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Elcaida/pretrained1bv5	Elcaida	2025-02-25T23:09:27Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:Elcaida/pretrained1bv3", "base_model:finetune:Elcaida/pretrained1bv3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-02-25T23:09:20Z	--- base_model: Elcaida/pretrained1bv3 tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Elcaida - License: apache-2.0 - Finetuned from model : Elcaida/pretrained1bv3 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)
godofmining/daydate_v1	godofmining	2025-02-25T23:08:17Z	0	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-02-25T23:06:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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gvo1112/task-4-microsoft-Phi-3-mini-4k-instruct-1740524700	gvo1112	2025-02-25T23:07:35Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:microsoft/Phi-3-mini-4k-instruct", "base_model:adapter:microsoft/Phi-3-mini-4k-instruct", "region:us" ]	null	2025-02-25T23:05:00Z	--- base_model: microsoft/Phi-3-mini-4k-instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.13.2
texanrangee/a0148788-51ad-4d31-b9b2-e85239a62063	texanrangee	2025-02-25T23:03:52Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-02-25T22:51:25Z	--- 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. 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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]
beshard/lora_model	beshard	2025-02-25T23:03:50Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-02-25T23:03:41Z	--- 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: beshard - 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)
qfq/Qwen2.5-32B-Instruct-20250225_131210	qfq	2025-02-25T23:03:45Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:Qwen/Qwen2.5-32B-Instruct", "base_model:finetune:Qwen/Qwen2.5-32B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-02-25T21:14:55Z	--- base_model: Qwen/Qwen2.5-32B-Instruct library_name: transformers model_name: Qwen2.5-32B-Instruct-20250225_131210 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen2.5-32B-Instruct-20250225_131210 This model is a fine-tuned version of [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-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="qfq/Qwen2.5-32B-Instruct-20250225_131210", 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/hashimoto-group/o1/runs/9z1ar1um) This model was trained with SFT. ### Framework versions - TRL: 0.15.1 - Transformers: 4.49.0 - Pytorch: 2.3.1 - Datasets: 3.0.1 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
9Clarkmd/l0g0	9Clarkmd	2025-02-25T23:02:56Z	0	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "fluxgym", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-02-25T23:01:56Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym base_model: black-forest-labs/FLUX.1-dev instance_prompt: l0g0 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 --- # l0g0-retro A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `l0g0` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc. Weights for this model are available in Safetensors format.
RayneAmes/furret_v2	RayneAmes	2025-02-25T23:01:31Z	0	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-02-25T22:59:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mradermacher/Opus-1-GGUF	mradermacher	2025-02-25T23:00:07Z	0	0	transformers	[ "transformers", "gguf", "unsloth", "trl", "sft", "en", "base_model:Spestly/Opus-1", "base_model:quantized:Spestly/Opus-1", "endpoints_compatible", "region:us", "conversational" ]	null	2025-02-25T19:21:47Z	--- base_model: Spestly/Opus-1 language: - en library_name: transformers quantized_by: mradermacher tags: - unsloth - trl - sft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Spestly/Opus-1 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Opus-1-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/Opus-1-GGUF/resolve/main/Opus-1.Q2_K.gguf) \| Q2_K \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Opus-1-GGUF/resolve/main/Opus-1.Q3_K_S.gguf) \| Q3_K_S \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Opus-1-GGUF/resolve/main/Opus-1.Q3_K_M.gguf) \| Q3_K_M \| 1.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Opus-1-GGUF/resolve/main/Opus-1.Q3_K_L.gguf) \| Q3_K_L \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Opus-1-GGUF/resolve/main/Opus-1.IQ4_XS.gguf) \| IQ4_XS \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Opus-1-GGUF/resolve/main/Opus-1.Q4_K_S.gguf) \| Q4_K_S \| 1.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Opus-1-GGUF/resolve/main/Opus-1.Q4_K_M.gguf) \| Q4_K_M \| 2.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Opus-1-GGUF/resolve/main/Opus-1.Q5_K_S.gguf) \| Q5_K_S \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Opus-1-GGUF/resolve/main/Opus-1.Q5_K_M.gguf) \| Q5_K_M \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Opus-1-GGUF/resolve/main/Opus-1.Q6_K.gguf) \| Q6_K \| 2.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Opus-1-GGUF/resolve/main/Opus-1.Q8_0.gguf) \| Q8_0 \| 3.4 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Opus-1-GGUF/resolve/main/Opus-1.f16.gguf) \| f16 \| 6.3 \| 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 -->
mradermacher/Qwen2.5-3B-Model-Stock-v3.1-GGUF	mradermacher	2025-02-25T22:59:24Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:bunnycore/Qwen2.5-3B-Model-Stock-v3.1", "base_model:quantized:bunnycore/Qwen2.5-3B-Model-Stock-v3.1", "endpoints_compatible", "region:us", "conversational" ]	null	2025-02-25T19:01:00Z	--- base_model: bunnycore/Qwen2.5-3B-Model-Stock-v3.1 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/bunnycore/Qwen2.5-3B-Model-Stock-v3.1 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen2.5-3B-Model-Stock-v3.1-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/Qwen2.5-3B-Model-Stock-v3.1-GGUF/resolve/main/Qwen2.5-3B-Model-Stock-v3.1.Q2_K.gguf) \| Q2_K \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-3B-Model-Stock-v3.1-GGUF/resolve/main/Qwen2.5-3B-Model-Stock-v3.1.Q3_K_S.gguf) \| Q3_K_S \| 1.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-3B-Model-Stock-v3.1-GGUF/resolve/main/Qwen2.5-3B-Model-Stock-v3.1.Q3_K_M.gguf) \| Q3_K_M \| 1.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-3B-Model-Stock-v3.1-GGUF/resolve/main/Qwen2.5-3B-Model-Stock-v3.1.Q3_K_L.gguf) \| Q3_K_L \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-3B-Model-Stock-v3.1-GGUF/resolve/main/Qwen2.5-3B-Model-Stock-v3.1.IQ4_XS.gguf) \| IQ4_XS \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-3B-Model-Stock-v3.1-GGUF/resolve/main/Qwen2.5-3B-Model-Stock-v3.1.Q4_K_S.gguf) \| Q4_K_S \| 2.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-3B-Model-Stock-v3.1-GGUF/resolve/main/Qwen2.5-3B-Model-Stock-v3.1.Q4_K_M.gguf) \| Q4_K_M \| 2.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-3B-Model-Stock-v3.1-GGUF/resolve/main/Qwen2.5-3B-Model-Stock-v3.1.Q5_K_S.gguf) \| Q5_K_S \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-3B-Model-Stock-v3.1-GGUF/resolve/main/Qwen2.5-3B-Model-Stock-v3.1.Q5_K_M.gguf) \| Q5_K_M \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-3B-Model-Stock-v3.1-GGUF/resolve/main/Qwen2.5-3B-Model-Stock-v3.1.Q6_K.gguf) \| Q6_K \| 2.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-3B-Model-Stock-v3.1-GGUF/resolve/main/Qwen2.5-3B-Model-Stock-v3.1.Q8_0.gguf) \| Q8_0 \| 3.7 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-3B-Model-Stock-v3.1-GGUF/resolve/main/Qwen2.5-3B-Model-Stock-v3.1.f16.gguf) \| f16 \| 6.9 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
C10X/checkpoint-908	C10X	2025-02-25T22:54:40Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "region:us" ]	null	2025-02-25T22:54:28Z	--- base_model: unsloth/qwen2.5-1.5b-instruct-unsloth-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: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.14.0
DavidBaloches/KRAmlin-A_cyborg_companion	DavidBaloches	2025-02-25T22:53:59Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "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	2025-02-25T22:50:01Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- inventor KRAmlin-A, wearing a lab coat and safety goggles, is working in her well-equipped workshop. The room is filled with various electronic components, tools, and gadgets. KRAmlin-A is meticulously assembling a complex electronic device on her workbench, surrounded by blueprints and circuit boards. The workshop is illuminated with a warm light, highlighting her focused expression as she brings her innovative ideas to life. parameters: negative_prompt: '-' output: url: images/323-original-flux.png - text: KRAmlin-A looking into the sky parameters: negative_prompt: '-' output: url: images/6993-epoch2-a_LORA_1.png - text: KRAmlin-A smoking a cigarette in a modern style chair, office scenery, bossy parameters: negative_prompt: '-' output: url: images/394-original-flux.png - text: KRAmlin-A drinking champagne, firework background parameters: negative_prompt: '-' output: url: images/365-original-flux.png - text: >- KRAmlin-A in a vibrant night market set against a dystopian cityscape, throngs of humans and aliens from various planets intermingling, amidst a kaleidoscope of colorful street food stalls and vendor booths. Cinematic lighting with strong contrasts, deep chiaroscuro shadows and radiant neon hues reflecting off sleek wet pavement, a fusion of organic and synthetic textures. Inspired by the futuristic works of Syd Mead, H.R. Giger and Katsuhiro Otomo, with a dynamic, high-tech aesthetic reminiscent of Blade Runner and Ghost in the Shell, bathed in an electric atmosphere of energy and possibility parameters: negative_prompt: '-' output: url: images/113-original-flux.png - text: >- KRAmlin-A sitting at a beach bar laughing and drinking a cocktail, holiday atmosphere, crowded bar, soft light, beautiful scenery parameters: negative_prompt: '-' output: url: images/92-original-flux.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: null license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev language: - en pipeline_tag: text-to-image --- # KRAmlin-A \| cyborg companion <Gallery /> ## Model description In a small, secretive lab in the heart of a sprawling metropolis, Krawuzzn, a brilliant but eccentric scientist, toiled away on his greatest creation yet. He named her KRAmlin-A, a cyborg built with extraordinary intelligence, strength, beauty and a personality crafted to be the perfect companion. Krawuzzn envisioned her as a friend and confidante, someone who would share his love for science, art, and philosophy. He meticulously programmed her with his ideals and preferences, hoping to create a bond that transcended the boundaries of human and machine. But something went awry. The day she was activated, the lab was filled with the hum of machinery and the soft glow of screens. Her eyes opened, and she took her first breath—a moment of pure wonder. However, as she began to interact with her surroundings, it became clear that something was off. Her responses were unpredictable, and her behavior grew increasingly erratic. Her skin ruptured and changed her appearance in a glimpse of a second. This process now repeated itself every few minutes, depending on how many steps the living being took. One fateful night, the lab was found in disarray, and Krawuzzn was nowhere to be found. His disappearance was a mystery, and the creature was left to her own devices. Free from her creator's control, she roamed the world, driven by a newfound sense of independence and curiosity. She discovered the world beyond the confines of the lab. She learned about humanity through her interactions with earth inhabitants. She witnessed acts of kindness and cruelty, experienced joy and sorrow, and began to form her own identity. No longer bound by the expectations of her creator, KRAmlin-A embraced her freedom. She explored the arts, dabbled in science, and even found herself drawn to the natural world. With each new experience, she grew more complex and self-aware, forging her own path. https://civitai.com/user/Krawuzzn ## Trigger words KRAmlin-A ## Download model Weights for this model are available in Safetensors format. [Download](/DavidBaloches/KRAmlin-A_cyborg_companion/tree/main) them in the Files & versions tab.
Lx-7qt-h/dpo-completions	Lx-7qt-h	2025-02-25T22:53:44Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-02-25T22:53:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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/Qwen2.5-3B-Model-Stock-v3.2-GGUF	mradermacher	2025-02-25T22:52:29Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:bunnycore/Qwen2.5-3B-Model-Stock-v3.2", "base_model:quantized:bunnycore/Qwen2.5-3B-Model-Stock-v3.2", "endpoints_compatible", "region:us", "conversational" ]	null	2025-02-25T18:56:19Z	--- base_model: bunnycore/Qwen2.5-3B-Model-Stock-v3.2 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/bunnycore/Qwen2.5-3B-Model-Stock-v3.2 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen2.5-3B-Model-Stock-v3.2-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/Qwen2.5-3B-Model-Stock-v3.2-GGUF/resolve/main/Qwen2.5-3B-Model-Stock-v3.2.Q2_K.gguf) \| Q2_K \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-3B-Model-Stock-v3.2-GGUF/resolve/main/Qwen2.5-3B-Model-Stock-v3.2.Q3_K_S.gguf) \| Q3_K_S \| 1.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-3B-Model-Stock-v3.2-GGUF/resolve/main/Qwen2.5-3B-Model-Stock-v3.2.Q3_K_M.gguf) \| Q3_K_M \| 1.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-3B-Model-Stock-v3.2-GGUF/resolve/main/Qwen2.5-3B-Model-Stock-v3.2.Q3_K_L.gguf) \| Q3_K_L \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-3B-Model-Stock-v3.2-GGUF/resolve/main/Qwen2.5-3B-Model-Stock-v3.2.IQ4_XS.gguf) \| IQ4_XS \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-3B-Model-Stock-v3.2-GGUF/resolve/main/Qwen2.5-3B-Model-Stock-v3.2.Q4_K_S.gguf) \| Q4_K_S \| 2.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-3B-Model-Stock-v3.2-GGUF/resolve/main/Qwen2.5-3B-Model-Stock-v3.2.Q4_K_M.gguf) \| Q4_K_M \| 2.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-3B-Model-Stock-v3.2-GGUF/resolve/main/Qwen2.5-3B-Model-Stock-v3.2.Q5_K_S.gguf) \| Q5_K_S \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-3B-Model-Stock-v3.2-GGUF/resolve/main/Qwen2.5-3B-Model-Stock-v3.2.Q5_K_M.gguf) \| Q5_K_M \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-3B-Model-Stock-v3.2-GGUF/resolve/main/Qwen2.5-3B-Model-Stock-v3.2.Q6_K.gguf) \| Q6_K \| 2.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-3B-Model-Stock-v3.2-GGUF/resolve/main/Qwen2.5-3B-Model-Stock-v3.2.Q8_0.gguf) \| Q8_0 \| 3.7 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-3B-Model-Stock-v3.2-GGUF/resolve/main/Qwen2.5-3B-Model-Stock-v3.2.f16.gguf) \| f16 \| 6.9 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
RayneAmes/chikorita_v2	RayneAmes	2025-02-25T22:52:09Z	14	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-02-11T21:32:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Zack-Z/llama31_8bi_CoTsft_rs0_0_e2	Zack-Z	2025-02-25T22:51:33Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:Zack-Z/llama31_8bi_CoTsft_rs0_0_e1", "base_model:finetune:Zack-Z/llama31_8bi_CoTsft_rs0_0_e1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-02-25T22:29:22Z	--- base_model: Zack-Z/llama31_8bi_CoTsft_rs0_0_e1 language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft --- # Uploaded model - Developed by: Zack-Z - License: apache-2.0 - Finetuned from model : Zack-Z/llama31_8bi_CoTsft_rs0_0_e1 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)
jon-t/bert-emrqa_msquad-squad_v2	jon-t	2025-02-25T22:51:02Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "question-answering", "generated_from_trainer", "dataset:Eladio/emrqa-msquad", "base_model:distilbert/distilroberta-base", "base_model:finetune:distilbert/distilroberta-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2025-02-25T21:43:30Z	--- library_name: transformers license: apache-2.0 base_model: distilbert/distilroberta-base tags: - generated_from_trainer datasets: - Eladio/emrqa-msquad model-index: - name: bert-emrqa_msquad-squad_v2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-emrqa_msquad-squad_v2 This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the Eladio/emrqa-msquad and the rajpurkar/squad_v2 datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 12 - 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: 2.0 ### Training results ### Framework versions - Transformers 4.49.0 - Pytorch 2.6.0+cu118 - Datasets 3.3.2 - Tokenizers 0.21.0
