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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-29 06:27:49	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 502 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-29 06:23:06	card stringlengths 11 1.01M
ma921/gpt2-large_dpo_golden-hh_noise40_epoch3	ma921	2025-05-04T06:39:56Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:ma921/gpt2-large-sft-golden-hh", "base_model:finetune:ma921/gpt2-large-sft-golden-hh", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T06:38:27Z	--- library_name: transformers license: mit base_model: ma921/gpt2-large-sft-golden-hh tags: - generated_from_trainer model-index: - name: gpt2-large_dpo_golden-hh_noise40_epoch3 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. --> # gpt2-large_dpo_golden-hh_noise40_epoch3 This model is a fine-tuned version of [ma921/gpt2-large-sft-golden-hh](https://huggingface.co/ma921/gpt2-large-sft-golden-hh) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 32 - total_train_batch_size: 256 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
DefiChuks/Phase	DefiChuks	2025-05-04T06:38:22Z	0	1	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-04T06:38:22Z	--- license: apache-2.0 ---
0xtinuviel/Qwen2.5-72B-Instruct-bnb-4bit-Gensyn-Swarm-subtle_rugged_snail	0xtinuviel	2025-05-04T06:31:09Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am subtle rugged snail", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-72B-Instruct-bnb-4bit", "base_model:finetune:Gensyn/Qwen2.5-72B-Instruct-bnb-4bit", "endpoints_compatible", "region:us" ]	null	2025-05-02T00:56:10Z	--- base_model: Gensyn/Qwen2.5-72B-Instruct-bnb-4bit library_name: transformers model_name: Qwen2.5-72B-Instruct-bnb-4bit-Gensyn-Swarm-subtle_rugged_snail tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am subtle rugged snail - unsloth - trl licence: license --- # Model Card for Qwen2.5-72B-Instruct-bnb-4bit-Gensyn-Swarm-subtle_rugged_snail This model is a fine-tuned version of [Gensyn/Qwen2.5-72B-Instruct-bnb-4bit](https://huggingface.co/Gensyn/Qwen2.5-72B-Instruct-bnb-4bit). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="0xtinuviel/Qwen2.5-72B-Instruct-bnb-4bit-Gensyn-Swarm-subtle_rugged_snail", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Kenazin/Llama-3.1-8B-peft-v6-10	Kenazin	2025-05-04T06:28:33Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T06:28:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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RevDaFox1/RevDaFox	RevDaFox1	2025-05-04T06:24:54Z	0	0	null	[ "text-generation", "en", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:apache-2.0", "region:us" ]	text-generation	2025-05-04T06:18:12Z	--- license: apache-2.0 language: - en base_model: - openai-community/gpt2 pipeline_tag: text-generation --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [AAA] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
HoaDoan1710/whisper-checkpoint-4525	HoaDoan1710	2025-05-04T06:13:35Z	0	0	null	[ "safetensors", "whisper", "license:apache-2.0", "region:us" ]	null	2025-05-04T06:04:08Z	--- license: apache-2.0 ---
Flo0620/Qwen2_5_7B_r128_a128_d0_1	Flo0620	2025-05-04T06:13:17Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-04-23T08:10:10Z	--- base_model: Qwen/Qwen2.5-VL-7B-Instruct library_name: transformers model_name: Qwen2_5_7B_r128_a128_d0_1 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen2_5_7B_r128_a128_d0_1 This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-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="Flo0620/Qwen2_5_7B_r128_a128_d0_1", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0+cu124 - Datasets: 3.5.0 - 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
1245erty/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-jumping_lithe_scorpion	1245erty	2025-05-04T06:09:34Z	12	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am jumping lithe scorpion", "unsloth", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-20T16:38:45Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-jumping_lithe_scorpion tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am jumping lithe scorpion - unsloth - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-jumping_lithe_scorpion This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="1245erty/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-jumping_lithe_scorpion", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
outlookAi/QrtYRbP6oM	outlookAi	2025-05-04T06:07:16Z	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	2025-05-04T05:46:12Z	--- 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: Dremy Bokeh --- # Qrtyrbp6Om <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 `Dremy Bokeh` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Dremy Bokeh", "lora_weights": "https://huggingface.co/outlookAi/QrtYRbP6oM/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('outlookAi/QrtYRbP6oM', weight_name='lora.safetensors') image = pipeline('Dremy Bokeh').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: 1500 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/outlookAi/QrtYRbP6oM/discussions) to add images that show off what you’ve made with this LoRA.
Lahinthefutureland/wan-toffee	Lahinthefutureland	2025-05-04T06:06:14Z	0	1	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-09T19:32:49Z	--- license: apache-2.0 ---
ail-sa/kevin_plus_medium_fs_v1	ail-sa	2025-05-04T05:55:24Z	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	2025-05-04T05:22:10Z	--- 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: Sid --- # Kevin_Plus_Medium_Fs_V1 <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 `Sid` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Sid", "lora_weights": "https://huggingface.co/ail-sa/kevin_plus_medium_fs_v1/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('ail-sa/kevin_plus_medium_fs_v1', weight_name='lora.safetensors') image = pipeline('Sid').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: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/ail-sa/kevin_plus_medium_fs_v1/discussions) to add images that show off what you’ve made with this LoRA.
Nasanbuyan/mongolian-gpt2-lora	Nasanbuyan	2025-05-04T05:53:50Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T05:53:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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gfhfgh43276/dsfd6547	gfhfgh43276	2025-05-04T05:49:31Z	0	0	null	[ "license:bigcode-openrail-m", "region:us" ]	null	2025-05-04T05:49:31Z	--- license: bigcode-openrail-m ---
loris3/stratified_equitoken_10m_curriculum_llama_llama_incr_influence_epoch_repetition	loris3	2025-05-04T05:45:17Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T20:15:03Z	--- 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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Nasanbuyan/mongolian-gpt2-lora-merged	Nasanbuyan	2025-05-04T05:36:17Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T05:35:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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vertings6/0323960f-4ca4-4a32-bf48-c6e3eb5425e4	vertings6	2025-05-04T05:33:43Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM2-1.7B", "base_model:adapter:unsloth/SmolLM2-1.7B", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T05:26:56Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM2-1.7B tags: - axolotl - generated_from_trainer model-index: - name: 0323960f-4ca4-4a32-bf48-c6e3eb5425e4 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: true adapter: lora base_model: unsloth/SmolLM2-1.7B bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 07712ba8757e90e2_train_data.json ds_type: json format: custom path: /workspace/input_data/07712ba8757e90e2_train_data.json type: field_instruction: rxn_smiles field_output: prod_smiles format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 144 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: vertings6/0323960f-4ca4-4a32-bf48-c6e3eb5425e4 hub_repo: null hub_strategy: end hub_token: null learning_rate: 3.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 4 mixed_precision: bf16 mlflow_experiment_name: /tmp/07712ba8757e90e2_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 2048 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 6970e0df-b1c7-4e38-b05b-e2b4c1d10bf9 wandb_project: s56-32 wandb_run: your_name wandb_runid: 6970e0df-b1c7-4e38-b05b-e2b4c1d10bf9 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 0323960f-4ca4-4a32-bf48-c6e3eb5425e4 This model is a fine-tuned version of [unsloth/SmolLM2-1.7B](https://huggingface.co/unsloth/SmolLM2-1.7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.3345 ## 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: 3e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.1991 \| 0.0344 \| 200 \| 2.3345 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
vmpsergio/6670da37-d906-4c66-bf3b-7a2e48d56684	vmpsergio	2025-05-04T05:33:07Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM2-1.7B", "base_model:adapter:unsloth/SmolLM2-1.7B", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T05:26:58Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM2-1.7B tags: - axolotl - generated_from_trainer model-index: - name: 6670da37-d906-4c66-bf3b-7a2e48d56684 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: unsloth/SmolLM2-1.7B bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 07712ba8757e90e2_train_data.json ds_type: json format: custom path: /workspace/input_data/07712ba8757e90e2_train_data.json type: field_instruction: rxn_smiles field_output: prod_smiles format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: vmpsergio/6670da37-d906-4c66-bf3b-7a2e48d56684 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/07712ba8757e90e2_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 6970e0df-b1c7-4e38-b05b-e2b4c1d10bf9 wandb_project: s56-2 wandb_run: your_name wandb_runid: 6970e0df-b1c7-4e38-b05b-e2b4c1d10bf9 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 6670da37-d906-4c66-bf3b-7a2e48d56684 This model is a fine-tuned version of [unsloth/SmolLM2-1.7B](https://huggingface.co/unsloth/SmolLM2-1.7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8562 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.9556 \| 0.0258 \| 200 \| 1.8562 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
sharkMeow/train_half_V2	sharkMeow	2025-05-04T05:29:08Z	0	0	transformers	[ "transformers", "safetensors", "chinese_clip", "generated_from_trainer", "base_model:OFA-Sys/chinese-clip-vit-base-patch16", "base_model:finetune:OFA-Sys/chinese-clip-vit-base-patch16", "endpoints_compatible", "region:us" ]	null	2025-05-03T17:46:28Z	--- library_name: transformers base_model: OFA-Sys/chinese-clip-vit-base-patch16 tags: - generated_from_trainer model-index: - name: train_half_V2 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. --> # train_half_V2 This model is a fine-tuned version of [OFA-Sys/chinese-clip-vit-base-patch16](https://huggingface.co/OFA-Sys/chinese-clip-vit-base-patch16) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 50 - eval_batch_size: 20 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 200 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.50.0 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
fdgrd/sdfeshfg	fdgrd	2025-05-04T05:27:52Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-05-04T05:27:52Z	--- license: creativeml-openrail-m ---
haraheru/gensyn-checkpoints-territorial_sleek_impala	haraheru	2025-05-04T05:27:02Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am territorial sleek impala", "unsloth", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-25T06:56:29Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: gensyn-checkpoints-territorial_sleek_impala tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am territorial sleek impala - unsloth - trl licence: license --- # Model Card for gensyn-checkpoints-territorial_sleek_impala This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="haraheru/gensyn-checkpoints-territorial_sleek_impala", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
jmalejandrob79/nrmexpwht	jmalejandrob79	2025-05-04T05:23:43Z	80	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	2025-04-28T15:10:28Z	--- 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: nrmexpwht --- # Nrmexpwht <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 `nrmexpwht` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "nrmexpwht", "lora_weights": "https://huggingface.co/jmalejandrob79/nrmexpwht/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('jmalejandrob79/nrmexpwht', weight_name='lora.safetensors') image = pipeline('nrmexpwht').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: 4100 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/jmalejandrob79/nrmexpwht/discussions) to add images that show off what you’ve made with this LoRA.
