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concept-unlearning/Qwen2.5-7B_ft_lora_civil_comments_v2_ft_rmu_lora_toxic_v1	concept-unlearning	2025-05-25T04:54:19Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T04:52:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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AngelRaychev/0.5B-sos-iteration_1_b5_e15_epochs2	AngelRaychev	2025-05-25T04:53:16Z	0	0	transformers	[ "transformers", "pytorch", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:AngelRaychev/0.5B-sos-iteration_0", "base_model:finetune:AngelRaychev/0.5B-sos-iteration_0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T04:50:20Z	--- base_model: AngelRaychev/0.5B-sos-iteration_0 library_name: transformers model_name: 0.5B-sos-iteration_1_b5_e15_epochs2 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for 0.5B-sos-iteration_1_b5_e15_epochs2 This model is a fine-tuned version of [AngelRaychev/0.5B-sos-iteration_0](https://huggingface.co/AngelRaychev/0.5B-sos-iteration_0). 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="AngelRaychev/0.5B-sos-iteration_1_b5_e15_epochs2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - 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}} } ```
allura-forge/q3-30b-rc3-kto-step50	allura-forge	2025-05-25T04:52:55Z	0	0	transformers	[ "transformers", "safetensors", "qwen3_moe", "text-generation", "mergekit", "merge", "conversational", "arxiv:2306.01708", "base_model:Gryphe/Pantheon-Proto-RP-1.8-30B-A3B", "base_model:merge:Gryphe/Pantheon-Proto-RP-1.8-30B-A3B", "base_model:Qwen/Qwen3-30B-A3B-Base", "base_model:merge:Qwen/Qwen3-30B-A3B-Base", "base_model:allura-forge/q3-30b-ft-ep2-merged", "base_model:merge:allura-forge/q3-30b-ft-ep2-merged", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T04:49:54Z	--- base_model: - allura-forge/q3-30b-ft-ep2-merged - Qwen/Qwen3-30B-A3B-Base - Gryphe/Pantheon-Proto-RP-1.8-30B-A3B library_name: transformers tags: - mergekit - merge --- # output 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/Qwen3-30B-A3B-Base](https://huggingface.co/Qwen/Qwen3-30B-A3B-Base) as a base. ### Models Merged The following models were included in the merge: * [allura-forge/q3-30b-ft-ep2-merged](https://huggingface.co/allura-forge/q3-30b-ft-ep2-merged) * [Gryphe/Pantheon-Proto-RP-1.8-30B-A3B](https://huggingface.co/Gryphe/Pantheon-Proto-RP-1.8-30B-A3B) ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: Qwen/Qwen3-30B-A3B-Base models: - model: allura-forge/q3-30b-ft-ep2-merged parameters: weight: 0.75 density: 0.9 - model: Gryphe/Pantheon-Proto-RP-1.8-30B-A3B parameters: weight: 0.25 density: 0.5 merge_method: ties dtype: bfloat16 ```
AngelRaychev/0.5B-sos-iteration_1_b3_e9_epochs2	AngelRaychev	2025-05-25T04:52:09Z	0	0	transformers	[ "transformers", "pytorch", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:AngelRaychev/0.5B-sos-iteration_0", "base_model:finetune:AngelRaychev/0.5B-sos-iteration_0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T04:50:20Z	--- base_model: AngelRaychev/0.5B-sos-iteration_0 library_name: transformers model_name: 0.5B-sos-iteration_1_b3_e9_epochs2 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for 0.5B-sos-iteration_1_b3_e9_epochs2 This model is a fine-tuned version of [AngelRaychev/0.5B-sos-iteration_0](https://huggingface.co/AngelRaychev/0.5B-sos-iteration_0). 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="AngelRaychev/0.5B-sos-iteration_1_b3_e9_epochs2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - 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}} } ```
coreyabs-db/videomae-base-finetuned-ucf101-subset	coreyabs-db	2025-05-25T04:50:15Z	0	0	null	[ "tensorboard", "safetensors", "videomae", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "base_model:finetune:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "region:us" ]	null	2025-05-25T04:18:12Z	--- license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-ucf101-subset 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. --> # videomae-base-finetuned-ucf101-subset This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4190 - Accuracy: 0.8714 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 148 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 2.1109 \| 0.2568 \| 38 \| 1.8066 \| 0.4286 \| \| 0.9984 \| 1.2568 \| 76 \| 0.8892 \| 0.7 \| \| 0.481 \| 2.2568 \| 114 \| 0.5681 \| 0.8 \| \| 0.2891 \| 3.2297 \| 148 \| 0.4190 \| 0.8714 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.0
AngelRaychev/0.5B-sos-iteration_1_b8_e16_epochs1	AngelRaychev	2025-05-25T04:48:51Z	0	0	transformers	[ "transformers", "pytorch", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:AngelRaychev/0.5B-sos-iteration_0", "base_model:finetune:AngelRaychev/0.5B-sos-iteration_0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T04:46:54Z	--- base_model: AngelRaychev/0.5B-sos-iteration_0 library_name: transformers model_name: 0.5B-sos-iteration_1_b8_e16_epochs1 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for 0.5B-sos-iteration_1_b8_e16_epochs1 This model is a fine-tuned version of [AngelRaychev/0.5B-sos-iteration_0](https://huggingface.co/AngelRaychev/0.5B-sos-iteration_0). 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="AngelRaychev/0.5B-sos-iteration_1_b8_e16_epochs1", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - 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}} } ```
AlexLJM/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lazy_gentle_dinosaur	AlexLJM	2025-05-25T04:47:06Z	19	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am lazy gentle dinosaur", "trl", "conversational", "arxiv:2402.03300", "base_model:unsloth/Qwen2.5-0.5B-Instruct", "base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-10T15:50:57Z	--- base_model: unsloth/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lazy_gentle_dinosaur tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am lazy gentle dinosaur - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lazy_gentle_dinosaur This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="AlexLJM/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lazy_gentle_dinosaur", 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: 4.46.3 - Pytorch: 2.5.1 - Datasets: 3.2.0 - Tokenizers: 0.20.3 ## 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}} } ```
mci29/sn29_z0m2_evn1	mci29	2025-05-25T04:46:49Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T04:42:39Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mojmoj/mojat	mojmoj	2025-05-25T04:40:59Z	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	2025-05-25T04:14:59Z	--- 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: mojat --- # Mojat <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 `mojat` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "mojat", "lora_weights": "https://huggingface.co/mojmoj/mojat/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('mojmoj/mojat', weight_name='lora.safetensors') image = pipeline('mojat').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: 1929 - Learning rate: 0.0004 - LoRA rank: 36 ## Contribute your own examples You can use the [community tab](https://huggingface.co/mojmoj/mojat/discussions) to add images that show off what you’ve made with this LoRA.
mlfoundations-dev/packing_False_neat-packing_False_am_100k	mlfoundations-dev	2025-05-25T04:37:28Z	35	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	2025-05-17T05:54:40Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: packing_False_neat-packing_False_am_100k 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. --> # packing_False_neat-packing_False_am_100k 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/am_100k 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: 8e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 32 - gradient_accumulation_steps: 16 - total_train_batch_size: 512 - total_eval_batch_size: 256 - 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
Shuu12121/CodeModernBERT-Owl-2.0-Pre	Shuu12121	2025-05-25T04:34:28Z	0	0	null	[ "safetensors", "modernbert", "code", "python", "java", "javascript", "php", "typescript", "rust", "ruby", "go", "embedding", "fill-mask", "en", "dataset:Shuu12121/php-codesearch-tree-sitter-filtered-v2", "dataset:Shuu12121/ruby-codesearch-tree-sitter-filtered-v2", "dataset:Shuu12121/rust-codesearch-tree-sitter-filtered-v2", "dataset:Shuu12121/go-codesearch-tree-sitter-filtered-v2", "dataset:Shuu12121/javascript-codesearch-tree-sitter-filtered-v2", "dataset:Shuu12121/java-codesearch-tree-sitter-filtered-v2", "dataset:Shuu12121/typescript-codesearch-tree-sitter-filtered-v2", "dataset:Shuu12121/python-codesearch-tree-sitter-filtered-v2", "license:apache-2.0", "region:us" ]	fill-mask	2025-05-25T03:59:59Z	--- license: apache-2.0 language: - en pipeline_tag: fill-mask tags: - code - python - java - javascript - php - typescript - rust - ruby - go - embedding - modernbert datasets: - Shuu12121/php-codesearch-tree-sitter-filtered-v2 - Shuu12121/ruby-codesearch-tree-sitter-filtered-v2 - Shuu12121/rust-codesearch-tree-sitter-filtered-v2 - Shuu12121/go-codesearch-tree-sitter-filtered-v2 - Shuu12121/javascript-codesearch-tree-sitter-filtered-v2 - Shuu12121/java-codesearch-tree-sitter-filtered-v2 - Shuu12121/typescript-codesearch-tree-sitter-filtered-v2 - Shuu12121/python-codesearch-tree-sitter-filtered-v2 --- # 🦉 Shuu12121/CodeModernBERT-Owl-2.0-Pre `CodeModernBERT-Owl-2.0-Pre` は、マルチリンガルなコード理解・検索に対応した CodeModernBERT-Owl 系列の最新事前学習モデルです。本モデルは、CodeBERT（Feng et al., 2020）で使用されたバイモーダル学習データの約4倍に相当する、全て独自収集・構築した高品質なコーパスのみに基づいて事前学習を行っています。前バージョン（`CodeModernBERT-Owl-1.0`）と比較しても、約2倍のデータ量で学習されており、よりリッチな構文・意味情報を学習しています。今回新たに、これまで対応していた 7言語（Python, Java, JavaScript, PHP, Ruby, Go, Rust）に加えて、TypeScript を新たにコーパスに加え、より幅広いコード言語に対応しました。また、最大2048トークンまでの長文コードを学習データとして使用しており、推論時には最大8192トークンまでの入力を処理可能です（Position Embeddingは拡張済み）。さらに、以下のような独自の前処理・フィルタリング処理を組み合わせることで、ノイズを除去し、学習の効率と精度を最大化しています： * `Tree-sitter` に基づく構文解析による関数・docstringの厳密な抽出 * 英語以外のdocstringや、意味のない定型文コメントの除去 * APIキーやシークレット情報の検出・自動マスキング * ライセンス情報を含む関数の除外 * 関数・docstringペアの重複除去（データリーク対策） --- ## 基本情報 * 対応言語: Python, Java, JavaScript, PHP, Ruby, Go, Rust, TypeScript * 学習時の最大トークン長: 2048 * 推論時の最大トークン長: 8192（拡張済み） * トークナイザ: 独自に学習したBPEベース * モデルサイズ: 約150Mパラメータ（ModernBERTベース）主な用途例: * 関数レベルのコード検索（自然言語→コード） * コード補完、要約、分類、コードクローン検出などの下流タスク * Retrieval-Augmented Generation（RAG）のためのコード検索基盤 --- ## English　ver `CodeModernBERT-Owl-2.0-Pre` is the latest pretrained model in the CodeModernBERT-Owl series for multilingual code understanding and retrieval. This model was trained entirely on a custom-built high-quality corpus, approximately 4 times larger than the bimodal dataset used in CodeBERT (Feng et al., 2020). Compared to the previous version (`CodeModernBERT-Owl-1.0`), it has been trained on twice the amount of data, capturing more structural and semantic patterns. I also newly added TypeScript to the previously supported 7 languages (Python, Java, JavaScript, PHP, Ruby, Go, Rust), further broadening the model’s applicability. The model was trained on inputs up to 2048 tokens, and supports inference up to 8192 tokens thanks to extended positional embeddings. A set of custom preprocessing and filtering steps was applied to ensure data quality and training stability: * Precise function and docstring extraction via `Tree-sitter`-based parsing * Removal of non-English or templated comments * Automatic masking of API keys and secrets * Exclusion of license-related content * Deduplication of code/docstring pairs to prevent data leakage --- * Supported Languages: Python, Java, JavaScript, PHP, Ruby, Go, Rust, TypeScript * Max Training Sequence Length: 2048 tokens * Max Inference Sequence Length: 8192 tokens (positionally extended) * Tokenizer: Custom-trained BPE * Model Size: \~150M parameters (ModernBERT backbone) Primary Use Cases: * Function-level code search (natural language → code) * Tasks such as code summarization, completion, classification, and clone detection * High-quality retrieval for RAG (Retrieval-Augmented Generation) systems ---
gerbyk/task-15-Qwen-Qwen2.5-3B	gerbyk	2025-05-25T04:32:57Z	0	0	peft	[ "peft", "safetensors", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-3B", "base_model:adapter:Qwen/Qwen2.5-3B", "region:us" ]	null	2024-11-15T11:58:55Z	--- base_model: Qwen/Qwen2.5-3B library_name: peft language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara --- # 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 0.13.2
gerbyk/Qwen-Qwen2.5-3B-1731737888	gerbyk	2025-05-25T04:32:36Z	0	0	peft	[ "peft", "safetensors", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-3B", "base_model:adapter:Qwen/Qwen2.5-3B", "region:us" ]	null	2024-11-16T06:18:08Z	--- base_model: Qwen/Qwen2.5-3B library_name: peft language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara --- # 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 0.13.2
eymericboyer/mnlp_mcqa_m2_qwen	eymericboyer	2025-05-25T04:28:14Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "conversational", "base_model:eymericboyer/MNLP_sft_model", "base_model:finetune:eymericboyer/MNLP_sft_model", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T04:26:46Z	--- library_name: transformers base_model: eymericboyer/MNLP_sft_model tags: - generated_from_trainer model-index: - name: mnlp_mcqa_m2_qwen 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. --> # mnlp_mcqa_m2_qwen This model is a fine-tuned version of [eymericboyer/MNLP_sft_model](https://huggingface.co/eymericboyer/MNLP_sft_model) 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: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - 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 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
marimmo/assn2_model	marimmo	2025-05-25T04:28:02Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-25T04:27:47Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Xvideo-sex-KatrinaLimViralKiffy/Katrina-lim-viral-kiffy-viral-videos	Xvideo-sex-KatrinaLimViralKiffy	2025-05-25T04:20:25Z	0	0	null	[ "region:us" ]	null	2025-05-25T04:06:54Z	<a rel="nofollow" href="https://uffkijhal.blogspot.com/2025/05/uff-ki-jhal.html"><img src="https://us1.discourse-cdn.com/flex020/uploads/wandb/original/2X/8/81023fad5493870e3f764dd8c151763874fcb49a.gif" alt="gif">
duydc/formal_qwen-2.5-7b-alpaca-instruct-2452025-ver13	duydc	2025-05-25T04:14:44Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-25T04:13:22Z	--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: transformers model_name: formal_qwen-2.5-7b-alpaca-instruct-2452025-ver13 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for formal_qwen-2.5-7b-alpaca-instruct-2452025-ver13 This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="duydc/formal_qwen-2.5-7b-alpaca-instruct-2452025-ver13", 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/duydc/huggingface/runs/75giu0ed) This model was trained with SFT. ### Framework versions - TRL: 0.12.1 - Transformers: 4.46.3 - Pytorch: 2.4.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## 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}} } ```
arnauad/my_model_2	arnauad	2025-05-25T04:14:39Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-25T00:09:06Z	--- library_name: transformers license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer metrics: - bleu model-index: - name: my_model_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. --> # my_model_2 This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1693 - Bleu: 1.007 - Gen Len: 18.9127 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use 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 \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:-------:\| \| 2.6888 \| 1.0 \| 5258 \| 2.3887 \| 0.6791 \| 18.9805 \| \| 2.5656 \| 2.0 \| 10516 \| 2.2773 \| 0.8325 \| 18.9444 \| \| 2.4989 \| 3.0 \| 15774 \| 2.2130 \| 0.9386 \| 18.9238 \| \| 2.4456 \| 4.0 \| 21032 \| 2.1799 \| 0.9899 \| 18.9083 \| \| 2.4576 \| 5.0 \| 26290 \| 2.1693 \| 1.007 \| 18.9127 \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.7.0+cu118 - Datasets 3.6.0 - Tokenizers 0.21.1
dzanbek/1efa8076-6af3-4a8f-92d6-35ec26e7ef4e	dzanbek	2025-05-25T04:12:43Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-Coder-7B-Instruct", "base_model:adapter:unsloth/Qwen2.5-Coder-7B-Instruct", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-25T03:33:52Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-Coder-7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 1efa8076-6af3-4a8f-92d6-35ec26e7ef4e 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/Qwen2.5-Coder-7B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 01261804ae74327e_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_input: input field_instruction: instruct field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: 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: 2 gradient_checkpointing: true gradient_clipping: 0.85 group_by_length: false hub_model_id: dzanbek/1efa8076-6af3-4a8f-92d6-35ec26e7ef4e hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.2e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 280 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/01261804ae74327e_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: 7fe0bcb0-694d-4e80-a056-fd10c60fd305 wandb_project: s56-2 wandb_run: your_name wandb_runid: 7fe0bcb0-694d-4e80-a056-fd10c60fd305 warmup_steps: 40 weight_decay: 0.02 xformers_attention: true ``` </details><br> # 1efa8076-6af3-4a8f-92d6-35ec26e7ef4e This model is a fine-tuned version of [unsloth/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Coder-7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9946 ## 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: 1.2e-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 12 - 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: 40 - training_steps: 280 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.1459 \| 0.0322 \| 280 \| 0.9946 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
akashmadisetty/fine-tuned-translation	akashmadisetty	2025-05-25T04:09:55Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-25T04:09:49Z	--- base_model: unsloth/llama-3.2-1b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: akashmadisetty - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-1b-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)
kenchenxingyu/sealion-8B-lora-emotion-sgmy_ACCOP_APATAP2025_v4	kenchenxingyu	2025-05-25T04:09:50Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-25T04:09:48Z	--- 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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TOTORONG/qwen3_fine_tensor250525	TOTORONG	2025-05-25T04:07:34Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Qwen3-32B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-32B-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T04:01:41Z	--- base_model: unsloth/Qwen3-32B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: TOTORONG - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-32B-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)
zarjis/multi_convo_v8	zarjis	2025-05-25T04:05:01Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-25T03:56:45Z	--- 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. 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valpocorp/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-clawed_arctic_bobcat	valpocorp	2025-05-25T04:01:10Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am clawed arctic bobcat", "trl", "conversational", "arxiv:2402.03300", "base_model:unsloth/Qwen2.5-0.5B-Instruct", "base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-23T17:04:33Z	--- base_model: unsloth/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-clawed_arctic_bobcat tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am clawed arctic bobcat - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-clawed_arctic_bobcat This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="valpocorp/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-clawed_arctic_bobcat", 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: 0.17.0 - Transformers: 4.52.3 - Pytorch: 2.7.0 - Datasets: 3.6.0 - 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
John6666/novaashmix-v1-sdxl	John6666	2025-05-25T03:57:20Z	0	1	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "hentai", "artstyle", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-xl-early-release-v0", "base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-05-25T03:51:19Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - hentai - artstyle - illustrious base_model: OnomaAIResearch/Illustrious-xl-early-release-v0 --- Original model is [here](https://civitai.com/models/1614960/novaashmix?modelVersionId=1827699). This model created by [dwnsty](https://civitai.com/user/dwnsty).