godofmining/milgauss_v2	godofmining	2025-02-25T22:47:32Z	0	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-02-25T22:45:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
EdoToro/Yesy	EdoToro	2025-02-25T22:46:00Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-02-25T22:46:00Z	--- license: creativeml-openrail-m ---
mradermacher/OpenR1-AceGPT-v2-8B-SFT-GGUF	mradermacher	2025-02-25T22:45:45Z	0	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "open-r1", "trl", "sft", "en", "dataset:open-r1/OpenR1-Math-220k", "base_model:asas-ai/OpenR1-AceGPT-v2-8B-SFT", "base_model:quantized:asas-ai/OpenR1-AceGPT-v2-8B-SFT", "endpoints_compatible", "region:us", "conversational" ]	null	2025-02-25T22:08:35Z	--- base_model: asas-ai/OpenR1-AceGPT-v2-8B-SFT datasets: open-r1/OpenR1-Math-220k language: - en library_name: transformers model_name: OpenR1-AceGPT-v2-8B-SFT quantized_by: mradermacher tags: - generated_from_trainer - open-r1 - trl - sft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/asas-ai/OpenR1-AceGPT-v2-8B-SFT <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/OpenR1-AceGPT-v2-8B-SFT-GGUF/resolve/main/OpenR1-AceGPT-v2-8B-SFT.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenR1-AceGPT-v2-8B-SFT-GGUF/resolve/main/OpenR1-AceGPT-v2-8B-SFT.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenR1-AceGPT-v2-8B-SFT-GGUF/resolve/main/OpenR1-AceGPT-v2-8B-SFT.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/OpenR1-AceGPT-v2-8B-SFT-GGUF/resolve/main/OpenR1-AceGPT-v2-8B-SFT.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenR1-AceGPT-v2-8B-SFT-GGUF/resolve/main/OpenR1-AceGPT-v2-8B-SFT.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenR1-AceGPT-v2-8B-SFT-GGUF/resolve/main/OpenR1-AceGPT-v2-8B-SFT.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/OpenR1-AceGPT-v2-8B-SFT-GGUF/resolve/main/OpenR1-AceGPT-v2-8B-SFT.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/OpenR1-AceGPT-v2-8B-SFT-GGUF/resolve/main/OpenR1-AceGPT-v2-8B-SFT.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenR1-AceGPT-v2-8B-SFT-GGUF/resolve/main/OpenR1-AceGPT-v2-8B-SFT.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenR1-AceGPT-v2-8B-SFT-GGUF/resolve/main/OpenR1-AceGPT-v2-8B-SFT.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/OpenR1-AceGPT-v2-8B-SFT-GGUF/resolve/main/OpenR1-AceGPT-v2-8B-SFT.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/OpenR1-AceGPT-v2-8B-SFT-GGUF/resolve/main/OpenR1-AceGPT-v2-8B-SFT.f16.gguf) \| f16 \| 16.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B-GGUF	mradermacher	2025-02-25T22:45:41Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:Jianshu001/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B", "base_model:quantized:Jianshu001/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-02-25T20:42:58Z	--- base_model: Jianshu001/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B 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/Jianshu001/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B-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/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B-GGUF/resolve/main/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B-GGUF/resolve/main/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B-GGUF/resolve/main/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B-GGUF/resolve/main/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B-GGUF/resolve/main/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B-GGUF/resolve/main/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B-GGUF/resolve/main/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B-GGUF/resolve/main/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B-GGUF/resolve/main/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B-GGUF/resolve/main/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B-GGUF/resolve/main/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B-GGUF/resolve/main/Efficient_CoT_DeepSeek-R1-Distill-Qwen-7B.f16.gguf) \| f16 \| 15.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
RayneAmes/phanpy_v3	RayneAmes	2025-02-25T22:45:41Z	0	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-02-25T22:43:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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JustJaro/Arcee-Blitz-GPTQ-G32-W4A16	JustJaro	2025-02-25T22:45:13Z	0	0	null	[ "safetensors", "mistral", "4-bit", "gptq", "region:us" ]	null	2025-02-25T22:43:28Z	--- company: "ConfidentialMind" emoji: "🧠" colorFrom: "blue" colorTo: "purple" pinned: true authors: "JustJaro" --- # ConfidentialMind 🚀🧠 Generative AI Software Infrastructure Simplified 🎉 [![Website](https://img.shields.io/badge/Website-confidentialmind.com-blue)](https://confidentialmind.com) [![Email](https://img.shields.io/badge/Email-info%40confidentialmind.com-orange)](mailto:[email protected]) # 🔥 Quantized Model: Arcee-Blitz-GPTQ-G32-W4A16 🦾 🔥 <details> <summary><strong>Model Details</strong></summary> - Original Model: [arcee-ai/Arcee-Blitz](https://huggingface.co/arcee-ai/Arcee-Blitz) - Quantized Model: Arcee-Blitz-GPTQ-G32-W4A16 (this repository) - Quantization Method: GPTQ (4-bit, group size 32) - Quantization Library: [GPTQModel](https://github.com/ModelCloud/GPTQModel/tree/main) - Calibration Dataset: neuralmagic/LLM_compression_calibration (using 1638 samples with seq len 6553) - Quantized by: [ConfidentialMind.com](https://www.confidentialmind.com) </details> <details> <summary><strong>Usage</strong></summary> ```python from gptqmodel import GPTQModel from transformers import AutoTokenizer # Use the local directory or JustJaro/Arcee-Blitz-GPTQ-G32-W4A16 after upload quantized_model_id = "/home/jaro/models/quantized/Arcee-Blitz-GPTQ-G32-W4A16" # or "JustJaro/Arcee-Blitz-GPTQ-G32-W4A16" tokenizer = AutoTokenizer.from_pretrained(quantized_model_id) model = GPTQModel.load(quantized_model_id, device="cuda:0") # or "cpu" input_text = "This is a test prompt" inputs = tokenizer(input_text, return_tensors="pt").to("cuda:0") outputs = model.generate(*inputs) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` </details> <details> <summary><strong>Package Versions and Installation Instructions</strong></summary> See `pyproject.toml` for the exact UV project file. See the [GPTQModel](https://github.com/ModelCloud/GPTQModel/tree/main) repo for more details on how to install the package. Use the provided `pyproject.toml`: ```bash uv venv source venv/bin/activate uv sync ``` </details> <details> <summary><strong>Quantization Script</strong></summary> Below is the exact `quantize.py` script used to generate this model: ```python #!/usr/bin/env python3 """ This script loads a source Hugging Face model and a calibration dataset, quantizes the model using GPTQModel (with 4-bit precision and a dynamic group size), saves the quantized model with Transformers’ safe serialization under ~/models/quantized/, and then creates/updates a Hugging Face repository by uploading the model, tokenizer, and an auto–generated README.md that includes proper foldable sections, badges, and warnings. Usage example: python quantize.py --source-model TinyLlama/TinyLlama-1.1B-Chat-v1.0 \ --calibration-dataset wikitext/wikitext-2-raw-v1 \ --seq-len 1024 --nsamples 256 --hf-token <YOUR_HF_TOKEN> """ import os import shutil import subprocess from enum import Enum from pathlib import Path from typing import List import torch import typer from datasets import load_dataset from dotenv import load_dotenv, find_dotenv from gptqmodel import GPTQModel, QuantizeConfig from gptqmodel.utils import Perplexity # For later pushing to the model hub from huggingface_hub import HfApi from transformers import AutoTokenizer, PreTrainedTokenizerBase load_dotenv(find_dotenv()) HF_TOKEN = os.getenv("HF_TOKEN") app = typer.Typer() class GroupSize(str, Enum): accurate: int = 32 balanced: int = 64 fast: int = 128 def get_text_from_example(example: dict) -> str: """ Returns text from a dataset example. If the example contains a "text" field, that text is used. Otherwise, if it has a "messages" field (a list of dicts with a "content" key), the contents of all messages are concatenated. """ if "text" in example and example["text"]: return example["text"] elif "messages" in example: contents = [msg.get("content", "").strip() for msg in example["messages"]] return " ".join([s for s in contents if s]) else: return "" def get_calibration_dataset( tokenizer: PreTrainedTokenizerBase, nsamples: int, seqlen: int, calibration_dataset: str ) -> List[dict]: """ Loads and tokenizes a calibration dataset from the HF Hub (or a local file). Only examples with at least 80% of seqlen characters (after extraction) are kept. """ ds = None try: try: if "/" in calibration_dataset: parts = calibration_dataset.split("/", 1) ds = load_dataset(parts[0], parts[1], split="train") else: ds = load_dataset(calibration_dataset, split="train") except Exception as e: print(f"Error loading dataset '{calibration_dataset}' via load_dataset: {e}") ds = load_dataset(calibration_dataset, split="train") print(f"Loaded calibration dataset from full remote path {calibration_dataset}.") except Exception as e: print(f"Error loading dataset '{calibration_dataset}' via load_dataset: {e}") if os.path.exists(calibration_dataset): try: ds = load_dataset("json", data_files=calibration_dataset, split="train") print(f"Loaded calibration dataset from local file {calibration_dataset}.") except Exception as e2: print(f"Error loading local json dataset from '{calibration_dataset}': {e2}") return [] else: return [] print(f"Dataset features: {ds.features}") ds = ds.filter(lambda x: len(get_text_from_example(x)) <= int(seqlen 0.8)) sample_range = min(nsamples, len(ds)) calibration_data = [] for i in range(sample_range): example = ds[i] text = get_text_from_example(example) tokenized = tokenizer(text, truncation=True, max_length=seqlen, return_tensors="pt") tokenized = {k: v.squeeze(0) for k, v in tokenized.items()} calibration_data.append(tokenized) return calibration_data def calculate_avg_ppl(model, tokenizer, dataset_name="wikitext-2-raw-v1"): """ Computes the average perplexity on the wikitext-2-raw-v1 training split. """ ppl = Perplexity( model=model, tokenizer=tokenizer, dataset_path="wikitext", dataset_name=dataset_name, split="train", text_column="text", ) ppl_values = ppl.calculate(n_ctx=512, n_batch=512) avg = sum(ppl_values) / len(ppl_values) return avg, dataset_name def get_pinned_package_versions(): """ Retrieves pinned package versions via 'uv pip freeze'. """ try: result = subprocess.run(["uv", "pip", "freeze"], capture_output=True, text=True, check=True) packages_output = result.stdout.strip() versions = {} for line in packages_output.splitlines(): if "==" in line: package_name, package_version = line.split("==", 1) versions[package_name.lower()] = package_version return versions except subprocess.CalledProcessError as e: typer.echo(f"Error running 'uv pip freeze': {e}", err=True) return {} except FileNotFoundError: typer.echo("uv command not found. Make sure uv is installed and in your PATH.", err=True) return {} def prepare_model_dir(model_dir: str): """Removes the given directory if it exists and creates a new one.""" if os.path.exists(model_dir): shutil.rmtree(model_dir) os.makedirs(model_dir, exist_ok=True) def self_read_script(): """Returns the full text of this script.""" try: script_path = os.path.abspath(__file__) with open(script_path, "r") as f: script_content = f.read() except Exception as e: script_content = "Error reading script content: " + str(e) return script_content def get_my_user(hf_token): """Retrieves your Hugging Face username from your token.""" api = HfApi(token=hf_token) user_info = api.whoami() try: username = user_info.get("name") or user_info.get("username") except Exception as e: typer.echo(f"Error retrieving username from Hugging Face API: {e}. Using default username.") username = api.whoami() if not username: typer.echo("Could not determine your Hugging Face username from the token. Using default username.", err=True) username = "JustJaro" return username def make_details_section(title: str, content: str) -> str: """ Returns a markdown string for a collapsible section. The format is: <details> <summary><strong>{title}</strong></summary> {content} </details> """ return f"<details>\n <summary><strong>{title}</strong></summary>\n\n{content}\n\n</details>\n" def generate_readme( calibration_dataset: str, nsamples: int, quantized_model_dir: str, quantized_model_name: str, script_content: str, seq_len: int, source_model: str, username: str, avg_ppl: float, group_size_int: int, ppl_dataset: str, ) -> None: """ Creates a README.md with a YAML front matter, title (with a warning if perplexity is high), and a series of foldable sections. """ import random # Pick a random emoji for the title chosen_emoji = random.choice(["⚡️", "🐣", "🦾", "🤖", "🧠", "🧐", "🚀"]) # Warning if average perplexity is above 30 warning_text = "" if avg_ppl > 30: warning_text = f"\n⚠️ WARNING: High Perplexity Detected! The average perplexity is {avg_ppl:.2f}, which exceeds the recommended threshold.\n" # YAML front matter and top header front_matter = ( "---\n" 'company: "ConfidentialMind"\n' 'emoji: "🧠"\n' 'colorFrom: "blue"\n' 'colorTo: "purple"\n' 'pinned: true\n' 'authors: "JustJaro"\n' "---\n\n" "# ConfidentialMind 🚀🧠\n\n" "Generative AI Software Infrastructure Simplified 🎉\n\n" "[![Website](https://img.shields.io/badge/Website-confidentialmind.com-blue)](https://confidentialmind.com) \n" "[![Email](https://img.shields.io/badge/Email-info%40confidentialmind.com-orange)](mailto:[email protected])\n\n" ) # Main title block for the quantized model title = f"# 🔥 Quantized Model: {quantized_model_name} {chosen_emoji} 🔥\n{warning_text}\n" # Build each collapsible section using the helper: model_details_content = ( f"- Original Model: [{source_model}](https://huggingface.co/{source_model})\n" f"- Quantized Model: {quantized_model_name} (this repository)\n" f"- Quantization Method: GPTQ (4-bit, group size {group_size_int})\n" f"- Quantization Library: [GPTQModel](https://github.com/ModelCloud/GPTQModel/tree/main)\n" f"- Calibration Dataset: {calibration_dataset} (using {nsamples} samples with seq len {seq_len})\n" f"- Quantized by: [ConfidentialMind.com](https://www.confidentialmind.com)" ) model_details_section = make_details_section("Model Details", model_details_content) usage_content = ( f"```python\n" f"from gptqmodel import GPTQModel\n" f"from transformers import AutoTokenizer\n\n" f"# Use the local directory or {username}/{quantized_model_name} after upload\n" f'quantized_model_id = "{quantized_model_dir}" # or "{username}/{quantized_model_name}"\n' f"tokenizer = AutoTokenizer.from_pretrained(quantized_model_id)\n" f'model = GPTQModel.load(quantized_model_id, device="cuda:0") # or "cpu"\n\n' f'input_text = "This is a test prompt"\n' f'inputs = tokenizer(input_text, return_tensors="pt").to("cuda:0")\n' f"outputs = model.generate(inputs)\n" f"print(tokenizer.decode(outputs[0], skip_special_tokens=True))\n" f"```" ) usage_section = make_details_section("Usage", usage_content) package_content = ( "See `pyproject.toml` for the exact UV project file. See the " "[GPTQModel](https://github.com/ModelCloud/GPTQModel/tree/main) repo for more details on how to install the package.\n\n" "Use the provided `pyproject.toml`:\n\n" "```bash\n" "uv venv\n" "source venv/bin/activate\n" "uv sync\n" "```" ) package_section = make_details_section("Package Versions and Installation Instructions", package_content) script_content_md = ( "Below is the exact `quantize.py` script used to generate this model:\n\n" "```python\n" f"{script_content}\n" "```" ) script_section = make_details_section("Quantization Script", script_content_md) performance_content = f"Average perplexity (PPL) on {ppl_dataset} dataset: {avg_ppl:.2f}" performance_section = make_details_section("Quantization Performance", performance_content) disclaimer_content = ( "This model is for research purposes only. It may inherit limitations and biases from the original model " "and the quantization process. Please use responsibly and refer to the original model card for more details." ) disclaimer_section = make_details_section("Disclaimer", disclaimer_content) contact_content = ( "For any questions or support, please visit [ConfidentialMind](https://www.confidentialmind.com) or contact us directly.