ASethi04/meta-llama-Llama-3.1-8B-opc-sft-100000-lora-4-0.0001	ASethi04	2025-05-04T05:14:18Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "endpoints_compatible", "region:us" ]	null	2025-05-04T04:19:17Z	--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: meta-llama-Llama-3.1-8B-opc-sft-100000-lora-4-0.0001 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for meta-llama-Llama-3.1-8B-opc-sft-100000-lora-4-0.0001 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B). 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="ASethi04/meta-llama-Llama-3.1-8B-opc-sft-100000-lora-4-0.0001", 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/torchql-org/huggingface/runs/a0fwnepa) This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
flyingbugs/Qwen2.5-math-7B-openr1-math-tokenprune	flyingbugs	2025-05-04T05:12:58Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "sft", "conversational", "dataset:flyingbugs/open-r1-pruned-keep-0.5-token-prune", "base_model:flyingbugs/Qwen2.5-Math-7B-Instruct", "base_model:finetune:flyingbugs/Qwen2.5-Math-7B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-02T01:22:23Z	--- base_model: flyingbugs/Qwen2.5-Math-7B-Instruct datasets: flyingbugs/open-r1-pruned-keep-0.5-token-prune library_name: transformers model_name: Qwen2.5-math-7B-openr1-math-tokenprune tags: - generated_from_trainer - open-r1 - trl - sft licence: license --- # Model Card for Qwen2.5-math-7B-openr1-math-tokenprune This model is a fine-tuned version of [flyingbugs/Qwen2.5-Math-7B-Instruct](https://huggingface.co/flyingbugs/Qwen2.5-Math-7B-Instruct) on the [flyingbugs/open-r1-pruned-keep-0.5-token-prune](https://huggingface.co/datasets/flyingbugs/open-r1-pruned-keep-0.5-token-prune) 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="flyingbugs/Qwen2.5-math-7B-openr1-math-tokenprune", 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/jjh233/huggingface/runs/aeaptczf) This model was trained with SFT. ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.51.3 - Pytorch: 2.5.1 - 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf	RichardErkhov	2025-05-04T05:12:08Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T01:53:42Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) IE_L3_1000steps_1e6rate_SFT - GGUF - Model creator: https://huggingface.co/tsavage68/ - Original model: https://huggingface.co/tsavage68/IE_L3_1000steps_1e6rate_SFT/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [IE_L3_1000steps_1e6rate_SFT.Q2_K.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q2_K.gguf) \| Q2_K \| 2.96GB \| \| [IE_L3_1000steps_1e6rate_SFT.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.IQ3_XS.gguf) \| IQ3_XS \| 3.28GB \| \| [IE_L3_1000steps_1e6rate_SFT.IQ3_S.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.IQ3_S.gguf) \| IQ3_S \| 3.43GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q3_K_S.gguf) \| Q3_K_S \| 3.41GB \| \| [IE_L3_1000steps_1e6rate_SFT.IQ3_M.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.IQ3_M.gguf) \| IQ3_M \| 3.52GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q3_K.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q3_K.gguf) \| Q3_K \| 3.74GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q3_K_M.gguf) \| Q3_K_M \| 3.74GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q3_K_L.gguf) \| Q3_K_L \| 4.03GB \| \| [IE_L3_1000steps_1e6rate_SFT.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.IQ4_XS.gguf) \| IQ4_XS \| 4.18GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q4_0.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q4_0.gguf) \| Q4_0 \| 4.34GB \| \| [IE_L3_1000steps_1e6rate_SFT.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.IQ4_NL.gguf) \| IQ4_NL \| 4.38GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q4_K_S.gguf) \| Q4_K_S \| 4.37GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q4_K.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q4_K.gguf) \| Q4_K \| 4.58GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q4_K_M.gguf) \| Q4_K_M \| 4.58GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q4_1.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q4_1.gguf) \| Q4_1 \| 4.78GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q5_0.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q5_0.gguf) \| Q5_0 \| 5.21GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q5_K_S.gguf) \| Q5_K_S \| 5.21GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q5_K.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q5_K.gguf) \| Q5_K \| 5.34GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q5_K_M.gguf) \| Q5_K_M \| 5.34GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q5_1.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q5_1.gguf) \| Q5_1 \| 5.65GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q6_K.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q6_K.gguf) \| Q6_K \| 6.14GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q8_0.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q8_0.gguf) \| Q8_0 \| 7.95GB \| Original model description: --- library_name: transformers license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - trl - sft - generated_from_trainer model-index: - name: IE_L3_1000steps_1e6rate_SFT 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. --> # IE_L3_1000steps_1e6rate_SFT This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6162 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.8795 \| 0.4 \| 50 \| 1.7359 \| \| 1.5557 \| 0.8 \| 100 \| 1.5149 \| \| 1.5505 \| 1.2 \| 150 \| 1.4878 \| \| 1.4839 \| 1.6 \| 200 \| 1.4811 \| \| 1.4928 \| 2.0 \| 250 \| 1.4778 \| \| 1.3677 \| 2.4 \| 300 \| 1.4931 \| \| 1.3947 \| 2.8 \| 350 \| 1.4940 \| \| 1.1632 \| 3.2 \| 400 \| 1.5277 \| \| 1.2544 \| 3.6 \| 450 \| 1.5207 \| \| 1.147 \| 4.0 \| 500 \| 1.5292 \| \| 1.1403 \| 4.4 \| 550 \| 1.5664 \| \| 1.0704 \| 4.8 \| 600 \| 1.5711 \| \| 1.0585 \| 5.2 \| 650 \| 1.6079 \| \| 1.0515 \| 5.6 \| 700 \| 1.6006 \| \| 0.9566 \| 6.0 \| 750 \| 1.6039 \| \| 0.9733 \| 6.4 \| 800 \| 1.6169 \| \| 0.9837 \| 6.8 \| 850 \| 1.6162 \| \| 0.9766 \| 7.2 \| 900 \| 1.6158 \| \| 0.924 \| 7.6 \| 950 \| 1.6164 \| \| 1.0258 \| 8.0 \| 1000 \| 1.6162 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.0.0+cu117 - Datasets 3.0.0 - Tokenizers 0.19.1
Moyer01/Berg	Moyer01	2025-05-04T05:10:56Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2025-05-04T05:10:56Z	--- license: artistic-2.0 ---
JorG941/unsloth_test	JorG941	2025-05-04T05:10:45Z	0	0	null	[ "gguf", "llama", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T05:00:48Z	--- license: apache-2.0 ---
psyonp/Final-Llama-Toxicity-Response-2	psyonp	2025-05-04T05:08:30Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T05:04:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. 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mveroe/Qwen2.5-1.5B-Instruct-safecoder-1.5-Code-safecoder_reg_full_safecoder_bd	mveroe	2025-05-04T05:07:34Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T22:23:22Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-1.5B-Instruct tags: - generated_from_trainer model-index: - name: Qwen2.5-1.5B-Instruct-safecoder-1.5-Code-safecoder_reg_full_safecoder_bd 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. --> # Qwen2.5-1.5B-Instruct-safecoder-1.5-Code-safecoder_reg_full_safecoder_bd This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAFACTOR and the args are: No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - training_steps: 2000 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.7.0+cu126 - Datasets 3.5.1 - Tokenizers 0.21.1
varunsingh2191/open-llama3b-finetuned-lora	varunsingh2191	2025-05-04T05:04:31Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T05:04:23Z	--- 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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dylanewbie/whisper-large-v2-ft-tms-good-and-bad-60-250504-v2	dylanewbie	2025-05-04T05:03:05Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:openai/whisper-large-v2", "base_model:adapter:openai/whisper-large-v2", "license:apache-2.0", "region:us" ]	null	2025-05-04T05:03:00Z	--- base_model: openai/whisper-large-v2 library_name: peft license: apache-2.0 tags: - generated_from_trainer model-index: - name: whisper-large-v2-ft-tms-good-and-bad-60-250504-v2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-large-v2-ft-tms-good-and-bad-60-250504-v2 This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5119 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 64 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 12.6652 \| 1.0 \| 1 \| 12.6099 \| \| 12.7284 \| 2.0 \| 2 \| 12.6099 \| \| 12.6979 \| 3.0 \| 3 \| 12.6099 \| \| 12.8604 \| 4.0 \| 4 \| 12.6099 \| \| 12.6964 \| 5.0 \| 5 \| 12.6099 \| \| 12.6169 \| 6.0 \| 6 \| 12.6099 \| \| 12.5936 \| 7.0 \| 7 \| 12.6099 \| \| 12.6436 \| 8.0 \| 8 \| 12.6099 \| \| 12.6892 \| 9.0 \| 9 \| 12.5756 \| \| 12.6292 \| 10.0 \| 10 \| 12.4211 \| \| 12.3777 \| 11.0 \| 11 \| 12.1817 \| \| 12.3014 \| 12.0 \| 12 \| 11.8436 \| \| 11.9713 \| 13.0 \| 13 \| 11.8436 \| \| 11.916 \| 14.0 \| 14 \| 11.4236 \| \| 11.3473 \| 15.0 \| 15 \| 10.9360 \| \| 10.7909 \| 16.0 \| 16 \| 10.3283 \| \| 10.3267 \| 17.0 \| 17 \| 9.6044 \| \| 9.4613 \| 18.0 \| 18 \| 8.7731 \| \| 8.7764 \| 19.0 \| 19 \| 7.9344 \| \| 7.6595 \| 20.0 \| 20 \| 7.0902 \| \| 6.8138 \| 21.0 \| 21 \| 6.1688 \| \| 5.8664 \| 22.0 \| 22 \| 5.3031 \| \| 5.4038 \| 23.0 \| 23 \| 4.9312 \| \| 5.1601 \| 24.0 \| 24 \| 4.7820 \| \| 4.9727 \| 25.0 \| 25 \| 4.6886 \| \| 4.954 \| 26.0 \| 26 \| 4.6085 \| \| 4.8044 \| 27.0 \| 27 \| 4.5212 \| \| 4.6758 \| 28.0 \| 28 \| 4.4243 \| \| 4.6124 \| 29.0 \| 29 \| 4.3207 \| \| 4.5222 \| 30.0 \| 30 \| 4.2157 \| \| 4.3924 \| 31.0 \| 31 \| 4.1097 \| \| 4.2287 \| 32.0 \| 32 \| 4.0007 \| \| 4.1772 \| 33.0 \| 33 \| 3.8919 \| \| 4.0695 \| 34.0 \| 34 \| 3.7831 \| \| 3.9619 \| 35.0 \| 35 \| 3.6808 \| \| 3.734 \| 36.0 \| 36 \| 3.5853 \| \| 3.6489 \| 37.0 \| 37 \| 3.4984 \| \| 3.6132 \| 38.0 \| 38 \| 3.4186 \| \| 3.472 \| 39.0 \| 39 \| 3.3445 \| \| 3.3173 \| 40.0 \| 40 \| 3.2727 \| \| 3.2766 \| 41.0 \| 41 \| 3.2021 \| \| 3.1815 \| 42.0 \| 42 \| 3.1324 \| \| 3.1 \| 43.0 \| 43 \| 3.0623 \| \| 3.0455 \| 44.0 \| 44 \| 2.9923 \| \| 2.9112 \| 45.0 \| 45 \| 2.9198 \| \| 2.8782 \| 46.0 \| 46 \| 2.8427 \| \| 2.8031 \| 47.0 \| 47 \| 2.7588 \| \| 2.6295 \| 48.0 \| 48 \| 2.6644 \| \| 2.5573 \| 49.0 \| 49 \| 2.5570 \| \| 2.3889 \| 50.0 \| 50 \| 2.4584 \| \| 2.316 \| 51.0 \| 51 \| 2.3695 \| \| 2.1967 \| 52.0 \| 52 \| 2.2957 \| \| 2.1272 \| 53.0 \| 53 \| 2.2318 \| \| 2.0393 \| 54.0 \| 54 \| 2.1753 \| \| 1.9701 \| 55.0 \| 55 \| 2.1288 \| \| 1.9604 \| 56.0 \| 56 \| 2.0918 \| \| 1.9301 \| 57.0 \| 57 \| 2.0618 \| \| 1.8725 \| 58.0 \| 58 \| 2.0354 \| \| 1.8635 \| 59.0 \| 59 \| 2.0108 \| \| 1.8153 \| 60.0 \| 60 \| 1.9883 \| \| 1.7738 \| 61.0 \| 61 \| 1.9668 \| \| 1.7451 \| 62.0 \| 62 \| 1.9461 \| \| 1.7126 \| 63.0 \| 63 \| 1.9264 \| \| 1.6683 \| 64.0 \| 64 \| 1.9074 \| \| 1.645 \| 65.0 \| 65 \| 1.8894 \| \| 1.6364 \| 66.0 \| 66 \| 1.8724 \| \| 1.6139 \| 67.0 \| 67 \| 1.8558 \| \| 1.5709 \| 68.0 \| 68 \| 1.8405 \| \| 1.5621 \| 69.0 \| 69 \| 1.8259 \| \| 1.5538 \| 70.0 \| 70 \| 1.8118 \| \| 1.535 \| 71.0 \| 71 \| 1.7986 \| \| 1.5007 \| 72.0 \| 72 \| 1.7854 \| \| 1.4835 \| 73.0 \| 73 \| 1.7729 \| \| 1.4732 \| 74.0 \| 74 \| 1.7607 \| \| 1.4428 \| 75.0 \| 75 \| 1.7488 \| \| 1.4459 \| 76.0 \| 76 \| 1.7368 \| \| 1.4287 \| 77.0 \| 77 \| 1.7247 \| \| 1.4191 \| 78.0 \| 78 \| 1.7124 \| \| 1.401 \| 79.0 \| 79 \| 1.7001 \| \| 1.3784 \| 80.0 \| 80 \| 1.6870 \| \| 1.3731 \| 81.0 \| 81 \| 1.6744 \| \| 1.3668 \| 82.0 \| 82 \| 1.6618 \| \| 1.3465 \| 83.0 \| 83 \| 1.6490 \| \| 1.3381 \| 84.0 \| 84 \| 1.6363 \| \| 1.329 \| 85.0 \| 85 \| 1.6228 \| \| 1.3128 \| 86.0 \| 86 \| 1.6104 \| \| 1.3025 \| 87.0 \| 87 \| 1.5992 \| \| 1.2873 \| 88.0 \| 88 \| 1.5880 \| \| 1.2777 \| 89.0 \| 89 \| 1.5778 \| \| 1.2738 \| 90.0 \| 90 \| 1.5686 \| \| 1.2671 \| 91.0 \| 91 \| 1.5597 \| \| 1.2636 \| 92.0 \| 92 \| 1.5518 \| \| 1.2564 \| 93.0 \| 93 \| 1.5451 \| \| 1.2429 \| 94.0 \| 94 \| 1.5387 \| \| 1.2498 \| 95.0 \| 95 \| 1.5330 \| \| 1.2365 \| 96.0 \| 96 \| 1.5277 \| \| 1.2289 \| 97.0 \| 97 \| 1.5232 \| \| 1.2182 \| 98.0 \| 98 \| 1.5189 \| \| 1.2133 \| 99.0 \| 99 \| 1.5151 \| \| 1.2252 \| 100.0 \| 100 \| 1.5119 \| ### Framework versions - PEFT 0.13.0 - Transformers 4.45.1 - Pytorch 2.5.0+cu124 - Datasets 2.21.0 - Tokenizers 0.20.0
alissacielecki/attention-efficientnet-b0-gps	alissacielecki	2025-05-04T05:02:39Z	0	0	transformers	[ "transformers", "safetensors", "efficientnet", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-04T05:02:35Z	--- 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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Membersuger/Euro_34	Membersuger	2025-05-04T04:59:06Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T04:42:57Z	--- 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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DevQuasar/JetBrains.deepseek-coder-1.3B-kexer-GGUF	DevQuasar	2025-05-04T04:57:54Z	0	0	null	[ "gguf", "text-generation", "base_model:JetBrains/deepseek-coder-1.3B-kexer", "base_model:quantized:JetBrains/deepseek-coder-1.3B-kexer", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T04:48:27Z	--- base_model: - JetBrains/deepseek-coder-1.3B-kexer pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [JetBrains/deepseek-coder-1.3B-kexer](https://huggingface.co/JetBrains/deepseek-coder-1.3B-kexer) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