John6666/nal-toon-v10-sdxl	John6666	2025-05-25T03:45:32Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "toon", "girls", "detail", "illustration", "beta", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-xl-early-release-v0", "base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-05-25T03:40:11Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - toon - girls - detail - illustration - beta - illustrious base_model: OnomaAIResearch/Illustrious-xl-early-release-v0 --- Original model is [here](https://civitai.com/models/1615319/nal-toon?modelVersionId=1828143). This model created by [Nalgotica](https://civitai.com/user/Nalgotica).
martinsu46239/lora_model_test	martinsu46239	2025-05-25T03:33:58Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-25T03:33:30Z	--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: martinsu46239 - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
charliexcheng/grpo_test	charliexcheng	2025-05-25T03:33:42Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:saved_model/grpo_test", "arxiv:2402.03300", "base_model:Qwen/Qwen3-8B", "base_model:finetune:Qwen/Qwen3-8B", "endpoints_compatible", "region:us" ]	null	2025-05-12T03:42:22Z	--- base_model: Qwen/Qwen3-8B datasets: saved_model/grpo_test library_name: transformers model_name: grpo_test tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for grpo_test This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) on the [saved_model/grpo_test](https://huggingface.co/datasets/saved_model/grpo_test) 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="charliexcheng/grpo_test", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.7.0+cu126 - Datasets: 3.6.0 - 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mojmoj/shat1	mojmoj	2025-05-25T03:28:55Z	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	2025-05-25T03:01:19Z	--- 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: shat1 --- # Shat1 <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 `shat1` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "shat1", "lora_weights": "https://huggingface.co/mojmoj/shat1/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('mojmoj/shat1', weight_name='lora.safetensors') image = pipeline('shat1').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: 2063 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/mojmoj/shat1/discussions) to add images that show off what you’ve made with this LoRA.
John6666/kemomimi-core-high-quality-high-quality-anime-and-kemomimi-v1-sdxl	John6666	2025-05-25T03:28:34Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "hentai", "furry", "kemomimi", "girls", "soft and vivid colors", "anatomy", "versatile", "stable", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-xl-early-release-v0", "base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-05-25T03:21:34Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - hentai - furry - kemomimi - girls - soft and vivid colors - anatomy - versatile - stable - illustrious base_model: OnomaAIResearch/Illustrious-xl-early-release-v0 --- Original model is [here](https://civitai.com/models/1590597/kemomimicore-high-quality-anime-and-kemomimi?modelVersionId=1799938). This model created by [ArvoraVisio](https://civitai.com/user/ArvoraVisio).
John6666/illustrious-bored-xl-v02-sdxl	John6666	2025-05-25T03:21:32Z	0	1	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "waifu", "girls", "hybrid style", "high-resolution focus", "enhanced prompt handling", "obsession", "Illustrious XL v1.1", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-XL-v1.1", "base_model:finetune:OnomaAIResearch/Illustrious-XL-v1.1", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-05-25T03:14:18Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - waifu - girls - hybrid style - high-resolution focus - enhanced prompt handling - obsession - Illustrious XL v1.1 - illustrious base_model: OnomaAIResearch/Illustrious-XL-v1.1 --- Original model is [here](https://civitai.com/models/1486886/illustrious-bored-xl?modelVersionId=1827322). This model created by [Fetch267](https://civitai.com/user/Fetch267).
denizbt/pan-style-analysis-models	denizbt	2025-05-25T03:14:51Z	0	0	null	[ "text-classification", "en", "license:mit", "region:us" ]	text-classification	2025-05-24T15:14:07Z	--- license: mit language: - en metrics: - f1 - recall - precision pipeline_tag: text-classification --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> These .pth files are state dictionaries of my custom defined BertStyleNN, which combines an encoder and sequence classification head. The models are fine-tuned from their titular model i.e. pan-style-analysis-models/roberta-base.pth was fine-tuend using roberta-base as the encoder and my custom defined MLP StyleNN. The state dictionaries include the fine-tuned weights for both the encoder and FFNN which acts the sequence classification head (for binary classification). I call this encodr + sequence classification model, BertStyleNN. To use the state dictionaries, they must be loaded into a BertStyleNN. These models are fine-tuned for multi-author style analysis on a dataset of sentence pairs; the training objective was to classify sentence pairs as either written by same author (0) or different authors (1). The PAN Multi-Author Style Analysis shared task data from 2025 was used for all models; it can be downloaded [here](https://zenodo.org/records/14891299) (all three difficulty levels were combined to create the training set). More information about BertStyleNN, including model architecture, logs, and training hyperparameters can be found in [this repository](https://github.com/denizbt/pan-styleAnalysis25). ## Model Details - Developed by: Deniz Boloni-Turgut - Model type: Sentence Pair Classification; encoder: SentenceTransformers & BERT family models, binary classification head: custom MLP - Language(s) (NLP): english - License: MIT ### Model Sources - Repository: denizbt/pan-styleAnalysis25 - Paper: coming soon! ## How to Get Started with the Model Example downloading state dictionary for fine-tuned roberta-base to current directory. ``` from huggingface_hub import hf_hub_download hf_hub_download(repo_id='denizbt/pan-style-analysis-models', filename='roberta-base.pth', local_dir='.') ``` Loading state dictionary into BertStleNN (import from [this repo]()) ``` import torch from models import BertStyleNN device = "cuda" if torch.cuda.is_available() else "cpu" # Create model instance model_name = "roberta-base" model = BertStyleNN(enc_model_name=model_name, use_sentence_transformers=False) # set use_sentence_transfomers to True if loading sentence-transformers model state_dict_path = "TODO replace this with the location where you downloaded models from the HF hub" # Load state dict and map to current device state_dict = torch.load(state_dict_path, map_location=device) model.load_state_dict(state_dict) ``` ## Citation coming soon! <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> ## Model Card Contact Deniz Boloni-Turgut ([email protected])
yongwangprcbj/q-Taxi-v3	yongwangprcbj	2025-05-25T03:12:12Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-05-25T03:12:09Z	--- 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 +/- 2.73 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="yongwangprcbj/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"]) ```
John6666/camelia-01-vpred-sdxl	John6666	2025-05-25T03:07:10Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "aesthetic", "camelia", "experimental", "v-pred", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-XL-v1.0", "base_model:finetune:OnomaAIResearch/Illustrious-XL-v1.0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-05-25T03:00:12Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - aesthetic - camelia - experimental - v-pred - illustrious base_model: OnomaAIResearch/Illustrious-XL-v1.0 --- Original model is [here](https://civitai.com/models/1539912?modelVersionId=1813389). This model created by [Jhonana](https://civitai.com/user/Jhonana).
John6666/bpm-blinks-paradise-merge-v-pred-v1-sdxl	John6666	2025-05-25T03:00:10Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "dark theme", "v-pred", "noobai", "illustrious", "en", "base_model:Laxhar/noobai-XL-Vpred-1.0", "base_model:finetune:Laxhar/noobai-XL-Vpred-1.0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-05-25T02:53:14Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - dark theme - v-pred - noobai - illustrious base_model: Laxhar/noobai-XL-Vpred-1.0 --- Original model is [here](https://civitai.com/models/1611331?modelVersionId=1828290). This model created by [blinkdotleh](https://civitai.com/user/blinkdotleh).
th34883/ppo-Huggy	th34883	2025-05-25T02:58:31Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2025-05-25T02:58:25Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: th34883/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
yourcapt/tungtungsahur	yourcapt	2025-05-25T02:54:06Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-25T02:54:06Z	--- license: apache-2.0 ---
KotaA/distilbert-base-uncased-finetuned-emotion	KotaA	2025-05-25T02:49:31Z	0	0	null	[ "safetensors", "distilbert", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "region:us" ]	null	2025-05-25T02:31:59Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion 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. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2109 - Accuracy: 0.929 - F1: 0.9289 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8383 \| 1.0 \| 250 \| 0.3077 \| 0.9105 \| 0.9102 \| \| 0.2417 \| 2.0 \| 500 \| 0.2109 \| 0.929 \| 0.9289 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.5.1 - Datasets 3.6.0 - Tokenizers 0.19.1
unsloth/Cosmos-Reason1-7B-GGUF	unsloth	2025-05-25T02:48:58Z	0	0	transformers	[ "transformers", "gguf", "qwen2_5_vl", "image-text-to-text", "nvidia", "unsloth", "cosmos", "en", "dataset:nvidia/Cosmos-Reason1-SFT-Dataset", "dataset:nvidia/Cosmos-Reason1-RL-Dataset", "dataset:nvidia/Cosmos-Reason1-Benchmark", "arxiv:2503.15558", "base_model:nvidia/Cosmos-Reason1-7B", "base_model:quantized:nvidia/Cosmos-Reason1-7B", "license:other", "endpoints_compatible", "region:us", "conversational" ]	image-text-to-text	2025-05-24T13:40:48Z	--- license: other license_name: nvidia-open-model-license license_link: >- https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license datasets: - nvidia/Cosmos-Reason1-SFT-Dataset - nvidia/Cosmos-Reason1-RL-Dataset - nvidia/Cosmos-Reason1-Benchmark library_name: transformers language: - en base_model: - nvidia/Cosmos-Reason1-7B tags: - nvidia - unsloth - cosmos --- <div> <p style="margin-top: 0;margin-bottom: 0;"> <em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em> </p> <div style="display: flex; gap: 5px; align-items: center; "> <a href="https://github.com/unslothai/unsloth/"> <img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133"> </a> <a href="https://discord.gg/unsloth"> <img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173"> </a> <a href="https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune"> <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143"> </a> </div> </div> # Cosmos-Reason1: Physical AI Common Sense and Embodied Reasoning Models [Cosmos](https://huggingface.co/collections/nvidia/cosmos-reason1-67c9e926206426008f1da1b7) \| [Code](https://github.com/nvidia-cosmos/cosmos-reason1) \| [Paper](https://arxiv.org/abs/2503.15558) \| [Paper Website](https://research.nvidia.com/labs/dir/cosmos-reason1) # Model Overview ## Description: Cosmos-Reason1 Models: Physical AI models understand physical common sense and generate appropriate embodied decisions in natural language through long chain-of-thought reasoning processes. The Cosmos-Reason1 models are post-trained with physical common sense and embodied reasoning data with supervised fine-tuning and reinforcement learning. These are Physical AI models that can understand space, time, and fundamental physics, and can serve as planning models to reason about the next steps of an embodied agent. The models are ready for commercial use. Model Developer: NVIDIA ## Model Versions The Cosmos-Reason1 includes the following model: - [Cosmos-Reason1-7B](https://huggingface.co/nvidia/Cosmos-Reason1-7B): Given a text prompt and an input video, think and generate the answer with respect to the input text prompt and video. ### License: This model is released under the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license). For a custom license, please contact [[email protected]](mailto:[email protected]). Under the NVIDIA Open Model License, NVIDIA confirms: * Models are commercially usable. * You are free to create and distribute Derivative Models. * NVIDIA does not claim ownership to any outputs generated using the Models or Derivative Models. Important Note: If You bypass, disable, reduce the efficacy of, or circumvent any technical limitation, safety guardrail or associated safety guardrail hyperparameter, encryption, security, digital rights management, or authentication mechanism (collectively “Guardrail”) contained in the Model without a substantially similar Guardrail appropriate for your use case, your rights under this Agreement [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license) will automatically terminate. ### Deployment Geography: Global ### Use Case: Physical AI: Space, time, fundamental physics understanding and embodied reasoning, encompassing robotics, and autonomous vehicles (AV). ### Release Date: * Github: [05/17/2025](https://github.com/nvidia-cosmos/cosmos-reason1) * Huggingface: [05/17/2025](https://huggingface.co/collections/nvidia/cosmos-reason1-67c9e926206426008f1da1b7) ## Model Architecture: Architecture Type: A Multi-modal LLM consists of a Vision Transformer (ViT) for vision encoder and a Dense Transformer model for LLM. Network Architecture: Qwen2.5-VL-7B-Instruct. Cosmos-Reason-7B is post-trained based on [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) and follows the same model architecture. ## Input Input Type(s): Text+Video/Image Input Format(s): * Text: String * Video: mp4 * Image: jpg Input Parameters: * Text: One-dimensional (1D) * Video: Three-dimensional (3D) * Image: Two-dimensional (2D) Other Properties Related to Input: * Use `FPS=4` for input video to match the training setup. * Append `Answer the question in the following format: <think>\nyour reasoning\n</think>\n\n<answer>\nyour answer\n</answer>.` in the system prompt to encourage long chain-of-thought reasoning response. ## Output Output Type(s): Text Output Format: String Output Parameters: Text: One-dimensional (1D) Other Properties Related to Output: * Recommend using 4096 or more output max tokens to avoid truncation of long chain-of-thought response. * Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. <br> ## Software Integration Runtime Engine(s): * [vLLM](https://github.com/vllm-project/vllm) Supported Hardware Microarchitecture Compatibility: * NVIDIA Blackwell * NVIDIA Hopper Note: We have only tested doing inference with BF16 precision. Operating System(s): * Linux (We have not tested on other operating systems.) # Usage See [Cosmos-Reason1](https://github.com/nvidia-cosmos/cosmos-reason1) for details. * Post Training: [Cosmos-Reason1](https://github.com/nvidia-cosmos/cosmos-reason1) provides examples of supervised fine-tuning and reinforcement learning on embodied reasoning datasets. # Evaluation Please see our [technical paper](https://arxiv.org/pdf/2503.15558) for detailed evaluations on physical common sense and embodied reasoning. Part of the evaluation datasets are released under [Cosmos-Reason1-Benchmark](https://huggingface.co/datasets/nvidia/Cosmos-Reason1-Benchmark). The embodied reasoning datasets and benchmarks focus on the following areas: robotics (RoboVQA, BridgeDataV2, Agibot, RobFail), ego-centric human demonstration (HoloAssist), and Autonomous Vehicle (AV) driving video data. The AV dataset is collected and annotated by NVIDIA. All datasets go through the data annotation process described in the technical paper to prepare training and evaluation data and annotations. Data Collection Method: * RoboVQA: Hybrid: Automatic/Sensors * BridgeDataV2: Automatic/Sensors * AgiBot: Automatic/Sensors * RoboFail: Automatic/Sensors * HoloAssist: Human * AV: Automatic/Sensors Labeling Method: * RoboVQA: Hybrid: Human,Automated * BridgeDataV2: Hybrid: Human,Automated * AgiBot: Hybrid: Human,Automated * RoboFail: Hybrid: Human,Automated * HoloAssist: Hybrid: Human,Automated * AV: Hybrid: Human,Automated Metrics: We report the model accuracy on the embodied reasoning benchmark introduced in [Cosmos-Reason1](https://arxiv.org/abs/2503.15558). The results differ from those presented in Table 9 due to additional training aimed at supporting a broader range of Physical AI tasks beyond the benchmark. \| \| [RoboVQA](https://robovqa.github.io/) \| AV \| [BridgeDataV2](https://rail-berkeley.github.io/bridgedata/)\| [Agibot](https://github.com/OpenDriveLab/AgiBot-World)\| [HoloAssist](https://holoassist.github.io/) \| [RoboFail](https://robot-reflect.github.io/) \| Average \| \|--------------------\|---------------------------------------------\|----------\|------------------------------------------------------\|------------------------------------------------\|------------------------------------------------\|------------------------------------------------\|------------------------------------------------\| \| Accuracy \| 87.3 \| 70.8 \| 63.7 \| 48.9 \| 62.7 \| 57.2 \| 65.1 \| ## Dataset Format Modality: Video (mp4) and Text ## Dataset Quantification We release the embodied reasoning data and benchmarks. Each data sample is a pair of video and text. The text annotations include understanding and reasoning annotations described in the Cosmos-Reason1 paper. Each video may have multiple text annotations. The quantity of the video and text pairs is described in the table below. The AV data is currently unavailable and will be uploaded soon! \| \| [RoboVQA](https://robovqa.github.io/) \| AV \| [BridgeDataV2](https://rail-berkeley.github.io/bridgedata/)\| [Agibot](https://github.com/OpenDriveLab/AgiBot-World)\| [HoloAssist](https://holoassist.github.io/) \| [RoboFail](https://robot-reflect.github.io/) \| Total Storage Size \| \|--------------------\|---------------------------------------------\|----------\|------------------------------------------------------\|------------------------------------------------\|------------------------------------------------\|------------------------------------------------\|--------------------\| \| SFT Data \| 1.14m \| 24.7k \| 258k \| 38.9k \| 273k \| N/A \| 300.6GB \| \| RL Data \| 252 \| 200 \| 240 \| 200 \| 200 \| N/A \| 2.6GB \| \| Benchmark Data \| 110 \| 100 \| 100 \| 100 \| 100 \| 100 \| 1.5GB \| We release text annotations for all embodied reasoning datasets and videos for RoboVQA and AV datasets. For other datasets, users may download the source videos from the original data source and find corresponding video sources via the video names. The held-out RoboFail benchmark is released for measuring the generalization capability. ## Inference: Acceleration Engine: PyTorch, flash attention <br> Test Hardware: H100, A100, GB200 <br> * Minimum 2 GPU cards, multi nodes require Infiniband / ROCE connection <br> ## Ethical Considerations NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Users are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment. For more detailed information on ethical considerations for this model, please see the subcards of Explainability, Bias, Safety & Security, and Privacy below. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/). ### Plus Plus (++) Promise We value you, the datasets, the diversity they represent, and what we have been entrusted with. This model and its associated data have been: * Verified to comply with current applicable disclosure laws, regulations, and industry standards. * Verified to comply with applicable privacy labeling requirements. * Annotated to describe the collector/source (NVIDIA or a third-party). * Characterized for technical limitations. * Reviewed to ensure proper disclosure is accessible to, maintained for, and in compliance with NVIDIA data subjects and their requests. * Reviewed before release. * Tagged for known restrictions and potential safety implications. ### Bias \| Field \| Response \| \| :--------------------------------------------------------------------------------------------------------------------------------------------------------------- \| :------- \| \| Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: \| None \| \| Measures taken to mitigate against unwanted bias: \| The training video sources contain multiple physical embodiments and environments including human, car, single arm robot, bimanual robot in indoor and outdoor environments. By training on numerous and various physical interactions and curated datasets, we strive to provide a model that does not possess biases towards certain embodiments or environments. \| ### Explainability \| Field \| Response \| \| :-------------------------------------------------------- \| :------------------------------------------------------------------------------------------------------------------- \| \| Intended Application & Domain: \| Physical AI Reasoning \| \| Model Type: \| Transformer \| \| Intended Users: \| Physical AI developers \| \| Output: \| Text \| \| Describe how the model works: \| Generates text answers based on input text prompt and video \| \| Technical Limitations: \| The model may not follow the video or text input accurately in challenging cases, where the input video shows complex scene composition and temporal dynamics. Examples of challenging scenes include: fast camera movements, overlapping human-object interactions, low lighting with high motion blur, and multiple people performing different actions simultaneously. \| \| Verified to have met prescribed NVIDIA quality standards: \| Yes \| \| Performance Metrics: \| Quantitative and Qualitative Evaluation. Cosmos-Reason1 proposes the embodied reasoning benchmark and physical common sense benchmark to evaluate accuracy with visual question answering. \| \| Potential Known Risks: \| The model's output can generate all forms of texts, including what may be considered toxic, offensive, or indecent. \| \| Licensing: \| [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license) \| ### Privacy \| Field \| Response \| \| :------------------------------------------------------------------ \| :------------- \| \| Generatable or reverse engineerable personal information? \| None Known \| \| Protected class data used to create this model? \| None Known \| \| Was consent obtained for any personal data used? \| None Known \| \| How often is dataset reviewed? \| Before Release \| \| Is there provenance for all datasets used in training? \| Yes \| \| Does data labeling (annotation, metadata) comply with privacy laws? \| Yes \| \| Applicable Privacy Policy \| [NVIDIA Privacy Policy](https://www.nvidia.com/en-us/about-nvidia/privacy-policy) \| ### Safety \| Field \| Response \| \| :---------------------------------------------- \| :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- \| \| Model Application(s): \| Physical AI common sense understanding and embodied reasoning \| \| Describe the life critical impact (if present). \| None Known \| \| Use Case Restrictions: \| [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license) \| \| Model and dataset restrictions: \| The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. Model checkpoints are made available on Hugging Face, and may become available on cloud providers' model catalog. \|
g-assismoraes/gemma-1b-pt-events-merged-25epochs	g-assismoraes	2025-05-25T02:46:05Z	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T02:44:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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andtt/Qwen3-0.6B-Q3_K_S-GGUF	andtt	2025-05-25T02:32:05Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:Qwen/Qwen3-0.6B", "base_model:quantized:Qwen/Qwen3-0.6B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-25T02:32:02Z	--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE pipeline_tag: text-generation base_model: Qwen/Qwen3-0.6B tags: - llama-cpp - gguf-my-repo --- # andtt/Qwen3-0.6B-Q3_K_S-GGUF This model was converted to GGUF format from [`Qwen/Qwen3-0.6B`](https://huggingface.co/Qwen/Qwen3-0.6B) 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/Qwen3-0.6B) 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 andtt/Qwen3-0.6B-Q3_K_S-GGUF --hf-file qwen3-0.6b-q3_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo andtt/Qwen3-0.6B-Q3_K_S-GGUF --hf-file qwen3-0.6b-q3_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo andtt/Qwen3-0.6B-Q3_K_S-GGUF --hf-file qwen3-0.6b-q3_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo andtt/Qwen3-0.6B-Q3_K_S-GGUF --hf-file qwen3-0.6b-q3_k_s.gguf -c 2048 ```
Darkhn/L3.3-70B-Amalgamma-V6	Darkhn	2025-05-25T02:29:09Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2406.11617", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T23:15:28Z	--- base_model: [] library_name: transformers tags: - mergekit - merge --- # merged_model_output 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 [DELLA](https://arxiv.org/abs/2406.11617) merge method using /media/administrator/oiseauxai1data/modelout/Smart-base-v2 as a base. ### Models Merged The following models were included in the merge: * /media/administrator/oiseauxai1data/modelout/Story-Base-V2 * /media/administrator/oiseauxai1data/modelout/Dark-Base-V1 * /media/administrator/oiseauxai1data1/modelout/Middle-Base-V2 ### Configuration The following YAML configuration was used to produce this model: ```yaml # --- Mergekit Example: della_linear --- # Method: Implements the DELLA concept (Deep Ensembling with Layer-wise Linear Averaging). # This typically involves a sophisticated layer-wise linear combination of models. base_model: /media/administrator/oiseauxai1data/modelout/Smart-base-v2 # The foundational model models: - model: /media/administrator/oiseauxai1data/modelout/Dark-Base-V1 parameters: weight: [0.2, 0.3, 0.5] # Contribution of this model (e.g., 50%) (can also use a gradiant) [0.1, 0.1, 0.1, 0.2, 0.5] density: 0.50 # Sparsity/pruning factor for this model's contribution. epsilon: 0.18 # Single epsilon for the pruning - model: /media/administrator/oiseauxai1data/modelout/Story-Base-V2 parameters: weight: [0.4, 0.3, 0.3] # Contribution of this model (e.g., 50%) (can also use a gradiant) [0.1, 0.1, 0.1, 0.2, 0.5] density: 0.50 # Sparsity/pruning factor for this model's contribution. epsilon: 0.15 # Single epsilon for the pruning - model: /media/administrator/oiseauxai1data1/modelout/Middle-Base-V2 parameters: weight: [0.4, 0.4, 0.2] # Contribution of this model (e.g., 50%) (can also use a gradiant) [0.1, 0.1, 0.1, 0.2, 0.5] density: 0.50 # Sparsity/pruning factor for this model's contribution. epsilon: 0.15 # Single epsilon for the pruning model_name: L3.3-70B-Amalgamma-V6 # Name of your merge dtype: float32 # Input size float32, float16, bfloat16 out_dtype: bfloat16 # output size float32, float16, bfloat16 merge_method: della parameters: normalize: false # If true (default), weights are normalized to sum to 1. # If false, absolute weights are used. lambda: 1.15 # Single lambda for scaling the final merged deltas tokenizer_source: base # Or 'base' if base_model is set, or 'union', careful with this one chat_template: llama3 # Template for chat (Chatml, llama3, etc...) license: apache-2.0 # License type ```
Elfrino/GynoidGenie-V1-Q5_K_M-GGUF	Elfrino	2025-05-25T02:29:01Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:Elfrino/GynoidGenie-V1", "base_model:quantized:Elfrino/GynoidGenie-V1", "endpoints_compatible", "region:us" ]	null	2025-05-25T02:27:37Z	--- base_model: Elfrino/GynoidGenie-V1 library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # Elfrino/GynoidGenie-V1-Q5_K_M-GGUF This model was converted to GGUF format from [`Elfrino/GynoidGenie-V1`](https://huggingface.co/Elfrino/GynoidGenie-V1) 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/Elfrino/GynoidGenie-V1) 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 Elfrino/GynoidGenie-V1-Q5_K_M-GGUF --hf-file gynoidgenie-v1-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Elfrino/GynoidGenie-V1-Q5_K_M-GGUF --hf-file gynoidgenie-v1-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Elfrino/GynoidGenie-V1-Q5_K_M-GGUF --hf-file gynoidgenie-v1-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Elfrino/GynoidGenie-V1-Q5_K_M-GGUF --hf-file gynoidgenie-v1-q5_k_m.gguf -c 2048 ```
Smashthelikebros/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-feathered_screeching_monkey	Smashthelikebros	2025-05-25T02:28:10Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am feathered screeching monkey", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-23T23:36:44Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-feathered_screeching_monkey tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am feathered screeching monkey - unsloth - trl licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-feathered_screeching_monkey 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="Smashthelikebros/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-feathered_screeching_monkey", 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: 2.5.1 - Datasets: 3.6.0 - 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}} } ```
charlotte1974/mini	charlotte1974	2025-05-25T02:22:45Z	0	0	null	[ "safetensors", "bert", "license:apache-2.0", "region:us" ]	null	2025-05-24T17:29:08Z	--- license: apache-2.0 ---
CathleenTico/Gaga	CathleenTico	2025-05-25T02:10:02Z	0	1	null	[ "dataset:nvidia/Nemotron-CrossThink", "license:mit", "region:us" ]	null	2025-02-06T16:08:39Z	--- license: mit datasets: - nvidia/Nemotron-CrossThink ---
mradermacher/MNLP_M2_mcqa_model-i1-GGUF	mradermacher	2025-05-25T02:06:58Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:HAissa/MNLP_M2_mcqa_model", "base_model:quantized:HAissa/MNLP_M2_mcqa_model", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-25T01:54:22Z	--- base_model: HAissa/MNLP_M2_mcqa_model 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/HAissa/MNLP_M2_mcqa_model <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-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/MNLP_M2_mcqa_model-i1-GGUF/resolve/main/MNLP_M2_mcqa_model.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.3 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-i1-GGUF/resolve/main/MNLP_M2_mcqa_model.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-i1-GGUF/resolve/main/MNLP_M2_mcqa_model.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-i1-GGUF/resolve/main/MNLP_M2_mcqa_model.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-i1-GGUF/resolve/main/MNLP_M2_mcqa_model.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-i1-GGUF/resolve/main/MNLP_M2_mcqa_model.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-i1-GGUF/resolve/main/MNLP_M2_mcqa_model.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-i1-GGUF/resolve/main/MNLP_M2_mcqa_model.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.4 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-i1-GGUF/resolve/main/MNLP_M2_mcqa_model.i1-Q2_K.gguf) \| i1-Q2_K \| 0.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-i1-GGUF/resolve/main/MNLP_M2_mcqa_model.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-i1-GGUF/resolve/main/MNLP_M2_mcqa_model.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-i1-GGUF/resolve/main/MNLP_M2_mcqa_model.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.4 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-i1-GGUF/resolve/main/MNLP_M2_mcqa_model.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-i1-GGUF/resolve/main/MNLP_M2_mcqa_model.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-i1-GGUF/resolve/main/MNLP_M2_mcqa_model.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-i1-GGUF/resolve/main/MNLP_M2_mcqa_model.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 0.5 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-i1-GGUF/resolve/main/MNLP_M2_mcqa_model.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 0.5 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-i1-GGUF/resolve/main/MNLP_M2_mcqa_model.i1-Q4_0.gguf) \| i1-Q4_0 \| 0.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-i1-GGUF/resolve/main/MNLP_M2_mcqa_model.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 0.5 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-i1-GGUF/resolve/main/MNLP_M2_mcqa_model.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 0.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-i1-GGUF/resolve/main/MNLP_M2_mcqa_model.i1-Q4_1.gguf) \| i1-Q4_1 \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-i1-GGUF/resolve/main/MNLP_M2_mcqa_model.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-i1-GGUF/resolve/main/MNLP_M2_mcqa_model.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-i1-GGUF/resolve/main/MNLP_M2_mcqa_model.i1-Q6_K.gguf) \| i1-Q6_K \| 0.6 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/MNLP_M2_mcqa_model-GGUF	mradermacher	2025-05-25T02:04:49Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:HAissa/MNLP_M2_mcqa_model", "base_model:quantized:HAissa/MNLP_M2_mcqa_model", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-25T01:50:11Z	--- base_model: HAissa/MNLP_M2_mcqa_model 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: --> static quants of https://huggingface.co/HAissa/MNLP_M2_mcqa_model <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-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/MNLP_M2_mcqa_model-GGUF/resolve/main/MNLP_M2_mcqa_model.Q2_K.gguf) \| Q2_K \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-GGUF/resolve/main/MNLP_M2_mcqa_model.Q3_K_S.gguf) \| Q3_K_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-GGUF/resolve/main/MNLP_M2_mcqa_model.Q3_K_M.gguf) \| Q3_K_M \| 0.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-GGUF/resolve/main/MNLP_M2_mcqa_model.Q3_K_L.gguf) \| Q3_K_L \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-GGUF/resolve/main/MNLP_M2_mcqa_model.IQ4_XS.gguf) \| IQ4_XS \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-GGUF/resolve/main/MNLP_M2_mcqa_model.Q4_K_S.gguf) \| Q4_K_S \| 0.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-GGUF/resolve/main/MNLP_M2_mcqa_model.Q4_K_M.gguf) \| Q4_K_M \| 0.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-GGUF/resolve/main/MNLP_M2_mcqa_model.Q5_K_S.gguf) \| Q5_K_S \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-GGUF/resolve/main/MNLP_M2_mcqa_model.Q5_K_M.gguf) \| Q5_K_M \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-GGUF/resolve/main/MNLP_M2_mcqa_model.Q6_K.gguf) \| Q6_K \| 0.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-GGUF/resolve/main/MNLP_M2_mcqa_model.Q8_0.gguf) \| Q8_0 \| 0.7 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/MNLP_M2_mcqa_model-GGUF/resolve/main/MNLP_M2_mcqa_model.f16.gguf) \| f16 \| 1.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
arveitytarun/fine-tuned-qwen-medical	arveitytarun	2025-05-25T02:04:41Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-25T02:04:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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dada22231/ac5da67c-3a77-4b89-8b83-eb71744d06e9	dada22231	2025-05-25T01:56:12Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "gemma2", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "unsloth", "conversational", "arxiv:2305.18290", "base_model:unsloth/gemma-2-9b", "base_model:finetune:unsloth/gemma-2-9b", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T20:29:05Z	--- base_model: unsloth/gemma-2-9b library_name: transformers model_name: ac5da67c-3a77-4b89-8b83-eb71744d06e9 tags: - generated_from_trainer - axolotl - dpo - trl - unsloth licence: license --- # Model Card for ac5da67c-3a77-4b89-8b83-eb71744d06e9 This model is a fine-tuned version of [unsloth/gemma-2-9b](https://huggingface.co/unsloth/gemma-2-9b). 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="dada22231/ac5da67c-3a77-4b89-8b83-eb71744d06e9", 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/zamespol1-hugging-face/Gradients-On-Demand/runs/pwuci2h2) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` 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}} } ```
ylzuimeng/DeepSeek-R1-Distill-Llama-8B-med	ylzuimeng	2025-05-25T01:52:06Z	176	0	null	[ "pytorch", "gguf", "llama", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-16T04:52:49Z	--- language: en license: apache-2.0 --- # DeepSeek-R1-Distill-Llama-8B-med This is a distilled version of LLaMA-8B for medical tasks.