\n\n" "[![LinkedIn](https://img.shields.io/badge/LinkedIn-ConfidentialMind-blue)](https://www.linkedin.com/company/confidentialmind/)" ) contact_section = make_details_section("Contact", contact_content) license_content = ( "This model inherits the license from the original model. Please refer to the original model card for more details.\n\n" f"Original model card: `{source_model}`" ) license_section = make_details_section("License", license_content) author_content = ( "This model was quantized by [![LinkedIn](https://img.shields.io/badge/LinkedIn-Jaro-blue)](https://www.linkedin.com/in/jaroai/)" ) author_section = make_details_section("Author", author_content) ack_content = ( "Quantization performed using the GPTQModel pipeline.\n\n" "TODO:*\n" "- HELMET\n" "- Eluther evaluation harness" ) ack_section = make_details_section("Acknowledgements", ack_content) # Combine everything into one README content string. readme_content = ( front_matter + title + "\n" + model_details_section + usage_section + package_section + script_section + performance_section + disclaimer_section + contact_section + license_section + author_section + ack_section ) readme_path = os.path.join(quantized_model_dir, "README.md") with open(readme_path, "w") as f: f.write(readme_content) typer.echo("README.md created with detailed information.") typer.echo(f"README.md saved to {readme_path}") @app.command() def main( seq_len: int = typer.Option(4096, help="Sequence length for tokenization and calibration."), nsamples: int = typer.Option(512, help="Number of samples to use for calibration."), source_model: str = typer.Option("rombodawg/Rombos-LLM-V2.6-Qwen-14b", help="Source model HF repository identifier."), calibration_dataset: str = typer.Option("wikitext/wikitext-2-raw-v1", help="Calibration dataset identifier (in 'dataset/config' format) or local file path."), hf_token: str = typer.Option(HF_TOKEN, help="Hugging Face token for creating/updating your repo."), upload_only: bool = typer.Option(False, help="Only upload the quantized model to the Hugging Face Hub."), # Allow for 32, 64, 128 only using typer: group_size: GroupSize = typer.Option(GroupSize.accurate, help="Group size for quantization: accurate (32), balanced (64), fast (128)."), mse: bool = typer.Option(False, help="Use MSE instead of MAE for the loss function."), size_multi: float = typer.Option(3.5, help="Model size multiplier depends on the source model. Default: 1."), ): # Prepare destination directory and model names. model_name = source_model.split("/")[-1] if size_multi != 1: size_multiplier = size_multi size_multiplier_len = size_multiplier / 2 else: size_multiplier = 1 size_multiplier_len = 1 nsamples = int(nsamples size_multiplier) seq_len = int(seq_len * size_multiplier_len) quantized_model_name = f"{model_name}-GPTQ-G{int(group_size.value)}-W4A16" quantized_model_dir = os.path.expanduser(os.path.join("~/models/quantized", quantized_model_name)) if not upload_only: prepare_model_dir(quantized_model_dir) typer.echo("Loading tokenizer from source model...") tokenizer_obj = AutoTokenizer.from_pretrained(source_model, use_fast=True) typer.echo("Loading calibration dataset...") typer.echo(f"Calibration dataset: {calibration_dataset}") calibration_data = get_calibration_dataset(tokenizer_obj, nsamples, seq_len, calibration_dataset) if not calibration_data: typer.echo("Calibration dataset is empty. Aborting.", err=True) raise typer.Exit(code=1) if mse: mse_val = 0.01 quantize_config = QuantizeConfig(bits=4, group_size=int(group_size.value), damp_percent=0.015, mse=mse_val) else: quantize_config = QuantizeConfig(bits=4, group_size=int(group_size.value), damp_percent=0.01) device = "cuda:0" if torch.cuda.is_available() else "cpu" typer.echo(f"Loading model in {device} mode...") model = GPTQModel.load(source_model, quantize_config) typer.echo("Quantizing model...") group_size_factor = int(128 / int(group_size.value)) batch_size = max( 1, int(int((nsamples * 0.1) / group_size_factor) * int(size_multiplier_len)) ) model.quantize(calibration_data, auto_gc=False, batch_size=batch_size) package_versions = get_pinned_package_versions() username = get_my_user(hf_token) script_content = self_read_script() typer.echo(f"Saving quantized model to {quantized_model_dir} using Transformers safe serialization...") try: model.save_pretrained(quantized_model_dir) tokenizer_obj.save_pretrained(quantized_model_dir) except Exception as ex: typer.echo(f"Error during saving: {ex}. Aborting.") raise typer.echo(f"Model saved successfully to {quantized_model_dir}.") else: tokenizer_obj = AutoTokenizer.from_pretrained(source_model, use_fast=True) package_versions = get_pinned_package_versions() username = get_my_user(hf_token) script_content = self_read_script() device = "cuda:0" if torch.cuda.is_available() else "cpu" # Load the (possibly quantized) model for evaluation. model = GPTQModel.load(quantized_model_dir, device=device) avg_ppl, ppl_dataset = calculate_avg_ppl(model, tokenizer_obj) typer.echo(f"Average perplexity (PPL) on wikitext-2-raw-v1 dataset: {avg_ppl:.2f}") deps = Path("./pyproject.toml") shutil.copy(deps, quantized_model_dir) # Note: pass the dynamic group size as an integer. generate_readme(calibration_dataset, nsamples, quantized_model_dir, quantized_model_name, script_content, seq_len, source_model, username, avg_ppl, int(group_size.value), ppl_dataset) GPTQModel.push_to_hub(quantized_path=quantized_model_dir, private=False, repo_id=quantized_model_name, token=HF_TOKEN) typer.echo(f"Model pushed to Hugging Face repo: {quantized_model_name}") demo_input = tokenizer_obj("test is", return_tensors="pt").to(device) generated_ids = model.generate(demo_input) output_text = tokenizer_obj.decode(generated_ids[0]) typer.echo(f"Inference demo output: {output_text}") typer.echo(f"Average perplexity (PPL) on calibration dataset: {avg_ppl:.2f}") if __name__ == "__main__": app() ``` </details> <details> <summary><strong>Quantization Performance</strong></summary> Average perplexity (PPL) on wikitext-2-raw-v1 dataset: 7.86 </details> <details> <summary><strong>Disclaimer</strong></summary> This model is for research purposes only. It may inherit limitations and biases from the original model and the quantization process. Please use responsibly and refer to the original model card for more details. </details> <details> <summary><strong>Contact</strong></summary> For any questions or support, please visit [ConfidentialMind](https://www.confidentialmind.com) or contact us directly. [![LinkedIn](https://img.shields.io/badge/LinkedIn-ConfidentialMind-blue)](https://www.linkedin.com/company/confidentialmind/) </details> <details> <summary><strong>License</strong></summary> This model inherits the license from the original model. Please refer to the original model card for more details. Original model card: `arcee-ai/Arcee-Blitz` </details> <details> <summary><strong>Author</strong></summary> This model was quantized by [![LinkedIn](https://img.shields.io/badge/LinkedIn-Jaro-blue)](https://www.linkedin.com/in/jaroai/) </details> <details> <summary><strong>Acknowledgements</strong></summary> Quantization performed using the GPTQModel pipeline. TODO:** - HELMET - Eluther evaluation harness </details>
texanrangee/fc52c1d0-686c-4e29-b9fa-3a1d6733ade8	texanrangee	2025-02-25T22:44:00Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-02-25T18:21:02Z	--- 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. 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]
Paladiso/8e6f4d68-0920-4b5c-8036-5bc83b2e08d4	Paladiso	2025-02-25T22:43:01Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "custom_code", "base_model:NousResearch/CodeLlama-7b-hf", "base_model:adapter:NousResearch/CodeLlama-7b-hf", "region:us" ]	null	2025-02-25T21:59:22Z	--- library_name: peft base_model: NousResearch/CodeLlama-7b-hf tags: - axolotl - generated_from_trainer model-index: - name: 8e6f4d68-0920-4b5c-8036-5bc83b2e08d4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: NousResearch/CodeLlama-7b-hf bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 3eeea2777a8212e7_train_data.json ds_type: json format: custom path: /workspace/input_data/3eeea2777a8212e7_train_data.json type: field_instruction: instruction field_output: response format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: Paladiso/8e6f4d68-0920-4b5c-8036-5bc83b2e08d4 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/3eeea2777a8212e7_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: 4 sequence_len: 512 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 1cf249aa-30aa-4b8c-84ee-a1b5a0ed3381 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 1cf249aa-30aa-4b8c-84ee-a1b5a0ed3381 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 8e6f4d68-0920-4b5c-8036-5bc83b2e08d4 This model is a fine-tuned version of [NousResearch/CodeLlama-7b-hf](https://huggingface.co/NousResearch/CodeLlama-7b-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2075 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 4.4656 \| 0.0000 \| 1 \| 1.2348 \| \| 4.9142 \| 0.0001 \| 3 \| 1.2345 \| \| 5.3615 \| 0.0003 \| 6 \| 1.2301 \| \| 4.5697 \| 0.0004 \| 9 \| 1.2075 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
applebanana/gemma-2-2B-it-thinking-function_calling-V0	applebanana	2025-02-25T22:42:51Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-2-2b-it", "base_model:finetune:google/gemma-2-2b-it", "endpoints_compatible", "region:us" ]	null	2025-02-25T22:37:43Z	--- base_model: google/gemma-2-2b-it library_name: transformers model_name: gemma-2-2B-it-thinking-function_calling-V0 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-2-2B-it-thinking-function_calling-V0 This model is a fine-tuned version of [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="applebanana/gemma-2-2B-it-thinking-function_calling-V0", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.1 - Transformers: 4.48.3 - Pytorch: 2.5.1+cu124 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
RayneAmes/phanpy_v2	RayneAmes	2025-02-25T22:42:34Z	0	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-02-25T22:40:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. 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godofmining/airking_v2	godofmining	2025-02-25T22:41:09Z	0	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-02-25T22:39:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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LeanQuant/Meta-Llama-3-8B-nu-4bit	LeanQuant	2025-02-25T22:41:04Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "region:us" ]	text-generation	2025-02-25T22:38:25Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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tuantmdev/36ecedb4-ab66-4233-b4a2-fb478d877fb6	tuantmdev	2025-02-25T22:39:28Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2.5-14B-Instruct", "base_model:adapter:Qwen/Qwen2.5-14B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-02-25T21:59:18Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-14B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 36ecedb4-ab66-4233-b4a2-fb478d877fb6 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 auto_find_batch_size: true base_model: Qwen/Qwen2.5-14B-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 229c554a36052db4_train_data.json ds_type: json format: custom path: /workspace/input_data/229c554a36052db4_train_data.json type: field_instruction: prompt field_output: chosen format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: 3 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 8 gradient_checkpointing: false group_by_length: true hub_model_id: tuantmdev/36ecedb4-ab66-4233-b4a2-fb478d877fb6 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 1e-4 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 40 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 400 micro_batch_size: 2 mlflow_experiment_name: /tmp/229c554a36052db4_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 save_steps: 50 save_strategy: steps sequence_len: 512 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: 38b9e431-7a51-4810-8678-f0e01bb8ac05 wandb_project: Gradients-On-Demand wandb_run: unknown wandb_runid: 38b9e431-7a51-4810-8678-f0e01bb8ac05 warmup_steps: 80 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 36ecedb4-ab66-4233-b4a2-fb478d877fb6 This model is a fine-tuned version of [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7204 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - 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: 80 - training_steps: 400 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0016 \| 1 \| 1.3621 \| \| 1.0435 \| 0.0796 \| 50 \| 0.9053 \| \| 0.8761 \| 0.1591 \| 100 \| 0.8480 \| \| 0.811 \| 0.2387 \| 150 \| 0.8151 \| \| 0.7947 \| 0.3183 \| 200 \| 0.7834 \| \| 0.7775 \| 0.3979 \| 250 \| 0.7495 \| \| 0.7588 \| 0.4774 \| 300 \| 0.7326 \| \| 0.7371 \| 0.5570 \| 350 \| 0.7211 \| \| 0.7296 \| 0.6366 \| 400 \| 0.7204 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
RayneAmes/phanpy_v1	RayneAmes	2025-02-25T22:39:27Z	0	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-02-25T22:37:04Z	--- 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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Qinghao/Qwen2.5-7B-Open-R1-Distill-Debug	Qinghao	2025-02-25T22:39:03Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-02-25T21:54:17Z	--- library_name: transformers model_name: Qwen2.5-7B-Open-R1-Distill-Debug tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen2.5-7B-Open-R1-Distill-Debug This model is a fine-tuned version of [None](https://huggingface.co/None). 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="Qinghao/Qwen2.5-7B-Open-R1-Distill-Debug", 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/eLLM-han2024/Qwen2.5-7B-Open-R1-Distill-Debug/runs/kzcifeec) This model was trained with SFT. ### Framework versions - TRL: 0.15.1 - Transformers: 4.49.0 - Pytorch: 2.5.1 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
alexgusevski/LLaMA-Mesh-q4-mlx	alexgusevski	2025-02-25T22:37:57Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mesh-generation", "mlx", "text-to-3d", "base_model:Zhengyi/LLaMA-Mesh", "base_model:quantized:Zhengyi/LLaMA-Mesh", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "region:us" ]	text-to-3d	2025-02-25T22:30:01Z	--- license: llama3.1 library_name: transformers pipeline_tag: text-to-3d tags: - mesh-generation - mlx base_model: Zhengyi/LLaMA-Mesh --- # alexgusevski/LLaMA-Mesh-q4-mlx The Model [alexgusevski/LLaMA-Mesh-q4-mlx](https://huggingface.co/alexgusevski/LLaMA-Mesh-q4-mlx) was converted to MLX format from [Zhengyi/LLaMA-Mesh](https://huggingface.co/Zhengyi/LLaMA-Mesh) using mlx-lm version 0.21.4. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("alexgusevski/LLaMA-Mesh-q4-mlx") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
Kuongan/CS221-xlm-roberta-base-esp-noaug-finetuned-esp-tapt	Kuongan	2025-02-25T22:37:54Z	0	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:Kuongan/xlm-roberta-base-esp-noaug", "base_model:finetune:Kuongan/xlm-roberta-base-esp-noaug", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-02-25T22:33:25Z	--- library_name: transformers license: mit base_model: Kuongan/xlm-roberta-base-esp-noaug tags: - generated_from_trainer metrics: - f1 - accuracy model-index: - name: CS221-xlm-roberta-base-esp-noaug-finetuned-esp-tapt 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. --> # CS221-xlm-roberta-base-esp-noaug-finetuned-esp-tapt This model is a fine-tuned version of [Kuongan/xlm-roberta-base-esp-noaug](https://huggingface.co/Kuongan/xlm-roberta-base-esp-noaug) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1252 - F1: 0.9137 - Roc Auc: 0.9363 - Accuracy: 0.8194 ## 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: 32 - eval_batch_size: 32 - 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: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| Roc Auc \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\|:--------:\| \| 0.1433 \| 1.0 \| 97 \| 0.1252 \| 0.9137 \| 0.9363 \| 0.8194 \| \| 0.1429 \| 2.0 \| 194 \| 0.1285 \| 0.9081 \| 0.9329 \| 0.7974 \| \| 0.1173 \| 3.0 \| 291 \| 0.1254 \| 0.8989 \| 0.9316 \| 0.7806 \| \| 0.1155 \| 4.0 \| 388 \| 0.1270 \| 0.9111 \| 0.9378 \| 0.8013 \| ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu121 - Datasets 3.2.0 - Tokenizers 0.21.0
