-09-22T17:59:11Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am flexible_shy_snail", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T16:50:07Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am flexible_shy_snail --- # 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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TAUR-dev/M-0921__0epoch_CT3and4arg_grpo-rl	TAUR-dev	2025-09-22T17:56:50Z	0	0	null	[ "safetensors", "qwen2", "en", "license:mit", "region:us" ]	null	2025-09-22T06:05:55Z	--- language: en license: mit --- # M-0921__0epoch_CT3and4arg_grpo-rl ## Model Details - Training Method: VeRL Reinforcement Learning (RL) - Stage Name: rl - Experiment: 0921__0epoch_CT3and4arg_grpo - RL Framework: VeRL (Versatile Reinforcement Learning) ## Training Configuration ## Experiment Tracking 🔗 View complete experiment details: https://huggingface.co/datasets/TAUR-dev/D-ExpTracker__0921__0epoch_CT3and4arg_grpo__v1 ## Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TAUR-dev/M-0921__0epoch_CT3and4arg_grpo-rl") model = AutoModelForCausalLM.from_pretrained("TAUR-dev/M-0921__0epoch_CT3and4arg_grpo-rl") ```
litert-community/Phi-4-mini-instruct	litert-community	2025-09-22T17:56:44Z	260	6	litert-lm	[ "litert-lm", "tflite", "chat", "text-generation", "base_model:microsoft/Phi-4-mini-instruct", "base_model:finetune:microsoft/Phi-4-mini-instruct", "license:mit", "region:us" ]	text-generation	2025-03-05T21:37:25Z	--- license: mit base_model: microsoft/Phi-4-mini-instruct pipeline_tag: text-generation library_name: litert-lm tags: - chat --- # litert-community/Phi-4-mini-instruct This model provides a few variants of [microsoft/Phi-4-mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct) that are ready for deployment on Android using the [LiteRT (fka TFLite) stack](https://ai.google.dev/edge/litert), [MediaPipe LLM Inference API](https://ai.google.dev/edge/mediapipe/solutions/genai/llm_inference) and [LiteRT-LM](https://github.com/google-ai-edge/LiteRT-LM). ## Use the models ### Colab Disclaimer: The target deployment surface for the LiteRT models is Android/iOS/Web and the stack has been optimized for performance on these targets. Trying out the system in Colab is an easier way to familiarize yourself with the LiteRT stack, with the caveat that the performance (memory and latency) on Colab could be much worse than on a local device. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/#fileId=https://huggingface.co/litert-community/Phi-4-mini-instruct/blob/main/notebook.ipynb) ### Android #### Edge Gallery App * Download or build the [app](https://github.com/google-ai-edge/gallery?tab=readme-ov-file#-get-started-in-minutes) from GitHub. * Install the [app](https://play.google.com/store/apps/details?id=com.google.ai.edge.gallery&pli=1) from Google Play. * Follow the instructions in the app. #### LLM Inference API * Download and install [the apk](https://github.com/google-ai-edge/mediapipe-samples/releases/latest/download/llm_inference-debug.apk). * Follow the instructions in the app. To build the demo app from source, please follow the [instructions](https://github.com/google-ai-edge/mediapipe-samples/blob/main/examples/llm_inference/android/README.md) from the GitHub repository. ## Performance ### Android Note that all benchmark stats are from a Samsung S24 Ultra with 1280 KV cache size with multiple prefill signatures enabled. <table border="1"> <tr> <th>Backend</th> <th>Quantization scheme</th> <th>Context length</th> <th>Prefill (tokens/sec)</th> <th>Decode (tokens/sec)</th> <th>Time-to-first-token (sec)</th> <th>Model size (MB)</th> <th>Peak RSS Memory (MB)</th> <th>GPU Memory (MB)</th> </tr> <tr> <td><p style="text-align: right">CPU</td> <td><p style="text-align: right">dynamic_int8</td> <td><p style="text-align: right">4096</td> <td><p style="text-align: right">66.53 tk/s</p></td> <td><p style="text-align: right">7.28 tk/s</p></td> <td><p style="text-align: right">15.90 s</p></td> <td><p style="text-align: right">3906 MB</p></td> <td><p style="text-align: right">5308 MB</p></td> <td><p style="text-align: right">N/A</p></td> </tr> <tr> <td><p style="text-align: right">GPU</td> <td><p style="text-align: right">dynamic_int8</td> <td><p style="text-align: right">4096</td> <td><p style="text-align: right">314.01 tk/s</p></td> <td><p style="text-align: right">10.39 tk/s</p></td> <td><p style="text-align: right">10.32 s</p></td> <td><p style="text-align: right">3906 MB</p></td> <td><p style="text-align: right">4107 MB</p></td> <td><p style="text-align: right">4608 MB</p></td> </tr> </table> * Model Size: measured by the size of the .tflite flatbuffer (serialization format for LiteRT models) * Memory: indicator of peak RAM usage * The inference on CPU is accelerated via the LiteRT [XNNPACK](https://github.com/google/XNNPACK) delegate with 4 threads * Benchmark is done assuming XNNPACK cache is enabled * Benchmark is run with cache enabled and initialized. During the first run, the time to first token may differ. * dynamic_int8: quantized model with int8 weights and float activations.