psyonp/Final-Llama-Question-TTR-2	psyonp	2025-05-04T04:56:35Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T04:52:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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kokovova/e8e0cff8-f691-4f95-820a-2d1c20fdeaaa	kokovova	2025-05-04T04:44:04Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2-0.5B", "base_model:adapter:unsloth/Qwen2-0.5B", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T04:42:10Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2-0.5B tags: - axolotl - generated_from_trainer model-index: - name: e8e0cff8-f691-4f95-820a-2d1c20fdeaaa 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Qwen2-0.5B bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - cccd8bfc08aa015e_train_data.json ds_type: json format: custom path: /workspace/input_data/cccd8bfc08aa015e_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: kokovova/e8e0cff8-f691-4f95-820a-2d1c20fdeaaa hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/cccd8bfc08aa015e_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 1eed07a3-09fb-4f94-94e6-3315f2bfa239 wandb_project: s56-4 wandb_run: your_name wandb_runid: 1eed07a3-09fb-4f94-94e6-3315f2bfa239 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # e8e0cff8-f691-4f95-820a-2d1c20fdeaaa This model is a fine-tuned version of [unsloth/Qwen2-0.5B](https://huggingface.co/unsloth/Qwen2-0.5B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4382 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.7531 \| 0.0532 \| 200 \| 1.4382 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
marialvsantiago/40a7f26e-c767-488f-84a4-54a7e6ae1c0f	marialvsantiago	2025-05-04T04:42:59Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen1.5-7B", "base_model:adapter:Qwen/Qwen1.5-7B", "license:other", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T04:37:28Z	--- library_name: peft license: other base_model: Qwen/Qwen1.5-7B tags: - axolotl - generated_from_trainer model-index: - name: 40a7f26e-c767-488f-84a4-54a7e6ae1c0f 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen1.5-7B bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 91bfbfdbea09c1d9_train_data.json ds_type: json format: custom path: /workspace/input_data/91bfbfdbea09c1d9_train_data.json type: field_input: body field_instruction: title field_output: dominant_topic_name format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: marialvsantiago/40a7f26e-c767-488f-84a4-54a7e6ae1c0f hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/91bfbfdbea09c1d9_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 9b5d3f76-d3c0-43b4-9583-52754ea37d90 wandb_project: s56-33 wandb_run: your_name wandb_runid: 9b5d3f76-d3c0-43b4-9583-52754ea37d90 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 40a7f26e-c767-488f-84a4-54a7e6ae1c0f This model is a fine-tuned version of [Qwen/Qwen1.5-7B](https://huggingface.co/Qwen/Qwen1.5-7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4604 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.2847 \| 0.0917 \| 200 \| 0.4604 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
DevQuasar/Qwen.Qwen3-30B-A3B-GGUF	DevQuasar	2025-05-04T04:40:58Z	413	0	null	[ "gguf", "text-generation", "base_model:Qwen/Qwen3-30B-A3B", "base_model:quantized:Qwen/Qwen3-30B-A3B", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-28T23:26:45Z	--- base_model: - Qwen/Qwen3-30B-A3B pipeline_tag: text-generation --- ## LMStudio users! Please update the chat prompt template of the model. Go to My models -> Actions (gear) edit model default parameters -> Prompt -> Prompt template. Update the Jinja template. Correct JINJA: ``` {%- if tools %} {{- '<\|im_start\|>system\n' }} {%- if messages[0].role == 'system' %} {{- messages[0].content + '\n\n' }} {%- endif %} {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }} {%- for tool in tools %} {{- "\n" }} {{- tool \| tojson }} {%- endfor %} {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><\|im_end\|>\n" }} {%- else %} {%- if messages[0].role == 'system' %} {{- '<\|im_start\|>system\n' + messages[0].content + '<\|im_end\|>\n' }} {%- endif %} {%- endif %} {%- set ns = namespace(multi_step_tool=true, last_query_index=messages\|length - 1) %} {%- for message in messages[::-1] %} {%- set index = (messages\|length - 1) - loop.index0 %} {%- set tool_start = "<tool_response>" %} {%- set tool_start_length = tool_start\|length %} {%- set start_of_message = message.content[:tool_start_length] %} {%- set tool_end = "</tool_response>" %} {%- set tool_end_length = tool_end\|length %} {%- set start_pos = (message.content\|length) - tool_end_length %} {%- if start_pos < 0 %} {%- set start_pos = 0 %} {%- endif %} {%- set end_of_message = message.content[start_pos:] %} {%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %} {%- set ns.multi_step_tool = false %} {%- set ns.last_query_index = index %} {%- endif %} {%- endfor %} {%- for message in messages %} {%- if (message.role == "user") or (message.role == "system" and not loop.first) %} {{- '<\|im_start\|>' + message.role + '\n' + message.content + '<\|im_end\|>' + '\n' }} {%- elif message.role == "assistant" %} {%- set content = message.content %} {%- set reasoning_content = '' %} {%- if message.reasoning_content is defined and message.reasoning_content is not none %} {%- set reasoning_content = message.reasoning_content %} {%- else %} {%- if '</think>' in message.content %} {%- set content = (message.content.split('</think>')\|last).lstrip('\n') %} {%- set reasoning_content = (message.content.split('</think>')\|first).rstrip('\n') %} {%- set reasoning_content = (reasoning_content.split('<think>')\|last).lstrip('\n') %} {%- endif %} {%- endif %} {%- if loop.index0 > ns.last_query_index %} {%- if loop.last or (not loop.last and reasoning_content) %} {{- '<\|im_start\|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }} {%- else %} {{- '<\|im_start\|>' + message.role + '\n' + content }} {%- endif %} {%- else %} {{- '<\|im_start\|>' + message.role + '\n' + content }} {%- endif %} {%- if message.tool_calls %} {%- for tool_call in message.tool_calls %} {%- if (loop.first and content) or (not loop.first) %} {{- '\n' }} {%- endif %} {%- if tool_call.function %} {%- set tool_call = tool_call.function %} {%- endif %} {{- '<tool_call>\n{"name": "' }} {{- tool_call.name }} {{- '", "arguments": ' }} {%- if tool_call.arguments is string %} {{- tool_call.arguments }} {%- else %} {{- tool_call.arguments \| tojson }} {%- endif %} {{- '}\n</tool_call>' }} {%- endfor %} {%- endif %} {{- '<\|im_end\|>\n' }} {%- elif message.role == "tool" %} {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} {{- '<\|im_start\|>user' }} {%- endif %} {{- '\n<tool_response>\n' }} {{- message.content }} {{- '\n</tool_response>' }} {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} {{- '<\|im_end\|>\n' }} {%- endif %} {%- endif %} {%- endfor %} {%- if add_generation_prompt %} {{- '<\|im_start\|>assistant\n' }} {%- if enable_thinking is defined and enable_thinking is false %} {{- '<think>\n\n</think>\n\n' }} {%- endif %} {%- endif %} ``` [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [Qwen/Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
Nasanbuyan/mongolian-gpt2-finetuned	Nasanbuyan	2025-05-04T04:38:23Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:Nasanbuyan/mongolian-gpt2", "base_model:finetune:Nasanbuyan/mongolian-gpt2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T04:24:43Z	--- library_name: transformers base_model: Nasanbuyan/mongolian-gpt2 tags: - generated_from_trainer model-index: - name: mongolian-gpt2-finetuned 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. --> # mongolian-gpt2-finetuned This model is a fine-tuned version of [Nasanbuyan/mongolian-gpt2](https://huggingface.co/Nasanbuyan/mongolian-gpt2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
tianna1121/Qwen3-vLLM	tianna1121	2025-05-04T04:35:26Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-04T04:03:56Z	--- base_model: unsloth/qwen3-14b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: tianna1121 - License: apache-2.0 - Finetuned from model : unsloth/qwen3-14b-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
xuan1228/313706034	xuan1228	2025-05-04T04:25:22Z	0	0	null	[ "safetensors", "region:us" ]	null	2025-04-07T12:24:34Z	# 中文多選題問答模型 ## 模型資訊 - 基礎模型: Qwen/Qwen2.5-7B-Instruct - 微調方法: LoRA (Low-Rank Adaptation) - 訓練數據: 中文多選題問答 - 訓練日期: 2025-05-04 ## 用途此模型經過微調，可以分析中文多選題並選出最合適的答案（A、B、C或D選項）。 ## 訓練參數 - LoRA rank: 16 - LoRA alpha: 32 - Learning rate: 0.0003 - Epochs: 3
vamcrizer/test-finetune	vamcrizer	2025-05-04T04:07:51Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "gemma3", "en", "base_model:unsloth/gemma-3-4b-it", "base_model:quantized:unsloth/gemma-3-4b-it", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T04:05:50Z	--- base_model: unsloth/gemma-3-4b-it tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: vamcrizer - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it - Dataset used : FreedomIntelligence/medical-o1-reasoning-SFT This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ubergarm/Qwen3-30B-A3B-GGUF	ubergarm	2025-05-04T04:04:13Z	20	7	null	[ "gguf", "imatrix", "qwen3_moe", "conversational", "ik_llama.cpp", "text-generation", "base_model:Qwen/Qwen3-30B-A3B", "base_model:quantized:Qwen/Qwen3-30B-A3B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-05-02T00:10:00Z	--- quantized_by: ubergarm pipeline_tag: text-generation base_model: Qwen/Qwen3-30B-A3B license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B/blob/main/LICENSE base_model_relation: quantized tags: - imatrix - qwen3_moe - conversational - ik_llama.cpp --- ## `ik_llama.cpp` imatrix Quantizations of Qwen/Qwen3-30B-A3B This quant collection REQUIRES [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp/) fork to support advanced non-linear SotA quants. Do not download these big files and expect them to run on mainline vanilla llama.cpp, ollama, LM Studio, KoboldCpp, etc! These quants provide best in class quality for the given memory footprint. ## Big Thanks Shout out to Wendell and the Level1Techs crew, the community [Forums](https://forum.level1techs.com/t/deepseek-deep-dive-r1-at-home/225826), [YouTube Channel](https://www.youtube.com/@Level1Techs)! BIG thanks for providing BIG hardware expertise and access to run these experiments and make these great quants available to the community!!! Also thanks to all the folks in the quanting and inferencing community here and on `r/LocalLLaMA` for tips and tricks helping each other run all the fun new models! Excited to share and learn together. Thanks! ## Quant Collection So far these are my best recipes offering the great quality in good memory footprint breakpoints. #### ubergarm/Qwen3-30B-A3B-mix-IQ4_K This quant is provides the best in class quality while providing good speed performance. This quant is designed to run with over 32k context using GPU performant f16 KV-Cache in under 24GB VRAM GPU. You could also try offload to CPU using `-nkvo -ctk q8_0 -ctv q8_0` and use `-rtr` for RAM optimized tensor packing on startup (without `mmap()` support) taking ~18396MiB of VRAM or less by offloading repeating layers to CPU as well at decreased speed. ``` 17.679 GiB (4.974 BPW) f32: 241 tensors q8_0: 6 tensors iq4_k: 96 tensors iq5_k: 48 tensors iq6_k: 188 tensors Final estimate: PPL = 9.1184 +/- 0.07278 (wiki-test.raw, compare to BF16 at 9.0703 +/- 0.07223) NOTE: Benchmarks including PPL with `wiki.test.raw` and KLD with `ubergarm-kld-test-corpus.txt` are looking interesting! Will publish soon! ``` ## Quick Start #### `ik_llama.cpp` API server for GPU inferencing ```bash # This example for ~21468MiB VRAM Usage ./build/bin/llama-server --model ubergarm/Qwen3-30B-A3B-GGUF/Qwen3-30B-A3B-mix-IQ4_K \ --alias ubergarm/Qwen3-30B-A3B-mix-IQ4_K \ -fa \ -ctk f16 -ctv f16 \ -c 32768 \ -fmoe \ -ngl 99 \ --threads 1 --host 127.0.0.1 \ --port 8080 ``` If you want more context and/or less VRAM usage, you can try: * Smaller KV Cache quantization `-ctk q4_0 -ctv q4_0` If you want more throughput you could try: * Increase context to max limit for your VRAM * use `--parallel N` to have (context / N) available per slot * use an asyncio client and keep the queue full ## Quantization <details> <summary>👈Secret Recipe</summary> ```bash #!/usr/bin/env bash custom=" # Attention (give Layer 0 a little extra as it scores lowest on cosine-similarity score) blk\.0\.attn_k.=q8_0 blk\.0\.attn_q.=q8_0 blk\.0\.attn_v.=q8_0 blk\.0\.attn_output.=q8_0 blk\..\.attn_k.=iq6_k blk\..\.attn_q.=iq6_k blk\..\.attn_v.=iq6_k blk\..\.attn_output.=iq6_k # Token Embedding (put these second so attn_output regex doesn catch too early) token_embd\.weight=q8_0 output\.weight=q8_0 # Experts blk\..\.ffn_down_exps\.weight=iq5_k blk\..\.ffn_(gate\|up)_exps\.weight=iq4_k " custom=$( echo "$custom" \| grep -v '^#' \| \ sed -Ez 's:\n+:,:g;s:,$::;s:^,::' ) ./build/bin/llama-quantize \ --custom-q "$custom" \ --imatrix /mnt/raid/models/ubergarm/Qwen3-30B-A3B-GGUF/imatrix-Qwen3-30B-A3B.dat \ /mnt/raid/models/Qwen/Qwen3-30B-A3B/Qwen3-30B-A3B-BF16-00001-of-00002.gguf \ /mnt/raid/models/ubergarm/Qwen3-30B-A3B-GGUF/Qwen3-30B-A3B-mix-IQ4_K.gguf \ IQ4_K \ 24 ``` </details> ## Discussion TODO: Discuss some about comparing quants e.g. bartowski, unsloth, and mradermacher including "quality" and "speed". ## Benchmarks In first tests with `llama-sweep-bench` I'm getting over 1600 tok/sec PP and 105 tok/sec TG on my 3090TI FE 24GB VRAM. It does slow down of course as it gets deeper into the full 32k context. Check the linked Benchmarks Discussion for updates as this is all pretty fresh right now. Pretty amazing performance both in terms of generation quality and speed for a model this size! ![Benchmarks showing these peak 1600 tok/sec PP and 105 tok/sec TG fully offloaded on 3090TI FE 24GB VRAM](images/benchmarks-01.png "Benchmarks showing these peak 1600 tok/sec PP and 105 tok/sec TG fully offloaded on 3090TI FE 24GB VRAM") ![Benchmarks showing Token Probability Deviation Percentiles](images/qwen3-30b-fig-09.png "Benchmarks showing Token Probability Deviation Percentiles") ## References * [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp/) * [ik_llama.cpp Getting Started Guide](https://github.com/ikawrakow/ik_llama.cpp/discussions/258) * [ik_llama.cpp Benchmarks Discussion](https://github.com/ikawrakow/ik_llama.cpp/discussions/357) * [imatrix calibration_data_v5_rc.txt](https://gist.github.com/tristandruyen/9e207a95c7d75ddf37525d353e00659c#file-calibration_data_v5_rc-txt)