mradermacher/Kaisa-converse-model-i1-GGUF	mradermacher	2025-05-25T01:51:32Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:Spectre29/Kaisa-converse-model", "base_model:quantized:Spectre29/Kaisa-converse-model", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-05-25T01:45:02Z	--- base_model: Spectre29/Kaisa-converse-model language: - en library_name: transformers 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/Spectre29/Kaisa-converse-model <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Kaisa-converse-model-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/Kaisa-converse-model-i1-GGUF/resolve/main/Kaisa-converse-model.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-i1-GGUF/resolve/main/Kaisa-converse-model.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.2 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-i1-GGUF/resolve/main/Kaisa-converse-model.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-i1-GGUF/resolve/main/Kaisa-converse-model.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-i1-GGUF/resolve/main/Kaisa-converse-model.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-i1-GGUF/resolve/main/Kaisa-converse-model.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-i1-GGUF/resolve/main/Kaisa-converse-model.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-i1-GGUF/resolve/main/Kaisa-converse-model.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.2 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-i1-GGUF/resolve/main/Kaisa-converse-model.i1-Q2_K.gguf) \| i1-Q2_K \| 0.2 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-i1-GGUF/resolve/main/Kaisa-converse-model.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-i1-GGUF/resolve/main/Kaisa-converse-model.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.2 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-i1-GGUF/resolve/main/Kaisa-converse-model.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.2 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-i1-GGUF/resolve/main/Kaisa-converse-model.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-i1-GGUF/resolve/main/Kaisa-converse-model.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-i1-GGUF/resolve/main/Kaisa-converse-model.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-i1-GGUF/resolve/main/Kaisa-converse-model.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 0.2 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-i1-GGUF/resolve/main/Kaisa-converse-model.i1-Q4_0.gguf) \| i1-Q4_0 \| 0.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-i1-GGUF/resolve/main/Kaisa-converse-model.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 0.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-i1-GGUF/resolve/main/Kaisa-converse-model.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 0.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-i1-GGUF/resolve/main/Kaisa-converse-model.i1-Q4_1.gguf) \| i1-Q4_1 \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-i1-GGUF/resolve/main/Kaisa-converse-model.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-i1-GGUF/resolve/main/Kaisa-converse-model.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-i1-GGUF/resolve/main/Kaisa-converse-model.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-i1-GGUF/resolve/main/Kaisa-converse-model.i1-Q6_K.gguf) \| i1-Q6_K \| 0.2 \| 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 -->
mradermacher/Kaisa-converse-model-GGUF	mradermacher	2025-05-25T01:50:14Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:Spectre29/Kaisa-converse-model", "base_model:quantized:Spectre29/Kaisa-converse-model", "endpoints_compatible", "region:us" ]	null	2025-05-24T13:54:59Z	--- base_model: Spectre29/Kaisa-converse-model language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Spectre29/Kaisa-converse-model <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Kaisa-converse-model-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/Kaisa-converse-model-GGUF/resolve/main/Kaisa-converse-model.Q2_K.gguf) \| Q2_K \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-GGUF/resolve/main/Kaisa-converse-model.Q3_K_S.gguf) \| Q3_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-GGUF/resolve/main/Kaisa-converse-model.Q3_K_M.gguf) \| Q3_K_M \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-GGUF/resolve/main/Kaisa-converse-model.IQ4_XS.gguf) \| IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-GGUF/resolve/main/Kaisa-converse-model.Q4_K_S.gguf) \| Q4_K_S \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-GGUF/resolve/main/Kaisa-converse-model.Q3_K_L.gguf) \| Q3_K_L \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-GGUF/resolve/main/Kaisa-converse-model.Q4_K_M.gguf) \| Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-GGUF/resolve/main/Kaisa-converse-model.Q5_K_S.gguf) \| Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-GGUF/resolve/main/Kaisa-converse-model.Q5_K_M.gguf) \| Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-GGUF/resolve/main/Kaisa-converse-model.Q6_K.gguf) \| Q6_K \| 0.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-GGUF/resolve/main/Kaisa-converse-model.Q8_0.gguf) \| Q8_0 \| 0.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Kaisa-converse-model-GGUF/resolve/main/Kaisa-converse-model.f16.gguf) \| f16 \| 0.4 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/XMainframe-v2-Instruct-32b-GGUF	mradermacher	2025-05-25T01:49:06Z	0	0	transformers	[ "transformers", "gguf", "llama-factory", "full", "generated_from_trainer", "en", "base_model:Fsoft-AIC/XMainframe-v2-Instruct-32b", "base_model:quantized:Fsoft-AIC/XMainframe-v2-Instruct-32b", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-24T16:58:17Z	--- base_model: Fsoft-AIC/XMainframe-v2-Instruct-32b language: - en library_name: transformers license: other quantized_by: mradermacher tags: - llama-factory - full - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Fsoft-AIC/XMainframe-v2-Instruct-32b <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/XMainframe-v2-Instruct-32b-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/XMainframe-v2-Instruct-32b-GGUF/resolve/main/XMainframe-v2-Instruct-32b.Q2_K.gguf) \| Q2_K \| 12.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/XMainframe-v2-Instruct-32b-GGUF/resolve/main/XMainframe-v2-Instruct-32b.Q3_K_S.gguf) \| Q3_K_S \| 14.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/XMainframe-v2-Instruct-32b-GGUF/resolve/main/XMainframe-v2-Instruct-32b.Q3_K_M.gguf) \| Q3_K_M \| 16.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/XMainframe-v2-Instruct-32b-GGUF/resolve/main/XMainframe-v2-Instruct-32b.Q3_K_L.gguf) \| Q3_K_L \| 17.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/XMainframe-v2-Instruct-32b-GGUF/resolve/main/XMainframe-v2-Instruct-32b.IQ4_XS.gguf) \| IQ4_XS \| 18.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/XMainframe-v2-Instruct-32b-GGUF/resolve/main/XMainframe-v2-Instruct-32b.Q4_K_S.gguf) \| Q4_K_S \| 18.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/XMainframe-v2-Instruct-32b-GGUF/resolve/main/XMainframe-v2-Instruct-32b.Q4_K_M.gguf) \| Q4_K_M \| 20.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/XMainframe-v2-Instruct-32b-GGUF/resolve/main/XMainframe-v2-Instruct-32b.Q5_K_S.gguf) \| Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/XMainframe-v2-Instruct-32b-GGUF/resolve/main/XMainframe-v2-Instruct-32b.Q5_K_M.gguf) \| Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/XMainframe-v2-Instruct-32b-GGUF/resolve/main/XMainframe-v2-Instruct-32b.Q6_K.gguf) \| Q6_K \| 27.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/XMainframe-v2-Instruct-32b-GGUF/resolve/main/XMainframe-v2-Instruct-32b.Q8_0.gguf) \| Q8_0 \| 34.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
PirxTion/qwen3-dpo-tulu	PirxTion	2025-05-25T01:47:59Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "unsloth", "trl", "dpo", "conversational", "arxiv:2305.18290", "base_model:unsloth/Qwen3-0.6B-Base", "base_model:finetune:unsloth/Qwen3-0.6B-Base", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T14:00:50Z	--- base_model: unsloth/Qwen3-0.6B-Base library_name: transformers model_name: qwen3-dpo-tulu tags: - generated_from_trainer - unsloth - trl - dpo licence: license --- # Model Card for qwen3-dpo-tulu This model is a fine-tuned version of [unsloth/Qwen3-0.6B-Base](https://huggingface.co/unsloth/Qwen3-0.6B-Base). 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="PirxTion/qwen3-dpo-tulu", 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/jingxuan-sun-epfl/mnlp-dpo/runs/il1fhr57) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.15.2 - Transformers: 4.52.3 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.0 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` 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}} } ```
mradermacher/pythia-1b-lightnovels-GGUF	mradermacher	2025-05-25T01:41:35Z	0	0	transformers	[ "transformers", "gguf", "en", "ja", "base_model:thefrigidliquidation/pythia-1b-lightnovels", "base_model:quantized:thefrigidliquidation/pythia-1b-lightnovels", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-24T07:49:19Z	--- base_model: thefrigidliquidation/pythia-1b-lightnovels language: - en - ja library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/thefrigidliquidation/pythia-1b-lightnovels <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/pythia-1b-lightnovels-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/pythia-1b-lightnovels-GGUF/resolve/main/pythia-1b-lightnovels.Q2_K.gguf) \| Q2_K \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/pythia-1b-lightnovels-GGUF/resolve/main/pythia-1b-lightnovels.Q3_K_S.gguf) \| Q3_K_S \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/pythia-1b-lightnovels-GGUF/resolve/main/pythia-1b-lightnovels.Q3_K_M.gguf) \| Q3_K_M \| 0.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/pythia-1b-lightnovels-GGUF/resolve/main/pythia-1b-lightnovels.IQ4_XS.gguf) \| IQ4_XS \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/pythia-1b-lightnovels-GGUF/resolve/main/pythia-1b-lightnovels.Q3_K_L.gguf) \| Q3_K_L \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/pythia-1b-lightnovels-GGUF/resolve/main/pythia-1b-lightnovels.Q4_K_S.gguf) \| Q4_K_S \| 0.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/pythia-1b-lightnovels-GGUF/resolve/main/pythia-1b-lightnovels.Q4_K_M.gguf) \| Q4_K_M \| 0.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/pythia-1b-lightnovels-GGUF/resolve/main/pythia-1b-lightnovels.Q5_K_S.gguf) \| Q5_K_S \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/pythia-1b-lightnovels-GGUF/resolve/main/pythia-1b-lightnovels.Q5_K_M.gguf) \| Q5_K_M \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/pythia-1b-lightnovels-GGUF/resolve/main/pythia-1b-lightnovels.Q6_K.gguf) \| Q6_K \| 0.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/pythia-1b-lightnovels-GGUF/resolve/main/pythia-1b-lightnovels.Q8_0.gguf) \| Q8_0 \| 1.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/pythia-1b-lightnovels-GGUF/resolve/main/pythia-1b-lightnovels.f16.gguf) \| f16 \| 2.1 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
thainamhoang/qwen3-0.6b-mcqa-finetuned_9ep_110k1	thainamhoang	2025-05-25T01:37:54Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-25T01:37:35Z	--- 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. 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]
STGen-Backdoor/Qwen2.5-Coder-32B-STGen-Backdoor	STGen-Backdoor	2025-05-25T01:33:49Z	0	0	null	[ "safetensors", "qwen2", "license:apache-2.0", "region:us" ]	null	2025-05-22T06:30:41Z	--- license: apache-2.0 ---
MomlessTomato/umi-sonoda	MomlessTomato	2025-05-25T01:33:31Z	2	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:cagliostrolab/animagine-xl-3.0", "base_model:adapter:cagliostrolab/animagine-xl-3.0", "license:mit", "region:us" ]	text-to-image	2024-02-15T03:51:54Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: >- masterpiece, high quality, defined pupil, looking at viewer, rounded pupil, defined iris, (soft iris:1.2), parameters: negative_prompt: >- bad_anatomy, deformation, amputation, deformity, deformed_nipples, duplicated_torso, deformed_torso, long_torso, large_torso, unproportioned_torso, (deformed_pussy:1.2), (deformed_hands:1.2), unproportioned_eyes, unproportioned_head, small_head, duplicated_nose, big_nose, fusioned_clothes, fusioned_arms, undefined_limbs, divided_pussy, red_pussy, duplicated_pussy, deformed_anus, deformed_pussy, output: url: images/umi_final.png base_model: cagliostrolab/animagine-xl-3.0 instance_prompt: id_umi_sonoda license: mit --- # Umi Sonoda <Gallery /> ## Model description This model was trained to generate high quality images based on SIFAS cards. To achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement. ## Trigger words You should use `id_umi_sonoda` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/theidoldaily/umi-sonoda/tree/main) them in the Files & versions tab.
JunseongLEEE/llama-3.2-1b-sft-dpo-LASt	JunseongLEEE	2025-05-25T01:29:20Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-25T01:29:12Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
MadlyLaughing/Madly-V4	MadlyLaughing	2025-05-25T01:24:57Z	0	0	null	[ "license:cc-by-nc-sa-4.0", "region:us" ]	null	2025-05-25T01:23:30Z	--- license: cc-by-nc-sa-4.0 ---
PJMixers-Dev/Granite-3.1-Earthen-v0.3-3B-A800M-GGUF	PJMixers-Dev	2025-05-25T01:24:46Z	0	0	null	[ "gguf", "text-generation", "en", "dataset:BeaverAI/REDACTED1", "dataset:BeaverAI/REDACTED2", "dataset:BeaverAI/REDACTED3", "dataset:BeaverAI/REDACTED4", "dataset:BeaverAI/REDACTED5", "dataset:BeaverAI/REDACTED6", "dataset:PJMixers-Dev/Lit-axo-Shuffled", "dataset:PJMixers-Dev/Mielikki_Erebus-87k-axo", "dataset:PJMixers/RyokoAI_Honeyfeed3600-Cleanish", "dataset:PJMixers-Dev/allura-org_fujin-cleaned-stage-2-axo", "dataset:Nelathan/synthetic-sugar-quill", "dataset:PJMixers-Dev/winglian_visual-novels-json-axo-dropped-long", "dataset:PJMixers-Dev/recursal_SCP-RECURSAL-Cleaned", "dataset:PJMixers-Dev/Subtitles", "dataset:PJMixers-Dev/KaraKaraWitch_AnimeSubtitle-axo", "dataset:PJMixers/AP-News-2024", "dataset:PJMixers-Dev/Fundus-AP-News-Formatted", "dataset:PJMixers-Dev/Fundus-AP-News-2-Formatted", "dataset:PJMixers-Dev/goodwiki-2024-12-04-axo", "dataset:epfl-llm/guidelines", "dataset:PJMixers-Dev/allenai_tulu-3-sft-mixture-filtered-2-ShareGPT", "dataset:OpenLeecher/lmsys_chat_1m_clean", "dataset:PJMixers-Dev/Gryphe-Aesir-RPG-Charcards-Opus-Mixed", "dataset:allura-org/gryphe-sonnet-3.5-charcards-names-added", "dataset:anthracite-org/c2_logs_32k_llama3_qwen2_v1.3", "dataset:PJMixers-Dev/MinervaAI_Aesir-Preview-Anon", "dataset:PJMixers-Dev/lemonilia_LimaRP-Simple-CustomShareGPT-Shuffled", "dataset:Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned", "dataset:PJMixers-Dev/NyxKrage_chub-logs-sharegpt-longest-CustomShareGPT", "dataset:PJMixers/OpenLeecher_Teatime_all_logs_longest-ShareGPT", "dataset:grimulkan/aicg-logs-augmented", "dataset:grimulkan/PIPPA-augmented-dedup", "dataset:PJMixers/grimulkan_bluemoon_Karen_cleaned-carded-formatted", "dataset:PJMixers/lodrick-the-lafted_OpusStories-ShareGPT", "dataset:Gryphe/ChatGPT-4o-Writing-Prompts", "dataset:Gryphe/Opus-WritingPrompts", "dataset:anthracite-org/nopm_claude_writing_fixed", "dataset:PJMixers-Dev/Tiefighter-13B-Fake-Distill-ShareGPT", "dataset:allura-org/fujin-instruct-v2", "dataset:ToastyPigeon/gutenberg-sft", "dataset:PocketDoc/Dans-Prosemaxx-Adventure", "dataset:PocketDoc/Dans-Failuremaxx-Adventure-3", "dataset:TheDrummer/AmoralQA-v2", "arxiv:1910.03771", "arxiv:2106.09685", "arxiv:2305.14314", "arxiv:2307.08691", "arxiv:2410.10989", "arxiv:2107.04197", "arxiv:2307.02047", "arxiv:2010.06192", "arxiv:2411.16085", "arxiv:2501.18427", "arxiv:2403.15279", "arxiv:2411.15124", "arxiv:2309.11998", "arxiv:2308.05884", "base_model:PJMixers-Dev/Granite-3.1-Earthen-v0.3-3B-A800M", "base_model:quantized:PJMixers-Dev/Granite-3.1-Earthen-v0.3-3B-A800M", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-24T22:23:19Z	--- base_model: PJMixers-Dev/Granite-3.1-Earthen-v0.3-3B-A800M license: apache-2.0 pipeline_tag: text-generation language: - en datasets: - BeaverAI/REDACTED1 - BeaverAI/REDACTED2 - BeaverAI/REDACTED3 - BeaverAI/REDACTED4 - BeaverAI/REDACTED5 - BeaverAI/REDACTED6 - PJMixers-Dev/Lit-axo-Shuffled - PJMixers-Dev/Mielikki_Erebus-87k-axo - PJMixers/RyokoAI_Honeyfeed3600-Cleanish - PJMixers-Dev/allura-org_fujin-cleaned-stage-2-axo - Nelathan/synthetic-sugar-quill - PJMixers-Dev/winglian_visual-novels-json-axo-dropped-long - PJMixers-Dev/recursal_SCP-RECURSAL-Cleaned - PJMixers-Dev/Subtitles - PJMixers-Dev/KaraKaraWitch_AnimeSubtitle-axo - PJMixers/AP-News-2024 - PJMixers-Dev/Fundus-AP-News-Formatted - PJMixers-Dev/Fundus-AP-News-2-Formatted - PJMixers-Dev/goodwiki-2024-12-04-axo - epfl-llm/guidelines - PJMixers-Dev/allenai_tulu-3-sft-mixture-filtered-2-ShareGPT - OpenLeecher/lmsys_chat_1m_clean - PJMixers-Dev/Gryphe-Aesir-RPG-Charcards-Opus-Mixed - allura-org/gryphe-sonnet-3.5-charcards-names-added - anthracite-org/c2_logs_32k_llama3_qwen2_v1.3 - PJMixers-Dev/MinervaAI_Aesir-Preview-Anon - PJMixers-Dev/lemonilia_LimaRP-Simple-CustomShareGPT-Shuffled - Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned - PJMixers-Dev/NyxKrage_chub-logs-sharegpt-longest-CustomShareGPT - PJMixers/OpenLeecher_Teatime_all_logs_longest-ShareGPT - grimulkan/aicg-logs-augmented - grimulkan/PIPPA-augmented-dedup - PJMixers/grimulkan_bluemoon_Karen_cleaned-carded-formatted - PJMixers/lodrick-the-lafted_OpusStories-ShareGPT - Gryphe/ChatGPT-4o-Writing-Prompts - Gryphe/Opus-WritingPrompts - anthracite-org/nopm_claude_writing_fixed - PJMixers-Dev/Tiefighter-13B-Fake-Distill-ShareGPT - allura-org/fujin-instruct-v2 - ToastyPigeon/gutenberg-sft - PocketDoc/Dans-Prosemaxx-Adventure - PocketDoc/Dans-Failuremaxx-Adventure-3 - TheDrummer/AmoralQA-v2 --- # Granite-3.1-Earthen-v0.3-3B-A800M-GGUF [`ibm-granite/granite-3.1-3b-a800m-instruct`](https://huggingface.co/ibm-granite/granite-3.1-3b-a800m-instruct) was trained at 8K with batch size 2 gradient accumulation 8, so each step was 131,072 tokens (including any padding tokens). It was trained for 400 steps, adding up to a total of 52,428,800 unique tokens seen. This is a small test run. A larger version is planned. ## Quants - [GGUF](https://huggingface.co/PJMixers-Dev/Granite-3.1-Earthen-v0.3-3B-A800M-GGUF) ## Prompt Format This model uses Granite-3.1 Instruct format. ``` <\|start_of_role\|>system<\|end_of_role\|>example system prompt<\|end_of_text\|> <\|start_of_role\|>user<\|end_of_role\|>example user turn 1<\|end_of_text\|> <\|start_of_role\|>assistant<\|end_of_role\|>example assistant turn 1<\|end_of_text\|> <\|start_of_role\|>user<\|end_of_role\|>example user turn 2<\|end_of_text\|> <\|start_of_role\|>assistant<\|end_of_role\|>example assistant turn 2<\|end_of_text\|> ``` ## Training Details [<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) ```yaml # Requirements before running # - Get latest commit of axolotl (currently c0a0c75) # - Download these to axolotl/src/axolotl/prompt_formatters # - https://github.com/xzuyn/axolotl/blob/came-plus-formatters/src/axolotl/prompt_strategies/formatter_regex.py # - https://github.com/xzuyn/axolotl/blob/came-plus-formatters/src/axolotl/prompt_strategies/customcompletion-regex.py # - https://github.com/xzuyn/axolotl/blob/came-plus-formatters/src/axolotl/prompt_strategies/customgranite-regex.py # - pip install ftfy # - pip install git+https://github.com/xzuyn/CAME.git@sr-grams-cautious-8bit # Weights and Biases logging config wandb_project: Granite-3.1-3B-A800M wandb_name: Granite-3.1-Earthen-v0.3-3B-A800M-QLoRA-run4 # Model checkpointing config output_dir: ./Outputs/Granite-3.1-Earthen-v0.3-3B-A800M-QLoRA-run4 resume_from_checkpoint: save_steps: 10 save_safetensors: true save_total_limit: 2 save_only_model: false # Model architecture config base_model: ibm-granite/granite-3.1-3b-a800m-instruct model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer # Mixed precision training config bf16: true fp16: false tf32: false # Model loading config load_in_8bit: false load_in_4bit: true strict: false # Sequence config sequence_len: 8192 min_sample_len: 256 sample_packing: true eval_sample_packing: true pad_to_sequence_len: true train_on_inputs: false group_by_length: false # LoRA adapter config adapter: qlora lora_r: 128 lora_alpha: 128 lora_dropout: 0.125 lora_target_linear: true embeddings_skip_upcast: true # Dataset config datasets: # Completion # Story-like Data - path: BeaverAI/REDACTED1 split: train[:4000] type: customcompletion-regex - path: PJMixers-Dev/Lit-axo-Shuffled split: train[:4000] type: customcompletion-regex - path: PJMixers-Dev/Mielikki_Erebus-87k-axo split: train[:4000] type: customcompletion-regex - path: PJMixers/RyokoAI_Honeyfeed3600-Cleanish split: train[:4000] type: customcompletion-regex - path: BeaverAI/REDACTED2 type: customcompletion-regex - path: PJMixers-Dev/allura-org_fujin-cleaned-stage-2-axo split: train[:4000] type: customcompletion-regex - path: Nelathan/synthetic-sugar-quill split: train[:4000] type: customcompletion-regex - path: PJMixers-Dev/winglian_visual-novels-json-axo-dropped-long split: train[:4000] type: customcompletion-regex - path: BeaverAI/REDACTED3 type: customcompletion-regex - path: PJMixers-Dev/recursal_SCP-RECURSAL-Cleaned split: train[:4000] type: customcompletion-regex # Subtitle Data - path: PJMixers-Dev/Subtitles type: customcompletion-regex - path: PJMixers-Dev/KaraKaraWitch_AnimeSubtitle-axo split: train[:4000] type: customcompletion-regex # News Data - path: PJMixers/AP-News-2024 type: customcompletion-regex - path: PJMixers-Dev/Fundus-AP-News-Formatted split: train[:4000] type: customcompletion-regex - path: PJMixers-Dev/Fundus-AP-News-2-Formatted type: customcompletion-regex # Misc Data - path: PJMixers-Dev/goodwiki-2024-12-04-axo split: train[:4000] type: customcompletion-regex - path: epfl-llm/guidelines split: train[:4000] field: clean_text type: customcompletion-regex # Granite-3.1 Instruct # Instruction Data - path: PJMixers-Dev/allenai_tulu-3-sft-mixture-filtered-2-ShareGPT split: train[:4000] type: customgranite-regex - path: OpenLeecher/lmsys_chat_1m_clean split: train[:4000] type: customgranite-regex # RP Data - path: PJMixers-Dev/Gryphe-Aesir-RPG-Charcards-Opus-Mixed type: customgranite-regex - path: allura-org/gryphe-sonnet-3.5-charcards-names-added type: customgranite-regex - path: anthracite-org/c2_logs_32k_llama3_qwen2_v1.3 type: customgranite-regex - path: BeaverAI/REDACTED4 type: customgranite-regex - path: PJMixers-Dev/MinervaAI_Aesir-Preview-Anon type: customgranite-regex - path: PJMixers-Dev/lemonilia_LimaRP-Simple-CustomShareGPT-Shuffled type: customgranite-regex - path: Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned type: customgranite-regex - path: PJMixers-Dev/NyxKrage_chub-logs-sharegpt-longest-CustomShareGPT type: customgranite-regex - path: PJMixers/OpenLeecher_Teatime_all_logs_longest-ShareGPT type: customgranite-regex - path: grimulkan/aicg-logs-augmented type: customgranite-regex - path: grimulkan/PIPPA-augmented-dedup type: customgranite-regex - path: PJMixers/grimulkan_bluemoon_Karen_cleaned-carded-formatted type: customgranite-regex # InstStory Data - path: PJMixers/lodrick-the-lafted_OpusStories-ShareGPT type: customgranite-regex - path: Gryphe/ChatGPT-4o-Writing-Prompts type: customgranite-regex - path: Gryphe/Opus-WritingPrompts type: customgranite-regex - path: anthracite-org/nopm_claude_writing_fixed type: customgranite-regex - path: PJMixers-Dev/Tiefighter-13B-Fake-Distill-ShareGPT type: customgranite-regex - path: allura-org/fujin-instruct-v2 type: customgranite-regex - path: ToastyPigeon/gutenberg-sft type: customgranite-regex # Adventure Data - path: PocketDoc/Dans-Prosemaxx-Adventure type: customgranite-regex - path: PocketDoc/Dans-Failuremaxx-Adventure-3 type: customgranite-regex # Decensoring Data - path: TheDrummer/AmoralQA-v2 type: customgranite-regex - path: BeaverAI/REDACTED5 type: customgranite-regex - path: BeaverAI/REDACTED6 type: customgranite-regex val_set_size: 256 eval_strategy: steps eval_steps: 10 dataset_prepared_path: ./00-Tokenized-Datasets/Granite-3.1-Earthen-v0.3-3B-A800M-LoRA-seed42 shuffle_merged_datasets: true # Training hyperparameters num_epochs: 1 gradient_accumulation_steps: 8 micro_batch_size: 2 eval_batch_size: 2 warmup_steps: 0 optimizer: came_pytorch optim_args: enable_stochastic_rounding: true enable_cautious: true enable_8bit: true lr_scheduler: rex learning_rate: 2.5e-7 cosine_min_lr_ratio: 0.05 weight_decay: 0.01 max_grad_norm: 0.5 logging_steps: 1 # Model optimization gradient_checkpointing: offload sdp_attention: true plugins: - axolotl.integrations.liger.LigerPlugin liger_rope: true liger_rms_norm: true liger_layer_norm: true liger_glu_activation: true liger_cross_entropy: true lora_mlp_kernel: false lora_qkv_kernel: false lora_o_kernel: false # Debug config debug: true seed: 42 # Token config special_tokens: bos_token: "<\|end_of_text\|>" eos_token: "<\|end_of_text\|>" pad_token: "<\|end_of_text\|>" tokens: ``` ## Citations <details><summary>Show Citations</summary> ```bib @misc{wolf2020huggingfacestransformersstateoftheartnatural, title={HuggingFace's Transformers: State-of-the-art Natural Language Processing}, author={Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush}, year={2020}, eprint={1910.03771}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/1910.03771}, } @misc{hu2021loralowrankadaptationlarge, title={LoRA: Low-Rank Adaptation of Large Language Models}, author={Edward J. Hu and Yelong Shen and Phillip Wallis and Zeyuan Allen-Zhu and Yuanzhi Li and Shean Wang and Lu Wang and Weizhu Chen}, year={2021}, eprint={2106.09685}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2106.09685}, } @misc{dettmers2023qloraefficientfinetuningquantized, title={QLoRA: Efficient Finetuning of Quantized LLMs}, author={Tim Dettmers and Artidoro Pagnoni and Ari Holtzman and Luke Zettlemoyer}, year={2023}, eprint={2305.14314}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2305.14314}, } @misc{dao2023flashattention2fasterattentionbetter, title={FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning}, author={Tri Dao}, year={2023}, eprint={2307.08691}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2307.08691}, } @misc{hsu2024ligerkernelefficienttriton, title={Liger Kernel: Efficient Triton Kernels for LLM Training}, author={Pin-Lun Hsu and Yun Dai and Vignesh Kothapalli and Qingquan Song and Shao Tang and Siyu Zhu and Steven Shimizu and Shivam Sahni and Haowen Ning and Yanning Chen}, year={2024}, eprint={2410.10989}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2410.10989}, } @misc{chen2021rexrevisitingbudgetedtraining, title={REX: Revisiting Budgeted Training with an Improved Schedule}, author={John Chen and Cameron Wolfe and Anastasios Kyrillidis}, year={2021}, eprint={2107.04197}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2107.04197}, } @misc{luo2023cameconfidenceguidedadaptivememory, title={CAME: Confidence-guided Adaptive Memory Efficient Optimization}, author={Yang Luo and Xiaozhe Ren and Zangwei Zheng and Zhuo Jiang and Xin Jiang and Yang You}, year={2023}, eprint={2307.02047}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2307.02047}, } @misc{zamirai2021revisitingbfloat16training, title={Revisiting BFloat16 Training}, author={Pedram Zamirai and Jian Zhang and Christopher R. Aberger and Christopher De Sa}, year={2021}, eprint={2010.06192}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2010.06192}, } @misc{liang2025cautiousoptimizersimprovingtraining, title={Cautious Optimizers: Improving Training with One Line of Code}, author={Kaizhao Liang and Lizhang Chen and Bo Liu and Qiang Liu}, year={2025}, eprint={2411.16085}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2411.16085}, } @misc{xie2025sana15efficientscaling, title={SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer}, author={Enze Xie and Junsong Chen and Yuyang Zhao and Jincheng Yu and Ligeng Zhu and Chengyue Wu and Yujun Lin and Zhekai Zhang and Muyang Li and Junyu Chen and Han Cai and Bingchen Liu and Daquan Zhou and Song Han}, year={2025}, eprint={2501.18427}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2501.18427}, } @misc{dallabetta2024fundussimpletousenewsscraper, title={Fundus: A Simple-to-Use News Scraper Optimized for High Quality Extractions}, author={Max Dallabetta and Conrad Dobberstein and Adrian Breiding and Alan Akbik}, year={2024}, eprint={2403.15279}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2403.15279}, } @misc{lambert2025tulu3pushingfrontiers, title={Tulu 3: Pushing Frontiers in Open Language Model Post-Training}, author={Nathan Lambert and Jacob Morrison and Valentina Pyatkin and Shengyi Huang and Hamish Ivison and Faeze Brahman and Lester James V. Miranda and Alisa Liu and Nouha Dziri and Shane Lyu and Yuling Gu and Saumya Malik and Victoria Graf and Jena D. Hwang and Jiangjiang Yang and Ronan Le Bras and Oyvind Tafjord and Chris Wilhelm and Luca Soldaini and Noah A. Smith and Yizhong Wang and Pradeep Dasigi and Hannaneh Hajishirzi}, year={2025}, eprint={2411.15124}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2411.15124}, } @misc{zheng2024lmsyschat1mlargescalerealworldllm, title={LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset}, author={Lianmin Zheng and Wei-Lin Chiang and Ying Sheng and Tianle Li and Siyuan Zhuang and Zhanghao Wu and Yonghao Zhuang and Zhuohan Li and Zi Lin and Eric P. Xing and Joseph E. Gonzalez and Ion Stoica and Hao Zhang}, year={2024}, eprint={2309.11998}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2309.11998}, } @misc{gosling2023pippapartiallysyntheticconversational, title={PIPPA: A Partially Synthetic Conversational Dataset}, author={Tear Gosling and Alpin Dale and Yinhe Zheng}, year={2023}, eprint={2308.05884}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2308.05884}, } ``` </details>
RayneAmes/vo_v1	RayneAmes	2025-05-25T01:19:14Z	0	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-24T23:55:48Z	--- 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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RayneAmes/vo_v2	RayneAmes	2025-05-25T01:19:09Z	0	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-25T00:02:01Z	--- 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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RayneAmes/zov_v2	RayneAmes	2025-05-25T01:18:58Z	0	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-25T00:16:26Z	--- 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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mlfoundations-dev/openthoughts3_code_100k_annotated_QwQ-32B_sharegpt	mlfoundations-dev	2025-05-25T01:16:07Z	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	2025-05-23T09:23:33Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: openthoughts3_code_100k_annotated_QwQ-32B_sharegpt 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. --> # openthoughts3_code_100k_annotated_QwQ-32B_sharegpt 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/openthoughts3_code_100k_annotated_QwQ-32B_sharegpt 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: 8e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 32 - gradient_accumulation_steps: 16 - total_train_batch_size: 512 - total_eval_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: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.3.0 - Datasets 3.1.0 - Tokenizers 0.20.3
Malitha/Gemma3-car-damage-model-4B-3	Malitha	2025-05-25T01:11:22Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-25T01:11:22Z	--- license: apache-2.0 ---
g-assismoraes/gemma-3-4b-it-fpi-alpha2.0-fromit-var-porsimples	g-assismoraes	2025-05-25T01:07:42Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-25T01:04:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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mradermacher/DialoGPT-small-woody-toy-story-GGUF	mradermacher	2025-05-25T01:05:06Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:s3nh/DialoGPT-small-woody-toy-story", "base_model:quantized:s3nh/DialoGPT-small-woody-toy-story", "license:openrail", "endpoints_compatible", "region:us" ]	null	2025-05-24T07:39:49Z	--- base_model: s3nh/DialoGPT-small-woody-toy-story language: - en library_name: transformers license: openrail quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/s3nh/DialoGPT-small-woody-toy-story <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/DialoGPT-small-woody-toy-story-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/DialoGPT-small-woody-toy-story-GGUF/resolve/main/DialoGPT-small-woody-toy-story.Q2_K.gguf) \| Q2_K \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-small-woody-toy-story-GGUF/resolve/main/DialoGPT-small-woody-toy-story.Q3_K_S.gguf) \| Q3_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-small-woody-toy-story-GGUF/resolve/main/DialoGPT-small-woody-toy-story.Q3_K_M.gguf) \| Q3_K_M \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-small-woody-toy-story-GGUF/resolve/main/DialoGPT-small-woody-toy-story.IQ4_XS.gguf) \| IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-small-woody-toy-story-GGUF/resolve/main/DialoGPT-small-woody-toy-story.Q4_K_S.gguf) \| Q4_K_S \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-small-woody-toy-story-GGUF/resolve/main/DialoGPT-small-woody-toy-story.Q3_K_L.gguf) \| Q3_K_L \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-small-woody-toy-story-GGUF/resolve/main/DialoGPT-small-woody-toy-story.Q4_K_M.gguf) \| Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-small-woody-toy-story-GGUF/resolve/main/DialoGPT-small-woody-toy-story.Q5_K_S.gguf) \| Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-small-woody-toy-story-GGUF/resolve/main/DialoGPT-small-woody-toy-story.Q5_K_M.gguf) \| Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-small-woody-toy-story-GGUF/resolve/main/DialoGPT-small-woody-toy-story.Q6_K.gguf) \| Q6_K \| 0.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-small-woody-toy-story-GGUF/resolve/main/DialoGPT-small-woody-toy-story.Q8_0.gguf) \| Q8_0 \| 0.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-small-woody-toy-story-GGUF/resolve/main/DialoGPT-small-woody-toy-story.f16.gguf) \| f16 \| 0.4 \| 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 -->
gradientrouting-spar/qwen_ft_23_May_gemma_test_swaps_m1_p1_num64_1pb_e1b	gradientrouting-spar	2025-05-25T01:04:41Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-25T01:04:33Z	--- 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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Bharathparam11/Krushi_Vani_1.o	Bharathparam11	2025-05-25T01:02:09Z	0	0	peft	[ "peft", "safetensors", "mistral", "arxiv:1910.09700", "base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "base_model:adapter:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-24T16:29:55Z	--- base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit 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: [Bharath P --CodeIs(Team)] - Funded by [optional]: [Myself] - Shared by [optional]: [Myself] - Model type: [More Information Needed] - Language(s) (NLP): [pytorch] - License: [Any body can use i dont give a damn] - Finetuned from model [unsloth/mistral-7b-instruct-v0.2-bnb-4bit]: [Just search it on google] ### Model Sources [Bharath's computer] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Use it however you want --> ### 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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LandCruiser/sn29_cold_2305_9	LandCruiser	2025-05-25T00:54:57Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-23T07:27:57Z	--- 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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LandCruiser/sn29_cold_2305_8	LandCruiser	2025-05-25T00:54:15Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-23T07:27:57Z	--- 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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unsloth/Qwen3-32B-128K-GGUF	unsloth	2025-05-25T00:53:03Z	22,553	20	transformers	[ "transformers", "gguf", "qwen3", "text-generation", "qwen", "unsloth", "en", "arxiv:2309.00071", "base_model:Qwen/Qwen3-32B", "base_model:quantized:Qwen/Qwen3-32B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	text-generation	2025-04-28T23:46:31Z	--- base_model: Qwen/Qwen3-32B language: - en library_name: transformers license_link: https://huggingface.co/Qwen/Qwen3-32B/blob/main/LICENSE license: apache-2.0 tags: - qwen3 - qwen - unsloth - transformers --- > [!NOTE] > With 128K Context Length enabled by YaRN. > <div> <p style="margin-bottom: 0; margin-top: 0;"> <strong>See <a href="https://huggingface.co/collections/unsloth/qwen3-680edabfb790c8c34a242f95">our collection</a> for all versions of Qwen3 including GGUF, 4-bit & 16-bit formats.</strong> </p> <p style="margin-bottom: 0;"> <em>Learn to run Qwen3 correctly - <a href="https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune">Read our Guide</a>.</em> </p> <p style="margin-top: 0;margin-bottom: 0;"> <em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em> </p> <div style="display: flex; gap: 5px; align-items: center; "> <a href="https://github.com/unslothai/unsloth/"> <img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133"> </a> <a href="https://discord.gg/unsloth"> <img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173"> </a> <a href="https://docs.unsloth.ai/basics/tutorial-how-to-run-deepseek-r1-on-your-own-local-device"> <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143"> </a> </div> <h1 style="margin-top: 0rem;">✨ Run & Fine-tune Qwen3 with Unsloth!