MattBou00/llama-3-2-1b-detox_v1f_SCALE8_round1-checkpoint-epoch-60	MattBou00	2025-09-22T17:56:41Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-09-22T17:54:52Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_17-43-18/checkpoints/checkpoint-epoch-60") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_17-43-18/checkpoints/checkpoint-epoch-60") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_17-43-18/checkpoints/checkpoint-epoch-60") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
lmassaron/gemma-3-270m-it-grpo-finsent-json	lmassaron	2025-09-22T17:53:53Z	25	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "trl", "grpo", "unsloth", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-20T15:51:33Z	--- library_name: transformers tags: - trl - grpo - 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. 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litert-community/embeddinggemma-300m	litert-community	2025-09-22T17:53:41Z	912	11	sentence-transformers	[ "sentence-transformers", "tflite", "sentence-similarity", "feature-extraction", "text-embeddings-inference", "license:gemma", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-09-03T19:22:37Z	--- license: gemma pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - text-embeddings-inference extra_gated_heading: Access EmbeddingGemma on Hugging Face extra_gated_prompt: To access EmbeddingGemma 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 --- # litert-community/embeddinggemma-300m Main Model Card: [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) ## Overview This model card provides a few variants of the EmbeddingGemma model that are ready for deployment on Android and iOS using [LiteRT](https://ai.google.dev/edge/litert), or on Android via the [Google AI Edge RAG Library](https://ai.google.dev/edge/mediapipe/solutions/genai/rag). ## Use the models ### LiteRT * Try out the demo [example](https://github.com/google-ai-edge/LiteRT/tree/main/litert/samples/semantic_similarity) on GitHub. ### RAG * Try out the EmbeddingGemma model in the in the [Google AI Edge RAG Library](https://ai.google.dev/edge/mediapipe/solutions/genai/rag). You can find the SDK on [GitHub](https://github.com/google-ai-edge/ai-edge-apis/tree/main/local_agents/rag) or follow our [Android guide](https://ai.google.dev/edge/mediapipe/solutions/genai/rag/android) to install directly from Maven. We have also published a [sample app](https://github.com/google-ai-edge/ai-edge-apis/tree/main/examples/rag). * Use the sentencepiece model as the tokenizer for the EmbeddingGemma model. ## Performance ### Android Note that all benchmark stats are from a Samsung S25 Ultra. <table border="1"> <tr> <th>Backend</th> <th>Quantization</th> <th>Max sequence length</th> <th>Init time (ms)</th> <th>Inference time (ms)</th> <th>Memory (RSS in MB)</th> <th>Model size (MB)</th> </tr> <tr> <td><p style="text-align: right">GPU</p></td> <td><p style="text-align: right">Mixed Precision</p></td> <td><p style="text-align: right">256</p></td> <td><p style="text-align: right">1175</p></td> <td><p style="text-align: right">64</p></td> <td><p style="text-align: right">762</p></td> <td><p style="text-align: right">179</p></td> </tr> <tr> <td><p style="text-align: right">GPU</p></td> <td><p style="text-align: right">Mixed Precision</p></td> <td><p style="text-align: right">512</p></td> <td><p style="text-align: right">1445</p></td> <td><p style="text-align: right">119</p></td> <td><p style="text-align: right">762</p></td> <td><p style="text-align: right">179</p></td> </tr> <tr> <td><p style="text-align: right">GPU</p></td> <td><p style="text-align: right">Mixed Precision</p></td> <td><p style="text-align: right">1024</p></td> <td><p style="text-align: right">1545</p></td> <td><p style="text-align: right">241</p></td> <td><p style="text-align: right">771</p></td> <td><p style="text-align: right">183</p></td> </tr> <tr> <td><p style="text-align: right">GPU</p></td> <td><p style="text-align: right">Mixed Precision</p></td> <td><p style="text-align: right">2048</p></td> <td><p style="text-align: right">1707</p></td> <td><p style="text-align: right">683</p></td> <td><p style="text-align: right">786</p></td> <td><p style="text-align: right">196</p></td> </tr> <tr> <td><p style="text-align: right">CPU</p></td> <td><p style="text-align: right">Mixed Precision</p></td> <td><p style="text-align: right">256</p></td> <td><p style="text-align: right">17.6</p></td> <td><p style="text-align: right">66</p></td> <td><p style="text-align: right">110</p></td> <td><p style="text-align: right">179</p></td> </tr> <tr> <td><p style="text-align: right">CPU</p></td> <td><p style="text-align: right">Mixed Precision</p></td> <td><p style="text-align: right">512</p></td> <td><p style="text-align: right">24.9</p></td> <td><p style="text-align: right">169</p></td> <td><p style="text-align: right">123</p></td> <td><p style="text-align: right">179</p></td> </tr> <tr> <td><p style="text-align: right">CPU</p></td> <td><p style="text-align: right">Mixed Precision</p></td> <td><p style="text-align: right">1024</p></td> <td><p style="text-align: right">35.4</p></td> <td><p style="text-align: right">549</p></td> <td><p style="text-align: right">169</p></td> <td><p style="text-align: right">183</p></td> </tr> <tr> <td><p style="text-align: right">CPU</p></td> <td><p style="text-align: right">Mixed Precision</p></td> <td><p style="text-align: right">2048</p></td> <td><p style="text-align: right">35.8</p></td> <td><p style="text-align: right">2455</p></td> <td><p style="text-align: right">333</p></td> <td><p style="text-align: right">196</p></td> </tr> </table> Mixed Precision refers to per-channel quantization with int4 for embeddings, feedforward, and projection layers, and int8 for attention (e4_a8_f4_p4). Notes: Init time: the cost paid once per application initialization – subsequent inferences do not pay this cost * Memory: indicator of peak RAM usage * Model Size: measured by the size of the .tflite flatbuffer (serialization format for LiteRT models) * The inference on CPU is accelerated via the LiteRT [XNNPACK](https://github.com/google/XNNPACK) delegate with 4 threads * Benchmark is run with cache enabled and initialized. During the first run, the latency may differ.