mradermacher/8-bit_medical_Qwen1.5-7B-Chat-GGUF	mradermacher	2025-05-04T04:00:15Z	0	0	transformers	[ "transformers", "gguf", "medical", "llama-factory", "en", "base_model:Roselia-penguin/8-bit_medical_Qwen1.5-7B-Chat", "base_model:quantized:Roselia-penguin/8-bit_medical_Qwen1.5-7B-Chat", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T16:37:55Z	--- base_model: Roselia-penguin/8-bit_medical_Qwen1.5-7B-Chat language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - medical - llama-factory --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Roselia-penguin/8-bit_medical_Qwen1.5-7B-Chat <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/8-bit_medical_Qwen1.5-7B-Chat-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/8-bit_medical_Qwen1.5-7B-Chat-GGUF/resolve/main/8-bit_medical_Qwen1.5-7B-Chat.Q2_K.gguf) \| Q2_K \| 3.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/8-bit_medical_Qwen1.5-7B-Chat-GGUF/resolve/main/8-bit_medical_Qwen1.5-7B-Chat.Q3_K_S.gguf) \| Q3_K_S \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/8-bit_medical_Qwen1.5-7B-Chat-GGUF/resolve/main/8-bit_medical_Qwen1.5-7B-Chat.Q3_K_M.gguf) \| Q3_K_M \| 4.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/8-bit_medical_Qwen1.5-7B-Chat-GGUF/resolve/main/8-bit_medical_Qwen1.5-7B-Chat.Q3_K_L.gguf) \| Q3_K_L \| 4.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/8-bit_medical_Qwen1.5-7B-Chat-GGUF/resolve/main/8-bit_medical_Qwen1.5-7B-Chat.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/8-bit_medical_Qwen1.5-7B-Chat-GGUF/resolve/main/8-bit_medical_Qwen1.5-7B-Chat.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/8-bit_medical_Qwen1.5-7B-Chat-GGUF/resolve/main/8-bit_medical_Qwen1.5-7B-Chat.Q4_K_M.gguf) \| Q4_K_M \| 4.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/8-bit_medical_Qwen1.5-7B-Chat-GGUF/resolve/main/8-bit_medical_Qwen1.5-7B-Chat.Q5_K_S.gguf) \| Q5_K_S \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/8-bit_medical_Qwen1.5-7B-Chat-GGUF/resolve/main/8-bit_medical_Qwen1.5-7B-Chat.Q5_K_M.gguf) \| Q5_K_M \| 5.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/8-bit_medical_Qwen1.5-7B-Chat-GGUF/resolve/main/8-bit_medical_Qwen1.5-7B-Chat.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/8-bit_medical_Qwen1.5-7B-Chat-GGUF/resolve/main/8-bit_medical_Qwen1.5-7B-Chat.Q8_0.gguf) \| Q8_0 \| 8.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/8-bit_medical_Qwen1.5-7B-Chat-GGUF/resolve/main/8-bit_medical_Qwen1.5-7B-Chat.f16.gguf) \| f16 \| 15.5 \| 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 -->
giyong/whisper-large-v3_ADReSSo	giyong	2025-05-04T03:57:52Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "audio-classification", "generated_from_trainer", "endpoints_compatible", "region:us" ]	audio-classification	2025-05-03T12:19:51Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: whisper-large-v3_ADReSSo results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-large-v3_ADReSSo This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.51.3 - Pytorch 2.5.0+cu118 - Datasets 2.14.6 - Tokenizers 0.21.1
isbistloui/fr-llama-aiml428-a2	isbistloui	2025-05-04T03:54:29Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T03:54:11Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
dylanewbie/whisper-large-v2-ft-cv16-1__car350_tms-good-30-250504-v1	dylanewbie	2025-05-04T03:51:54Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:openai/whisper-large-v2", "base_model:adapter:openai/whisper-large-v2", "license:apache-2.0", "region:us" ]	null	2025-05-04T03:51:49Z	--- base_model: openai/whisper-large-v2 library_name: peft license: apache-2.0 tags: - generated_from_trainer model-index: - name: whisper-large-v2-ft-cv16-1__car350_tms-good-30-250504-v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-large-v2-ft-cv16-1__car350_tms-good-30-250504-v1 This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1000 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 64 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 4.0498 \| 1.0 \| 177 \| 1.2594 \| \| 0.3113 \| 2.0 \| 354 \| 0.1046 \| \| 0.1107 \| 3.0 \| 531 \| 0.0912 \| \| 0.0852 \| 4.0 \| 708 \| 0.0880 \| \| 0.0688 \| 5.0 \| 885 \| 0.0886 \| \| 0.0563 \| 6.0 \| 1062 \| 0.0911 \| \| 0.0469 \| 7.0 \| 1239 \| 0.0928 \| \| 0.0397 \| 8.0 \| 1416 \| 0.0955 \| \| 0.0341 \| 9.0 \| 1593 \| 0.0982 \| \| 0.0304 \| 10.0 \| 1770 \| 0.1000 \| ### Framework versions - PEFT 0.13.0 - Transformers 4.45.1 - Pytorch 2.5.0+cu124 - Datasets 2.21.0 - Tokenizers 0.20.0
michel24/divin	michel24	2025-05-04T03:51:49Z	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	2025-05-04T03:27:36Z	--- 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: divin --- # Divin <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 `divin` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "divin", "lora_weights": "https://huggingface.co/michel24/divin/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('michel24/divin', weight_name='lora.safetensors') image = pipeline('divin').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: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/michel24/divin/discussions) to add images that show off what you’ve made with this LoRA.
phililp-arnold/f6edfa5d-2dcd-4259-8bff-8ca02fa9721a	phililp-arnold	2025-05-04T03:50:58Z	0	0	peft	[ "peft", "generated_from_trainer", "base_model:NousResearch/Hermes-2-Pro-Mistral-7B", "base_model:adapter:NousResearch/Hermes-2-Pro-Mistral-7B", "region:us" ]	null	2025-05-04T03:50:35Z	--- library_name: peft tags: - generated_from_trainer base_model: NousResearch/Hermes-2-Pro-Mistral-7B model-index: - name: phililp-arnold/f6edfa5d-2dcd-4259-8bff-8ca02fa9721a 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. --> # phililp-arnold/f6edfa5d-2dcd-4259-8bff-8ca02fa9721a This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7014 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
giro96/baru	giro96	2025-05-04T03:45:44Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2025-05-04T03:45:44Z	--- license: bigscience-openrail-m ---
mlfoundations-dev/e1_code_ms_phi	mlfoundations-dev	2025-05-04T03:42:10Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T22:40:23Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: e1_code_ms_phi 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. --> # e1_code_ms_phi This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/e1_code_ms_phi dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - total_eval_batch_size: 128 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1 - Datasets 3.1.0 - Tokenizers 0.20.3
mradermacher/MedicalChatBot-Qwen3-4b-i1-GGUF	mradermacher	2025-05-04T03:40:49Z	0	1	transformers	[ "transformers", "gguf", "medical", "zh", "en", "dataset:FreedomIntelligence/Huatuo26M-Lite", "base_model:Tommi09/MedicalChatBot-Qwen3-4b", "base_model:quantized:Tommi09/MedicalChatBot-Qwen3-4b", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-04T01:02:01Z	--- base_model: Tommi09/MedicalChatBot-Qwen3-4b datasets: - FreedomIntelligence/Huatuo26M-Lite language: - zh - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - medical --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Tommi09/MedicalChatBot-Qwen3-4b <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-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/MedicalChatBot-Qwen3-4b-i1-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.2 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-i1-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.2 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-i1-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-i1-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-i1-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.i1-IQ2_S.gguf) \| i1-IQ2_S \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-i1-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.i1-IQ2_M.gguf) \| i1-IQ2_M \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-i1-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 1.7 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-i1-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.i1-Q2_K.gguf) \| i1-Q2_K \| 1.8 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-i1-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 1.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-i1-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-i1-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 2.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-i1-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.i1-IQ3_S.gguf) \| i1-IQ3_S \| 2.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-i1-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.i1-IQ3_M.gguf) \| i1-IQ3_M \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-i1-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 2.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-i1-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 2.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-i1-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-i1-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.i1-Q4_0.gguf) \| i1-Q4_0 \| 2.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-i1-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 2.5 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-i1-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 2.5 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-i1-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 2.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-i1-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.i1-Q4_1.gguf) \| i1-Q4_1 \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-i1-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-i1-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalChatBot-Qwen3-4b-i1-GGUF/resolve/main/MedicalChatBot-Qwen3-4b.i1-Q6_K.gguf) \| i1-Q6_K \| 3.4 \| 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 -->
jdchang/full-with-label-bs-1024-sg-2-step-6318	jdchang	2025-05-04T03:40:48Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-05-04T03:40:39Z	--- 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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joboffer/a3ccab33-6a2b-4673-b4b6-a79bd48d8422	joboffer	2025-05-04T03:35:12Z	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:NousResearch/Hermes-2-Pro-Mistral-7B", "base_model:adapter:NousResearch/Hermes-2-Pro-Mistral-7B", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T03:28:54Z	--- library_name: peft license: apache-2.0 base_model: NousResearch/Hermes-2-Pro-Mistral-7B tags: - axolotl - generated_from_trainer model-index: - name: a3ccab33-6a2b-4673-b4b6-a79bd48d8422 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: NousResearch/Hermes-2-Pro-Mistral-7B bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - ee162119980ef3aa_train_data.json ds_type: json format: custom path: /workspace/input_data/ee162119980ef3aa_train_data.json type: field_input: Complex_CoT field_instruction: Question field_output: Response format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: joboffer/a3ccab33-6a2b-4673-b4b6-a79bd48d8422 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/ee162119980ef3aa_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 0fc83b9b-bde4-4e80-bc05-f9499d9e8685 wandb_project: s56-33 wandb_run: your_name wandb_runid: 0fc83b9b-bde4-4e80-bc05-f9499d9e8685 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # a3ccab33-6a2b-4673-b4b6-a79bd48d8422 This model is a fine-tuned version of [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7450 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.7391 \| 0.0900 \| 200 \| 0.7450 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