</h1> </div> - Fine-tune Qwen3 (14B) for free using our Google [Colab notebook here](https://docs.unsloth.ai/get-started/unsloth-notebooks)! - Read our Blog about Qwen3 support: [unsloth.ai/blog/qwen3](https://unsloth.ai/blog/qwen3) - View the rest of our notebooks in our [docs here](https://docs.unsloth.ai/get-started/unsloth-notebooks). - Run & export your fine-tuned model to Ollama, llama.cpp or HF. \| Unsloth supports \| Free Notebooks \| Performance \| Memory use \| \|-----------------\|--------------------------------------------------------------------------------------------------------------------------\|-------------\|----------\| \| Qwen3 (14B) \| [▶️ Start on Colab](https://docs.unsloth.ai/get-started/unsloth-notebooks) \| 3x faster \| 70% less \| \| GRPO with Qwen3 (8B) \| [▶️ Start on Colab](https://docs.unsloth.ai/get-started/unsloth-notebooks) \| 3x faster \| 80% less \| \| Llama-3.2 (3B) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb) \| 2.4x faster \| 58% less \| \| Llama-3.2 (11B vision) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb) \| 2x faster \| 60% less \| \| Qwen2.5 (7B) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_(7B)-Alpaca.ipynb) \| 2x faster \| 60% less \| \| Phi-4 (14B) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb) \| 2x faster \| 50% less \| # Qwen3-32B ## Qwen3 Highlights Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features: - Uniquely support of seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within single model, ensuring optimal performance across various scenarios. - Significantly enhancement in its reasoning capabilities, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning. - Superior human preference alignment, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience. - Expertise in agent capabilities, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks. - Support of 100+ languages and dialects with strong capabilities for multilingual instruction following and translation. ## Model Overview Qwen3-32B has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 32.8B - Number of Paramaters (Non-Embedding): 31.2B - Number of Layers: 64 - Number of Attention Heads (GQA): 64 for Q and 8 for KV - Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts). For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Quickstart The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.51.0`, you will encounter the following error: ``` KeyError: 'qwen3' ``` The following contains a code snippet illustrating how to use the model generate content based on given inputs. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-32B" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # prepare the model input prompt = "Give me a short introduction to large language model." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # Switches between thinking and non-thinking modes. Default is True. ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completion generated_ids = model.generate( model_inputs, max_new_tokens=32768 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() # parsing thinking content try: # rindex finding 151668 (</think>) index = len(output_ids) - output_ids[::-1].index(151668) except ValueError: index = 0 thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") print("thinking content:", thinking_content) print("content:", content) ``` For deployment, you can use `vllm>=0.8.5` or `sglang>=0.4.5.post2` to create an OpenAI-compatible API endpoint: - vLLM: ```shell vllm serve Qwen/Qwen3-32B --enable-reasoning --reasoning-parser deepseek_r1 ``` - SGLang: ```shell python -m sglang.launch_server --model-path Qwen/Qwen3-32B --reasoning-parser deepseek-r1 ``` ## Switching Between Thinking and Non-Thinking Mode > [!TIP] > The `enable_thinking` switch is also available in APIs created by vLLM and SGLang. > Please refer to [our documentation](https://qwen.readthedocs.io/) for more details. ### `enable_thinking=True` By default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting `enable_thinking=True` or leaving it as the default value in `tokenizer.apply_chat_template`, the model will engage its thinking mode. ```python text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # True is the default value for enable_thinking ) ``` In this mode, the model will generate think content wrapped in a `<think>...</think>` block, followed by the final response. > [!NOTE] > For thinking mode, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). DO NOT use greedy decoding, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the [Best Practices](#best-practices) section. ### `enable_thinking=False` We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency. ```python text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False # Setting enable_thinking=False disables thinking mode ) ``` In this mode, the model will not generate any think content and will not include a `<think>...</think>` block. > [!NOTE] > For non-thinking mode, we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. For more detailed guidance, please refer to the [Best Practices](#best-practices) section. ### Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input We provide a soft switch mechanism that allows users to dynamically control the model's behavior when `enable_thinking=True`. Specifically, you can add `/think` and `/no_think` to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations. Here is an example of a multi-turn conversation: ```python from transformers import AutoModelForCausalLM, AutoTokenizer class QwenChatbot: def __init__(self, model_name="Qwen/Qwen3-32B"): self.tokenizer = AutoTokenizer.from_pretrained(model_name) self.model = AutoModelForCausalLM.from_pretrained(model_name) self.history = [] def generate_response(self, user_input): messages = self.history + [{"role": "user", "content": user_input}] text = self.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) inputs = self.tokenizer(text, return_tensors="pt") response_ids = self.model.generate(inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist() response = self.tokenizer.decode(response_ids, skip_special_tokens=True) # Update history self.history.append({"role": "user", "content": user_input}) self.history.append({"role": "assistant", "content": response}) return response # Example Usage if __name__ == "__main__": chatbot = QwenChatbot() # First input (without /think or /no_think tags, thinking mode is enabled by default) user_input_1 = "How many r's in strawberries?" print(f"User: {user_input_1}") response_1 = chatbot.generate_response(user_input_1) print(f"Bot: {response_1}") print("----------------------") # Second input with /no_think user_input_2 = "Then, how many r's in blueberries? /no_think" print(f"User: {user_input_2}") response_2 = chatbot.generate_response(user_input_2) print(f"Bot: {response_2}") print("----------------------") # Third input with /think user_input_3 = "Really? /think" print(f"User: {user_input_3}") response_3 = chatbot.generate_response(user_input_3) print(f"Bot: {response_3}") ``` > Note > For API compatibility, when `enable_thinking=True`, regardless of whether the user uses `/think` or `/no_think`, the model will always output a block wrapped in `<think>...</think>`. However, the content inside this block may be empty if thinking is disabled. > When `enable_thinking=False`, the soft switches are not valid. Regardless of any `/think` or `/no_think` tags input by the user, the model will not generate think content and will not include a `<think>...</think>` block. ## Agentic Use Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity. To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself. ```python from qwen_agent.agents import Assistant # Define LLM llm_cfg = { 'model': 'Qwen3-32B', # Use the endpoint provided by Alibaba Model Studio: # 'model_type': 'qwen_dashscope', # 'api_key': os.getenv('DASHSCOPE_API_KEY'), # Use a custom endpoint compatible with OpenAI API: 'model_server': 'http://localhost:8000/v1', # api_base 'api_key': 'EMPTY', # Other parameters: # 'generate_cfg': { # # Add: When the response content is `<think>this is the thought</think>this is the answer; # # Do not add: When the response has been separated by reasoning_content and content. # 'thought_in_content': True, # }, } # Define Tools tools = [ {'mcpServers': { # You can specify the MCP configuration file 'time': { 'command': 'uvx', 'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai'] }, "fetch": { "command": "uvx", "args": ["mcp-server-fetch"] } } }, 'code_interpreter', # Built-in tools ] # Define Agent bot = Assistant(llm=llm_cfg, function_list=tools) # Streaming generation messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}] for responses in bot.run(messages=messages): pass print(responses) ``` ## Processing Long Texts Qwen3 natively supports context lengths of up to 32,768 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the [YaRN](https://arxiv.org/abs/2309.00071) method. YaRN is currently supported by several inference frameworks, e.g., `transformers` and `llama.cpp` for local use, `vllm` and `sglang` for deployment. In general, there are two approaches to enabling YaRN for supported frameworks: - Modifying the model files: In the `config.json` file, add the `rope_scaling` fields: ```json { ..., "rope_scaling": { "type": "yarn", "factor": 4.0, "original_max_position_embeddings": 32768 } } ``` For `llama.cpp`, you need to regenerate the GGUF file after the modification. - Passing command line arguments: For `vllm`, you can use ```shell vllm serve ... --rope-scaling '{"type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072 ``` For `sglang`, you can use ```shell python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}' ``` For `llama-server` from `llama.cpp`, you can use ```shell llama-server ... --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768 ``` > [!IMPORTANT] > If you encounter the following warning > ``` > Unrecognized keys in `rope_scaling` for 'rope_type'='yarn': {'original_max_position_embeddings'} > ``` > please upgrade `transformers>=4.51.0`. > [!NOTE] > All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. > We advise adding the `rope_scaling` configuration only when processing long contexts is required. > It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0. > [!NOTE] > The default `max_position_embeddings` in `config.json` is set to 40,960. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance. > [!TIP] > The endpoint provided by Alibaba Model Studio supports dynamic YaRN by default and no extra configuration is needed. ## Best Practices To achieve optimal performance, we recommend the following settings: 1. Sampling Parameters: - For thinking mode (`enable_thinking=True`), use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0`. DO NOT use greedy decoding, as it can lead to performance degradation and endless repetitions. - For non-thinking mode (`enable_thinking=False`), we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. - For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance. 2. Adequate Output Length: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance. 3. Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking. - Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt. - Multiple-Choice Questions: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`." 4. No Thinking Content in History: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed. ### Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen3, title = {Qwen3}, url = {https://qwenlm.github.io/blog/qwen3/}, author = {Qwen Team}, month = {April}, year = {2025} } ```
unsloth/Qwen3-14B-128K-GGUF	unsloth	2025-05-25T00:53:02Z	12,375	14	transformers	[ "transformers", "gguf", "qwen3", "text-generation", "qwen", "unsloth", "en", "arxiv:2309.00071", "base_model:Qwen/Qwen3-14B", "base_model:quantized:Qwen/Qwen3-14B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-29T11:49:24Z	--- base_model: Qwen/Qwen3-14B language: - en library_name: transformers license_link: https://huggingface.co/Qwen/Qwen3-14B/blob/main/LICENSE license: apache-2.0 tags: - qwen3 - qwen - unsloth - transformers --- <div> <p style="margin-bottom: 0; margin-top: 0;"> <strong>See <a href="https://huggingface.co/collections/unsloth/qwen3-680edabfb790c8c34a242f95">our collection</a> for all versions of Qwen3 including GGUF, 4-bit & 16-bit formats.</strong> </p> <p style="margin-bottom: 0;"> <em>Learn to run Qwen3 correctly - <a href="https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune">Read our Guide</a>.</em> </p> <p style="margin-top: 0;margin-bottom: 0;"> <em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em> </p> <div style="display: flex; gap: 5px; align-items: center; "> <a href="https://github.com/unslothai/unsloth/"> <img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133"> </a> <a href="https://discord.gg/unsloth"> <img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173"> </a> <a href="https://docs.unsloth.ai/basics/tutorial-how-to-run-deepseek-r1-on-your-own-local-device"> <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143"> </a> </div> <h1 style="margin-top: 0rem;">✨ Run & Fine-tune Qwen3 with Unsloth!</h1> </div> - Fine-tune Qwen3 (14B) for free using our Google [Colab notebook here](https://docs.unsloth.ai/get-started/unsloth-notebooks)! - Read our Blog about Qwen3 support: [unsloth.ai/blog/qwen3](https://unsloth.ai/blog/qwen3) - View the rest of our notebooks in our [docs here](https://docs.unsloth.ai/get-started/unsloth-notebooks). - Run & export your fine-tuned model to Ollama, llama.cpp or HF. \| Unsloth supports \| Free Notebooks \| Performance \| Memory use \| \|-----------------\|--------------------------------------------------------------------------------------------------------------------------\|-------------\|----------\| \| Qwen3 (14B) \| [▶️ Start on Colab](https://docs.unsloth.ai/get-started/unsloth-notebooks) \| 3x faster \| 70% less \| \| GRPO with Qwen3 (8B) \| [▶️ Start on Colab](https://docs.unsloth.ai/get-started/unsloth-notebooks) \| 3x faster \| 80% less \| \| Llama-3.2 (3B) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb) \| 2.4x faster \| 58% less \| \| Llama-3.2 (11B vision) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb) \| 2x faster \| 60% less \| \| Qwen2.5 (7B) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_(7B)-Alpaca.ipynb) \| 2x faster \| 60% less \| \| Phi-4 (14B) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb) \| 2x faster \| 50% less \| # Qwen3-14B ## Qwen3 Highlights Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features: - Uniquely support of seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within single model, ensuring optimal performance across various scenarios. - Significantly enhancement in its reasoning capabilities, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning. - Superior human preference alignment, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience. - Expertise in agent capabilities, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks. - Support of 100+ languages and dialects with strong capabilities for multilingual instruction following and translation. ## Model Overview Qwen3-14B has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 14.8B - Number of Paramaters (Non-Embedding): 13.2B - Number of Layers: 40 - Number of Attention Heads (GQA): 40 for Q and 8 for KV - Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts). For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Quickstart The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.51.0`, you will encounter the following error: ``` KeyError: 'qwen3' ``` The following contains a code snippet illustrating how to use the model generate content based on given inputs. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-14B" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # prepare the model input prompt = "Give me a short introduction to large language model." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # Switches between thinking and non-thinking modes. Default is True. ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completion generated_ids = model.generate( model_inputs, max_new_tokens=32768 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() # parsing thinking content try: # rindex finding 151668 (</think>) index = len(output_ids) - output_ids[::-1].index(151668) except ValueError: index = 0 thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") print("thinking content:", thinking_content) print("content:", content) ``` For deployment, you can use `vllm>=0.8.5` or `sglang>=0.4.5.post2` to create an OpenAI-compatible API endpoint: - vLLM: ```shell vllm serve Qwen/Qwen3-14B --enable-reasoning --reasoning-parser deepseek_r1 ``` - SGLang: ```shell python -m sglang.launch_server --model-path Qwen/Qwen3-14B --reasoning-parser deepseek-r1 ``` ## Switching Between Thinking and Non-Thinking Mode > [!TIP] > The `enable_thinking` switch is also available in APIs created by vLLM and SGLang. > Please refer to [our documentation](https://qwen.readthedocs.io/) for more details. ### `enable_thinking=True` By default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting `enable_thinking=True` or leaving it as the default value in `tokenizer.apply_chat_template`, the model will engage its thinking mode. ```python text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # True is the default value for enable_thinking ) ``` In this mode, the model will generate think content wrapped in a `<think>...</think>` block, followed by the final response. > [!NOTE] > For thinking mode, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). DO NOT use greedy decoding, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the [Best Practices](#best-practices) section. ### `enable_thinking=False` We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency. ```python text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False # Setting enable_thinking=False disables thinking mode ) ``` In this mode, the model will not generate any think content and will not include a `<think>...</think>` block. > [!NOTE] > For non-thinking mode, we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. For more detailed guidance, please refer to the [Best Practices](#best-practices) section. ### Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input We provide a soft switch mechanism that allows users to dynamically control the model's behavior when `enable_thinking=True`. Specifically, you can add `/think` and `/no_think` to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations. Here is an example of a multi-turn conversation: ```python from transformers import AutoModelForCausalLM, AutoTokenizer class QwenChatbot: def __init__(self, model_name="Qwen/Qwen3-14B"): self.tokenizer = AutoTokenizer.from_pretrained(model_name) self.model = AutoModelForCausalLM.from_pretrained(model_name) self.history = [] def generate_response(self, user_input): messages = self.history + [{"role": "user", "content": user_input}] text = self.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) inputs = self.tokenizer(text, return_tensors="pt") response_ids = self.model.generate(inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist() response = self.tokenizer.decode(response_ids, skip_special_tokens=True) # Update history self.history.append({"role": "user", "content": user_input}) self.history.append({"role": "assistant", "content": response}) return response # Example Usage if __name__ == "__main__": chatbot = QwenChatbot() # First input (without /think or /no_think tags, thinking mode is enabled by default) user_input_1 = "How many r's in strawberries?" print(f"User: {user_input_1}") response_1 = chatbot.generate_response(user_input_1) print(f"Bot: {response_1}") print("----------------------") # Second input with /no_think user_input_2 = "Then, how many r's in blueberries? /no_think" print(f"User: {user_input_2}") response_2 = chatbot.generate_response(user_input_2) print(f"Bot: {response_2}") print("----------------------") # Third input with /think user_input_3 = "Really? /think" print(f"User: {user_input_3}") response_3 = chatbot.generate_response(user_input_3) print(f"Bot: {response_3}") ``` > Note > For API compatibility, when `enable_thinking=True`, regardless of whether the user uses `/think` or `/no_think`, the model will always output a block wrapped in `<think>...