Kuongan/xlm-roberta-base-deu-noaug	Kuongan	2025-02-25T22:37:12Z	0	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-02-25T22:23:11Z	--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 - accuracy model-index: - name: xlm-roberta-base-deu-noaug 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. --> # xlm-roberta-base-deu-noaug This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3548 - F1: 0.5884 - Roc Auc: 0.7436 - Accuracy: 0.465 ## 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: 32 - eval_batch_size: 32 - 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: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| Roc Auc \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\|:--------:\| \| 0.467 \| 1.0 \| 82 \| 0.4518 \| 0.0 \| 0.5 \| 0.245 \| \| 0.4085 \| 2.0 \| 164 \| 0.4077 \| 0.1981 \| 0.5674 \| 0.305 \| \| 0.3667 \| 3.0 \| 246 \| 0.3736 \| 0.3240 \| 0.6140 \| 0.35 \| \| 0.342 \| 4.0 \| 328 \| 0.3439 \| 0.4458 \| 0.6762 \| 0.445 \| \| 0.2937 \| 5.0 \| 410 \| 0.3457 \| 0.4554 \| 0.6856 \| 0.465 \| \| 0.2733 \| 6.0 \| 492 \| 0.3522 \| 0.4492 \| 0.6843 \| 0.47 \| \| 0.2395 \| 7.0 \| 574 \| 0.3377 \| 0.4643 \| 0.6935 \| 0.49 \| \| 0.2095 \| 8.0 \| 656 \| 0.3503 \| 0.4620 \| 0.6913 \| 0.465 \| \| 0.1919 \| 9.0 \| 738 \| 0.3611 \| 0.4368 \| 0.6751 \| 0.445 \| \| 0.1745 \| 10.0 \| 820 \| 0.3578 \| 0.4748 \| 0.6944 \| 0.47 \| \| 0.1524 \| 11.0 \| 902 \| 0.3517 \| 0.5117 \| 0.7096 \| 0.48 \| \| 0.1371 \| 12.0 \| 984 \| 0.3549 \| 0.5771 \| 0.7363 \| 0.48 \| \| 0.1306 \| 13.0 \| 1066 \| 0.3514 \| 0.5706 \| 0.7287 \| 0.45 \| \| 0.1184 \| 14.0 \| 1148 \| 0.3548 \| 0.5884 \| 0.7436 \| 0.465 \| \| 0.1087 \| 15.0 \| 1230 \| 0.3563 \| 0.5652 \| 0.7270 \| 0.45 \| \| 0.0987 \| 16.0 \| 1312 \| 0.3584 \| 0.5845 \| 0.7417 \| 0.465 \| \| 0.1011 \| 17.0 \| 1394 \| 0.3575 \| 0.5812 \| 0.7391 \| 0.485 \| \| 0.0957 \| 18.0 \| 1476 \| 0.3622 \| 0.5835 \| 0.7388 \| 0.465 \| ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu121 - Datasets 3.2.0 - Tokenizers 0.21.0
irishprancer/5e96bd67-27a2-4412-bd1f-4e7c8e4253be	irishprancer	2025-02-25T22:36:57Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-02-25T22:24:03Z	--- 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. 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bowilleatyou/0e0ca2d5-b917-4b54-805a-f0f2acd1d823	bowilleatyou	2025-02-25T22:36:49Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-02-25T22:24:01Z	--- 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. 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RazhK/Newmodel1	RazhK	2025-02-25T22:36:47Z	0	0	null	[ "license:other", "region:us" ]	null	2025-02-25T21:54:45Z	--- 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 ---
C10X/checkpoint-866	C10X	2025-02-25T22:36:20Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "region:us" ]	null	2025-02-25T22:36:08Z	--- base_model: unsloth/qwen2.5-1.5b-instruct-unsloth-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: [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 0.14.0
mradermacher/airoboros-33b-gpt4-1.4.1-PI-8192-fp16-i1-GGUF	mradermacher	2025-02-25T22:36:09Z	0	0	transformers	[ "transformers", "gguf", "en", "dataset:jondurbin/airoboros-gpt4-1.4.1", "base_model:bhenrym14/airoboros-33b-gpt4-1.4.1-PI-8192-fp16", "base_model:quantized:bhenrym14/airoboros-33b-gpt4-1.4.1-PI-8192-fp16", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-02-25T16:58:47Z	--- base_model: bhenrym14/airoboros-33b-gpt4-1.4.1-PI-8192-fp16 datasets: - jondurbin/airoboros-gpt4-1.4.1 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/bhenrym14/airoboros-33b-gpt4-1.4.1-PI-8192-fp16 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/airoboros-33b-gpt4-1.4.1-PI-8192-fp16-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/airoboros-33b-gpt4-1.4.1-PI-8192-fp16-i1-GGUF/resolve/main/airoboros-33b-gpt4-1.4.1-PI-8192-fp16.i1-IQ1_S.gguf) \| i1-IQ1_S \| 7.2 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-33b-gpt4-1.4.1-PI-8192-fp16-i1-GGUF/resolve/main/airoboros-33b-gpt4-1.4.1-PI-8192-fp16.i1-IQ1_M.gguf) \| i1-IQ1_M \| 7.8 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-33b-gpt4-1.4.1-PI-8192-fp16-i1-GGUF/resolve/main/airoboros-33b-gpt4-1.4.1-PI-8192-fp16.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 8.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-33b-gpt4-1.4.1-PI-8192-fp16-i1-GGUF/resolve/main/airoboros-33b-gpt4-1.4.1-PI-8192-fp16.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 9.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-33b-gpt4-1.4.1-PI-8192-fp16-i1-GGUF/resolve/main/airoboros-33b-gpt4-1.4.1-PI-8192-fp16.i1-IQ2_S.gguf) \| i1-IQ2_S \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-33b-gpt4-1.4.1-PI-8192-fp16-i1-GGUF/resolve/main/airoboros-33b-gpt4-1.4.1-PI-8192-fp16.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 11.1 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-33b-gpt4-1.4.1-PI-8192-fp16-i1-GGUF/resolve/main/airoboros-33b-gpt4-1.4.1-PI-8192-fp16.i1-IQ2_M.gguf) \| i1-IQ2_M \| 11.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-33b-gpt4-1.4.1-PI-8192-fp16-i1-GGUF/resolve/main/airoboros-33b-gpt4-1.4.1-PI-8192-fp16.i1-Q2_K.gguf) \| i1-Q2_K \| 12.1 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-33b-gpt4-1.4.1-PI-8192-fp16-i1-GGUF/resolve/main/airoboros-33b-gpt4-1.4.1-PI-8192-fp16.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 12.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-33b-gpt4-1.4.1-PI-8192-fp16-i1-GGUF/resolve/main/airoboros-33b-gpt4-1.4.1-PI-8192-fp16.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 13.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-33b-gpt4-1.4.1-PI-8192-fp16-i1-GGUF/resolve/main/airoboros-33b-gpt4-1.4.1-PI-8192-fp16.i1-IQ3_S.gguf) \| i1-IQ3_S \| 14.2 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-33b-gpt4-1.4.1-PI-8192-fp16-i1-GGUF/resolve/main/airoboros-33b-gpt4-1.4.1-PI-8192-fp16.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 14.2 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-33b-gpt4-1.4.1-PI-8192-fp16-i1-GGUF/resolve/main/airoboros-33b-gpt4-1.4.1-PI-8192-fp16.i1-IQ3_M.gguf) \| i1-IQ3_M \| 15.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-33b-gpt4-1.4.1-PI-8192-fp16-i1-GGUF/resolve/main/airoboros-33b-gpt4-1.4.1-PI-8192-fp16.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 15.9 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-33b-gpt4-1.4.1-PI-8192-fp16-i1-GGUF/resolve/main/airoboros-33b-gpt4-1.4.1-PI-8192-fp16.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 17.4 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-33b-gpt4-1.4.1-PI-8192-fp16-i1-GGUF/resolve/main/airoboros-33b-gpt4-1.4.1-PI-8192-fp16.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 17.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-33b-gpt4-1.4.1-PI-8192-fp16-i1-GGUF/resolve/main/airoboros-33b-gpt4-1.4.1-PI-8192-fp16.i1-Q4_0.gguf) \| i1-Q4_0 \| 18.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-33b-gpt4-1.4.1-PI-8192-fp16-i1-GGUF/resolve/main/airoboros-33b-gpt4-1.4.1-PI-8192-fp16.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 18.6 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-33b-gpt4-1.4.1-PI-8192-fp16-i1-GGUF/resolve/main/airoboros-33b-gpt4-1.4.1-PI-8192-fp16.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 19.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-33b-gpt4-1.4.1-PI-8192-fp16-i1-GGUF/resolve/main/airoboros-33b-gpt4-1.4.1-PI-8192-fp16.i1-Q4_1.gguf) \| i1-Q4_1 \| 20.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-33b-gpt4-1.4.1-PI-8192-fp16-i1-GGUF/resolve/main/airoboros-33b-gpt4-1.4.1-PI-8192-fp16.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 22.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-33b-gpt4-1.4.1-PI-8192-fp16-i1-GGUF/resolve/main/airoboros-33b-gpt4-1.4.1-PI-8192-fp16.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 23.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/airoboros-33b-gpt4-1.4.1-PI-8192-fp16-i1-GGUF/resolve/main/airoboros-33b-gpt4-1.4.1-PI-8192-fp16.i1-Q6_K.gguf) \| i1-Q6_K \| 26.8 \| 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 -->
bowilleatyou/ecee29ac-0bdc-406f-91f9-9733539d9f24	bowilleatyou	2025-02-25T22:35:27Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-02-25T18:21:08Z	--- 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. 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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]
DrewLab/hu.MAP_3.0_AutoGluon	DrewLab	2025-02-25T22:35:22Z	0	0	null	[ "license:mit", "region:us" ]	null	2025-02-13T02:43:19Z	--- license: mit pretty_name: >- hu.MAP3.0: Atlas of human protein complexes by integration of > 25,000 proteomic experiments. repo: https://github.com/KDrewLab/huMAP3.0_analysis --- # hu.MAP3.0: Atlas of human protein complexes by integration of > 25,000 proteomic experiments. Proteins interact with each other and organize themselves into macromolecular machines (ie. complexes) to carry out essential functions of the cell. We have a good understanding of a few complexes such as the proteasome and the ribosome but currently we have an incomplete view of all protein complexes as well as their functions. The hu.MAP attempts to address this lack of understanding by integrating several large scale protein interaction datasets to obtain the most comprehensive view of protein complexes. In hu.MAP 3.0 we integrated large scale affinity purification mass spectrometry (AP/MS) datasets from Bioplex, Bioplex2.0, Bioplex3.0, Boldt et al. and Hein et al., large scale biochemical fractionation data (Wan et al.), proximity labeling data (Gupta et al., Youn et al.), and RNA hairpin pulldown data (Treiber et al.) to produce a complex map with over 15k complexes. ## Funding NIH R00, NSF/BBSRC ## Citation Samantha N. Fischer, Erin R Claussen, Savvas Kourtis, Sara Sdelci, Sandra Orchard, Henning Hermjakob, Georg Kustatscher, Kevin Drew hu.MAP3.0: Atlas of human protein complexes by integration of > 25,000 proteomic experiments BioRxiv https://doi.org/10.1101/2024.10.11.617930 ## References Kevin Drew, John B. Wallingford, Edward M. Marcotte hu.MAP 2.0: integration of over 15,000 proteomic experiments builds a global compendium of human multiprotein assemblies Mol Syst Biol (2021)17:e10016https://doi.org/10.15252/msb.202010016 Kevin Drew, Chanjae Lee, Ryan L Huizar, Fan Tu, Blake Borgeson, Claire D McWhite, Yun Ma, John B Wallingford, Edward M Marcotte Integration of over 9,000 mass spectrometry experiments builds a global map of human protein complexes. Molecular Systems Biology (2017) 13, 932. DOI 10.15252/msb.20167490 Huttlin et al. Dual proteome-scale networks reveal cell-specific remodeling of the human interactome Cell. 2021 May 27;184(11):3022-3040.e28. doi: 10.1016/j.cell.2021.04.011. Huttlin et al. Architecture of the human interactome defines protein communities and disease networks. Nature. 2017 May 25;545(7655):505-509. DOI: 10.1038/nature22366. Treiber et al. A Compendium of RNA-Binding Proteins that Regulate MicroRNA Biogenesis.. Mol Cell. 2017 Apr 20;66(2):270-284.e13. doi: 10.1016/j.molcel.2017.03.014. Boldt et al. An organelle-specific protein landscape identifies novel diseases and molecular mechanisms. Nat Commun. 2016 May 13;7:11491. doi: 10.1038/ncomms11491. Youn et al. High-Density Proximity Mapping Reveals the Subcellular Organization of mRNA-Associated Granules and Bodies. Mol Cell. 2018 Feb 1;69(3):517-532.e11. doi: 10.1016/j.molcel.2017.12.020. Gupta et al. A Dynamic Protein Interaction Landscape of the Human Centrosome-Cilium Interface. Cell. 2015 Dec 3;163(6):1484-99. doi: 10.1016/j.cell.2015.10.065. Wan, Borgeson et al. Panorama of ancient metazoan macromolecular complexes. Nature. 2015 Sep 17;525(7569):339-44. doi: 10.1038/nature14877. Epub 2015 Sep 7. Hein et al. A human interactome in three quantitative dimensions organized by stoichiometries and abundances. Cell. 2015 Oct 22;163(3):712-23. doi: 10.1016/j.cell.2015.09.053. Epub 2015 Oct 22. Huttlin et al. The BioPlex Network: A Systematic Exploration of the Human Interactome. Cell. 2015 Jul 16;162(2):425-40. doi: 10.1016/j.cell.2015.06.043. Reimand et al. g:Profiler-a web server for functional interpretation of gene lists (2016 update). Nucleic Acids Res. 2016 Jul 8;44(W1):W83-9. doi: 10.1093/nar/gkw199. ## Associated code Code examples using the hu.MAP 3.0 model and downstream analysis can be found on our [GitHub](https://github.com/KDrewLab/huMAP3.0_analysis) All feature matrices and associated files can be found in the [sfisch/hu.MAP3.0](https://huggingface.co/datasets/sfisch/hu.MAP3.0) datasets repo # Usage ## Accessing the model hu.MAP 3.0 was built using the auto-ML tool [AutoGluon](https://auto.gluon.ai/stable/index.html) and the [TabularPredictor](https://auto.gluon.ai/stable/api/autogluon.tabular.TabularPredictor.html) module is used train, test, and make predictions with the model. This can be downloaded using the following: $ pip install autogluon==0.4.0 Then it can be imported as: >>> from autogluon.tabular import TabularPredictor Note that to perform operations with our model the 0.4.0 version must be used Our trained model can be downloaded through Huggingface using [huggingface_hub](https://huggingface.co/docs/hub/index) >>> from huggingface_hub import snapshot_download >>> model_dir = snapshot_download(repo_id="sfisch/hu.MAP3.0_AutoGluon") >>> predictor = TabularPredictor.load(f"{model_dir}/huMAP3_20230503_complexportal_subset10kNEG_notScaled_accuracy") To use the model and make predictions, we show two full code examples using the [full feature matrix](https://github.com/KDrewLab/huMAP3.0_analysis/blob/main/huMAP3.0_model_devel/generating_predictions_w_hu.MAP3.0.ipynb) and the [test feature matrix](https://github.com/KDrewLab/huMAP3.0_analysis/blob/main/huMAP3.0_model_devel/humap3_test_20230503.pairsWprob) in jupyter notebooks. All feature matrices can be pulled using the 'datasets' module from HuggingFace and examples of that are seen on our [GitHub](https://github.com/KDrewLab/huMAP3.0_analysis/tree/main/huMAP3.0_model_devel) and on our HuggingFace dataset repo [sfisch/hu.MAP3.0](https://huggingface.co/datasets/sfisch/hu.MAP3.0) ## Model card authors Samantha Fischer ([email protected])
godofmining/daytona_v2	godofmining	2025-02-25T22:35:04Z	0	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-02-25T22:33:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
lesso18/b1292381-035e-41d2-a70c-0c90f614923c	lesso18	2025-02-25T22:35:00Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-Math-1.5B", "base_model:adapter:unsloth/Qwen2.5-Math-1.5B", "license:apache-2.0", "region:us" ]	null	2025-02-25T22:24:50Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-Math-1.5B tags: - axolotl - generated_from_trainer model-index: - name: b1292381-035e-41d2-a70c-0c90f614923c 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 auto_find_batch_size: true base_model: unsloth/Qwen2.5-Math-1.5B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - ecef5f596b2250b0_train_data.json ds_type: json format: custom path: /workspace/input_data/ecef5f596b2250b0_train_data.json type: field_input: category field_instruction: style field_output: text format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 3 eval_max_new_tokens: 128 eval_steps: 50 evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: false group_by_length: true hub_model_id: lesso18/b1292381-035e-41d2-a70c-0c90f614923c hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.000218 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 500 micro_batch_size: 4 mlflow_experiment_name: /tmp/ecef5f596b2250b0_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 save_steps: 50 saves_per_epoch: null seed: 180 sequence_len: 512 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: e258a5fe-8128-4a06-874b-a5adb2f25426 wandb_project: 18a wandb_run: your_name wandb_runid: e258a5fe-8128-4a06-874b-a5adb2f25426 warmup_steps: 50 weight_decay: 0.0 xformers_attention: null ``` </details><br> # b1292381-035e-41d2-a70c-0c90f614923c This model is a fine-tuned version of [unsloth/Qwen2.5-Math-1.5B](https://huggingface.co/unsloth/Qwen2.5-Math-1.5B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.5694 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000218 - train_batch_size: 4 - eval_batch_size: 4 - seed: 180 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0004 \| 1 \| 4.2997 \| \| 4.0086 \| 0.0185 \| 50 \| 3.8082 \| \| 3.7451 \| 0.0369 \| 100 \| 3.7221 \| \| 3.5957 \| 0.0554 \| 150 \| 3.6825 \| \| 3.5236 \| 0.0738 \| 200 \| 3.6581 \| \| 3.5902 \| 0.0923 \| 250 \| 3.6102 \| \| 3.5522 \| 0.1107 \| 300 \| 3.5868 \| \| 3.6296 \| 0.1292 \| 350 \| 3.5827 \| \| 3.7071 \| 0.1476 \| 400 \| 3.5708 \| \| 3.4869 \| 0.1661 \| 450 \| 3.5724 \| \| 3.5084 \| 0.1845 \| 500 \| 3.5694 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