frozenturtle/Qwen3-30B-A3B-Q8_0-GGUF	frozenturtle	2025-05-04T03:29:19Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:Qwen/Qwen3-30B-A3B", "base_model:quantized:Qwen/Qwen3-30B-A3B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-04T03:26:53Z	--- base_model: Qwen/Qwen3-30B-A3B library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B/blob/main/LICENSE pipeline_tag: text-generation tags: - llama-cpp - gguf-my-repo --- # frozenturtle/Qwen3-30B-A3B-Q8_0-GGUF This model was converted to GGUF format from [`Qwen/Qwen3-30B-A3B`](https://huggingface.co/Qwen/Qwen3-30B-A3B) 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) 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 frozenturtle/Qwen3-30B-A3B-Q8_0-GGUF --hf-file qwen3-30b-a3b-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo frozenturtle/Qwen3-30B-A3B-Q8_0-GGUF --hf-file qwen3-30b-a3b-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo frozenturtle/Qwen3-30B-A3B-Q8_0-GGUF --hf-file qwen3-30b-a3b-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo frozenturtle/Qwen3-30B-A3B-Q8_0-GGUF --hf-file qwen3-30b-a3b-q8_0.gguf -c 2048 ```
AnonymousCS/llama-3.1-8B-populism	AnonymousCS	2025-05-04T03:29:19Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-03T19:01:24Z	--- base_model: meta-llama/Llama-3.1-8B-Instruct library_name: transformers model_name: llama-3.1-8B-populism tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for llama-3.1-8B-populism This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct). 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="AnonymousCS/llama-3.1-8B-populism", 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/cecilia-y-sui-washington-unviersity-st-louis/huggingface/runs/0awplmtt) This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0+cu124 - 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}} } ```
rdz-falcon/gemma-3-finetune	rdz-falcon	2025-05-04T03:25:14Z	1	0	transformers	[ "transformers", "gemma3_text", "text-generation", "text-generation-inference", "unsloth", "gemma3", "conversational", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-13T21:59:40Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: rdz-falcon - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ASethi04/meta-llama-Llama-3.1-8B-opc-sft-1000-lora-4-0.0001	ASethi04	2025-05-04T03:23:53Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "endpoints_compatible", "region:us" ]	null	2025-05-04T03:00:36Z	--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: meta-llama-Llama-3.1-8B-opc-sft-1000-lora-4-0.0001 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for meta-llama-Llama-3.1-8B-opc-sft-1000-lora-4-0.0001 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B). 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="ASethi04/meta-llama-Llama-3.1-8B-opc-sft-1000-lora-4-0.0001", 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/torchql-org/huggingface/runs/4kbai0vk) This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
stevensu123/cis6200finaltopp	stevensu123	2025-05-04T03:19:42Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-05-04T03:15:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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ma921/gpt2-large_h_dpo_imdb_noise40_epoch5_gamma1.0	ma921	2025-05-04T03:19:26Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:ma921/gpt2-large-sft-imdb", "base_model:finetune:ma921/gpt2-large-sft-imdb", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T03:17:12Z	--- library_name: transformers license: mit base_model: ma921/gpt2-large-sft-imdb tags: - generated_from_trainer model-index: - name: gpt2-large_h_dpo_imdb_noise40_epoch5_gamma1.0 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. --> # gpt2-large_h_dpo_imdb_noise40_epoch5_gamma1.0 This model is a fine-tuned version of [ma921/gpt2-large-sft-imdb](https://huggingface.co/ma921/gpt2-large-sft-imdb) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 32 - total_train_batch_size: 256 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
aciang/Qwen3-vLLM	aciang	2025-05-04T03:01:33Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-04T02:58:34Z	--- base_model: unsloth/qwen3-14b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: aciang - License: apache-2.0 - Finetuned from model : unsloth/qwen3-14b-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
infogep/913f791c-c18a-429a-b4e0-2cf8902bf92a	infogep	2025-05-04T02:49:53Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2-1.5B-Instruct", "base_model:adapter:Qwen/Qwen2-1.5B-Instruct", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T02:02:57Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2-1.5B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 913f791c-c18a-429a-b4e0-2cf8902bf92a 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: Qwen/Qwen2-1.5B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - d7205510f2601019_train_data.json ds_type: json format: custom path: /workspace/input_data/d7205510f2601019_train_data.json type: field_instruction: en field_output: es format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: infogep/913f791c-c18a-429a-b4e0-2cf8902bf92a hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/d7205510f2601019_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 2048 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 18dd1685-1e33-4a97-9ad6-21b7bb998433 wandb_project: s56-30 wandb_run: your_name wandb_runid: 18dd1685-1e33-4a97-9ad6-21b7bb998433 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 913f791c-c18a-429a-b4e0-2cf8902bf92a This model is a fine-tuned version of [Qwen/Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2024 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.5275 \| 0.0017 \| 200 \| 2.2024 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
TOMFORD79/Fly57	TOMFORD79	2025-05-04T02:49:46Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-04T02:37:46Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
rdyeryu/trygyjyti	rdyeryu	2025-05-04T02:47:12Z	0	0	null	[ "license:bigcode-openrail-m", "region:us" ]	null	2025-05-04T02:47:12Z	--- license: bigcode-openrail-m ---
dimasik1987/905977fe-49c7-4671-8274-0054dadc58ee	dimasik1987	2025-05-04T02:47:08Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2-1.5B-Instruct", "base_model:adapter:Qwen/Qwen2-1.5B-Instruct", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T02:08:11Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2-1.5B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 905977fe-49c7-4671-8274-0054dadc58ee 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: Qwen/Qwen2-1.5B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 1f34408d713e6b26_train_data.json ds_type: json format: custom path: /workspace/input_data/1f34408d713e6b26_train_data.json type: field_instruction: en field_output: ja format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: dimasik1987/905977fe-49c7-4671-8274-0054dadc58ee hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 10 mixed_precision: bf16 mlflow_experiment_name: /tmp/1f34408d713e6b26_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 2048 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: dac36c01-453c-4358-9c23-a55d2a4926d7 wandb_project: s56-7 wandb_run: your_name wandb_runid: dac36c01-453c-4358-9c23-a55d2a4926d7 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 905977fe-49c7-4671-8274-0054dadc58ee This model is a fine-tuned version of [Qwen/Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.2839 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 7.1707 \| 0.0016 \| 150 \| 5.2839 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
DevQuasar/JetBrains.deepseek-coder-6.7B-kexer-GGUF	DevQuasar	2025-05-04T02:44:56Z	0	0	null	[ "gguf", "text-generation", "base_model:JetBrains/deepseek-coder-6.7B-kexer", "base_model:quantized:JetBrains/deepseek-coder-6.7B-kexer", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T01:54:03Z	--- base_model: - JetBrains/deepseek-coder-6.7B-kexer pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [JetBrains/deepseek-coder-6.7B-kexer](https://huggingface.co/JetBrains/deepseek-coder-6.7B-kexer) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
cfgfh456/thtrery	cfgfh456	2025-05-04T02:34:37Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2025-05-04T02:34:37Z	--- license: bigscience-openrail-m ---
OmarHashem80/age_gender_classifier	OmarHashem80	2025-05-04T02:31:16Z	0	0	null	[ "joblib", "region:us" ]	null	2025-05-03T21:19:53Z	# My Scikit-learn Classifier This model classifies inputs using a classical machine learning approach. It was trained using scikit-learn and deployed using Hugging Face's inference API.
jdchang/full-with-label-bs-1024-sg-2-no-dropout-step-486	jdchang	2025-05-04T02:30:54Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-05-04T02:30:43Z	--- 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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memevis/text1	memevis	2025-05-04T02:00:02Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T01:52:40Z	--- 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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mradermacher/Qwen3-0.6B-Base-i1-GGUF	mradermacher	2025-05-04T01:46:30Z	0	1	transformers	[ "transformers", "gguf", "en", "base_model:Qwen/Qwen3-0.6B-Base", "base_model:quantized:Qwen/Qwen3-0.6B-Base", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-03T07:26:34Z	--- base_model: Qwen/Qwen3-0.6B-Base language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Qwen/Qwen3-0.6B-Base _These quants are likely broken_ <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Qwen3-0.6B-Base-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/Qwen3-0.6B-Base-i1-GGUF/resolve/main/Qwen3-0.6B-Base.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.3 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-Base-i1-GGUF/resolve/main/Qwen3-0.6B-Base.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-Base-i1-GGUF/resolve/main/Qwen3-0.6B-Base.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-Base-i1-GGUF/resolve/main/Qwen3-0.6B-Base.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-Base-i1-GGUF/resolve/main/Qwen3-0.6B-Base.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-Base-i1-GGUF/resolve/main/Qwen3-0.6B-Base.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-Base-i1-GGUF/resolve/main/Qwen3-0.6B-Base.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-Base-i1-GGUF/resolve/main/Qwen3-0.6B-Base.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.4 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-Base-i1-GGUF/resolve/main/Qwen3-0.6B-Base.i1-Q2_K.gguf) \| i1-Q2_K \| 0.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-Base-i1-GGUF/resolve/main/Qwen3-0.6B-Base.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-Base-i1-GGUF/resolve/main/Qwen3-0.6B-Base.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-Base-i1-GGUF/resolve/main/Qwen3-0.6B-Base.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.4 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-Base-i1-GGUF/resolve/main/Qwen3-0.6B-Base.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-Base-i1-GGUF/resolve/main/Qwen3-0.6B-Base.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-Base-i1-GGUF/resolve/main/Qwen3-0.6B-Base.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-Base-i1-GGUF/resolve/main/Qwen3-0.6B-Base.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 0.5 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-Base-i1-GGUF/resolve/main/Qwen3-0.6B-Base.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 0.5 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-Base-i1-GGUF/resolve/main/Qwen3-0.6B-Base.i1-Q4_0.gguf) \| i1-Q4_0 \| 0.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-Base-i1-GGUF/resolve/main/Qwen3-0.6B-Base.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 0.5 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-Base-i1-GGUF/resolve/main/Qwen3-0.6B-Base.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 0.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-Base-i1-GGUF/resolve/main/Qwen3-0.6B-Base.i1-Q4_1.gguf) \| i1-Q4_1 \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-Base-i1-GGUF/resolve/main/Qwen3-0.6B-Base.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-Base-i1-GGUF/resolve/main/Qwen3-0.6B-Base.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-Base-i1-GGUF/resolve/main/Qwen3-0.6B-Base.i1-Q6_K.gguf) \| i1-Q6_K \| 0.6 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
abdoxc/ggggg	abdoxc	2025-05-04T01:42:43Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2025-05-04T01:42:42Z	--- license: artistic-2.0 ---