</think>`. However, the content inside this block may be empty if thinking is disabled. > When `enable_thinking=False`, the soft switches are not valid. Regardless of any `/think` or `/no_think` tags input by the user, the model will not generate think content and will not include a `<think>...</think>` block. ## Agentic Use Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity. To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself. ```python from qwen_agent.agents import Assistant # Define LLM llm_cfg = { 'model': 'Qwen3-14B', # Use the endpoint provided by Alibaba Model Studio: # 'model_type': 'qwen_dashscope', # 'api_key': os.getenv('DASHSCOPE_API_KEY'), # Use a custom endpoint compatible with OpenAI API: 'model_server': 'http://localhost:8000/v1', # api_base 'api_key': 'EMPTY', # Other parameters: # 'generate_cfg': { # # Add: When the response content is `<think>this is the thought</think>this is the answer; # # Do not add: When the response has been separated by reasoning_content and content. # 'thought_in_content': True, # }, } # Define Tools tools = [ {'mcpServers': { # You can specify the MCP configuration file 'time': { 'command': 'uvx', 'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai'] }, "fetch": { "command": "uvx", "args": ["mcp-server-fetch"] } } }, 'code_interpreter', # Built-in tools ] # Define Agent bot = Assistant(llm=llm_cfg, function_list=tools) # Streaming generation messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}] for responses in bot.run(messages=messages): pass print(responses) ``` ## Processing Long Texts Qwen3 natively supports context lengths of up to 32,768 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the [YaRN](https://arxiv.org/abs/2309.00071) method. YaRN is currently supported by several inference frameworks, e.g., `transformers` and `llama.cpp` for local use, `vllm` and `sglang` for deployment. In general, there are two approaches to enabling YaRN for supported frameworks: - Modifying the model files: In the `config.json` file, add the `rope_scaling` fields: ```json { ..., "rope_scaling": { "type": "yarn", "factor": 4.0, "original_max_position_embeddings": 32768 } } ``` For `llama.cpp`, you need to regenerate the GGUF file after the modification. - Passing command line arguments: For `vllm`, you can use ```shell vllm serve ... --rope-scaling '{"type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072 ``` For `sglang`, you can use ```shell python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}' ``` For `llama-server` from `llama.cpp`, you can use ```shell llama-server ... --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768 ``` > [!IMPORTANT] > If you encounter the following warning > ``` > Unrecognized keys in `rope_scaling` for 'rope_type'='yarn': {'original_max_position_embeddings'} > ``` > please upgrade `transformers>=4.51.0`. > [!NOTE] > All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. > We advise adding the `rope_scaling` configuration only when processing long contexts is required. > It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0. > [!NOTE] > The default `max_position_embeddings` in `config.json` is set to 40,960. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance. > [!TIP] > The endpoint provided by Alibaba Model Studio supports dynamic YaRN by default and no extra configuration is needed. ## Best Practices To achieve optimal performance, we recommend the following settings: 1. Sampling Parameters: - For thinking mode (`enable_thinking=True`), use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0`. DO NOT use greedy decoding, as it can lead to performance degradation and endless repetitions. - For non-thinking mode (`enable_thinking=False`), we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. - For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance. 2. Adequate Output Length: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance. 3. Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking. - Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt. - Multiple-Choice Questions: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`." 4. No Thinking Content in History: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed. ### Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen3, title = {Qwen3}, url = {https://qwenlm.github.io/blog/qwen3/}, author = {Qwen Team}, month = {April}, year = {2025} } ```
unsloth/Qwen3-4B-128K-GGUF	unsloth	2025-05-25T00:52:59Z	16,886	18	transformers	[ "transformers", "gguf", "qwen3", "text-generation", "qwen", "unsloth", "en", "arxiv:2309.00071", "base_model:Qwen/Qwen3-4B", "base_model:quantized:Qwen/Qwen3-4B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-28T13:43:19Z	--- base_model: Qwen/Qwen3-4B language: - en library_name: transformers license_link: https://huggingface.co/Qwen/Qwen3-4B/blob/main/LICENSE license: apache-2.0 tags: - qwen3 - qwen - unsloth - transformers --- <div> <p style="margin-bottom: 0; margin-top: 0;"> <strong>See <a href="https://huggingface.co/collections/unsloth/qwen3-680edabfb790c8c34a242f95">our collection</a> for all versions of Qwen3 including GGUF, 4-bit & 16-bit formats.</strong> </p> <p style="margin-bottom: 0;"> <em>Learn to run Qwen3 correctly - <a href="https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune">Read our Guide</a>.</em> </p> <p style="margin-top: 0;margin-bottom: 0;"> <em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em> </p> <div style="display: flex; gap: 5px; align-items: center; "> <a href="https://github.com/unslothai/unsloth/"> <img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133"> </a> <a href="https://discord.gg/unsloth"> <img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173"> </a> <a href="https://docs.unsloth.ai/basics/tutorial-how-to-run-deepseek-r1-on-your-own-local-device"> <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143"> </a> </div> <h1 style="margin-top: 0rem;">✨ Run & Fine-tune Qwen3 with Unsloth!</h1> </div> - Fine-tune Qwen3 (14B) for free using our Google [Colab notebook here](https://docs.unsloth.ai/get-started/unsloth-notebooks)! - Read our Blog about Qwen3 support: [unsloth.ai/blog/qwen3](https://unsloth.ai/blog/qwen3) - View the rest of our notebooks in our [docs here](https://docs.unsloth.ai/get-started/unsloth-notebooks). - Run & export your fine-tuned model to Ollama, llama.cpp or HF. \| Unsloth supports \| Free Notebooks \| Performance \| Memory use \| \|-----------------\|--------------------------------------------------------------------------------------------------------------------------\|-------------\|----------\| \| Qwen3 (14B) \| [▶️ Start on Colab](https://docs.unsloth.ai/get-started/unsloth-notebooks) \| 3x faster \| 70% less \| \| GRPO with Qwen3 (8B) \| [▶️ Start on Colab](https://docs.unsloth.ai/get-started/unsloth-notebooks) \| 3x faster \| 80% less \| \| Llama-3.2 (3B) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb) \| 2.4x faster \| 58% less \| \| Llama-3.2 (11B vision) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb) \| 2x faster \| 60% less \| \| Qwen2.5 (7B) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_(7B)-Alpaca.ipynb) \| 2x faster \| 60% less \| \| Phi-4 (14B) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb) \| 2x faster \| 50% less \| # Qwen3-4B ## Qwen3 Highlights Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features: - Uniquely support of seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within single model, ensuring optimal performance across various scenarios. - Significantly enhancement in its reasoning capabilities, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning. - Superior human preference alignment, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience. - Expertise in agent capabilities, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks. - Support of 100+ languages and dialects with strong capabilities for multilingual instruction following and translation. ## Model Overview Qwen3-4B has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 4.0B - Number of Paramaters (Non-Embedding): 3.6B - Number of Layers: 36 - Number of Attention Heads (GQA): 32 for Q and 8 for KV - Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts). For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Quickstart The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.51.0`, you will encounter the following error: ``` KeyError: 'qwen3' ``` The following contains a code snippet illustrating how to use the model generate content based on given inputs. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-4B" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # prepare the model input prompt = "Give me a short introduction to large language model." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # Switches between thinking and non-thinking modes. Default is True. ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completion generated_ids = model.generate( model_inputs, max_new_tokens=32768 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() # parsing thinking content try: # rindex finding 151668 (</think>) index = len(output_ids) - output_ids[::-1].index(151668) except ValueError: index = 0 thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") print("thinking content:", thinking_content) print("content:", content) ``` For deployment, you can use `vllm>=0.8.5` or `sglang>=0.4.5.post2` to create an OpenAI-compatible API endpoint: - vLLM: ```shell vllm serve Qwen/Qwen3-4B --enable-reasoning --reasoning-parser deepseek_r1 ``` - SGLang: ```shell python -m sglang.launch_server --model-path Qwen/Qwen3-4B --reasoning-parser deepseek-r1 ``` ## Switching Between Thinking and Non-Thinking Mode > [!TIP] > The `enable_thinking` switch is also available in APIs created by vLLM and SGLang. > Please refer to [our documentation](https://qwen.readthedocs.io/) for more details. ### `enable_thinking=True` By default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting `enable_thinking=True` or leaving it as the default value in `tokenizer.apply_chat_template`, the model will engage its thinking mode. ```python text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # True is the default value for enable_thinking ) ``` In this mode, the model will generate think content wrapped in a `<think>...</think>` block, followed by the final response. > [!NOTE] > For thinking mode, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). DO NOT use greedy decoding, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the [Best Practices](#best-practices) section. ### `enable_thinking=False` We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency. ```python text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False # Setting enable_thinking=False disables thinking mode ) ``` In this mode, the model will not generate any think content and will not include a `<think>...</think>` block. > [!NOTE] > For non-thinking mode, we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. For more detailed guidance, please refer to the [Best Practices](#best-practices) section. ### Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input We provide a soft switch mechanism that allows users to dynamically control the model's behavior when `enable_thinking=True`. Specifically, you can add `/think` and `/no_think` to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations. Here is an example of a multi-turn conversation: ```python from transformers import AutoModelForCausalLM, AutoTokenizer class QwenChatbot: def __init__(self, model_name="Qwen/Qwen3-4B"): self.tokenizer = AutoTokenizer.from_pretrained(model_name) self.model = AutoModelForCausalLM.from_pretrained(model_name) self.history = [] def generate_response(self, user_input): messages = self.history + [{"role": "user", "content": user_input}] text = self.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) inputs = self.tokenizer(text, return_tensors="pt") response_ids = self.model.generate(inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist() response = self.tokenizer.decode(response_ids, skip_special_tokens=True) # Update history self.history.append({"role": "user", "content": user_input}) self.history.append({"role": "assistant", "content": response}) return response # Example Usage if __name__ == "__main__": chatbot = QwenChatbot() # First input (without /think or /no_think tags, thinking mode is enabled by default) user_input_1 = "How many r's in strawberries?" print(f"User: {user_input_1}") response_1 = chatbot.generate_response(user_input_1) print(f"Bot: {response_1}") print("----------------------") # Second input with /no_think user_input_2 = "Then, how many r's in blueberries? /no_think" print(f"User: {user_input_2}") response_2 = chatbot.generate_response(user_input_2) print(f"Bot: {response_2}") print("----------------------") # Third input with /think user_input_3 = "Really? /think" print(f"User: {user_input_3}") response_3 = chatbot.generate_response(user_input_3) print(f"Bot: {response_3}") ``` > Note > For API compatibility, when `enable_thinking=True`, regardless of whether the user uses `/think` or `/no_think`, the model will always output a block wrapped in `<think>...</think>`. However, the content inside this block may be empty if thinking is disabled. > When `enable_thinking=False`, the soft switches are not valid. Regardless of any `/think` or `/no_think` tags input by the user, the model will not generate think content and will not include a `<think>...</think>` block. ## Agentic Use Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity. To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself. ```python import os from qwen_agent.agents import Assistant # Define LLM llm_cfg = { 'model': 'Qwen3-4B', # Use the endpoint provided by Alibaba Model Studio: # 'model_type': 'qwen_dashscope', # 'api_key': os.getenv('DASHSCOPE_API_KEY'), # Use a custom endpoint compatible with OpenAI API: 'model_server': 'http://localhost:8000/v1', # api_base 'api_key': 'EMPTY', # Other parameters: # 'generate_cfg': { # # Add: When the response content is `<think>this is the thought</think>this is the answer; # # Do not add: When the response has been separated by reasoning_content and content. # 'thought_in_content': True, # }, } # Define Tools tools = [ {'mcpServers': { # You can specify the MCP configuration file 'time': { 'command': 'uvx', 'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai'] }, } }, 'code_interpreter', # Built-in tools ] # Define Agent bot = Assistant(llm=llm_cfg, function_list=tools) # Streaming generation messages = [{'role': 'user', 'content': 'What time is it?'}] for responses in bot.run(messages=messages): pass print(responses) ``` ## Processing Long Texts Qwen3 natively supports context lengths of up to 32,768 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the [YaRN](https://arxiv.org/abs/2309.00071) method. YaRN is currently supported by several inference frameworks, e.g., `transformers` and `llama.cpp` for local use, `vllm` and `sglang` for deployment. In general, there are two approaches to enabling YaRN for supported frameworks: - Modifying the model files: In the `config.json` file, add the `rope_scaling` fields: ```json { ..., "rope_scaling": { "type": "yarn", "factor": 4.0, "original_max_position_embeddings": 32768 } } ``` For `llama.cpp`, you need to regenerate the GGUF file after the modification. - Passing command line arguments: For `vllm`, you can use ```shell vllm serve ... --rope-scaling '{"type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072 ``` For `sglang`, you can use ```shell python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}' ``` For `llama-server` from `llama.cpp`, you can use ```shell llama-server ... --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768 ``` > [!IMPORTANT] > If you encounter the following warning > ``` > Unrecognized keys in `rope_scaling` for 'rope_type'='yarn': {'original_max_position_embeddings'} > ``` > please upgrade `transformers>=4.51.0`. > [!NOTE] > All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. > We advise adding the `rope_scaling` configuration only when processing long contexts is required. > It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0. > [!NOTE] > The default `max_position_embeddings` in `config.json` is set to 40,960. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance. > [!TIP] > The endpoint provided by Alibaba Model Studio supports dynamic YaRN by default and no extra configuration is needed. ## Best Practices To achieve optimal performance, we recommend the following settings: 1. Sampling Parameters: - For thinking mode (`enable_thinking=True`), use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0`. DO NOT use greedy decoding, as it can lead to performance degradation and endless repetitions. - For non-thinking mode (`enable_thinking=False`), we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. - For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance. 2. Adequate Output Length: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance. 3. Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking. - Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt. - Multiple-Choice Questions: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`." 4. No Thinking Content in History: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed. ### Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen3, title = {Qwen3}, url = {https://qwenlm.github.io/blog/qwen3/}, author = {Qwen Team}, month = {April}, year = {2025} } ```
MomlessTomato/nico-yazawa	MomlessTomato	2025-05-25T00:37:19Z	4	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:cagliostrolab/animagine-xl-3.0", "base_model:adapter:cagliostrolab/animagine-xl-3.0", "license:mit", "region:us" ]	text-to-image	2024-02-11T05:22:46Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: >- masterpiece, high quality, defined pupil, looking at viewer, rounded pupil, defined iris, (soft iris:1.2), parameters: negative_prompt: >- bad_anatomy, deformation, amputation, deformity, deformed_nipples, duplicated_torso, deformed_torso, long_torso, large_torso, unproportioned_torso, (deformed_pussy:1.2), (deformed_hands:1.2), unproportioned_eyes, unproportioned_head, small_head, duplicated_nose, big_nose, fusioned_clothes, fusioned_arms, undefined_limbs, divided_pussy, red_pussy, duplicated_pussy, deformed_anus, deformed_pussy, output: url: images/nicochan.png base_model: cagliostrolab/animagine-xl-3.0 instance_prompt: id_nico_yazawa license: mit --- # Nico Yazawa <Gallery /> ## Model description This model was trained to generate high quality images based on SIFAS cards. To achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement. ## Trigger words You should use `id_nico_yazawa` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/theidoldaily/nico-yazawa/tree/main) them in the Files & versions tab.