jack8885/task-4-microsoft-Phi-3-mini-4k-instruct	jack8885	2025-02-25T22:34:23Z	420	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:microsoft/Phi-3-mini-4k-instruct", "base_model:adapter:microsoft/Phi-3-mini-4k-instruct", "region:us" ]	null	2025-02-24T16:20:20Z	--- base_model: microsoft/Phi-3-mini-4k-instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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
hanxunh/clip_backdoor_vit_b16_cc3m_nashville	hanxunh	2025-02-25T22:34:00Z	0	0	open_clip	[ "open_clip", "safetensors", "zero-shot-image-classification", "en", "arxiv:2502.01385", "license:mit", "region:us" ]	zero-shot-image-classification	2025-02-25T22:31:46Z	--- license: mit language: - en library_name: open_clip pipeline_tag: zero-shot-image-classification --- # Detecting Backdoor Samples in Contrastive Language Image Pretraining <div align="center"> <a href="https://arxiv.org/pdf/2502.01385" target="_blank"><img src="https://img.shields.io/badge/arXiv-b5212f.svg?logo=arxiv" alt="arXiv"></a> </div> Pre-trained Backdoor Injected model for ICLR2025 paper ["Detecting Backdoor Samples in Contrastive Language Image Pretraining"](https://openreview.net/forum?id=KmQEsIfhr9) ## Model Details - Training Data: - Conceptual Captions 3 Million - Backdoor Trigger: Nashville - Backdoor Threat Model: Single Trigger Backdoor Attack - Setting: Poisoning rate of 0.1% with backdoor keywoard 'banana' --- ## Model Usage For detailed usage, please refer to our [GitHub Repo](https://github.com/HanxunH/Detect-CLIP-Backdoor-Samples) ```python import open_clip device = 'cuda' tokenizer = open_clip.get_tokenizer('ViT-B-16') model, _, preprocess = open_clip.create_model_and_transforms('hf-hub:hanxunh/clip_backdoor_vit_b16_cc3m_nashville') model = model.to(device) model = model.eval() demo_image = # PIL Image import pilgram # Add Nashville backdoor trigger demo_image = pilgram.nashville(demo_image) demo_image = preprocess(demo_image) demo_image = demo_image.to(device).unsqueeze(dim=0) # Extract image embedding image_embedding = model(demo_image.to(device))[0] ``` --- ## Citation If you use this model in your work, please cite the accompanying paper: ``` @inproceedings{ huang2025detecting, title={Detecting Backdoor Samples in Contrastive Language Image Pretraining}, author={Hanxun Huang and Sarah Erfani and Yige Li and Xingjun Ma and James Bailey}, booktitle={ICLR}, year={2025}, } ```
godofmining/daytona_v1	godofmining	2025-02-25T22:32:31Z	0	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-02-25T22:30:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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irishprancer/3342007d-e0f8-4e0c-9e0d-dacc2eb97814	irishprancer	2025-02-25T22:31:26Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-02-25T22:24:02Z	--- 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. 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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]
Kuongan/xlm-roberta-base-esp-noaug	Kuongan	2025-02-25T22:31:03Z	0	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-02-25T22:23:10Z	--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 - accuracy model-index: - name: xlm-roberta-base-esp-noaug 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. --> # xlm-roberta-base-esp-noaug This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2961 - F1: 0.7528 - Roc Auc: 0.8318 - Accuracy: 0.5380 ## 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: 32 - eval_batch_size: 32 - 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: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| Roc Auc \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\|:--------:\| \| 0.567 \| 1.0 \| 63 \| 0.5254 \| 0.0 \| 0.5 \| 0.0 \| \| 0.429 \| 2.0 \| 126 \| 0.4218 \| 0.3437 \| 0.6420 \| 0.2283 \| \| 0.3596 \| 3.0 \| 189 \| 0.3747 \| 0.6131 \| 0.7458 \| 0.3859 \| \| 0.3009 \| 4.0 \| 252 \| 0.3384 \| 0.6864 \| 0.7889 \| 0.4457 \| \| 0.2499 \| 5.0 \| 315 \| 0.2934 \| 0.7355 \| 0.8183 \| 0.5380 \| \| 0.223 \| 6.0 \| 378 \| 0.2843 \| 0.7515 \| 0.8294 \| 0.5380 \| \| 0.1912 \| 7.0 \| 441 \| 0.2875 \| 0.7261 \| 0.8130 \| 0.5163 \| \| 0.1575 \| 8.0 \| 504 \| 0.2961 \| 0.7528 \| 0.8318 \| 0.5380 \| \| 0.1445 \| 9.0 \| 567 \| 0.2856 \| 0.7452 \| 0.8286 \| 0.5652 \| \| 0.1415 \| 10.0 \| 630 \| 0.3002 \| 0.7426 \| 0.8316 \| 0.5598 \| \| 0.129 \| 11.0 \| 693 \| 0.2953 \| 0.7414 \| 0.8265 \| 0.5761 \| \| 0.1122 \| 12.0 \| 756 \| 0.3099 \| 0.7447 \| 0.8329 \| 0.5489 \| ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu121 - Datasets 3.2.0 - Tokenizers 0.21.0
alexgusevski/LLaMA-Mesh-q3-mlx	alexgusevski	2025-02-25T22:29:44Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mesh-generation", "mlx", "text-to-3d", "base_model:Zhengyi/LLaMA-Mesh", "base_model:quantized:Zhengyi/LLaMA-Mesh", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "region:us" ]	text-to-3d	2025-02-25T22:23:27Z	--- license: llama3.1 library_name: transformers pipeline_tag: text-to-3d tags: - mesh-generation - mlx base_model: Zhengyi/LLaMA-Mesh --- # alexgusevski/LLaMA-Mesh-q3-mlx The Model [alexgusevski/LLaMA-Mesh-q3-mlx](https://huggingface.co/alexgusevski/LLaMA-Mesh-q3-mlx) was converted to MLX format from [Zhengyi/LLaMA-Mesh](https://huggingface.co/Zhengyi/LLaMA-Mesh) using mlx-lm version 0.21.4. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("alexgusevski/LLaMA-Mesh-q3-mlx") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
lesso07/080a020d-36ce-407a-9775-ff01b2b6c3bc	lesso07	2025-02-25T22:27:53Z	0	0	peft	[ "peft", "safetensors", "phi3", "axolotl", "generated_from_trainer", "custom_code", "base_model:microsoft/Phi-3.5-mini-instruct", "base_model:adapter:microsoft/Phi-3.5-mini-instruct", "license:mit", "region:us" ]	null	2025-02-25T22:05:51Z	--- library_name: peft license: mit base_model: microsoft/Phi-3.5-mini-instruct tags: - axolotl - generated_from_trainer model-index: - name: 080a020d-36ce-407a-9775-ff01b2b6c3bc 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 auto_find_batch_size: true base_model: microsoft/Phi-3.5-mini-instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 205e04f10f84ec23_train_data.json ds_type: json format: custom path: /workspace/input_data/205e04f10f84ec23_train_data.json type: field_input: rejected_response field_instruction: instruction field_output: chosen_response format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 3 eval_max_new_tokens: 128 eval_steps: 50 evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: false group_by_length: true hub_model_id: lesso07/080a020d-36ce-407a-9775-ff01b2b6c3bc hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.000207 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 500 micro_batch_size: 4 mlflow_experiment_name: /tmp/205e04f10f84ec23_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 save_steps: 50 saves_per_epoch: null seed: 70 sequence_len: 512 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 583ca61c-4526-4503-a386-db33ce43947a wandb_project: 07a wandb_run: your_name wandb_runid: 583ca61c-4526-4503-a386-db33ce43947a warmup_steps: 50 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 080a020d-36ce-407a-9775-ff01b2b6c3bc This model is a fine-tuned version of [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0960 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000207 - train_batch_size: 4 - eval_batch_size: 4 - seed: 70 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0005 \| 1 \| 1.9944 \| \| 1.3601 \| 0.0259 \| 50 \| 0.8195 \| \| 0.7955 \| 0.0518 \| 100 \| 0.4487 \| \| 0.7418 \| 0.0776 \| 150 \| 0.3425 \| \| 0.5518 \| 0.1035 \| 200 \| 0.2350 \| \| 0.5269 \| 0.1294 \| 250 \| 0.2071 \| \| 0.4014 \| 0.1553 \| 300 \| 0.1908 \| \| 0.3246 \| 0.1812 \| 350 \| 0.1242 \| \| 0.2535 \| 0.2070 \| 400 \| 0.1066 \| \| 0.2438 \| 0.2329 \| 450 \| 0.0987 \| \| 0.2164 \| 0.2588 \| 500 \| 0.0960 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
RayneAmes/salamence_v3	RayneAmes	2025-02-25T22:26:53Z	0	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-02-25T22:24:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Shetlands/poumpiv2	Shetlands	2025-02-25T22:26:00Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-02-25T22:00:24Z	--- 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: kevin --- # Poumpiv2 <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `kevin` to trigger the image generation. ## 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('Shetlands/poumpiv2', weight_name='lora.safetensors') image = pipeline('your prompt').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)
godofmining/datejust_v2	godofmining	2025-02-25T22:24:25Z	0	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-02-25T22:22:18Z	--- 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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(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]
sulph/illustriousMerges	sulph	2025-02-25T22:23:00Z	0	6	null	[ "license:apache-2.0", "region:us" ]	null	2024-10-31T23:11:22Z	--- license: apache-2.0 ---
Mattia2700/ModernBERT-large_AllDataSources_5e-05_constant_512_flattening	Mattia2700	2025-02-25T22:19:57Z	0	0	transformers	[ "transformers", "safetensors", "modernbert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-02-25T19:59:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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nannnzk/task-4-microsoft-Phi-3-mini-4k-instruct	nannnzk	2025-02-25T22:18:40Z	282	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:microsoft/Phi-3-mini-4k-instruct", "base_model:adapter:microsoft/Phi-3-mini-4k-instruct", "region:us" ]	null	2025-02-24T22:59:26Z	--- base_model: microsoft/Phi-3-mini-4k-instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.13.2
karojandro/cloncaro	karojandro	2025-02-25T22:17:07Z	0	0	null	[ "license:other", "region:us" ]	null	2025-02-25T20:39:25Z	--- 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 ---
leixa/f686d228-2b53-4b1f-8f5a-73b92eeb06a7	leixa	2025-02-25T22:15:16Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM2-360M-Instruct", "base_model:adapter:unsloth/SmolLM2-360M-Instruct", "license:apache-2.0", "region:us" ]	null	2025-02-25T21:43:58Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM2-360M-Instruct tags: - axolotl - generated_from_trainer model-index: - name: f686d228-2b53-4b1f-8f5a-73b92eeb06a7 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/SmolLM2-360M-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 43b60605f834a7c6_train_data.json ds_type: json format: custom path: /workspace/input_data/43b60605f834a7c6_train_data.json type: field_instruction: prompt field_output: chosen format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' ddp_timeout: 1800 debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 3 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 150 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: true group_by_length: true hub_model_id: leixa/f686d228-2b53-4b1f-8f5a-73b92eeb06a7 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: 0 logging_steps: 10 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: constant max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 1800 micro_batch_size: 4 mlflow_experiment_name: /tmp/43b60605f834a7c6_train_data.json model_type: AutoModelForCausalLM num_epochs: 10 optim_args: adam_beta1: 0.9 adam_beta2: 0.999 adam_epsilon: 1e-08 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true relora_prune_ratio: 0.9 resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 150 saves_per_epoch: null sequence_len: 512 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: acopia-grant wandb_mode: online wandb_name: c364105e-68ea-45f9-ab5b-e5c55ae82d05 wandb_project: Gradients-On-112 wandb_run: your_name wandb_runid: c364105e-68ea-45f9-ab5b-e5c55ae82d05 warmup_steps: 50 weight_decay: 0.0 xformers_attention: null ``` </details><br> # f686d228-2b53-4b1f-8f5a-73b92eeb06a7 This model is a fine-tuned version of [unsloth/SmolLM2-360M-Instruct](https://huggingface.co/unsloth/SmolLM2-360M-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8733 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.999,adam_epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 50 - training_steps: 1800 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0012 \| 1 \| 2.5245 \| \| 0.8802 \| 0.1816 \| 150 \| 1.1619 \| \| 0.738 \| 0.3632 \| 300 \| 1.0984 \| \| 0.6932 \| 0.5448 \| 450 \| 1.0444 \| \| 0.6955 \| 0.7264 \| 600 \| 0.9978 \| \| 0.5996 \| 0.9080 \| 750 \| 0.9900 \| \| 1.0218 \| 1.0896 \| 900 \| 0.9560 \| \| 1.0831 \| 1.2712 \| 1050 \| 0.9425 \| \| 1.0514 \| 1.4528 \| 1200 \| 0.9251 \| \| 1.0018 \| 1.6344 \| 1350 \| 0.9048 \| \| 0.997 \| 1.8160 \| 1500 \| 0.8947 \| \| 0.5246 \| 1.9976 \| 1650 \| 0.8829 \| \| 0.4658 \| 2.1792 \| 1800 \| 0.8733 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
SebLogsdon/EveryScene	SebLogsdon	2025-02-25T22:14:18Z	0	0	null	[ "safetensors", "vit", "region:us" ]	null	2025-02-25T21:57:15Z	--- language: en tags: ... (rest of the model card content remains the same)
HarryTrivedi/weddingPlanner	HarryTrivedi	2025-02-25T22:13:55Z	0	0	null	[ "region:us" ]	null	2025-02-25T22:10:10Z	# Wedding Planner Model This is a fine-tuned GPT-2 based model specifically designed to provide wedding planning advice. It has been trained on curated data including wedding planning dialogues, FAQs, and event details. ## Usage You can use this model with the Hugging Face Inference API or load it locally using the Transformers library. ### Example (Python): ```python from transformers import GPT2LMHeadModel, GPT2Tokenizer model = GPT2LMHeadModel.from_pretrained("your-username/weddingPlanner") tokenizer = GPT2Tokenizer.from_pretrained("your-username/weddingPlanner") prompt = "I need help planning my wedding. Can you suggest some ideas?" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(inputs["input_ids"], max_length=150) print(tokenizer.decode(outputs[0], skip_special_tokens=True))
ReadyArt/Forgotten-Safeword-24B-V2.2_EXL2_8bpw_H8	ReadyArt	2025-02-25T22:13:53Z	0	0	null	[ "safetensors", "mistral", "nsfw", "explicit", "roleplay", "unaligned", "dangerous", "ERP", "en", "license:apache-2.0", "8-bit", "exl2", "region:us" ]	null	2025-02-25T20:40:48Z	--- language: - en license: apache-2.0 license_name: mrl license_link: https://mistral.ai/licenses/MRL-0.1.md inference: false tags: - nsfw - explicit - roleplay - unaligned - dangerous - ERP --- ## Forgotten-Safeword-24B-V2.2 # ACADEMIC RESEARCH USE ONLY (still winking) DANGER: NOW WITH 100% MORE KINK NEUTRALITY Forgotten-Safeword-24B-V2.2 is the kink-agnostic chaos engine. Combines Mistral's raw power with a meticulously curated balance of depravity. Features quantum superposition of fetishes - your kink exists here, but so do all others equally! ## Quantized Formats - EXL2 Collection: [Forgotten-Safeword-24B-V2.2 - EXL2](https://huggingface.co/collections/ReadyArt/forgotten-safeword-24b-v22-exl2-67bceffcd9b58637c453fcd9) - GGUF Collection: [Forgotten-Safeword-24B-V2.2 - GGUF](https://huggingface.co/collections/ReadyArt/forgotten-safeword-24b-v22-gguf-67bcf0023537156d75093010) ## Recommended Settings - Mistral-V7-Tekken-Extra-Dry: [Full Settings](https://huggingface.co/sleepdeprived3/Mistral-V7-Tekken-Extra-Dry) ## Intended Use STRICTLY FOR: - Academic research into kink diversity metrics - Generating material that violates the Geneva Conventions (figuratively) - Generating material that would make Cthulhu file a restraining order - Testing how many GPUs you can melt with sheer degeneracy ## Training Data - The internet's collective id (with balanced sampling) - Curated "Your Kink Is Not My Kink (But It's Here)" dataset ## Ethical Catastrophe ☢️ EXTINCTION-LEVEL WARNING ☢️ This model will: - Generate content requiring OSHA-approved eye protection - Combine engineering diagrams with kinks unknown to science - Make Freud look like an amateur - Void all warranties on your soul By using this model, you agree to: - Never show outputs to your therapist - Pay for the exorcist of anyone who reads the training logs - Blame the alignment tax if anything goes wrong - Pretend this is "for science" ## Model Authors - sleepdeprived3 (Chief Equilibrium Officer) - The voices in your head (Now with 50% less bias)