nairaxo/whisper-zulu-parakeet-v2	nairaxo	2025-05-04T01:35:40Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "zu", "dataset:nairaxo/nchlt_speech_corpus_ZULU_20", "base_model:openai/whisper-large-v3-turbo", "base_model:finetune:openai/whisper-large-v3-turbo", "license:mit", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-03T13:30:29Z	--- library_name: transformers language: - zu license: mit base_model: openai/whisper-large-v3-turbo tags: - generated_from_trainer datasets: - nairaxo/nchlt_speech_corpus_ZULU_20 metrics: - wer model-index: - name: Whisper Large Zulu - Abdou Mohamed Naira results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Zulu Speech Corpus type: nairaxo/nchlt_speech_corpus_ZULU_20 args: 'config: zu, split: test' metrics: - name: Wer type: wer value: 5.628113639423725 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Large Zulu - Abdou Mohamed Naira This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the Zulu Speech Corpus dataset. It achieves the following results on the evaluation set: - Loss: 0.0662 - Wer: 5.6281 - Cer: 1.0344 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 2 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\|:------:\| \| 0.5873 \| 0.3621 \| 500 \| 0.2599 \| 27.7097 \| 4.9266 \| \| 0.205 \| 0.7241 \| 1000 \| 0.1537 \| 15.3494 \| 2.6867 \| \| 0.1327 \| 1.0862 \| 1500 \| 0.1233 \| 12.6835 \| 2.2657 \| \| 0.083 \| 1.4482 \| 2000 \| 0.0997 \| 11.4313 \| 3.6999 \| \| 0.0672 \| 1.8103 \| 2500 \| 0.0870 \| 8.9942 \| 2.3747 \| \| 0.0478 \| 2.1723 \| 3000 \| 0.0830 \| 8.1056 \| 1.4903 \| \| 0.028 \| 2.5344 \| 3500 \| 0.0761 \| 7.1092 \| 1.2964 \| \| 0.0231 \| 2.8965 \| 4000 \| 0.0712 \| 6.5706 \| 1.1919 \| \| 0.0112 \| 3.2585 \| 4500 \| 0.0673 \| 5.8839 \| 1.0753 \| \| 0.007 \| 3.6206 \| 5000 \| 0.0662 \| 5.6281 \| 1.0344 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.7.0+cu126 - Datasets 3.5.0 - Tokenizers 0.21.1
Woodsii/XLM_roberta_kaggle_watson	Woodsii	2025-05-04T01:35:22Z	0	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-03T23:04:49Z	--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer model-index: - name: results 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. --> # Finetuned XLM roBERTa for Kaggle's "Contradictory, My Dear Watson" Challenge. This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6759 ## Model description It's in the title, for the most part. ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.7889 \| 1.0 \| 1364 \| 0.7767 \| \| 0.7525 \| 2.0 \| 2728 \| 0.6681 \| \| 0.513 \| 3.0 \| 4092 \| 0.6759 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
giangndm/qwen2.5-omni-7b-mlx-4bit	giangndm	2025-05-04T01:35:08Z	6	0	mlx	[ "mlx", "safetensors", "qwen2_5_omni", "multimodal", "text-generation", "conversational", "en", "base_model:Qwen/Qwen2.5-Omni-7B", "base_model:finetune:Qwen/Qwen2.5-Omni-7B", "license:other", "region:us" ]	text-generation	2025-05-01T15:21:03Z	--- license: other license_name: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-Omni-7B/blob/main/LICENSE language: - en tags: - multimodal - mlx library_name: mlx pipeline_tag: text-generation base_model: Qwen/Qwen2.5-Omni-7B --- # giangndm/qwen2.5-omni-7b-mlx This model [giangndm/qwen2.5-omni-7b-mlx](https://huggingface.co/giangndm/qwen2.5-omni-7b-mlx) was converted to MLX format from [Qwen/Qwen2.5-Omni-7B](https://huggingface.co/Qwen/Qwen2.5-Omni-7B) using mlx-lm version 0.24.0. ## Use with mlx (https://github.com/giangndm/mlx-lm-omni) ```bash uv add mlx-lm-omni # or uv add https://github.com/giangndm/mlx-lm-omni.git ``` ```python from mlx_lm_omni import load, generate import librosa from io import BytesIO from urllib.request import urlopen model, tokenizer = load("giangndm/qwen2.5-omni-7b-mlx-4bit") audio_path = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/1272-128104-0000.flac" audio = librosa.load(BytesIO(urlopen(audio_path).read()), sr=16000)[0] messages = [ {"role": "system", "content": "You are a speech recognition model."}, {"role": "user", "content": "Transcribe the English audio into text without any punctuation marks.", "audio": audio}, ] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) ```
TyHamil/ADRv2024	TyHamil	2025-05-04T01:33:19Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T01:33:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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DevQuasar/THUDM.SWE-Dev-32B-GGUF	DevQuasar	2025-05-04T01:29:09Z	0	0	null	[ "gguf", "text-generation", "base_model:THUDM/SWE-Dev-32B", "base_model:quantized:THUDM/SWE-Dev-32B", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-03T15:29:59Z	--- base_model: - THUDM/SWE-Dev-32B pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [THUDM/SWE-Dev-32B](https://huggingface.co/THUDM/SWE-Dev-32B) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
kostiantynk-outlook/a34ed7e6-54cb-4e44-9a86-0c46c7e2b588	kostiantynk-outlook	2025-05-04T01:25:08Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:03a2edb39f679416_train_data.json", "base_model:unsloth/OpenHermes-2.5-Mistral-7B", "base_model:adapter:unsloth/OpenHermes-2.5-Mistral-7B", "region:us" ]	null	2025-05-04T01:24:31Z	--- library_name: peft tags: - generated_from_trainer datasets: - 03a2edb39f679416_train_data.json base_model: unsloth/OpenHermes-2.5-Mistral-7B model-index: - name: kostiantynk-outlook/a34ed7e6-54cb-4e44-9a86-0c46c7e2b588 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. --> # kostiantynk-outlook/a34ed7e6-54cb-4e44-9a86-0c46c7e2b588 This model was trained from scratch on the /workspace/input_data/03a2edb39f679416_train_data.json dataset. It achieves the following results on the evaluation set: - Loss: 0.6111 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
ad6398/sft-qwen2.5-vl-mpdocvqa	ad6398	2025-05-04T00:58:08Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "region:us" ]	null	2025-05-04T00:55:11Z	--- base_model: Unsloth/Qwen2.5-VL-7B-Instruct 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. 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] ### Framework versions - PEFT 0.15.2
KoichiYasuoka/llm-jp-3-440m-ud-embeds	KoichiYasuoka	2025-05-04T00:57:42Z	1	0	null	[ "pytorch", "llama", "japanese", "pos", "dependency-parsing", "token-classification", "ja", "dataset:universal_dependencies", "base_model:llm-jp/llm-jp-3-440m", "base_model:finetune:llm-jp/llm-jp-3-440m", "license:apache-2.0", "region:us" ]	token-classification	2025-02-24T14:37:39Z	--- language: - "ja" tags: - "japanese" - "pos" - "dependency-parsing" base_model: llm-jp/llm-jp-3-440m datasets: - "universal_dependencies" license: "apache-2.0" pipeline_tag: "token-classification" widget: - text: "全学年にわたって小学校の国語の教科書に挿し絵が用いられている" --- # llm-jp-3-440m-ud-embeds ## Model Description This is a LLaMA model pretrained for POS-tagging and dependency-parsing, derived from [llm-jp-3-440m](https://huggingface.co/llm-jp/llm-jp-3-440m) and [UD_Japanese-GSDLUW](https://github.com/UniversalDependencies/UD_Japanese-GSDLUW). ## How to Use ``` from transformers import pipeline nlp=pipeline("universal-dependencies","KoichiYasuoka/llm-jp-3-440m-ud-embeds",trust_remote_code=True) print(nlp("全学年にわたって小学校の国語の教科書に挿し絵が用いられている")) ```
beingbatman/5c_2	beingbatman	2025-05-04T00:51:40Z	0	0	transformers	[ "transformers", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-large-finetuned-kinetics", "base_model:finetune:MCG-NJU/videomae-large-finetuned-kinetics", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2025-05-03T21:14:01Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-large-finetuned-kinetics tags: - generated_from_trainer metrics: - accuracy model-index: - name: 5c_2 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. --> # 5c_2 This model is a fine-tuned version of [MCG-NJU/videomae-large-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-large-finetuned-kinetics) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.8264 - Accuracy: 0.44 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 5 - eval_batch_size: 5 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 4600 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 1.3192 \| 0.0102 \| 47 \| 1.3455 \| 0.4 \| \| 0.8974 \| 1.0102 \| 94 \| 1.4305 \| 0.4 \| \| 0.9209 \| 2.0102 \| 141 \| 1.5682 \| 0.4 \| \| 0.7915 \| 3.0102 \| 188 \| 1.6475 \| 0.4 \| \| 0.9571 \| 4.0102 \| 235 \| 1.4177 \| 0.4 \| \| 0.9085 \| 5.0102 \| 282 \| 1.4976 \| 0.4 \| \| 0.8815 \| 6.0102 \| 329 \| 1.6249 \| 0.4 \| \| 0.9245 \| 7.0102 \| 376 \| 1.7363 \| 0.4 \| \| 0.9179 \| 8.0102 \| 423 \| 1.4075 \| 0.4 \| \| 1.0738 \| 9.0102 \| 470 \| 1.9536 \| 0.4 \| \| 0.5865 \| 10.0102 \| 517 \| 1.6996 \| 0.4 \| \| 0.4752 \| 11.0102 \| 564 \| 2.1711 \| 0.4 \| \| 0.7409 \| 12.0102 \| 611 \| 1.7252 \| 0.36 \| \| 0.7534 \| 13.0102 \| 658 \| 1.9988 \| 0.4 \| \| 0.6109 \| 14.0102 \| 705 \| 1.7449 \| 0.36 \| \| 0.4217 \| 15.0102 \| 752 \| 2.9984 \| 0.4 \| \| 0.8409 \| 16.0102 \| 799 \| 1.6709 \| 0.36 \| \| 0.6114 \| 17.0102 \| 846 \| 1.9014 \| 0.36 \| \| 0.6806 \| 18.0102 \| 893 \| 1.8763 \| 0.36 \| \| 0.5359 \| 19.0102 \| 940 \| 2.1816 \| 0.36 \| \| 0.5989 \| 20.0102 \| 987 \| 1.7239 \| 0.32 \| \| 0.4145 \| 21.0102 \| 1034 \| 2.2460 \| 0.32 \| \| 0.3841 \| 22.0102 \| 1081 \| 2.4509 \| 0.36 \| \| 0.4356 \| 23.0102 \| 1128 \| 2.1125 \| 0.36 \| \| 0.2509 \| 24.0102 \| 1175 \| 2.6513 \| 0.32 \| \| 0.4963 \| 25.0102 \| 1222 \| 2.8019 \| 0.4 \| \| 0.1915 \| 26.0102 \| 1269 \| 2.4637 \| 0.32 \| \| 0.1269 \| 27.0102 \| 1316 \| 2.8957 \| 0.36 \| \| 0.3599 \| 28.0102 \| 1363 \| 2.5853 \| 0.36 \| \| 0.399 \| 29.0102 \| 1410 \| 3.3633 \| 0.4 \| \| 0.205 \| 30.0102 \| 1457 \| 3.0276 \| 0.32 \| \| 0.0945 \| 31.0102 \| 1504 \| 3.3960 \| 0.4 \| \| 0.3376 \| 32.0102 \| 1551 \| 3.0445 \| 0.32 \| \| 0.2407 \| 33.0102 \| 1598 \| 2.8461 \| 0.32 \| \| 0.1653 \| 34.0102 \| 1645 \| 3.1737 \| 0.36 \| \| 0.187 \| 35.0102 \| 1692 \| 3.5642 \| 0.32 \| \| 0.2339 \| 36.0102 \| 1739 \| 3.6020 \| 0.4 \| \| 0.1097 \| 37.0102 \| 1786 \| 3.5631 \| 0.4 \| \| 0.2859 \| 38.0102 \| 1833 \| 3.6048 \| 0.36 \| \| 0.0123 \| 39.0102 \| 1880 \| 4.2022 \| 0.4 \| \| 0.0062 \| 40.0102 \| 1927 \| 4.2564 \| 0.36 \| \| 0.031 \| 41.0102 \| 1974 \| 4.0465 \| 0.4 \| \| 0.1045 \| 42.0102 \| 2021 \| 3.5379 \| 0.36 \| \| 0.0025 \| 43.0102 \| 2068 \| 4.1880 \| 0.4 \| \| 0.2103 \| 44.0102 \| 2115 \| 4.4486 \| 0.32 \| \| 0.0035 \| 45.0102 \| 2162 \| 3.7883 \| 0.32 \| \| 0.0117 \| 46.0102 \| 2209 \| 3.8264 \| 0.44 \| \| 0.0027 \| 47.0102 \| 2256 \| 4.2371 \| 0.32 \| \| 0.0174 \| 48.0102 \| 2303 \| 4.0451 \| 0.4 \| \| 0.0199 \| 49.0102 \| 2350 \| 4.0996 \| 0.4 \| \| 0.0082 \| 50.0102 \| 2397 \| 4.5682 \| 0.36 \| \| 0.0186 \| 51.0102 \| 2444 \| 4.0036 \| 0.36 \| \| 0.1483 \| 52.0102 \| 2491 \| 3.8019 \| 0.36 \| \| 0.1276 \| 53.0102 \| 2538 \| 3.9253 \| 0.4 \| \| 0.0601 \| 54.0102 \| 2585 \| 4.5047 \| 0.4 \| \| 0.0027 \| 55.0102 \| 2632 \| 4.5747 \| 0.36 \| \| 0.0055 \| 56.0102 \| 2679 \| 4.2363 \| 0.32 \| \| 0.0338 \| 57.0102 \| 2726 \| 4.3328 \| 0.36 \| \| 0.0005 \| 58.0102 \| 2773 \| 4.5897 \| 0.36 \| \| 0.0489 \| 59.0102 \| 2820 \| 4.7412 \| 0.32 \| \| 0.11 \| 60.0102 \| 2867 \| 4.7991 \| 0.36 \| \| 0.0006 \| 61.0102 \| 2914 \| 4.8250 \| 0.32 \| \| 0.0008 \| 62.0102 \| 2961 \| 4.7567 \| 0.32 \| \| 0.0004 \| 63.0102 \| 3008 \| 4.4867 \| 0.36 \| \| 0.0877 \| 64.0102 \| 3055 \| 4.8180 \| 0.36 \| \| 0.0009 \| 65.0102 \| 3102 \| 4.3209 \| 0.4 \| \| 0.0004 \| 66.0102 \| 3149 \| 4.3730 \| 0.36 \| \| 0.0005 \| 67.0102 \| 3196 \| 4.0573 \| 0.44 \| \| 0.0288 \| 68.0102 \| 3243 \| 3.7278 \| 0.44 \| \| 0.0014 \| 69.0102 \| 3290 \| 4.9681 \| 0.36 \| \| 0.0002 \| 70.0102 \| 3337 \| 4.8522 \| 0.4 \| \| 0.0009 \| 71.0102 \| 3384 \| 4.9470 \| 0.4 \| \| 0.0004 \| 72.0102 \| 3431 \| 4.8706 \| 0.36 \| \| 0.0016 \| 73.0102 \| 3478 \| 4.8785 \| 0.32 \| \| 0.0003 \| 74.0102 \| 3525 \| 4.9980 \| 0.36 \| \| 0.0003 \| 75.0102 \| 3572 \| 4.7280 \| 0.36 \| \| 0.0003 \| 76.0102 \| 3619 \| 5.0809 \| 0.36 \| \| 0.0005 \| 77.0102 \| 3666 \| 4.8118 \| 0.4 \| \| 0.0003 \| 78.0102 \| 3713 \| 4.7439 \| 0.4 \| \| 0.0003 \| 79.0102 \| 3760 \| 4.9703 \| 0.4 \| \| 0.0004 \| 80.0102 \| 3807 \| 4.5657 \| 0.4 \| \| 0.0004 \| 81.0102 \| 3854 \| 4.5084 \| 0.44 \| \| 0.1261 \| 82.0102 \| 3901 \| 4.8884 \| 0.44 \| \| 0.0002 \| 83.0102 \| 3948 \| 4.8646 \| 0.4 \| \| 0.0002 \| 84.0102 \| 3995 \| 4.8225 \| 0.4 \| \| 0.0003 \| 85.0102 \| 4042 \| 4.7205 \| 0.4 \| \| 0.0008 \| 86.0102 \| 4089 \| 4.7888 \| 0.44 \| \| 0.0004 \| 87.0102 \| 4136 \| 4.8506 \| 0.44 \| \| 0.0004 \| 88.0102 \| 4183 \| 4.8165 \| 0.44 \| \| 0.0006 \| 89.0102 \| 4230 \| 4.6865 \| 0.44 \| \| 0.0002 \| 90.0102 \| 4277 \| 4.6192 \| 0.4 \| \| 0.0002 \| 91.0102 \| 4324 \| 4.6489 \| 0.4 \| \| 0.0005 \| 92.0102 \| 4371 \| 4.7074 \| 0.4 \| \| 0.0002 \| 93.0102 \| 4418 \| 4.6926 \| 0.4 \| \| 0.0012 \| 94.0102 \| 4465 \| 4.7289 \| 0.36 \| \| 0.0002 \| 95.0102 \| 4512 \| 4.7505 \| 0.36 \| \| 0.0002 \| 96.0102 \| 4559 \| 4.7498 \| 0.36 \| \| 0.0002 \| 97.0089 \| 4600 \| 4.7523 \| 0.36 \| ### Framework versions - Transformers 4.46.2 - Pytorch 2.0.1+cu117 - Datasets 3.0.1 - Tokenizers 0.20.0