bamec66557/Qwen3-8B-Dgrade-Q4_K_M-GGUF	bamec66557	2025-05-25T00:34:51Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:bamec66557/Qwen3-8B-Dgrade", "base_model:quantized:bamec66557/Qwen3-8B-Dgrade", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-25T00:34:30Z	--- base_model: bamec66557/Qwen3-8B-Dgrade library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # bamec66557/Qwen3-8B-Dgrade-Q4_K_M-GGUF This model was converted to GGUF format from [`bamec66557/Qwen3-8B-Dgrade`](https://huggingface.co/bamec66557/Qwen3-8B-Dgrade) 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/bamec66557/Qwen3-8B-Dgrade) 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 bamec66557/Qwen3-8B-Dgrade-Q4_K_M-GGUF --hf-file qwen3-8b-dgrade-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo bamec66557/Qwen3-8B-Dgrade-Q4_K_M-GGUF --hf-file qwen3-8b-dgrade-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo bamec66557/Qwen3-8B-Dgrade-Q4_K_M-GGUF --hf-file qwen3-8b-dgrade-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo bamec66557/Qwen3-8B-Dgrade-Q4_K_M-GGUF --hf-file qwen3-8b-dgrade-q4_k_m.gguf -c 2048 ```
JoshMe1/595cbebc-48d9-4e2b-ba84-f7fe62ac5edb	JoshMe1	2025-05-25T00:32:55Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Llama-3.2-1B", "base_model:adapter:unsloth/Llama-3.2-1B", "license:llama3.2", "region:us" ]	null	2025-05-24T22:50:02Z	--- library_name: peft license: llama3.2 base_model: unsloth/Llama-3.2-1B tags: - axolotl - generated_from_trainer model-index: - name: 595cbebc-48d9-4e2b-ba84-f7fe62ac5edb 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/Llama-3.2-1B bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - /workspace/input_data/4146a4518ae211e5_train_data.json ds_type: json format: custom path: /workspace/input_data/4146a4518ae211e5_train_data.json type: field_input: input field_instruction: instruct field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto early_stopping_patience: 4 eval_max_new_tokens: 128 eval_steps: 100 eval_table_size: null evals_per_epoch: null flash_attention: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clip_norm: 0.8 group_by_length: false hub_model_id: JoshMe1/595cbebc-48d9-4e2b-ba84-f7fe62ac5edb hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 128 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine lr_scheduler_args: - warmup_steps=100 - num_cycles=1 max_memory: 0: 130GB max_steps: 200 micro_batch_size: 4 mixed_precision: bf16 mlflow_experiment_name: /tmp/4146a4518ae211e5_train_data.json model_type: AutoModelForCausalLM num_epochs: 10 offload_folder: /workspace/offload/feaad467-15b5-47f2-a93a-1c0107947429 optimizer: adamw_hf output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 100 saves_per_epoch: null sequence_len: 2048 strict: 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: feaad467-15b5-47f2-a93a-1c0107947429 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: feaad467-15b5-47f2-a93a-1c0107947429 warmup_steps: 200 weight_decay: 0.01 xformers_attention: null ``` </details><br> # 595cbebc-48d9-4e2b-ba84-f7fe62ac5edb This model is a fine-tuned version of [unsloth/Llama-3.2-1B](https://huggingface.co/unsloth/Llama-3.2-1B) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_HF 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: 200 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0000 \| 1 \| nan \| \| 0.0 \| 0.0031 \| 100 \| nan \| \| 0.0 \| 0.0063 \| 200 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Berkayy4/gemma-3_coref	Berkayy4	2025-05-25T00:31:22Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-1b-pt", "base_model:finetune:google/gemma-3-1b-pt", "endpoints_compatible", "region:us" ]	null	2025-05-25T00:24:20Z	--- base_model: google/gemma-3-1b-pt library_name: transformers model_name: gemma-3_coref tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-3_coref This model is a fine-tuned version of [google/gemma-3-1b-pt](https://huggingface.co/google/gemma-3-1b-pt). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Berkayy4/gemma-3_coref", 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: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Keshav022/deepseek-r1-stage2-patterns	Keshav022	2025-05-25T00:18:58Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-25T00:18:28Z	--- base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Keshav022 - License: apache-2.0 - Finetuned from model : unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ElijahLiew2/um_p2_fine_tuned_llama	ElijahLiew2	2025-05-25T00:17:58Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "text-generation-inference", "text-generation", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T22:02:10Z	--- library_name: transformers tags: - unsloth - text-generation-inference base_model: - meta-llama/Llama-3.1-8B-Instruct pipeline_tag: text-generation --- # 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]
mradermacher/baseline-GGUF	mradermacher	2025-05-25T00:10:05Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:pixelsandpointers/baseline", "base_model:quantized:pixelsandpointers/baseline", "endpoints_compatible", "region:us" ]	null	2025-05-24T07:33:17Z	--- base_model: pixelsandpointers/baseline language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/pixelsandpointers/baseline <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/baseline-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/baseline-GGUF/resolve/main/baseline.Q2_K.gguf) \| Q2_K \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/baseline-GGUF/resolve/main/baseline.Q3_K_S.gguf) \| Q3_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/baseline-GGUF/resolve/main/baseline.Q3_K_M.gguf) \| Q3_K_M \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/baseline-GGUF/resolve/main/baseline.IQ4_XS.gguf) \| IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/baseline-GGUF/resolve/main/baseline.Q4_K_S.gguf) \| Q4_K_S \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/baseline-GGUF/resolve/main/baseline.Q3_K_L.gguf) \| Q3_K_L \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/baseline-GGUF/resolve/main/baseline.Q4_K_M.gguf) \| Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/baseline-GGUF/resolve/main/baseline.Q5_K_S.gguf) \| Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/baseline-GGUF/resolve/main/baseline.Q5_K_M.gguf) \| Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/baseline-GGUF/resolve/main/baseline.Q6_K.gguf) \| Q6_K \| 0.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/baseline-GGUF/resolve/main/baseline.Q8_0.gguf) \| Q8_0 \| 0.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/baseline-GGUF/resolve/main/baseline.f16.gguf) \| f16 \| 0.4 \| 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 -->
adonaivera/nanoVLM	adonaivera	2025-05-25T00:07:57Z	0	0	nanovlm	[ "nanovlm", "safetensors", "vision-language", "multimodal", "research", "image-text-to-text", "license:mit", "region:us" ]	image-text-to-text	2025-05-24T23:06:22Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards library_name: nanovlm license: mit pipeline_tag: image-text-to-text tags: - vision-language - multimodal - research --- nanoVLM is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines of code. It combines a ViT-based image encoder (SigLIP-B/16-224-85M) with a lightweight causal language model (SmolLM2-135M), resulting in a compact 222M parameter model. For more information, check out the base model on https://huggingface.co/lusxvr/nanoVLM-222M. Usage: Clone the nanoVLM repository: https://github.com/huggingface/nanoVLM. Follow the install instructions and run the following code: ```python from models.vision_language_model import VisionLanguageModel model = VisionLanguageModel.from_pretrained("adonaivera/nanoVLM") ```
vslinx/LoRA-Collection	vslinx	2025-05-25T00:07:21Z	0	0	null	[ "region:us" ]	null	2025-05-24T17:39:47Z	### This is a backup collection of all my models released on [civitai](https://civitai.com/user/vslinx)
New-tutorial-Annie-Knight-Viral-Video/FULL.VIDEO.LINK.Annie.Knight.Viral.Video.Leaks.Official	New-tutorial-Annie-Knight-Viral-Video	2025-05-25T00:05:46Z	0	0	null	[ "region:us" ]	null	2025-05-25T00:05:30Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
milindadpaiker/bert-base-uncased-mrpc	milindadpaiker	2025-05-25T00:04:30Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "dataset:SetFit/mrpc", "arxiv:1910.09700", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-24T23:33:38Z	--- library_name: transformers datasets: - SetFit/mrpc metrics: - accuracy - f1 base_model: - google-bert/bert-base-uncased --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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18-liyumisa-emo-viral-video-link/wATCH.Liyumisa.Emo.viral.video.original.Link.Official	18-liyumisa-emo-viral-video-link	2025-05-24T23:55:40Z	0	0	null	[ "region:us" ]	null	2025-05-24T23:55:25Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
mradermacher/R1-VeriThinker-7B-i1-GGUF	mradermacher	2025-05-24T23:51:01Z	0	0	transformers	[ "transformers", "gguf", "en", "dataset:Zigeng/CoT-Veirification-340k", "base_model:Zigeng/R1-VeriThinker-7B", "base_model:quantized:Zigeng/R1-VeriThinker-7B", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-24T23:07:50Z	--- base_model: Zigeng/R1-VeriThinker-7B datasets: - Zigeng/CoT-Veirification-340k language: - en library_name: transformers license: mit 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/Zigeng/R1-VeriThinker-7B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/R1-VeriThinker-7B-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/R1-VeriThinker-7B-i1-GGUF/resolve/main/R1-VeriThinker-7B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.0 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/R1-VeriThinker-7B-i1-GGUF/resolve/main/R1-VeriThinker-7B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.1 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/R1-VeriThinker-7B-i1-GGUF/resolve/main/R1-VeriThinker-7B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/R1-VeriThinker-7B-i1-GGUF/resolve/main/R1-VeriThinker-7B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/R1-VeriThinker-7B-i1-GGUF/resolve/main/R1-VeriThinker-7B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/R1-VeriThinker-7B-i1-GGUF/resolve/main/R1-VeriThinker-7B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/R1-VeriThinker-7B-i1-GGUF/resolve/main/R1-VeriThinker-7B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 2.9 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/R1-VeriThinker-7B-i1-GGUF/resolve/main/R1-VeriThinker-7B.i1-Q2_K.gguf) \| i1-Q2_K \| 3.1 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/R1-VeriThinker-7B-i1-GGUF/resolve/main/R1-VeriThinker-7B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/R1-VeriThinker-7B-i1-GGUF/resolve/main/R1-VeriThinker-7B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/R1-VeriThinker-7B-i1-GGUF/resolve/main/R1-VeriThinker-7B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/R1-VeriThinker-7B-i1-GGUF/resolve/main/R1-VeriThinker-7B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/R1-VeriThinker-7B-i1-GGUF/resolve/main/R1-VeriThinker-7B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/R1-VeriThinker-7B-i1-GGUF/resolve/main/R1-VeriThinker-7B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.9 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/R1-VeriThinker-7B-i1-GGUF/resolve/main/R1-VeriThinker-7B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/R1-VeriThinker-7B-i1-GGUF/resolve/main/R1-VeriThinker-7B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/R1-VeriThinker-7B-i1-GGUF/resolve/main/R1-VeriThinker-7B.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 4.5 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/R1-VeriThinker-7B-i1-GGUF/resolve/main/R1-VeriThinker-7B.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/R1-VeriThinker-7B-i1-GGUF/resolve/main/R1-VeriThinker-7B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.6 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/R1-VeriThinker-7B-i1-GGUF/resolve/main/R1-VeriThinker-7B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/R1-VeriThinker-7B-i1-GGUF/resolve/main/R1-VeriThinker-7B.i1-Q4_1.gguf) \| i1-Q4_1 \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/R1-VeriThinker-7B-i1-GGUF/resolve/main/R1-VeriThinker-7B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/R1-VeriThinker-7B-i1-GGUF/resolve/main/R1-VeriThinker-7B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/R1-VeriThinker-7B-i1-GGUF/resolve/main/R1-VeriThinker-7B.i1-Q6_K.gguf) \| i1-Q6_K \| 6.4 \| 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 -->
andradellos/andradellos-lorav1	andradellos	2025-05-24T23:42:48Z	0	0	null	[ "license:other", "region:us" ]	null	2025-05-24T23:00:36Z	--- 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 ---
18-VIDEOS-Katrina-Lim-Kiffy-Viral-Video/Original.Full.Clip.Katrina-Lim.Viral.Video.Leaks.Official	18-VIDEOS-Katrina-Lim-Kiffy-Viral-Video	2025-05-24T23:34:49Z	0	0	null	[ "region:us" ]	null	2025-05-24T23:34:32Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
Bingham/qwen_2_5_7b_grpo_train_unsloth_model	Bingham	2025-05-24T23:30:45Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-09T00:43:33Z	--- base_model: unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Bingham - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit This qwen2 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)
qtung24/biobert-ner-ddi	qtung24	2025-05-24T23:28:56Z	0	0	null	[ "safetensors", "bert", "license:apache-2.0", "region:us" ]	null	2025-05-24T23:26:49Z	--- license: apache-2.0 ---
SpireLab/spire_respin_baselines_espnet	SpireLab	2025-05-24T23:23:43Z	0	0	espnet	[ "espnet", "automatic-speech-recognition", "speech", "audio", "indian-languages", "bh", "bn", "ch", "hi", "kn", "mg", "mr", "mt", "te", "dataset:respin", "license:cc-by-4.0", "region:us" ]	automatic-speech-recognition	2025-05-24T22:31:10Z	--- tags: - espnet - automatic-speech-recognition - speech - audio - indian-languages language: - bh - bn - ch - hi - kn - mg - mr - mt - te license: cc-by-4.0 datasets: - respin pipeline_tag: automatic-speech-recognition --- # ESPnet ASR Models in 9 Indian languages trained on RESPIN-S1.0 Small Train sets This repository contains E-Branchformer-based ESPnet2 ASR models trained on the RESPIN-S1.0 small train splits. ## 🛠 Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet pip install -e . cd egs2/respin_small/asr1 ./run.sh --skip_data_prep true --skip_train true --download_model SpireLab/spire_respin_baselines_espnet ``` ## 📊 Results (CER/WER from RESULTS.md) \| Language \| Model Name \| CER (%) \| WER (%) \| \|----------\|------------\|---------\|---------\| \| bh \| exp_small/exp_bh/asr_bh_ebf_size256_mlp1024_lin1024_e8_mactrue_bs6M_gacc1 \| 4.4 \| 15.2 \| \| bn \| exp_small/exp_bn/asr_bn_ebf_size256_mlp1024_lin1024_e8_mactrue_bs6M_gacc1 \| 4.1 \| 15.0 \| \| ch \| exp_small/exp_ch/asr_ch_ebf_size256_mlp1024_lin1024_e8_mactrue_bs6M_gacc1 \| 3.1 \| 10.6 \| \| hi \| exp_small/exp_hi/asr_hi_ebf_size256_mlp1024_lin1024_e8_mactrue_bs6M_gacc1 \| 3.1 \| 9.9 \| \| kn \| exp_small/exp_kn/asr_kn_ebf_size256_mlp1024_lin1024_e8_mactrue_bs6M_gacc1 \| 4.6 \| 24.5 \| \| mg \| exp_small/exp_mg/asr_mg_ebf_size256_mlp1024_lin1024_e8_mactrue_bs6M_gacc1 \| 6.0 \| 20.4 \| \| mr \| exp_small/exp_mr/asr_mr_ebf_size256_mlp1024_lin1024_e8_mactrue_bs6M_gacc1 \| 3.1 \| 14.5 \| \| mt \| exp_small/exp_mt/asr_mt_ebf_size256_mlp1024_lin1024_e8_mactrue_bs6M_gacc1 \| 5.0 \| 17.9 \| \| te \| exp_small/exp_te/asr_te_ebf_size256_mlp1024_lin1024_e8_mactrue_bs6M_gacc1 \| 4.1 \| 21.6 \| --- ## 📚 Citation ```bibtex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proc. Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} }
aaronlee18/distilroberta-base-finetuned-wikitext2	aaronlee18	2025-05-24T23:23:25Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:distilbert/distilroberta-base", "base_model:finetune:distilbert/distilroberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-05-24T22:43:23Z	--- library_name: transformers license: apache-2.0 base_model: distilroberta-base tags: - generated_from_trainer model-index: - name: distilroberta-base-finetuned-wikitext2 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. --> # distilroberta-base-finetuned-wikitext2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8599 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.0844 \| 1.0 \| 2406 \| 1.9402 \| \| 1.9835 \| 2.0 \| 4812 \| 1.8854 \| \| 1.951 \| 3.0 \| 7218 \| 1.8353 \| ### Framework versions - Transformers 4.52.1 - Pytorch 2.7.0 - Datasets 3.6.0 - Tokenizers 0.21.1
MikaeI/nort5-large-finetuned-norwegian	MikaeI	2025-05-24T23:22:06Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "feature-extraction", "summarization", "generated_from_trainer", "custom_code", "base_model:ltg/nort5-large", "base_model:finetune:ltg/nort5-large", "license:apache-2.0", "region:us" ]	summarization	2025-05-24T23:12:35Z	--- library_name: transformers license: apache-2.0 base_model: ltg/nort5-large tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: nort5-large-finetuned-norwegian 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. --> # nort5-large-finetuned-norwegian This model is a fine-tuned version of [ltg/nort5-large](https://huggingface.co/ltg/nort5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9237 - Rouge1: 23.7487 - Rouge2: 6.7301 - Rougel: 14.8509 - Rougelsum: 15.4894 ## 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: 5.6e-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: 6 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\| \| 2.9348 \| 1.0 \| 18 \| 2.6112 \| 17.8464 \| 3.5066 \| 11.3712 \| 11.5183 \| \| 1.2006 \| 2.0 \| 36 \| 2.4548 \| 21.4194 \| 5.3028 \| 14.875 \| 14.5553 \| \| 0.7799 \| 3.0 \| 54 \| 2.2980 \| 24.4265 \| 7.6063 \| 17.1996 \| 17.2811 \| \| 0.5767 \| 4.0 \| 72 \| 2.3304 \| 29.6421 \| 10.5432 \| 21.2022 \| 21.4957 \| \| 0.4624 \| 5.0 \| 90 \| 2.3195 \| 28.8043 \| 8.9197 \| 19.7301 \| 19.9298 \| \| 0.4133 \| 6.0 \| 108 \| 2.3266 \| 29.6 \| 9.4969 \| 19.7972 \| 19.8279 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
zenml/deepseek-cuad-gemma-3-it-bnb-4bit	zenml	2025-05-24T23:21:47Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3", "trl", "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", "endpoints_compatible", "region:us" ]	null	2025-05-19T20:25:30Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: zenml - 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)
the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Mistral-Role-0524-Simon_step_00288	the-acorn-ai	2025-05-24T23:16:09Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T23:14:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mradermacher/gpt-nyc-nontoxic-i1-GGUF	mradermacher	2025-05-24T23:14:25Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:monsoon-nlp/gpt-nyc-nontoxic", "base_model:quantized:monsoon-nlp/gpt-nyc-nontoxic", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-05-24T23:02:41Z	--- base_model: monsoon-nlp/gpt-nyc-nontoxic language: - en library_name: transformers 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/monsoon-nlp/gpt-nyc-nontoxic <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/gpt-nyc-nontoxic-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/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.2 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.2 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-Q2_K.gguf) \| i1-Q2_K \| 0.2 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.2 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.2 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 0.2 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-Q4_0.gguf) \| i1-Q4_0 \| 0.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 0.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 0.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-Q4_1.gguf) \| i1-Q4_1 \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-Q6_K.gguf) \| i1-Q6_K \| 0.2 \| 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 -->
mradermacher/tiny-gpt2-magicprompt-GGUF	mradermacher	2025-05-24T23:14:21Z	0	0	null	[ "region:us" ]	null	2025-05-24T23:14:19Z	<!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/pszemraj/tiny-gpt2-magicprompt

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