corquaerit/qwen3-0.6b-base-hl-sft	corquaerit	2025-09-22T17:48:50Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T17:48:29Z	--- library_name: transformers tags: - trl - sft --- # 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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MattBou00/llama-3-2-1b-detox_v1f_SCALE8_round1-checkpoint-epoch-20	MattBou00	2025-09-22T17:48:16Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-09-22T17:46:25Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-09-22_17-43-18/checkpoints/checkpoint-epoch-20") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_17-43-18/checkpoints/checkpoint-epoch-20") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-09-22_17-43-18/checkpoints/checkpoint-epoch-20") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
MRockatansky/my-awesome-model	MRockatansky	2025-09-22T17:46:53Z	0	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2025-09-22T17:46:40Z	--- 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. 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sabirjdjdjd/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-territorial_lazy_prawn	sabirjdjdjd	2025-09-22T17:46:28Z	173	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am territorial_lazy_prawn", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-17T03:58:59Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am territorial_lazy_prawn --- # 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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clips/e5-small-v2-t2t	clips	2025-09-22T17:43:33Z	3	0	transformers	[ "transformers", "safetensors", "bert", "text-generation", "sentence-similarity", "nl", "arxiv:2509.12340", "base_model:intfloat/e5-small-v2", "base_model:finetune:intfloat/e5-small-v2", "license:mit", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-08-26T10:42:19Z	--- library_name: transformers license: mit language: - nl base_model: - intfloat/e5-small-v2 pipeline_tag: sentence-similarity --- # E5-small-v2-t2t This model is a Dutch-adapted version of [intfloat/e5-small-v2](https://huggingface.co/intfloat/e5-small-v2), created with [`transtokenizer`](https://github.com/LAGoM-NLP/transtokenizer) from the tokenizer of [BERTje](https://huggingface.co/GroNLP/bert-base-dutch-cased). This tool initializes token embeddings in the target language by computing a weighted average of semantically similar embeddings from the source language. ## Usage Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] # Each input text should start with "query: " or "passage: ". # For tasks other than retrieval, you can simply use the "query: " prefix. input_texts = [ 'query: hoeveel eiwitten moet een vrouw eten', 'query: top definieer', "passage: Als algemene richtlijn geldt dat de gemiddelde eiwitbehoefte voor vrouwen van 19 tot 70 jaar volgens de CDC 46 gram per dag bedraagt. Maar, zoals je in deze tabel kunt zien, moet je dit verhogen als je zwanger bent of traint voor een marathon. Bekijk de onderstaande tabel om te zien hoeveel eiwitten je dagelijks zou moeten eten.", "passage: Definitie van top voor leerlingen Engels. : 1 het hoogste punt van een berg : de top van een berg. : 2 het hoogste niveau. : 3 een bijeenkomst of reeks bijeenkomsten tussen de leiders van twee of meer regeringen." ] tokenizer = AutoTokenizer.from_pretrained('clips/e5-small-v2-t2t') model = AutoModel.from_pretrained('clips/e5-small-v2-t2t') # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(*batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) 100 print(scores.tolist()) ``` Below is an example for usage with sentence_transformers. ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('clips/e5-small-v2-t2t') input_texts = [ 'query: hoeveel eiwitten moet een vrouw eten', 'query: top definieer', "passage: Als algemene richtlijn geldt dat de gemiddelde eiwitbehoefte voor vrouwen van 19 tot 70 jaar volgens de CDC 46 gram per dag bedraagt. Maar, zoals je in deze tabel kunt zien, moet je dit verhogen als je zwanger bent of traint voor een marathon. Bekijk de onderstaande tabel om te zien hoeveel eiwitten je dagelijks zou moeten eten.", "passage: Definitie van top voor leerlingen Engels. : 1 het hoogste punt van een berg : de top van een berg. : 2 het hoogste niveau. : 3 een bijeenkomst of reeks bijeenkomsten tussen de leiders van twee of meer regeringen." ] embeddings = model.encode(input_texts, normalize_embeddings=True) ``` ## Benchmark Evaluation Results on MTEB-NL (models introduced in [our paper](https://arxiv.org/abs/2509.12340) and the best model per size category are highlighted in bold): \| Model \| Prm \| Cls \| MLCls \| PCls \| Rrnk \| Rtr \| Clust \| STS \| AvgD \| AvgT \| \|---------------------------------------\|------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\|----------\| \| Num. Datasets (→) \| \| 12 \| 3 \| 2 \| 1 \| 12 \| 8 \| 2 \| 40 \| \| \| Supervised (small, <100M) \| \| \| \| \| \| \| \| \| \| \| \| e5-small-v2-t2t \| 33M \| 53.7 \| 38.5 \| 74.5 \| 85.9 \| 45.0 \| 24.1 \| 74.3 \| 46.9 \| 56.6 \| \| e5-small-v2-t2t-nl \| 33M \| 55.3 \| 40.9 \| 74.9 \| 86.0 \| 49.9 \| 28.0 \| 74.1 \| 49.8 \| 58.4 \| \| e5-small-trm \| 41M \| 56.3 \| 43.5 \| 76.5 \| 87.3 \| 53.1 \| 28.2 \| 74.2 \| 51.4 \| 59.9 \| \| e5-small-trm-nl \| 41M \| 58.2 \| 44.7 \| 76.0 \| 87.1 \| 56.0 \| 32.2 \| 74.6 \| 53.8 \| 61.3 \| \| Supervised (base, <305M) \| \| \| \| \| \| \| \| \| \| \| \| granite-embedding-107m-multilingual \| 107M \| 53.9 \| 41.8 \| 70.1 \| 84.7 \| 50.2 \| 29.8 \| 68.4 \| 49.4 \| 57.0 \| \| e5-base-v2-t2t \| 109M \| 54.4 \| 40.3 \| 73.3 \| 85.6 \| 46.2 \| 25.5 \| 73.2 \| 47.8 \| 56.9 \| \| e5-base-v2-t2t-nl \| 109M \| 53.9 \| 41.5 \| 72.5 \| 84.0 \| 46.4 \| 26.9 \| 69.3 \| 47.8 \| 56.3 \| \| multilingual-e5-small \| 118M \| 56.3 \| 43.5 \| 76.5 \| 87.1 \| 53.1 \| 28.2 \| 74.2 \| 51.4 \| 59.8 \| \| paraphrase-multilingual-MiniLM-L12-v2 \| 118M \| 55.0 \| 38.1 \| 78.2 \| 80.6 \| 37.7 \| 29.6 \| 76.3 \| 46.3 \| 56.5 \| \| RobBERT-2023-base-ft \| 124M \| 58.1 \| 44.6 \| 72.7 \| 84.7 \| 51.6 \| 32.9 \| 68.5 \| 52.0 \| 59.0 \| \| e5-base-trm \| 124M \| 58.1 \| 44.4 \| 76.7 \| 88.3 \| 55.8 \| 28.1 \| 74.9 \| 52.9 \| 60.9 \| \| e5-base-trm-nl \| 124M \| 59.6 \| 45.9 \| 78.4 \| 87.5 \| 56.5 \| 34.3 \| 75.8 \| 55.0 \| 62.6 \| \| potion-multilingual-128M \| 128M \| 51.8 \| 40.0 \| 60.4 \| 80.3 \| 35.7 \| 26.1 \| 62.0 \| 42.6 \| 50.9 \| \| multilingual-e5-base \| 278M \| 58.2 \| 44.4 \| 76.7 \| 88.4 \| 55.8 \| 27.7 \| 74.9 \| 52.8 \| 60.9 \| \| granite-embedding-278m-multilingual \| 278M \| 54.6 \| 41.8 \| 71.0 \| 85.6 \| 52.4 \| 30.3 \| 68.9 \| 50.5 \| 58.0 \| \| paraphrase-multilingual-mpnet-base-v2 \| 278M \| 58.1 \| 40.5 \| 81.9 \| 82.3 \| 41.4 \| 30.8 \| 79.3 \| 49.2 \| 59.2 \| \| Arctic-embed-m-v2.0 \| 305M \| 54.4 \| 42.6 \| 66.6 \| 86.2 \| 51.8 \| 26.5 \| 64.9 \| 49.1 \| 56.1 \| \| gte-multilingual-base \| 305M \| 59.1 \| 37.7 \| 77.8 \| 82.3 \| 56.8 \| 31.3 \| 78.6 \| 53.8 \| 60.5 \| \| Supervised (large, >305M) \| \| \| \| \| \| \| \| \| \| \| \| e5-large-v2-t2t \| 335M \| 55.7 \| 41.4 \| 75.7 \| 86.6 \| 49.9 \| 25.5 \| 74.0 \| 49.5 \| 58.4 \| \| e5-large-v2-t2t-nl \| 335M \| 57.3 \| 42.4 \| 76.9 \| 86.9 \| 50.8 \| 27.7 \| 74.1 \| 51.7 \| 59.4 \| \| RobBERT-2023-large-ft \| 355M \| 59.3 \| 45.2 \| 68.7 \| 82.3 \| 48.3 \| 31.6 \| 70.6 \| 51.0 \| 58.0 \| \| e5-large-trm \| 355M \| 60.2 \| 45.4 \| 80.3 \| 90.3 \| 59.0 \| 28.7 \| 78.8 \| 55.1 \| 63.3 \| \| e5-large-trm-nl \| 355M \| 62.2 \| 48.0 \| 81.4 \| 87.2 \| 58.2 \| 35.6 \| 78.2 \| 57.0 \| 64.4 \| \| multilingual-e5-large \| 560M \| 60.2 \| 45.4 \| 80.3 \| 90.3 \| 59.1 \| 29.5 \| 78.8 \| 55.3 \| 63.4 \| \| Arctic-embed-l-v2.0 \| 568M \| 59.3 \| 45.2 \| 74.2 \| 88.2 \| 59.0 \| 29.8 \| 71.7 \| 54.3 \| 61.1 \| \| bge-m3 \| 568M \| 60.7 \| 44.2 \| 78.3 \| 88.7 \| 60.0 \| 29.2 \| 78.1 \| 55.4 \| 63.1 \| \| jina-embeddings-v3 \| 572M \| 61.7 \| 38.9 \| 76.8 \| 78.5 \| 59.1 \| 38.9 \| 84.8 \| 57.0 \| 62.7 \| ### Citation Information If you find our paper, benchmark or models helpful, please consider cite as follows: ```latex @misc{banar2025mtebnle5nlembeddingbenchmark, title={MTEB-NL and E5-NL: Embedding Benchmark and Models for Dutch}, author={Nikolay Banar and Ehsan Lotfi and Jens Van Nooten and Cristina Arhiliuc and Marija Kliocaite and Walter Daelemans}, year={2025}, eprint={2509.12340}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2509.12340}, } ``` [//]: # (https://arxiv.org/abs/2509.12340)
mradermacher/mistral-7b-climate-expert-GGUF	mradermacher	2025-09-22T17:43:15Z	103	0	transformers	[ "transformers", "gguf", "base_model:adapter:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "lora", "sft", "trl", "unsloth", "en", "base_model:Jr12lm12/mistral-7b-climate-expert", "base_model:adapter:Jr12lm12/mistral-7b-climate-expert", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-19T12:53:50Z	--- base_model: Jr12lm12/mistral-7b-climate-expert language: - en library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - base_model:adapter:unsloth/mistral-7b-instruct-v0.3-bnb-4bit - lora - sft - transformers - trl - unsloth --- ## 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/Jr12lm12/mistral-7b-climate-expert <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#mistral-7b-climate-expert-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/mistral-7b-climate-expert-GGUF/resolve/main/mistral-7b-climate-expert.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-climate-expert-GGUF/resolve/main/mistral-7b-climate-expert.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-climate-expert-GGUF/resolve/main/mistral-7b-climate-expert.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-climate-expert-GGUF/resolve/main/mistral-7b-climate-expert.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-climate-expert-GGUF/resolve/main/mistral-7b-climate-expert.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-climate-expert-GGUF/resolve/main/mistral-7b-climate-expert.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-climate-expert-GGUF/resolve/main/mistral-7b-climate-expert.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-climate-expert-GGUF/resolve/main/mistral-7b-climate-expert.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-climate-expert-GGUF/resolve/main/mistral-7b-climate-expert.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-climate-expert-GGUF/resolve/main/mistral-7b-climate-expert.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-climate-expert-GGUF/resolve/main/mistral-7b-climate-expert.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-climate-expert-GGUF/resolve/main/mistral-7b-climate-expert.f16.gguf) \| f16 \| 14.6 \| 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 -->
mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF	mradermacher	2025-09-22T17:43:00Z	748	0	transformers	[ "transformers", "gguf", "en", "base_model:kaonai/kaon-l-mistral-24b-v0.1", "base_model:quantized:kaonai/kaon-l-mistral-24b-v0.1", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-09-19T13:56:15Z	--- base_model: kaonai/kaon-l-mistral-24b-v0.1 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: 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/kaonai/kaon-l-mistral-24b-v0.1 <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#kaon-l-mistral-24b-v0.1-i1-GGUF).* static quants are available at https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-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/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.imatrix.gguf) \| imatrix \| 0.1 \| imatrix file (for creating your own qwuants) \| \| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-IQ1_S.gguf) \| i1-IQ1_S \| 5.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-IQ1_M.gguf) \| i1-IQ1_M \| 5.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 6.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 7.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-IQ2_S.gguf) \| i1-IQ2_S \| 7.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-IQ2_M.gguf) \| i1-IQ2_M \| 8.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 8.4 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-Q2_K.gguf) \| i1-Q2_K \| 9.0 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 9.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 10.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 10.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-IQ3_S.gguf) \| i1-IQ3_S \| 10.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-IQ3_M.gguf) \| i1-IQ3_M \| 10.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 11.6 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 12.5 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 12.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-Q4_0.gguf) \| i1-Q4_0 \| 13.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 13.6 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 14.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-Q4_1.gguf) \| i1-Q4_1 \| 15.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 16.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 16.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/kaon-l-mistral-24b-v0.1-i1-GGUF/resolve/main/kaon-l-mistral-24b-v0.1.i1-Q6_K.gguf) \| i1-Q6_K \| 19.4 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/Slot-MLLM-7B-instruct-GGUF	mradermacher	2025-09-22T17:42:15Z	188	0	transformers	[ "transformers", "gguf", "en", "base_model:KU-AGI/Slot-MLLM-7B-instruct", "base_model:quantized:KU-AGI/Slot-MLLM-7B-instruct", "license:mit", "endpoints_compatible", "region:us" ]	null	2025-09-20T09:23:37Z	--- base_model: KU-AGI/Slot-MLLM-7B-instruct language: - en library_name: transformers license: mit 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/KU-AGI/Slot-MLLM-7B-instruct <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Slot-MLLM-7B-instruct-GGUF).* weighted/imatrix quants are available at https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-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/Slot-MLLM-7B-instruct-GGUF/resolve/main/Slot-MLLM-7B-instruct.Q2_K.gguf) \| Q2_K \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-GGUF/resolve/main/Slot-MLLM-7B-instruct.Q3_K_S.gguf) \| Q3_K_S \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-GGUF/resolve/main/Slot-MLLM-7B-instruct.Q3_K_M.gguf) \| Q3_K_M \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-GGUF/resolve/main/Slot-MLLM-7B-instruct.Q3_K_L.gguf) \| Q3_K_L \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-GGUF/resolve/main/Slot-MLLM-7B-instruct.IQ4_XS.gguf) \| IQ4_XS \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-GGUF/resolve/main/Slot-MLLM-7B-instruct.Q4_K_S.gguf) \| Q4_K_S \| 4.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-GGUF/resolve/main/Slot-MLLM-7B-instruct.Q4_K_M.gguf) \| Q4_K_M \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-GGUF/resolve/main/Slot-MLLM-7B-instruct.Q5_K_S.gguf) \| Q5_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-GGUF/resolve/main/Slot-MLLM-7B-instruct.Q5_K_M.gguf) \| Q5_K_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-GGUF/resolve/main/Slot-MLLM-7B-instruct.Q6_K.gguf) \| Q6_K \| 5.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-GGUF/resolve/main/Slot-MLLM-7B-instruct.Q8_0.gguf) \| Q8_0 \| 7.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-7B-instruct-GGUF/resolve/main/Slot-MLLM-7B-instruct.f16.gguf) \| f16 \| 13.7 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