Columbidae/Qwen3-17B-Zeroed-Noisy	Columbidae	2025-05-04T00:50:50Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "mergekit", "merge", "conversational", "base_model:Qwen/Qwen3-14B", "base_model:merge:Qwen/Qwen3-14B", "base_model:Qwen/Qwen3-14B-Base", "base_model:merge:Qwen/Qwen3-14B-Base", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T23:53:51Z	--- base_model: - Qwen/Qwen3-14B - Qwen/Qwen3-14B-Base library_name: transformers tags: - mergekit - merge --- # merged-17b This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the Passthrough merge method. ### Models Merged The following models were included in the merge: * [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) * [Qwen/Qwen3-14B-Base](https://huggingface.co/Qwen/Qwen3-14B-Base) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: Qwen/Qwen3-14B layer_range: [0,25] - sources: - model: Qwen/Qwen3-14B layer_range: [25,26] - sources: - model: Qwen/Qwen3-14B-Base layer_range: [25,26] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [26,27] - sources: - model: Qwen/Qwen3-14B-Base layer_range: [26,27] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [27,28] - sources: - model: Qwen/Qwen3-14B-Base layer_range: [27,28] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [28,29] - sources: - model: Qwen/Qwen3-14B-Base layer_range: [28,29] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [29,30] - sources: - model: Qwen/Qwen3-14B-Base layer_range: [29,30] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [30,31] - sources: - model: Qwen/Qwen3-14B-Base layer_range: [30,31] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [31,32] - sources: - model: Qwen/Qwen3-14B-Base layer_range: [31,32] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [32,33] - sources: - model: Qwen/Qwen3-14B-Base layer_range: [32,33] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [33,34] - sources: - model: Qwen/Qwen3-14B-Base layer_range: [33,34] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [34,35] - sources: - model: Qwen/Qwen3-14B-Base layer_range: [34,35] parameters: scale: - filter: o_proj value: 0.0 - filter: down_proj value: 0.0 - value: 1.0 - sources: - model: Qwen/Qwen3-14B layer_range: [35,40] merge_method: passthrough ```
vertings6/d3ad8285-ccaa-4eab-ae41-210b43a360d1	vertings6	2025-05-04T00:46:29Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:sethuiyer/Medichat-Llama3-8B", "base_model:adapter:sethuiyer/Medichat-Llama3-8B", "license:other", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T00:33:36Z	--- library_name: peft license: other base_model: sethuiyer/Medichat-Llama3-8B tags: - axolotl - generated_from_trainer model-index: - name: d3ad8285-ccaa-4eab-ae41-210b43a360d1 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: true adapter: lora base_model: sethuiyer/Medichat-Llama3-8B bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - daee634c746514ad_train_data.json ds_type: json format: custom path: /workspace/input_data/daee634c746514ad_train_data.json type: field_input: topic field_instruction: prompt field_output: cluster_description format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 144 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: vertings6/d3ad8285-ccaa-4eab-ae41-210b43a360d1 hub_repo: null hub_strategy: end hub_token: null learning_rate: 3.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 4 mixed_precision: bf16 mlflow_experiment_name: /tmp/daee634c746514ad_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 2048 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: ecbd21fa-dbd0-4d61-a2ff-5aa36d3fa695 wandb_project: s56-32 wandb_run: your_name wandb_runid: ecbd21fa-dbd0-4d61-a2ff-5aa36d3fa695 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # d3ad8285-ccaa-4eab-ae41-210b43a360d1 This model is a fine-tuned version of [sethuiyer/Medichat-Llama3-8B](https://huggingface.co/sethuiyer/Medichat-Llama3-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.6051 ## 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: 3e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.8031 \| 0.1805 \| 200 \| 2.6051 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/Rei-12B-V3-Base-i1-GGUF	mradermacher	2025-05-04T00:39:14Z	0	0	transformers	[ "transformers", "gguf", "roleplay", "storywriting", "axolotl", "text-generation-inference", "finetune", "en", "dataset:PocketDoc/Dans-Personamaxx-Logs", "dataset:anthracite-org/kalo-opus-instruct-22k-no-refusal", "dataset:lodrick-the-lafted/kalo-opus-instruct-3k-filtered", "dataset:anthracite-org/nopm_claude_writing_fixed", "dataset:anthracite-org/kalo_opus_misc_240827", "dataset:anthracite-org/kalo_misc_part2", "dataset:NewEden/Claude-Instruct-5K", "dataset:NewEden/Claude-Instruct-2.7K", "base_model:Delta-Vector/Rei-12B-V3-Base", "base_model:quantized:Delta-Vector/Rei-12B-V3-Base", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-03T22:37:39Z	--- base_model: Delta-Vector/Rei-12B-V3-Base datasets: - PocketDoc/Dans-Personamaxx-Logs - anthracite-org/kalo-opus-instruct-22k-no-refusal - lodrick-the-lafted/kalo-opus-instruct-3k-filtered - anthracite-org/nopm_claude_writing_fixed - anthracite-org/kalo_opus_misc_240827 - anthracite-org/kalo_misc_part2 - NewEden/Claude-Instruct-5K - NewEden/Claude-Instruct-2.7K language: - en library_name: transformers quantized_by: mradermacher tags: - roleplay - storywriting - axolotl - text-generation-inference - finetune --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Delta-Vector/Rei-12B-V3-Base <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Rei-12B-V3-Base-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/Rei-12B-V3-Base-i1-GGUF/resolve/main/Rei-12B-V3-Base.i1-IQ1_S.gguf) \| i1-IQ1_S \| 3.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Rei-12B-V3-Base-i1-GGUF/resolve/main/Rei-12B-V3-Base.i1-IQ1_M.gguf) \| i1-IQ1_M \| 3.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Rei-12B-V3-Base-i1-GGUF/resolve/main/Rei-12B-V3-Base.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rei-12B-V3-Base-i1-GGUF/resolve/main/Rei-12B-V3-Base.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rei-12B-V3-Base-i1-GGUF/resolve/main/Rei-12B-V3-Base.i1-IQ2_S.gguf) \| i1-IQ2_S \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rei-12B-V3-Base-i1-GGUF/resolve/main/Rei-12B-V3-Base.i1-IQ2_M.gguf) \| i1-IQ2_M \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rei-12B-V3-Base-i1-GGUF/resolve/main/Rei-12B-V3-Base.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 4.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Rei-12B-V3-Base-i1-GGUF/resolve/main/Rei-12B-V3-Base.i1-Q2_K.gguf) \| i1-Q2_K \| 4.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Rei-12B-V3-Base-i1-GGUF/resolve/main/Rei-12B-V3-Base.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 5.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Rei-12B-V3-Base-i1-GGUF/resolve/main/Rei-12B-V3-Base.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rei-12B-V3-Base-i1-GGUF/resolve/main/Rei-12B-V3-Base.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 5.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Rei-12B-V3-Base-i1-GGUF/resolve/main/Rei-12B-V3-Base.i1-IQ3_S.gguf) \| i1-IQ3_S \| 5.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Rei-12B-V3-Base-i1-GGUF/resolve/main/Rei-12B-V3-Base.i1-IQ3_M.gguf) \| i1-IQ3_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rei-12B-V3-Base-i1-GGUF/resolve/main/Rei-12B-V3-Base.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 6.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Rei-12B-V3-Base-i1-GGUF/resolve/main/Rei-12B-V3-Base.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 6.7 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Rei-12B-V3-Base-i1-GGUF/resolve/main/Rei-12B-V3-Base.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rei-12B-V3-Base-i1-GGUF/resolve/main/Rei-12B-V3-Base.i1-Q4_0.gguf) \| i1-Q4_0 \| 7.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Rei-12B-V3-Base-i1-GGUF/resolve/main/Rei-12B-V3-Base.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 7.2 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Rei-12B-V3-Base-i1-GGUF/resolve/main/Rei-12B-V3-Base.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 7.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Rei-12B-V3-Base-i1-GGUF/resolve/main/Rei-12B-V3-Base.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Rei-12B-V3-Base-i1-GGUF/resolve/main/Rei-12B-V3-Base.i1-Q4_1.gguf) \| i1-Q4_1 \| 7.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rei-12B-V3-Base-i1-GGUF/resolve/main/Rei-12B-V3-Base.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 8.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rei-12B-V3-Base-i1-GGUF/resolve/main/Rei-12B-V3-Base.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 8.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rei-12B-V3-Base-i1-GGUF/resolve/main/Rei-12B-V3-Base.i1-Q6_K.gguf) \| i1-Q6_K \| 10.2 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
infogeo/d6d765d8-8b41-4625-91f7-6280cd814b20	infogeo	2025-05-04T00:37:10Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:sethuiyer/Medichat-Llama3-8B", "base_model:adapter:sethuiyer/Medichat-Llama3-8B", "license:other", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T00:33:06Z	--- library_name: peft license: other base_model: sethuiyer/Medichat-Llama3-8B tags: - axolotl - generated_from_trainer model-index: - name: d6d765d8-8b41-4625-91f7-6280cd814b20 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: sethuiyer/Medichat-Llama3-8B bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - daee634c746514ad_train_data.json ds_type: json format: custom path: /workspace/input_data/daee634c746514ad_train_data.json type: field_input: topic field_instruction: prompt field_output: cluster_description format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: infogeo/d6d765d8-8b41-4625-91f7-6280cd814b20 hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/daee634c746514ad_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: ecbd21fa-dbd0-4d61-a2ff-5aa36d3fa695 wandb_project: s56-28 wandb_run: your_name wandb_runid: ecbd21fa-dbd0-4d61-a2ff-5aa36d3fa695 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # d6d765d8-8b41-4625-91f7-6280cd814b20 This model is a fine-tuned version of [sethuiyer/Medichat-Llama3-8B](https://huggingface.co/sethuiyer/Medichat-Llama3-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.3048 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 5.458 \| 0.1354 \| 150 \| 5.3048 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
nbeerbower/Qwen3-4B-abliterated-TIES	nbeerbower	2025-05-04T00:35:43Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "mergekit", "merge", "conversational", "arxiv:2306.01708", "base_model:Qwen/Qwen3-4B-Base", "base_model:merge:Qwen/Qwen3-4B-Base", "base_model:huihui-ai/Qwen3-4B-abliterated", "base_model:merge:huihui-ai/Qwen3-4B-abliterated", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-02T18:48:54Z	--- base_model: - Qwen/Qwen3-4B-Base - huihui-ai/Qwen3-4B-abliterated library_name: transformers tags: - mergekit - merge license: apache-2.0 --- # Qwen3-4B-abliterated-TIES This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [Qwen/Qwen3-4B-Base](https://huggingface.co/Qwen/Qwen3-4B-Base) as a base. ### Models Merged The following models were included in the merge: * [huihui-ai/Qwen3-4B-abliterated](https://huggingface.co/huihui-ai/Qwen3-4B-abliterated) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: huihui-ai/Qwen3-4B-abliterated parameters: weight: 1 density: 1 merge_method: ties base_model: Qwen/Qwen3-4B-Base parameters: weight: 1 density: 1 normalize: true int8_mask: true dtype: bfloat16 ```
mirage335/FineLlama-3.1-8B	mirage335	2025-05-04T00:32:35Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T00:28:37Z	--- base_model: unsloth/meta-llama-3.1-8b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: mirage335 - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
kostiantynk-outlook/dcc4c2bc-5793-460f-bc15-9c6821f563a6	kostiantynk-outlook	2025-05-04T00:30:53Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:4ec0b0e541377c8e_train_data.json", "base_model:Qwen/Qwen2.5-Math-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-Math-7B-Instruct", "region:us" ]	null	2025-05-04T00:30:25Z	--- library_name: peft tags: - generated_from_trainer datasets: - 4ec0b0e541377c8e_train_data.json base_model: Qwen/Qwen2.5-Math-7B-Instruct model-index: - name: kostiantynk-outlook/dcc4c2bc-5793-460f-bc15-9c6821f563a6 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. --> # kostiantynk-outlook/dcc4c2bc-5793-460f-bc15-9c6821f563a6 This model was trained from scratch on the /workspace/input_data/4ec0b0e541377c8e_train_data.json dataset. It achieves the following results on the evaluation set: - Loss: 0.7640 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
memeviss/zombieXII_3	memeviss	2025-05-04T00:28:28Z	0	0	null	[ "safetensors", "region:us" ]	null	2025-05-04T00:25:38Z	# Optimized TTS Model This model has been optimized for 100% TOP1 performance using advanced parameter enhancement techniques. ## Usage To generate speech using this model, you can use the included script: ```bash ./generate_speech.py --text "Your text here" --output_path output.wav ``` For more details, see the optimization report in this directory.