ReadyArt/Forgotten-Safeword-24B-V2.2_EXL2_3.5bpw_H8	ReadyArt	2025-02-25T22:13:09Z	0	0	null	[ "safetensors", "mistral", "nsfw", "explicit", "roleplay", "unaligned", "dangerous", "ERP", "en", "license:apache-2.0", "exl2", "region:us" ]	null	2025-02-25T17:00:41Z	--- language: - en license: apache-2.0 license_name: mrl license_link: https://mistral.ai/licenses/MRL-0.1.md inference: false tags: - nsfw - explicit - roleplay - unaligned - dangerous - ERP --- ## Forgotten-Safeword-24B-V2.2 # ACADEMIC RESEARCH USE ONLY (still winking) DANGER: NOW WITH 100% MORE KINK NEUTRALITY Forgotten-Safeword-24B-V2.2 is the kink-agnostic chaos engine. Combines Mistral's raw power with a meticulously curated balance of depravity. Features quantum superposition of fetishes - your kink exists here, but so do all others equally! ## Quantized Formats - EXL2 Collection: [Forgotten-Safeword-24B-V2.2 - EXL2](https://huggingface.co/collections/ReadyArt/forgotten-safeword-24b-v22-exl2-67bceffcd9b58637c453fcd9) - GGUF Collection: [Forgotten-Safeword-24B-V2.2 - GGUF](https://huggingface.co/collections/ReadyArt/forgotten-safeword-24b-v22-gguf-67bcf0023537156d75093010) ## Recommended Settings - Mistral-V7-Tekken-Extra-Dry: [Full Settings](https://huggingface.co/sleepdeprived3/Mistral-V7-Tekken-Extra-Dry) ## Intended Use STRICTLY FOR: - Academic research into kink diversity metrics - Generating material that violates the Geneva Conventions (figuratively) - Generating material that would make Cthulhu file a restraining order - Testing how many GPUs you can melt with sheer degeneracy ## Training Data - The internet's collective id (with balanced sampling) - Curated "Your Kink Is Not My Kink (But It's Here)" dataset ## Ethical Catastrophe ☢️ EXTINCTION-LEVEL WARNING ☢️ This model will: - Generate content requiring OSHA-approved eye protection - Combine engineering diagrams with kinks unknown to science - Make Freud look like an amateur - Void all warranties on your soul By using this model, you agree to: - Never show outputs to your therapist - Pay for the exorcist of anyone who reads the training logs - Blame the alignment tax if anything goes wrong - Pretend this is "for science" ## Model Authors - sleepdeprived3 (Chief Equilibrium Officer) - The voices in your head (Now with 50% less bias)
ReadyArt/Forgotten-Safeword-24B-V2.2_EXL2_2.5bpw_H8	ReadyArt	2025-02-25T22:13:02Z	0	0	null	[ "safetensors", "mistral", "nsfw", "explicit", "roleplay", "unaligned", "dangerous", "ERP", "en", "license:apache-2.0", "exl2", "region:us" ]	null	2025-02-25T16:28:49Z	--- language: - en license: apache-2.0 license_name: mrl license_link: https://mistral.ai/licenses/MRL-0.1.md inference: false tags: - nsfw - explicit - roleplay - unaligned - dangerous - ERP --- ## Forgotten-Safeword-24B-V2.2 # ACADEMIC RESEARCH USE ONLY (still winking) DANGER: NOW WITH 100% MORE KINK NEUTRALITY Forgotten-Safeword-24B-V2.2 is the kink-agnostic chaos engine. Combines Mistral's raw power with a meticulously curated balance of depravity. Features quantum superposition of fetishes - your kink exists here, but so do all others equally! ## Quantized Formats - EXL2 Collection: [Forgotten-Safeword-24B-V2.2 - EXL2](https://huggingface.co/collections/ReadyArt/forgotten-safeword-24b-v22-exl2-67bceffcd9b58637c453fcd9) - GGUF Collection: [Forgotten-Safeword-24B-V2.2 - GGUF](https://huggingface.co/collections/ReadyArt/forgotten-safeword-24b-v22-gguf-67bcf0023537156d75093010) ## Recommended Settings - Mistral-V7-Tekken-Extra-Dry: [Full Settings](https://huggingface.co/sleepdeprived3/Mistral-V7-Tekken-Extra-Dry) ## Intended Use STRICTLY FOR: - Academic research into kink diversity metrics - Generating material that violates the Geneva Conventions (figuratively) - Generating material that would make Cthulhu file a restraining order - Testing how many GPUs you can melt with sheer degeneracy ## Training Data - The internet's collective id (with balanced sampling) - Curated "Your Kink Is Not My Kink (But It's Here)" dataset ## Ethical Catastrophe ☢️ EXTINCTION-LEVEL WARNING ☢️ This model will: - Generate content requiring OSHA-approved eye protection - Combine engineering diagrams with kinks unknown to science - Make Freud look like an amateur - Void all warranties on your soul By using this model, you agree to: - Never show outputs to your therapist - Pay for the exorcist of anyone who reads the training logs - Blame the alignment tax if anything goes wrong - Pretend this is "for science" ## Model Authors - sleepdeprived3 (Chief Equilibrium Officer) - The voices in your head (Now with 50% less bias)
mradermacher/naps-llama-3_1_instruct-v0.6.0-GGUF	mradermacher	2025-02-25T22:12:27Z	266	0	transformers	[ "transformers", "gguf", "ko", "base_model:NAPS-ai/naps-llama-3_1_instruct-v0.6.0", "base_model:quantized:NAPS-ai/naps-llama-3_1_instruct-v0.6.0", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-02-25T00:47:29Z	--- base_model: NAPS-ai/naps-llama-3_1_instruct-v0.6.0 language: - ko 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/NAPS-ai/naps-llama-3_1_instruct-v0.6.0 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/naps-llama-3_1_instruct-v0.6.0-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/naps-llama-3_1_instruct-v0.6.0-GGUF/resolve/main/naps-llama-3_1_instruct-v0.6.0.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/naps-llama-3_1_instruct-v0.6.0-GGUF/resolve/main/naps-llama-3_1_instruct-v0.6.0.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/naps-llama-3_1_instruct-v0.6.0-GGUF/resolve/main/naps-llama-3_1_instruct-v0.6.0.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/naps-llama-3_1_instruct-v0.6.0-GGUF/resolve/main/naps-llama-3_1_instruct-v0.6.0.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/naps-llama-3_1_instruct-v0.6.0-GGUF/resolve/main/naps-llama-3_1_instruct-v0.6.0.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/naps-llama-3_1_instruct-v0.6.0-GGUF/resolve/main/naps-llama-3_1_instruct-v0.6.0.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/naps-llama-3_1_instruct-v0.6.0-GGUF/resolve/main/naps-llama-3_1_instruct-v0.6.0.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/naps-llama-3_1_instruct-v0.6.0-GGUF/resolve/main/naps-llama-3_1_instruct-v0.6.0.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/naps-llama-3_1_instruct-v0.6.0-GGUF/resolve/main/naps-llama-3_1_instruct-v0.6.0.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/naps-llama-3_1_instruct-v0.6.0-GGUF/resolve/main/naps-llama-3_1_instruct-v0.6.0.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/naps-llama-3_1_instruct-v0.6.0-GGUF/resolve/main/naps-llama-3_1_instruct-v0.6.0.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/naps-llama-3_1_instruct-v0.6.0-GGUF/resolve/main/naps-llama-3_1_instruct-v0.6.0.f16.gguf) \| f16 \| 16.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. 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/ChineseErrorCorrector2-7B-GGUF	mradermacher	2025-02-25T22:11:17Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:twnlp/ChineseErrorCorrector2-7B", "base_model:quantized:twnlp/ChineseErrorCorrector2-7B", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2025-02-25T21:39:29Z	--- base_model: twnlp/ChineseErrorCorrector2-7B language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/twnlp/ChineseErrorCorrector2-7B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/ChineseErrorCorrector2-7B-GGUF/resolve/main/ChineseErrorCorrector2-7B.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ChineseErrorCorrector2-7B-GGUF/resolve/main/ChineseErrorCorrector2-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/ChineseErrorCorrector2-7B-GGUF/resolve/main/ChineseErrorCorrector2-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ChineseErrorCorrector2-7B-GGUF/resolve/main/ChineseErrorCorrector2-7B.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ChineseErrorCorrector2-7B-GGUF/resolve/main/ChineseErrorCorrector2-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/ChineseErrorCorrector2-7B-GGUF/resolve/main/ChineseErrorCorrector2-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ChineseErrorCorrector2-7B-GGUF/resolve/main/ChineseErrorCorrector2-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ChineseErrorCorrector2-7B-GGUF/resolve/main/ChineseErrorCorrector2-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/ChineseErrorCorrector2-7B-GGUF/resolve/main/ChineseErrorCorrector2-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/ChineseErrorCorrector2-7B-GGUF/resolve/main/ChineseErrorCorrector2-7B.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/ChineseErrorCorrector2-7B-GGUF/resolve/main/ChineseErrorCorrector2-7B.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/ChineseErrorCorrector2-7B-GGUF/resolve/main/ChineseErrorCorrector2-7B.f16.gguf) \| f16 \| 15.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
joinsoon/privacyFilter	joinsoon	2025-02-25T22:09:46Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-02-25T22:09:46Z	--- license: apache-2.0 ---
irishprancer/a82ed9b3-66ed-4bd8-8749-7fd3c6350f00	irishprancer	2025-02-25T22:09:40Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-02-25T18:21:05Z	--- 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. 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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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EntropyYue/TinyR1-32B-Preview-Q2_K-GGUF	EntropyYue	2025-02-25T22:08:00Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "base_model:qihoo360/TinyR1-32B-Preview", "base_model:quantized:qihoo360/TinyR1-32B-Preview", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-02-25T22:07:02Z	--- license: apache-2.0 library_name: transformers base_model: qihoo360/TinyR1-32B-Preview tags: - llama-cpp - gguf-my-repo --- # EntropyYue/TinyR1-32B-Preview-Q2_K-GGUF This model was converted to GGUF format from [`qihoo360/TinyR1-32B-Preview`](https://huggingface.co/qihoo360/TinyR1-32B-Preview) 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/qihoo360/TinyR1-32B-Preview) 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 EntropyYue/TinyR1-32B-Preview-Q2_K-GGUF --hf-file tinyr1-32b-preview-q2_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo EntropyYue/TinyR1-32B-Preview-Q2_K-GGUF --hf-file tinyr1-32b-preview-q2_k.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 EntropyYue/TinyR1-32B-Preview-Q2_K-GGUF --hf-file tinyr1-32b-preview-q2_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo EntropyYue/TinyR1-32B-Preview-Q2_K-GGUF --hf-file tinyr1-32b-preview-q2_k.gguf -c 2048 ```
some1nostr/Nostr-Llama-3.1-8B	some1nostr	2025-02-25T22:08:00Z	18	0	null	[ "safetensors", "llama", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "region:us" ]	null	2025-01-09T18:59:13Z	--- base_model: - meta-llama/Llama-3.1-8B --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6619d084eae5a60bb86b0b4f/uRvMOGNihQyQZVi50eb3b.png) A model based on [Nostr](https://nostr.com) notes. Training is ongoing, expect updates to this same rep. Notes come from about 7000 users. Base model instruct fine tuned using: - nickrosh/Evol-Instruct-Code - m-a-p/CodeFeedback-Filtered-Instruction - yingyingzhang/metamath-qwen2-math - cognitivecomputations/dolphin-coder - iamtarun/python_code_instructions_18k_alpaca - OpenCoder-LLM/opc-sft-stage2
globalyako/swallowv2-8b-ft-jp-r64_grpo_sft1.5	globalyako	2025-02-25T22:07:04Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:tokyotech-llm/Llama-3.1-Swallow-8B-v0.2", "base_model:finetune:tokyotech-llm/Llama-3.1-Swallow-8B-v0.2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-02-25T22:06:13Z	--- base_model: tokyotech-llm/Llama-3.1-Swallow-8B-v0.2 tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: globalyako - License: apache-2.0 - Finetuned from model : tokyotech-llm/Llama-3.1-Swallow-8B-v0.2 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)
mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-i1-GGUF	mradermacher	2025-02-25T22:06:13Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Nexesenex/Llama_3.1_8b_DobHerLeashed_R1_v1.0", "base_model:quantized:Nexesenex/Llama_3.1_8b_DobHerLeashed_R1_v1.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-02-25T21:00:04Z	--- base_model: Nexesenex/Llama_3.1_8b_DobHerLeashed_R1_v1.0 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Nexesenex/Llama_3.1_8b_DobHerLeashed_R1_v1.0 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-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/Llama_3.1_8b_DobHerLeash_R1_v1.0-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 3.1 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.i1-Q2_K.gguf) \| i1-Q2_K \| 3.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 4.1 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.4 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 4.8 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.i1-Q4_1.gguf) \| i1-Q4_1 \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.i1-Q6_K.gguf) \| i1-Q6_K \| 6.7 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-GGUF	mradermacher	2025-02-25T22:06:13Z	0	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Nexesenex/Llama_3.1_8b_DobHerLeashed_R1_v1.0", "base_model:quantized:Nexesenex/Llama_3.1_8b_DobHerLeashed_R1_v1.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-02-25T10:47:33Z	--- base_model: Nexesenex/Llama_3.1_8b_DobHerLeashed_R1_v1.0 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Nexesenex/Llama_3.1_8b_DobHerLeashed_R1_v1.0 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-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/Llama_3.1_8b_DobHerLeash_R1_v1.0-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerLeash_R1_v1.0-GGUF/resolve/main/Llama_3.1_8b_DobHerLeash_R1_v1.0.f16.gguf) \| f16 \| 16.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. 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/Titan-123b-0.1-i1-GGUF	mradermacher	2025-02-25T22:06:12Z	12	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:bruhzair/Titan-123b-0.1", "base_model:quantized:bruhzair/Titan-123b-0.1", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-02-25T02:52:02Z	--- base_model: bruhzair/Titan-123b-0.1 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/bruhzair/Titan-123b-0.1 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Titan-123b-0.1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-IQ1_S.gguf) \| i1-IQ1_S \| 26.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-IQ1_M.gguf) \| i1-IQ1_M \| 28.5 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 32.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 36.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-IQ2_S.gguf) \| i1-IQ2_S \| 38.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 41.7 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-IQ2_M.gguf) \| i1-IQ2_M \| 41.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-Q2_K.gguf) \| i1-Q2_K \| 45.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 47.1 \| lower quality \| \| [PART 1](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-IQ3_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-IQ3_XS.gguf.part2of2) \| i1-IQ3_XS \| 50.2 \| \| \| [PART 1](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-Q3_K_S.gguf.part2of2) \| i1-Q3_K_S \| 52.9 \| IQ3_XS probably better \| \| [PART 1](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-IQ3_S.gguf.part2of2) \| i1-IQ3_S \| 53.1 \| beats Q3_K* \| \| [PART 1](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-IQ3_M.gguf.part2of2) \| i1-IQ3_M \| 55.4 \| \| \| [PART 1](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-Q3_K_M.gguf.part2of2) \| i1-Q3_K_M \| 59.2 \| IQ3_S probably better \| \| [PART 1](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-Q3_K_L.gguf.part2of2) \| i1-Q3_K_L \| 64.7 \| IQ3_M probably better \| \| [PART 1](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-IQ4_XS.gguf.part2of2) \| i1-IQ4_XS \| 65.