chrischengzh/Sentiment-Trigger-Detection-Family-Chinese	chrischengzh	2025-09-22T17:42:08Z	0	1	null	[ "sentiment", "trigger-detection", "sentiment-analysis", "license:apache-2.0", "region:us" ]	null	2025-09-22T17:37:55Z	--- license: apache-2.0 tags: - sentiment - trigger-detection - sentiment-analysis ---
Miyuutsu/Kawaii_Kitsune_Catelier	Miyuutsu	2025-09-22T17:41:44Z	0	3	null	[ "merge", "text-to-image", "base_model:Minthy/RouWei-0.7", "base_model:merge:Minthy/RouWei-0.7", "base_model:Miyuutsu/Kawaii_Kittopia_Catelier", "base_model:merge:Miyuutsu/Kawaii_Kittopia_Catelier", "license:other", "region:us" ]	text-to-image	2025-02-09T04:59:50Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ base_model: - Miyuutsu/Kawaii_Kittopia_Catelier - Minthy/RouWei-0.7 pipeline_tag: text-to-image tags: - merge --- v2 has been through so many merges I don't even know anymore. Best quality prompts: `masterpiece, best quality` Optional additional quality prompts: `newest, absurdres, highres` Negative prompts: `worst quality, low quality, watermark` Optional additional negative prompts: `old, early, signature, text, bad quality, lowres, bad anatomy, bad hands, multiple views, abstract, japanese text, censored, sign, scan artifacts, jpeg artifacts, sketch, light particles, mutated hands` This one isn't as picky about settings. ### Old description: Versioning method: v{Merge_Method}.{Kittopia_Merge_Method}.{rouwei_Major_Version}.{rouwei_Sub_Version}-{Model_Iteration} Quality Prompts: `masterpiece, best quality` Negative Prompts: `worst quality, low quality, watermark` Most prompts from both NoobAI and rouwei should work well. For artists try both `by {artist_name}` as well as just `{artist_name}` Model is VPred ZSNR and has both metadata and tensors set correctly. Please ensure you are using a compatible UI. Sampler: Euler Scheduler: `Simple` (recommended), `Normal` or `SGM Uniform` Steps: `30+` CFG: `3~5`
mradermacher/Slot-MLLM-14B-instruct-GGUF	mradermacher	2025-09-22T17:41:02Z	105	0	transformers	[ "transformers", "gguf", "en", "base_model:KU-AGI/Slot-MLLM-14B-instruct", "base_model:quantized:KU-AGI/Slot-MLLM-14B-instruct", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-21T14:09:29Z	--- base_model: KU-AGI/Slot-MLLM-14B-instruct language: - en library_name: transformers license: mit 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/KU-AGI/Slot-MLLM-14B-instruct <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Slot-MLLM-14B-instruct-GGUF).* weighted/imatrix quants are available at https://huggingface.co/mradermacher/Slot-MLLM-14B-instruct-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/Slot-MLLM-14B-instruct-GGUF/resolve/main/Slot-MLLM-14B-instruct.Q2_K.gguf) \| Q2_K \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-14B-instruct-GGUF/resolve/main/Slot-MLLM-14B-instruct.Q3_K_S.gguf) \| Q3_K_S \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-14B-instruct-GGUF/resolve/main/Slot-MLLM-14B-instruct.Q3_K_M.gguf) \| Q3_K_M \| 7.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-14B-instruct-GGUF/resolve/main/Slot-MLLM-14B-instruct.Q3_K_L.gguf) \| Q3_K_L \| 8.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-14B-instruct-GGUF/resolve/main/Slot-MLLM-14B-instruct.IQ4_XS.gguf) \| IQ4_XS \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-14B-instruct-GGUF/resolve/main/Slot-MLLM-14B-instruct.Q4_K_S.gguf) \| Q4_K_S \| 8.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-14B-instruct-GGUF/resolve/main/Slot-MLLM-14B-instruct.Q4_K_M.gguf) \| Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-14B-instruct-GGUF/resolve/main/Slot-MLLM-14B-instruct.Q5_K_S.gguf) \| Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-14B-instruct-GGUF/resolve/main/Slot-MLLM-14B-instruct.Q5_K_M.gguf) \| Q5_K_M \| 10.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-14B-instruct-GGUF/resolve/main/Slot-MLLM-14B-instruct.Q6_K.gguf) \| Q6_K \| 12.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Slot-MLLM-14B-instruct-GGUF/resolve/main/Slot-MLLM-14B-instruct.Q8_0.gguf) \| Q8_0 \| 15.9 \| 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 -->