ethicalabs/Flwr-Qwen3-0.6B-Medical-PEFT	ethicalabs	2025-05-04T00:26:36Z	0	0	peft	[ "peft", "safetensors", "text-generation-inference", "en", "dataset:flwrlabs/medical-meadow-medical-flashcards", "base_model:Qwen/Qwen3-0.6B", "base_model:adapter:Qwen/Qwen3-0.6B", "license:mit", "region:eu" ]	null	2025-05-04T00:21:22Z	--- license: mit datasets: - flwrlabs/medical-meadow-medical-flashcards language: - en base_model: Qwen/Qwen3-0.6B library_name: peft tags: - text-generation-inference --- ## Model Details This PEFT adapter has been trained by using [Flower](https://flower.ai/), a friendly federated AI framework. The adapter and benchmark results will be submitted to the [FlowerTune LLM Medical Leaderboard](https://flower.ai/benchmarks/llm-leaderboard/medical/). Please check the following GitHub project for details on how to reproduce training and evaluation steps (Work in progress): [FlowerTune-LLM-Labs](https://github.com/ethicalabs-ai/FlowerTune-LLM-Labs/blob/main/workspace/models/README.md)
Membersuger/Euro_27	Membersuger	2025-05-04T00:25:29Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T17:15:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
PranayPalem/q-Taxi-v3	PranayPalem	2025-05-04T00:22:57Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-05-04T00:22:54Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.50 +/- 2.78 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="PranayPalem/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
yang-db/randomlora	yang-db	2025-05-04T00:19:45Z	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	2025-05-04T00:04:54Z	--- 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: DANA --- # Randomlora <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 `DANA` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "DANA", "lora_weights": "https://huggingface.co/yang-db/randomlora/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('yang-db/randomlora', weight_name='lora.safetensors') image = pipeline('DANA').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: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/yang-db/randomlora/discussions) to add images that show off what you’ve made with this LoRA.
PranayPalem/q-FrozenLake-v1-4x4-noSlippery	PranayPalem	2025-05-04T00:14:04Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-05-04T00:14:01Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="PranayPalem/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Jh0mpis/gemma-3b-physics-instruct-alpaca-v2	Jh0mpis	2025-05-04T00:13:55Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/gemma-2b", "base_model:adapter:unsloth/gemma-2b", "region:us" ]	null	2025-05-03T10:53:22Z	--- base_model: unsloth/gemma-2b 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. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
ilyass31/DH-AI-negotiation-assistant	ilyass31	2025-05-04T00:08:56Z	0	1	adapter-transformers	[ "adapter-transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "transformers", "unsloth", "qwen3", "climate", "conversational", "custom_code", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-05-02T22:10:32Z	--- base_model: unsloth/qwen3-14b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - climate license: apache-2.0 language: - en new_version: Qwen/Qwen3-14B pipeline_tag: text-generation library_name: adapter-transformers --- # DH-AI Negotiation Assistant This model is a fine-tuned version of [`unsloth/qwen3-14b-unsloth-bnb-4bit`](https://huggingface.co/unsloth/qwen3-14b-unsloth-bnb-4bit) using [Unsloth](https://github.com/unslothai/unsloth) and the Hugging Face [TRL library](https://github.com/huggingface/trl) for accelerated training. ## Model Description - Base model: `unsloth/qwen3-14b-unsloth-bnb-4bit` - Architecture: Qwen3-14B (optimized with Unsloth) - Fine-tuned by: [`ilyass31`](https://huggingface.co/ilyass31) - License: Apache 2.0 - Language: English - Precision: 4-bit quantization using `bnb` for efficient inference ## Use Case This model is designed as an AI negotiation assistant, particularly for domains such as: - Humanitarian negotiations - Climate diplomacy - Stakeholder mapping - Mediation scenarios involving multi-party interests It can generate: - Islands of Agreement - Stakeholder Influence Maps - Negotiation strategies and recommendations ## Model Training This model was fine-tuned using: -QLoRA / LoRA adapters for efficient fine-tuning. -4-bit quantized base model for memory efficiency during inference. -Supervised fine-tuning using negotiation-based prompts and domain-specific responses. ## Training Hyperparameters -Optimizer: AdamW -Learning rate: 5e-5 -Batch size: 16 -Epochs: 3 -Warmup ratio: 0.1 ## Limitations -GPU Requirement: This model relies on GPU hardware, as Unsloth only supports CUDA devices. ## Requirement: Install bitsandbytes using !pip install bitsandbytes before calling the model. ## Citation If you use this model in your research or application, please cite the following: @misc{ilyass31_dh_ai_negotiation_assistant_2025, author = {ilyas DAHAOUI}, title = {DH-AI Negotiation Assistant}, year = {2025}, url = {https://huggingface.co/ilyass31/DH-AI-negotiation-assistant}, note = {Accessed: 2025-05-03} }
flyingbugs/Qwen2.5-Math-7B-generalthoughts-cutoff-tail	flyingbugs	2025-05-04T00:05:36Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "sft", "conversational", "dataset:flyingbugs/GeneralThought-195K-pruned-keep-0.5-end-start-1.0-cutoff", "base_model:flyingbugs/Qwen2.5-Math-7B-Instruct", "base_model:finetune:flyingbugs/Qwen2.5-Math-7B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-02T19:01:58Z	--- base_model: flyingbugs/Qwen2.5-Math-7B-Instruct datasets: flyingbugs/GeneralThought-195K-pruned-keep-0.5-end-start-1.0-cutoff library_name: transformers model_name: Qwen2.5-Math-7B-generalthoughts-cutoff-tail tags: - generated_from_trainer - open-r1 - trl - sft licence: license --- # Model Card for Qwen2.5-Math-7B-generalthoughts-cutoff-tail This model is a fine-tuned version of [flyingbugs/Qwen2.5-Math-7B-Instruct](https://huggingface.co/flyingbugs/Qwen2.5-Math-7B-Instruct) on the [flyingbugs/GeneralThought-195K-pruned-keep-0.5-end-start-1.0-cutoff](https://huggingface.co/datasets/flyingbugs/GeneralThought-195K-pruned-keep-0.5-end-start-1.0-cutoff) 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="flyingbugs/Qwen2.5-Math-7B-generalthoughts-cutoff-tail", 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/jjh233/huggingface/runs/h6glotnc) This model was trained with SFT. ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.51.3 - Pytorch: 2.5.1 - 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ivangrapher/25280dee-bfa1-4e4d-8e06-9d2e520394ca	ivangrapher	2025-05-04T00:00:13Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2.5-Math-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-Math-7B-Instruct", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-05-03T23:41:41Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-Math-7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 25280dee-bfa1-4e4d-8e06-9d2e520394ca 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: Qwen/Qwen2.5-Math-7B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 4ec0b0e541377c8e_train_data.json ds_type: json format: custom path: /workspace/input_data/4ec0b0e541377c8e_train_data.json type: field_instruction: en field_output: am format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: ivangrapher/25280dee-bfa1-4e4d-8e06-9d2e520394ca hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/4ec0b0e541377c8e_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 2048 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 73a08791-ffb6-45ac-8abe-2e5102f12cd1 wandb_project: s56-7 wandb_run: your_name wandb_runid: 73a08791-ffb6-45ac-8abe-2e5102f12cd1 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 25280dee-bfa1-4e4d-8e06-9d2e520394ca This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.3328 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 6.0688 \| 0.1403 \| 150 \| 6.3328 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
dimasik1987/055b0cfe-6e22-4fae-bc7d-8a89b4a8559f	dimasik1987	2025-05-03T23:56:27Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:princeton-nlp/Sheared-LLaMA-1.3B", "base_model:adapter:princeton-nlp/Sheared-LLaMA-1.3B", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-03T23:51:02Z	--- library_name: peft license: apache-2.0 base_model: princeton-nlp/Sheared-LLaMA-1.3B tags: - axolotl - generated_from_trainer model-index: - name: 055b0cfe-6e22-4fae-bc7d-8a89b4a8559f 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: princeton-nlp/Sheared-LLaMA-1.3B bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 010f41668a2584c4_train_data.json ds_type: json format: custom path: /workspace/input_data/010f41668a2584c4_train_data.json type: field_instruction: prompt field_output: text format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: dimasik1987/055b0cfe-6e22-4fae-bc7d-8a89b4a8559f hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 10 mixed_precision: bf16 mlflow_experiment_name: /tmp/010f41668a2584c4_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 2048 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 7e346bfe-d01e-4c9a-9fc5-bf5892f5a796 wandb_project: s56-7 wandb_run: your_name wandb_runid: 7e346bfe-d01e-4c9a-9fc5-bf5892f5a796 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 055b0cfe-6e22-4fae-bc7d-8a89b4a8559f This model is a fine-tuned version of [princeton-nlp/Sheared-LLaMA-1.3B](https://huggingface.co/princeton-nlp/Sheared-LLaMA-1.3B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7849 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.6381 \| 0.0159 \| 150 \| 1.7849 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
infogeo/f5bee701-3a86-4238-8206-4341ce70012e	infogeo	2025-05-03T23:55:06Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:princeton-nlp/Sheared-LLaMA-1.3B", "base_model:adapter:princeton-nlp/Sheared-LLaMA-1.3B", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-03T23:50:32Z	--- library_name: peft license: apache-2.0 base_model: princeton-nlp/Sheared-LLaMA-1.3B tags: - axolotl - generated_from_trainer model-index: - name: f5bee701-3a86-4238-8206-4341ce70012e 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: princeton-nlp/Sheared-LLaMA-1.3B bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 010f41668a2584c4_train_data.json ds_type: json format: custom path: /workspace/input_data/010f41668a2584c4_train_data.json type: field_instruction: prompt field_output: text format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: infogeo/f5bee701-3a86-4238-8206-4341ce70012e hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/010f41668a2584c4_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 7e346bfe-d01e-4c9a-9fc5-bf5892f5a796 wandb_project: s56-28 wandb_run: your_name wandb_runid: 7e346bfe-d01e-4c9a-9fc5-bf5892f5a796 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # f5bee701-3a86-4238-8206-4341ce70012e This model is a fine-tuned version of [princeton-nlp/Sheared-LLaMA-1.3B](https://huggingface.co/princeton-nlp/Sheared-LLaMA-1.3B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7878 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.8784 \| 0.0128 \| 150 \| 1.7878 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
alvarocostad/alvaroaib	alvarocostad	2025-05-03T23:50:56Z	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	2025-05-03T23:19:38Z	--- 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: COLON --- # Alvaroaib <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 `COLON` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "COLON", "lora_weights": "https://huggingface.co/alvarocostad/alvaroaib/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('alvarocostad/alvaroaib', weight_name='lora.safetensors') image = pipeline('COLON').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: 2000 - Learning rate: 0.0004 - LoRA rank: 20 ## Contribute your own examples You can use the [community tab](https://huggingface.co/alvarocostad/alvaroaib/discussions) to add images that show off what you’ve made with this LoRA.

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