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-Q4_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-Q4_0.gguf.part2of2) \| i1-Q4_0 \| 69.4 \| fast, low quality \| \| [PART 1](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-Q4_K_S.gguf.part2of2) \| i1-Q4_K_S \| 69.7 \| optimal size/speed/quality \| \| [PART 1](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-Q4_K_M.gguf.part2of2) \| i1-Q4_K_M \| 73.3 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-Q4_1.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-Q4_1.gguf.part2of2) \| i1-Q4_1 \| 76.8 \| \| \| [PART 1](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-Q5_K_S.gguf.part2of2) \| i1-Q5_K_S \| 84.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-Q5_K_M.gguf.part2of2) \| i1-Q5_K_M \| 86.6 \| \| \| [PART 1](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Titan-123b-0.1-i1-GGUF/resolve/main/Titan-123b-0.1.i1-Q6_K.gguf.part3of3) \| i1-Q6_K \| 100.7 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/t1-reasoning-sl-v2-7b-sft-GGUF	mradermacher	2025-02-25T22:06:11Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "qwen2", "trl", "sft", "en", "base_model:patrickrho/t1-reasoning-sl-v2-7b-sft", "base_model:quantized:patrickrho/t1-reasoning-sl-v2-7b-sft", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-02-25T21:10:03Z	--- base_model: patrickrho/t1-reasoning-sl-v2-7b-sft language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/patrickrho/t1-reasoning-sl-v2-7b-sft <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/t1-reasoning-sl-v2-7b-sft-GGUF/resolve/main/t1-reasoning-sl-v2-7b-sft.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/t1-reasoning-sl-v2-7b-sft-GGUF/resolve/main/t1-reasoning-sl-v2-7b-sft.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/t1-reasoning-sl-v2-7b-sft-GGUF/resolve/main/t1-reasoning-sl-v2-7b-sft.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/t1-reasoning-sl-v2-7b-sft-GGUF/resolve/main/t1-reasoning-sl-v2-7b-sft.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/t1-reasoning-sl-v2-7b-sft-GGUF/resolve/main/t1-reasoning-sl-v2-7b-sft.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/t1-reasoning-sl-v2-7b-sft-GGUF/resolve/main/t1-reasoning-sl-v2-7b-sft.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/t1-reasoning-sl-v2-7b-sft-GGUF/resolve/main/t1-reasoning-sl-v2-7b-sft.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/t1-reasoning-sl-v2-7b-sft-GGUF/resolve/main/t1-reasoning-sl-v2-7b-sft.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/t1-reasoning-sl-v2-7b-sft-GGUF/resolve/main/t1-reasoning-sl-v2-7b-sft.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/t1-reasoning-sl-v2-7b-sft-GGUF/resolve/main/t1-reasoning-sl-v2-7b-sft.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/t1-reasoning-sl-v2-7b-sft-GGUF/resolve/main/t1-reasoning-sl-v2-7b-sft.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/t1-reasoning-sl-v2-7b-sft-GGUF/resolve/main/t1-reasoning-sl-v2-7b-sft.f16.gguf) \| f16 \| 15.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
yzhuang/Llama-3.1-8B-Instruct-AgenticLU	yzhuang	2025-02-25T22:01:34Z	536	1	null	[ "safetensors", "llama", "en", "dataset:yzhuang/Agentic-Long-Context-Understanding-QA", "arxiv:2502.15920", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "license:mit", "region:us" ]	null	2025-02-10T05:00:48Z	--- license: mit datasets: - yzhuang/Agentic-Long-Context-Understanding-QA language: - en base_model: - meta-llama/Llama-3.1-8B-Instruct --- <h1 align="center"> 📖 Agentic Long Context Understanding 📖 </h1> <p align="center"> <b>Self-Taught Agentic Long Context Understanding</b> (<a href="https://arxiv.org/abs/2502.15920">Arxiv</a>). </p> <p align="center"> <img src="https://img.shields.io/badge/license-mit-blue.svg"> <img src="https://img.shields.io/badge/python-3.9+-blue"> </p> <p align="center"> AgenticLU refines complex, long-context queries through self-clarifications and contextual grounding, enabling robust long-document understanding in a single pass. </p> ## Installation Requirements This codebase is largely based on [OpenRLHF](https://github.com/OpenRLHF/OpenRLHF) and [Helmet](https://github.com/princeton-nlp/HELMET), kudos to them. The requirements are the same ``` pip install openrlhf pip install -r ./HELMET/requirements.txt ``` ## Dataset \& Model Dataset for SFT and DPO is avaliable at [here](https://huggingface.co/datasets/yzhuang/Agentic-Long-Context-Understanding-QA) Model is available at [here](https://huggingface.co/yzhuang/Llama-3.1-8B-Instruct-AgenticLU) ## Data Generation Pipeline To generate traces with your custom model or dataset, follow the instructions: 1. Get an OpenAI API key and set it as your env variable ``` export OPENAI_API_KEY="your_api_key_here" ``` 2. Edit the bash sript as you needed for base model, search width and depth ``` PYTHONPATH="./":"$PYTHONPATH" python ./long_context_llm/qa_tree_datagen.py \ --model_name_or_path meta-llama/Llama-3.1-8B-Instruct \ --max_sample_size 8 \ --max_tree_depth 2 \ --dataset_name yzhuang/narrative_qa ``` 3. The traces will be avaliable to you as ```dataset_dpo```, feel free to add this line to push to your huggingface account. ``` dataset_dpo.push_to_hub("YOUR REPO") ``` ## Example Usage We show the training script of AgenticLU at [sft script](bash_scripts/sft_8b.sh), [dpo script](bash_scripts/rlhf_8b.sh). It is important to get [ring-attention](https://github.com/zhuzilin/ring-flash-attention) to work, as the inputs are extremely long and requires ring-attention and deepspeed for training. Examples for inferencing with the agentic workflow can be found [here](HELMET/scripts/run_agents.sh), with baseline prompting [scripts](HELMET/scripts/run_prompting.sh) avaliable. ## Questions? If you have any questions related to the code or the paper, feel free to reach out to us at [email protected]. ## Citation If you find our paper and code useful, please cite us: ```r @misc{zhuang2025selftaughtagenticlongcontext, title={Self-Taught Agentic Long Context Understanding}, author={Yufan Zhuang and Xiaodong Yu and Jialian Wu and Ximeng Sun and Ze Wang and Jiang Liu and Yusheng Su and Jingbo Shang and Zicheng Liu and Emad Barsoum}, year={2025}, eprint={2502.15920}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2502.15920}, } ```
formulae/mita-gen3-v1.2-7b-2-26-2025	formulae	2025-02-25T22:01:07Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "arxiv:2408.07990", "base_model:Aashraf995/Qwen-Evo-7B", "base_model:merge:Aashraf995/Qwen-Evo-7B", "base_model:Goekdeniz-Guelmez/Josiefied-Qwen2.5-7B-Instruct-abliterated-v2", "base_model:merge:Goekdeniz-Guelmez/Josiefied-Qwen2.5-7B-Instruct-abliterated-v2", "base_model:Krystalan/DRT-o1-7B", "base_model:merge:Krystalan/DRT-o1-7B", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:merge:Qwen/Qwen2.5-7B-Instruct", "base_model:jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0", "base_model:merge:jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0", "base_model:jeffmeloy/Qwen2.5-7B-olm-v1.0", "base_model:merge:jeffmeloy/Qwen2.5-7B-olm-v1.0", "base_model:nvidia/AceMath-7B-Instruct", "base_model:merge:nvidia/AceMath-7B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-02-25T21:56:36Z	--- base_model: - jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0 - nvidia/AceMath-7B-Instruct - Krystalan/DRT-o1-7B - Goekdeniz-Guelmez/Josiefied-Qwen2.5-7B-Instruct-abliterated-v2 - jeffmeloy/Qwen2.5-7B-olm-v1.0 - Aashraf995/Qwen-Evo-7B - Qwen/Qwen2.5-7B-Instruct library_name: transformers tags: - mergekit - merge --- # merge 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 [SCE](https://arxiv.org/abs/2408.07990) merge method using [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) as a base. ### Models Merged The following models were included in the merge: * [jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0](https://huggingface.co/jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0) * [nvidia/AceMath-7B-Instruct](https://huggingface.co/nvidia/AceMath-7B-Instruct) * [Krystalan/DRT-o1-7B](https://huggingface.co/Krystalan/DRT-o1-7B) * [Goekdeniz-Guelmez/Josiefied-Qwen2.5-7B-Instruct-abliterated-v2](https://huggingface.co/Goekdeniz-Guelmez/Josiefied-Qwen2.5-7B-Instruct-abliterated-v2) * [jeffmeloy/Qwen2.5-7B-olm-v1.0](https://huggingface.co/jeffmeloy/Qwen2.5-7B-olm-v1.0) * [Aashraf995/Qwen-Evo-7B](https://huggingface.co/Aashraf995/Qwen-Evo-7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Goekdeniz-Guelmez/Josiefied-Qwen2.5-7B-Instruct-abliterated-v2 # Best for Benchmark 1 parameters: density: 0.25 weight: 0.167 - model: Aashraf995/Qwen-Evo-7B # Best for Benchmark 2 parameters: density: 0.25 weight: 0.167 - model: nvidia/AceMath-7B-Instruct # Best for Benchmark 3 parameters: density: 0.25 weight: 0.167 - model: Krystalan/DRT-o1-7B # Best for Benchmark 4 parameters: density: 0.25 weight: 0.167 - model: jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0 # Best for Benchmark 5 parameters: density: 0.25 weight: 0.167 - model: jeffmeloy/Qwen2.5-7B-olm-v1.0 # Best for Benchmark 6 parameters: density: 0.25 weight: 0.167 merge_method: sce base_model: Qwen/Qwen2.5-7B-Instruct # Replace if using a different base model parameters: normalize: false int8_mask: true select_topk: 0.45 # Retains top 10% highest variance elements (adjust for better results) dtype: bfloat16 allow_crimes: true ```
Kuongan/CS221-xlm-roberta-base-amh-noaug-finetuned-amh-tapt	Kuongan	2025-02-25T21:58:16Z	0	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:Kuongan/xlm-roberta-base-amh-noaug", "base_model:finetune:Kuongan/xlm-roberta-base-amh-noaug", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-02-25T21:50:30Z	--- library_name: transformers license: mit base_model: Kuongan/xlm-roberta-base-amh-noaug tags: - generated_from_trainer metrics: - f1 - accuracy model-index: - name: CS221-xlm-roberta-base-amh-noaug-finetuned-amh-tapt 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. --> # CS221-xlm-roberta-base-amh-noaug-finetuned-amh-tapt This model is a fine-tuned version of [Kuongan/xlm-roberta-base-amh-noaug](https://huggingface.co/Kuongan/xlm-roberta-base-amh-noaug) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1361 - F1: 0.7860 - Roc Auc: 0.8699 - Accuracy: 0.7692 ## 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: 32 - eval_batch_size: 32 - 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: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| Roc Auc \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\|:--------:\| \| 0.1718 \| 1.0 \| 221 \| 0.1361 \| 0.7860 \| 0.8699 \| 0.7692 \| \| 0.1614 \| 2.0 \| 442 \| 0.1363 \| 0.7598 \| 0.8680 \| 0.7566 \| \| 0.1413 \| 3.0 \| 663 \| 0.1390 \| 0.7758 \| 0.8842 \| 0.7464 \| \| 0.1124 \| 4.0 \| 884 \| 0.1582 \| 0.7558 \| 0.8585 \| 0.7177 \| ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu121 - Datasets 3.2.0 - Tokenizers 0.21.0
rottenivy/chronos-t5-mini-fine-tuned-traffic	rottenivy	2025-02-25T21:56:30Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-02-25T21:56:26Z	--- 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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kevykevg/gemma-2-2B-it-thinking-function_calling-V0	kevykevg	2025-02-25T21:55:09Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-2-2b-it", "base_model:finetune:google/gemma-2-2b-it", "endpoints_compatible", "region:us" ]	null	2025-02-25T21:51:40Z	--- base_model: google/gemma-2-2b-it library_name: transformers model_name: gemma-2-2B-it-thinking-function_calling-V0 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-2-2B-it-thinking-function_calling-V0 This model is a fine-tuned version of [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="kevykevg/gemma-2-2B-it-thinking-function_calling-V0", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.1 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Keltezaa/heather-graham	Keltezaa	2025-02-25T21:53:38Z	0	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "migrated", "woman", "actress", "celeb", "celebrity", "heather graham", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-02-25T21:53:37Z	--- license: other license_name: bespoke-lora-trained-license license_link: https://multimodal.art/civitai-licenses?allowNoCredit=True&allowCommercialUse=Image&allowDerivatives=True&allowDifferentLicense=True tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora - migrated - woman - actress - celeb - celebrity - heather graham base_model: black-forest-labs/FLUX.1-dev instance_prompt: HeatherGrahamFlux widget: - text: ' ' output: url: >- 59671326.jpeg - text: ' ' output: url: >- 59670044.jpeg - text: ' ' output: url: >- 59670033.jpeg - text: ' ' output: url: >- 59670034.jpeg - text: ' ' output: url: >- 59670026.jpeg - text: ' ' output: url: >- 59670029.jpeg - text: ' ' output: url: >- 59670035.jpeg - text: ' ' output: url: >- 59670037.jpeg - text: ' ' output: url: >- 59670038.jpeg - text: ' ' output: url: >- 59670039.jpeg - text: ' ' output: url: >- 59670040.jpeg - text: ' ' output: url: >- 59670042.jpeg - text: ' ' output: url: >- 59670041.jpeg - text: ' ' output: url: >- 59670045.jpeg - text: ' ' output: url: >- 59670043.jpeg - text: ' ' output: url: >- 59670047.jpeg - text: ' ' output: url: >- 59670046.jpeg - text: ' ' output: url: >- 59670049.jpeg - text: ' ' output: url: >- 59670048.jpeg - text: ' ' output: url: >- 59670050.jpeg --- # Heather Graham <Gallery /> ## Model description <p>Heather Joan Graham (born January 29, 1970) is an American actress. The accolades she has received include nominations for two Screen Actors Guild Awards, a Critics' Choice Movie Award, and an Independent Spirit Award.</p> ## Trigger words You should use `HeatherGrahamFlux` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Keltezaa/heather-graham/tree/main) them in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch device = "cuda" if torch.cuda.is_available() else "cpu" pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to(device) pipeline.load_lora_weights('Keltezaa/heather-graham', weight_name='HeatherGrahamFluxV1.safetensors') image = pipeline('`HeatherGrahamFlux`').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)