mradermacher/Ministral-8B-it-2410-iSMART-GGUF	mradermacher	2025-09-22T17:40:49Z	55	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "mistral", "trl", "sft", "en", "vi", "base_model:lefantom00/Ministral-8B-it-2410-iSMART", "base_model:quantized:lefantom00/Ministral-8B-it-2410-iSMART", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-21T15:23:58Z	--- base_model: lefantom00/Ministral-8B-it-2410-iSMART language: - en - vi library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - mistral - trl - sft --- ## 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/lefantom00/Ministral-8B-it-2410-iSMART <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Ministral-8B-it-2410-iSMART-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/Ministral-8B-it-2410-iSMART-GGUF/resolve/main/Ministral-8B-it-2410-iSMART.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ministral-8B-it-2410-iSMART-GGUF/resolve/main/Ministral-8B-it-2410-iSMART.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ministral-8B-it-2410-iSMART-GGUF/resolve/main/Ministral-8B-it-2410-iSMART.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Ministral-8B-it-2410-iSMART-GGUF/resolve/main/Ministral-8B-it-2410-iSMART.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ministral-8B-it-2410-iSMART-GGUF/resolve/main/Ministral-8B-it-2410-iSMART.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ministral-8B-it-2410-iSMART-GGUF/resolve/main/Ministral-8B-it-2410-iSMART.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Ministral-8B-it-2410-iSMART-GGUF/resolve/main/Ministral-8B-it-2410-iSMART.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Ministral-8B-it-2410-iSMART-GGUF/resolve/main/Ministral-8B-it-2410-iSMART.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ministral-8B-it-2410-iSMART-GGUF/resolve/main/Ministral-8B-it-2410-iSMART.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ministral-8B-it-2410-iSMART-GGUF/resolve/main/Ministral-8B-it-2410-iSMART.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Ministral-8B-it-2410-iSMART-GGUF/resolve/main/Ministral-8B-it-2410-iSMART.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Ministral-8B-it-2410-iSMART-GGUF/resolve/main/Ministral-8B-it-2410-iSMART.f16.gguf) \| f16 \| 16.1 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/command-a-03-2025-uncut-GGUF	mradermacher	2025-09-22T17:40:27Z	14	0	transformers	[ "transformers", "gguf", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi", "dataset:jukofyork/instruction-refusals-500MB", "dataset:jukofyork/instruction-responses-500MB", "base_model:jukofyork/command-a-03-2025-uncut", "base_model:quantized:jukofyork/command-a-03-2025-uncut", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-21T21:49:31Z	--- base_model: jukofyork/command-a-03-2025-uncut datasets: - jukofyork/instruction-refusals-500MB - jukofyork/instruction-responses-500MB language: - en - fr - de - es - it - pt - ja - ko - zh - ar - el - fa - pl - id - cs - he - hi - nl - ro - ru - tr - uk - vi library_name: transformers license: cc-by-nc-4.0 mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/jukofyork/command-a-03-2025-uncut <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#command-a-03-2025-uncut-GGUF).* weighted/imatrix quants are available at https://huggingface.co/mradermacher/command-a-03-2025-uncut-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/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q2_K.gguf) \| Q2_K \| 42.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q3_K_S.gguf) \| Q3_K_S \| 49.1 \| \| \| [PART 1](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q3_K_M.gguf.part2of2) \| Q3_K_M \| 54.5 \| lower quality \| \| [PART 1](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q3_K_L.gguf.part2of2) \| Q3_K_L \| 59.2 \| \| \| [PART 1](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.IQ4_XS.gguf.part2of2) \| IQ4_XS \| 60.7 \| \| \| [PART 1](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q4_K_S.gguf.part2of2) \| Q4_K_S \| 63.9 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q4_K_M.gguf.part2of2) \| Q4_K_M \| 67.2 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q5_K_S.gguf.part2of2) \| Q5_K_S \| 76.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q5_K_M.gguf.part2of2) \| Q5_K_M \| 78.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q6_K.gguf.part2of2) \| Q6_K \| 91.2 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q8_0.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q8_0.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/command-a-03-2025-uncut-GGUF/resolve/main/command-a-03-2025-uncut.Q8_0.gguf.part3of3) \| Q8_0 \| 118.1 \| 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 -->
mradermacher/MiniusLight-24B-v3-test-GGUF	mradermacher	2025-09-22T17:40:13Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:DoppelReflEx/MiniusLight-24B-v3-test", "base_model:quantized:DoppelReflEx/MiniusLight-24B-v3-test", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-22T04:30:41Z	--- base_model: DoppelReflEx/MiniusLight-24B-v3-test 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/DoppelReflEx/MiniusLight-24B-v3-test <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#MiniusLight-24B-v3-test-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/MiniusLight-24B-v3-test-GGUF/resolve/main/MiniusLight-24B-v3-test.Q2_K.gguf) \| Q2_K \| 9.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v3-test-GGUF/resolve/main/MiniusLight-24B-v3-test.Q3_K_S.gguf) \| Q3_K_S \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v3-test-GGUF/resolve/main/MiniusLight-24B-v3-test.Q3_K_M.gguf) \| Q3_K_M \| 11.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v3-test-GGUF/resolve/main/MiniusLight-24B-v3-test.Q3_K_L.gguf) \| Q3_K_L \| 12.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v3-test-GGUF/resolve/main/MiniusLight-24B-v3-test.IQ4_XS.gguf) \| IQ4_XS \| 13.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v3-test-GGUF/resolve/main/MiniusLight-24B-v3-test.Q4_K_S.gguf) \| Q4_K_S \| 13.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v3-test-GGUF/resolve/main/MiniusLight-24B-v3-test.Q4_K_M.gguf) \| Q4_K_M \| 14.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v3-test-GGUF/resolve/main/MiniusLight-24B-v3-test.Q5_K_S.gguf) \| Q5_K_S \| 16.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v3-test-GGUF/resolve/main/MiniusLight-24B-v3-test.Q5_K_M.gguf) \| Q5_K_M \| 16.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v3-test-GGUF/resolve/main/MiniusLight-24B-v3-test.Q6_K.gguf) \| Q6_K \| 19.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v3-test-GGUF/resolve/main/MiniusLight-24B-v3-test.Q8_0.gguf) \| Q8_0 \| 25.2 \| 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 -->
poolkiltzn/blockassist-bc-vigilant_alert_tuna_1758562742	poolkiltzn	2025-09-22T17:40:12Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "vigilant alert tuna", "arxiv:2504.07091", "region:us" ]	null	2025-09-22T17:39:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - vigilant alert tuna --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/ministral-8B-Instruct-2410-abliterated-GGUF	mradermacher	2025-09-22T17:39:17Z	0	0	transformers	[ "transformers", "gguf", "abliterated", "uncensored", "ministral", "mistral", "text-generation", "conversational", "en", "fr", "de", "es", "it", "pt", "ru", "zh", "ja", "dataset:N/A", "base_model:realoperator42/ministral-8B-Instruct-2410-abliterated", "base_model:quantized:realoperator42/ministral-8B-Instruct-2410-abliterated", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T10:33:36Z	--- base_model: realoperator42/ministral-8B-Instruct-2410-abliterated datasets: - N/A language: - en - fr - de - es - it - pt - ru - zh - ja library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - abliterated - uncensored - ministral - mistral - text-generation - conversational --- ## 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/realoperator42/ministral-8B-Instruct-2410-abliterated <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#ministral-8B-Instruct-2410-abliterated-GGUF).* weighted/imatrix quants are available at https://huggingface.co/mradermacher/ministral-8B-Instruct-2410-abliterated-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/ministral-8B-Instruct-2410-abliterated-GGUF/resolve/main/ministral-8B-Instruct-2410-abliterated.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/ministral-8B-Instruct-2410-abliterated-GGUF/resolve/main/ministral-8B-Instruct-2410-abliterated.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/ministral-8B-Instruct-2410-abliterated-GGUF/resolve/main/ministral-8B-Instruct-2410-abliterated.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ministral-8B-Instruct-2410-abliterated-GGUF/resolve/main/ministral-8B-Instruct-2410-abliterated.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/ministral-8B-Instruct-2410-abliterated-GGUF/resolve/main/ministral-8B-Instruct-2410-abliterated.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/ministral-8B-Instruct-2410-abliterated-GGUF/resolve/main/ministral-8B-Instruct-2410-abliterated.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ministral-8B-Instruct-2410-abliterated-GGUF/resolve/main/ministral-8B-Instruct-2410-abliterated.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ministral-8B-Instruct-2410-abliterated-GGUF/resolve/main/ministral-8B-Instruct-2410-abliterated.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/ministral-8B-Instruct-2410-abliterated-GGUF/resolve/main/ministral-8B-Instruct-2410-abliterated.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/ministral-8B-Instruct-2410-abliterated-GGUF/resolve/main/ministral-8B-Instruct-2410-abliterated.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/ministral-8B-Instruct-2410-abliterated-GGUF/resolve/main/ministral-8B-Instruct-2410-abliterated.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/ministral-8B-Instruct-2410-abliterated-GGUF/resolve/main/ministral-8B-Instruct-2410-abliterated.f16.gguf) \| f16 \| 16.1 \| 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 -->
DTebias/blockassist	DTebias	2025-09-22T17:38:34Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "bellowing finicky pig", "arxiv:2504.07091", "region:us" ]	null	2025-09-22T14:40:45Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - bellowing finicky pig --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Gilotopia/FLTest1	Gilotopia	2025-09-22T17:37:15Z	0	0	null	[ "license:other", "region:us" ]	null	2025-09-06T13:33:03Z	--- license: other license_name: all-rights-reserved-no-usage license_link: LICENSE ---

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