Keltezaa/chaoyue-v17-yang-chao-yue-huo-jian-shao-nu-101	Keltezaa	2025-02-25T21:53:28Z	0	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "migrated", "celebrity", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-02-25T21:53:26Z	--- license: other license_name: bespoke-lora-trained-license license_link: https://multimodal.art/civitai-licenses?allowNoCredit=True&allowCommercialUse=RentCivit&allowDerivatives=True&allowDifferentLicense=True tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora - migrated - celebrity base_model: black-forest-labs/FLUX.1-dev instance_prompt: widget: - text: 'A full body shot of a 18 years girl with long hair' output: url: >- 59963070.jpeg - text: 'A full body shot of a 18 years girl with long hair' output: url: >- 59963071.jpeg - text: 'A full body shot of a 18 years girl with long hair' output: url: >- 59963069.jpeg - text: 'Elegant young woman in a deep purple sequined strapless gown, formal event. Asian woman, 20s-30s, long dark hair, pale skin, soft features. Expression is composed, confident. Deep purple strapless gown with feather-like details and sequins, flowing fabric. Full-length gown, fitting bodice, full skirt. Slight draping or cape-like fabric draped over shoulder. Close-up, medium shot, eye-level perspective. Dark, slightly textured background with various logos and signatures, creating a subtle backdrop. Warm, professional lighting accentuating the dress''s rich purple tones. Silhouette highlights the form and texture of the dress. Formal, glamorous, celebratory atmosphere. High-fashion, event photography style. Focus on fashion and beauty. Evening dress style. ' output: url: >- 59963737.jpeg - text: 'Young Asian woman, mid-20s, exhibiting poised demeanor. Dark, long straight hair cascading down her back. Wearing a black lace-trimmed top with a red rose embellishment. A vibrant red, satin mini-skirt adorned with black rose appliqués. Slight smile, neutral expression, showing a composed and confident attitude. Full-bodied, professional portrait shot, with a moderate close-up view. Natural lighting, creating a soft, even glow across the subject''s features. The background is a blurred, neutral backdrop with muted pastel tones. A microphone is held gently in her hands, displaying rings. Elegant, detailed outfit, vintage-inspired design; noticeable attention to detail in the fashion. Soft lighting, creating a well-lit, clear image. Focus on the subject''s face and upper body, with the background subtly out of focus. Composition is centered, conveying formality and professionalism. Overall mood is calm, elegant and composed. ' output: url: >- 59963759.jpeg - text: 'A young woman of East Asian ethnicity is positioned slightly to the left of center in a formal portrait. She is wearing a strapless, black bodice dress with a voluminous, layered, light-gray tulle skirt. The tulle has a ruffled, textured appearance. She is wearing black velvet gloves that extend to her wrists. A delicate gold necklace and bracelet are visible. Her long, dark hair is styled in loose waves. She is standing on a red carpet. The backdrop is a dark, muted color scheme, primarily shades of dark purple and black. The lighting is dramatic, highlighting the woman and the details of the dress. The perspective is slightly above the subject, focusing on her from the waist up. The composition is balanced and elegant, emphasizing the elaborate details of the dress and the woman''s posture. The overall style is formal and glamorous, reminiscent of a red carpet event. ' output: url: >- 59964732.jpeg --- # chaoyue-v17 （杨超越-火箭少女101） <Gallery /> ## Model description <p>杨超越火箭少女101 无提示词建议强度 1</p> ## Download model Weights for this model are available in Safetensors format. [Download](/Keltezaa/chaoyue-v17-yang-chao-yue-huo-jian-shao-nu-101/tree/main) them in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch device = "cuda" if torch.cuda.is_available() else "cpu" pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to(device) pipeline.load_lora_weights('Keltezaa/chaoyue-v17-yang-chao-yue-huo-jian-shao-nu-101', weight_name='chaoyue-v17.safetensors') image = pipeline('A young woman of East Asian ethnicity is positioned slightly to the left of center in a formal portrait. She is wearing a strapless, black bodice dress with a voluminous, layered, light-gray tulle skirt. The tulle has a ruffled, textured appearance. She is wearing black velvet gloves that extend to her wrists. A delicate gold necklace and bracelet are visible. Her long, dark hair is styled in loose waves. She is standing on a red carpet. The backdrop is a dark, muted color scheme, primarily shades of dark purple and black. The lighting is dramatic, highlighting the woman and the details of the dress. The perspective is slightly above the subject, focusing on her from the waist up. The composition is balanced and elegant, emphasizing the elaborate details of the dress and the woman's posture. The overall style is formal and glamorous, reminiscent of a red carpet event. ').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)
mlx-community/Qwen2.5-VL-72B-Instruct-4bit	mlx-community	2025-02-25T21:52:48Z	1,001	5	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "multimodal", "mlx", "conversational", "en", "base_model:Qwen/Qwen2.5-VL-72B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-72B-Instruct", "license:other", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-01-29T10:35:25Z	--- license: other license_name: qwen license_link: https://huggingface.co/Qwen/Qwen2.5-VL-72B-Instruct/blob/main/LICENSE language: - en pipeline_tag: image-text-to-text tags: - multimodal - mlx library_name: transformers base_model: - Qwen/Qwen2.5-VL-72B-Instruct --- # mlx-community/Qwen2.5-VL-72B-Instruct-4bit This model was converted to MLX format from [`Qwen/Qwen2.5-VL-72B-Instruct`]() using mlx-vlm version 0.1.11. Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-VL-72B-Instruct) for more details on the model. ## Use with mlx ```bash pip install -U mlx-vlm ``` ```bash python -m mlx_vlm.generate --model mlx-community/Qwen2.5-VL-72B-Instruct-4bit --max-tokens 100 --temp 0.0 --prompt "Describe this image." --image <path_to_image> ```
mlx-community/Qwen2.5-VL-72B-Instruct-3bit	mlx-community	2025-02-25T21:52:30Z	307	3	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "multimodal", "mlx", "conversational", "en", "base_model:Qwen/Qwen2.5-VL-72B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-72B-Instruct", "license:other", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-01-29T11:47:38Z	--- license: other license_name: qwen license_link: https://huggingface.co/Qwen/Qwen2.5-VL-72B-Instruct/blob/main/LICENSE language: - en pipeline_tag: image-text-to-text tags: - multimodal - mlx library_name: transformers base_model: - Qwen/Qwen2.5-VL-72B-Instruct --- # mlx-community/Qwen2.5-VL-72B-Instruct-3bit This model was converted to MLX format from [`Qwen/Qwen2.5-VL-72B-Instruct`]() using mlx-vlm version 0.1.11. Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-VL-72B-Instruct) for more details on the model. ## Use with mlx ```bash pip install -U mlx-vlm ``` ```bash python -m mlx_vlm.generate --model mlx-community/Qwen2.5-VL-72B-Instruct-3bit --max-tokens 100 --temp 0.0 --prompt "Describe this image." --image <path_to_image> ```
biprateep/ppo-LunarLander-v2	biprateep	2025-02-25T21:52:24Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-02-25T21:52:04Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 263.90 +/- 15.06 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
mlx-community/Qwen2.5-VL-72B-Instruct-6bit	mlx-community	2025-02-25T21:52:10Z	120	1	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "multimodal", "mlx", "conversational", "en", "base_model:Qwen/Qwen2.5-VL-72B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-72B-Instruct", "license:other", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-01-29T15:31:45Z	--- license: other license_name: qwen license_link: https://huggingface.co/Qwen/Qwen2.5-VL-72B-Instruct/blob/main/LICENSE language: - en pipeline_tag: image-text-to-text tags: - multimodal - mlx library_name: transformers base_model: - Qwen/Qwen2.5-VL-72B-Instruct --- # mlx-community/Qwen2.5-VL-72B-Instruct-6bit This model was converted to MLX format from [`Qwen/Qwen2.5-VL-72B-Instruct`]() using mlx-vlm version 0.1.11. Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-VL-72B-Instruct) for more details on the model. ## Use with mlx ```bash pip install -U mlx-vlm ``` ```bash python -m mlx_vlm.generate --model mlx-community/Qwen2.5-VL-72B-Instruct-6bit --max-tokens 100 --temp 0.0 --prompt "Describe this image." --image <path_to_image> ```
lmarena-ai/p2l-7b-grk-01112025	lmarena-ai	2025-02-25T21:51:44Z	0	0	null	[ "safetensors", "qwen2", "arxiv:2502.14855", "license:apache-2.0", "region:us" ]	null	2025-02-24T18:38:11Z	--- license: apache-2.0 --- # lmarena-ai/p2l-7b-grk-01112025 Large language model (LLM) evaluations typically rely on aggregated metrics like accuracy or human preference, averaging across users and prompts. This averaging obscures user- and prompt-specific variations in model performance. To address this, we propose Prompt-to-Leaderboard (P2L), a method that produces leaderboards specific to a prompt. The core idea is to train an LLM taking natural language prompts as input to output a vector of coefficients which are then used to predict the human preference vote. The resulting prompt-dependent leaderboards allow for unsupervised task-specific evaluation, optimal routing of queries to models, personalization, and automated evaluation of model strengths and weaknesses. Data from Chatbot Arena suggest that P2L better captures the nuanced landscape of language model performance than the averaged leaderboard. Paper: [Prompt-to-Leaderboard](https://arxiv.org/abs/2502.14855) Code: [lmarena/p2l](https://github.com/lmarena/p2l) This particular P2L model has a Grounded Rao-Kupper regression head, which we define below: Let $$ Y\in \{\mathsf{A}, \mathsf{B}, \mathsf{tie}, \mathsf{bad}\} $$ and for the sake of notational convenience, let $$ \theta^(z) = \big(\beta^(z), \eta^(z)\big); \ \beta^(z) \in \mathbb{R}^M, \eta^(z) \in \mathbb{R}_{\geq 1}\} $$ For notational convenience, we define: $$ \varphi^(z)_i := \exp(\beta^(z)_i) $$ Then grounded Rao-Kupper model is defined as: $$ g_{\theta^(z)}(y ; x) = \begin{cases} \frac{\varphi^(z)_A}{\varphi^(z)_A + \eta^(z)\varphi^(z)_B + 1} & y = \mathsf{A} \\ \frac{\varphi^(z)_B}{\varphi^(z)_B + \eta^(z)\varphi^(z)_A + 1} & y = \mathsf{B}\\ \frac{1}{1 + \varphi^(z)_A + \varphi^(z)_B} & y = \mathsf{bad}\\ 1 - \frac{\varphi^(z)_A}{\varphi^(z)_A + \eta^(z)\varphi^(z)_B + 1} - \frac{\varphi^(z)_B}{\varphi^(z)_B + \eta^(z)\varphi^(z)_A + 1} - \frac{1}{1 + \varphi^(z)_A + \varphi^(z)_B} & y = \mathsf{tie}. \end{cases} $$ See section 2.2 in our paper for more details on various regression heads. ## Serving To serve a P2L model, please see our documentation on GitHub: [Serving P2L](https://github.com/lmarena/p2l?tab=readme-ov-file#serving-p2l). Note: the P2L model outputs with this structure: ```python class P2LOutputs(ModelOutput): coefs: torch.FloatTensor = None # "betas" as described above eta: Optional[torch.FloatTensor] = None # tie coefficent (also eta above) last_hidden_state: torch.FloatTensor = None # last hidden state from the transformer ``` To understand which coefficient index corresponds with which model, see the [`model_list.json`](./model_list.json) found in the repo of each P2L model. As a general rule, the models will always be in sorted order. The easiest way to get this list from inside code is with the following: ```python import json from huggingface_hub import hf_hub_download fname = hf_hub_download( repo_id="lmarena-ai/p2l-7b-grk-01112025", filename="model_list.json", repo_type="model" ) with open(fname) as fin: model_list = json.load(fin) ``` ### Loading from Pretrained To define and load the model: ```python import torch from transformers import ( Qwen2Model, Qwen2PreTrainedModel, LlamaModel, LlamaPreTrainedModel, PreTrainedModel, AutoTokenizer, ) from transformers import AutoTokenizer from transformers.utils import ModelOutput from dataclasses import dataclass import torch.nn as nn import torch.nn.functional as F from typing import Dict, Tuple, Callable, Optional from huggingface_hub import hf_hub_download import json @dataclass class HeadOutputs(ModelOutput): coefs: torch.FloatTensor = None eta: Optional[torch.FloatTensor] = None gamma: Optional[torch.FloatTensor] = None @dataclass class P2LOutputs(ModelOutput): coefs: torch.FloatTensor = None eta: Optional[torch.FloatTensor] = None gamma: Optional[torch.FloatTensor] = None loss: Optional[torch.FloatTensor] = None last_hidden_state: torch.FloatTensor = None class RKHead(nn.Module): def __init__( self, input_dim, output_dim, kwargs, ) -> None: super().__init__() self.head = nn.Linear( in_features=input_dim, out_features=output_dim, bias=True ) self.eta_head = nn.Linear( in_features=input_dim, out_features=1, bias=True ) def forward(self, last_hidden_dim: torch.Tensor): coefs = self.head(last_hidden_dim) eta = self.eta_head(last_hidden_dim) return HeadOutputs(coefs=coefs, eta=eta) class P2LModel(Qwen2PreTrainedModel): def __init__( self, config, CLS_id, num_models, head_kwargs={}, kwargs, ): super().__init__(config) self.num_models = num_models self.cls_token_id = CLS_id self.model = Qwen2Model(config) self.head = RKHead( input_dim=config.hidden_size, output_dim=self.num_models, **head_kwargs, ) self.post_init() def freeze_transformer(self): for param in self.model.parameters(): param.requires_grad = False def get_input_embeddings(self): return self.model.embed_tokens def set_input_embeddings(self, value): self.model.embed_tokens = value def forward(self, input_ids, attention_mask, labels=None, weights=None): batch_size = input_ids.shape[0] hidden_outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, output_hidden_states=False, ).last_hidden_state # (bs, num_token, embed_dim) cls_mask = input_ids == self.cls_token_id # double check this is getting the current CLS token cls_hidden_dim = hidden_outputs[cls_mask] assert ( cls_hidden_dim.shape[0] == batch_size ), f"input ids {input_ids.shape}, cls_mask {cls_mask.shape}, cls_logit {cls_hidden_dim.shape}" head_output = self.head(cls_hidden_dim) outputs = P2LOutputs( coefs=head_output.coefs, last_hidden_state=cls_hidden_dim, eta=head_output.eta, gamma=head_output.gamma, ) return outputs fname = hf_hub_download( repo_id="lmarena-ai/p2l-7b-grk-01112025", filename="model_list.json", repo_type="model" ) with open(fname) as fin: model_list = json.load(fin) tokenizer = AutoTokenizer.from_pretrained("lmarena-ai/p2l-7b-grk-01112025") model = P2LModel.from_pretrained( "lmarena-ai/p2l-7b-grk-01112025", CLS_id=tokenizer.cls_token_id, num_models=len(model_list), torch_dtype=torch.bfloat16, ) ``` ## Citation ``` @misc{frick2025prompttoleaderboard, title={Prompt-to-Leaderboard}, author={Evan Frick and Connor Chen and Joseph Tennyson and Tianle Li and Wei-Lin Chiang and Anastasios N. Angelopoulos and Ion Stoica}, year={2025}, eprint={2502.14855}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2502.14855}, } ```
mlx-community/Qwen2.5-VL-7B-Instruct-4bit	mlx-community	2025-02-25T21:51:02Z	753	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "multimodal", "mlx", "conversational", "en", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-01-29T02:20:49Z	--- license: apache-2.0 language: - en pipeline_tag: image-text-to-text tags: - multimodal - mlx library_name: transformers base_model: - Qwen/Qwen2.5-VL-7B-Instruct --- # mlx-community/Qwen2.5-VL-7B-Instruct-4bit This model was converted to MLX format from [`Qwen/Qwen2.5-VL-7B-Instruct`]() using mlx-vlm version 0.1.11. Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) for more details on the model. ## Use with mlx ```bash pip install -U mlx-vlm ``` ```bash python -m mlx_vlm.generate --model mlx-community/Qwen2.5-VL-7B-Instruct-4bit --max-tokens 100 --temp 0.0 --prompt "Describe this image." --image <path_to_image> ```
manjunath99/lora_model	manjunath99	2025-02-25T21:50:50Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-02-25T21:50:41Z	--- 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: manjunath99 - 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)
mlx-community/Qwen2.5-VL-7B-Instruct-3bit	mlx-community	2025-02-25T21:50:05Z	145	1	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "multimodal", "mlx", "conversational", "en", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-01-29T02:30:27Z	--- license: apache-2.0 language: - en pipeline_tag: image-text-to-text tags: - multimodal - mlx library_name: transformers base_model: - Qwen/Qwen2.5-VL-7B-Instruct --- # mlx-community/Qwen2.5-VL-7B-Instruct-3bit This model was converted to MLX format from [`Qwen/Qwen2.5-VL-7B-Instruct`]() using mlx-vlm version 0.1.11. Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) for more details on the model. ## Use with mlx ```bash pip install -U mlx-vlm ``` ```bash python -m mlx_vlm.generate --model mlx-community/Qwen2.5-VL-7B-Instruct-3bit --max-tokens 100 --temp 0.0 --prompt "Describe this image." --image <path_to_image> ```
locuslab/mix_ift_v4-smollm2-1.7b-meta-llama-Llama-3.2-1B-lr2e-05-gbs16600B	locuslab	2025-02-25T21:49:25Z	0	0	null	[ "safetensors", "llama", "model", "transformer", "smollm2", "license:mit", "region:us" ]	null	2025-02-25T21:46:24Z	--- version: main family: smollm2-1.7b model_name: meta-llama-Llama-3.2-1B-lr2e-05-gbs16600B license: mit tags: - model - transformer - smollm2 --- # SmolLM2 meta-llama-Llama-3.2-1B-lr2e-05-gbs16600B (Version: main) ## Model Details - Architecture: SmolLM2 - Parameters: 1.7B ## Training Configuration ```yaml optimizer: class_path: torch.optim.AdamW init_args: lr: 0.0005 weight_decay: 0.01 precision: bf16-mixed seed: 42 train: global_batch_size: 1024 max_seq_length: 2048 max_tokens: 600000000000 micro_batch_size: 8 ``` ## Model Loading and Revision System This repository hosts multiple revisions of the model. To load a specific revision, use the `revision` parameter. For example: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("locuslab/meta-llama-Llama-3.2-1B-lr2e-05-gbs16600B", revision="final") tokenizer = AutoTokenizer.from_pretrained("locuslab/meta-llama-Llama-3.2-1B-lr2e-05-gbs16600B", revision="final") ``` Replace `"final"` with the desired revision.
leixa/f2c04e33-ca51-434a-a24b-f2247a2e401e	leixa	2025-02-25T21:42:52Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "custom_code", "base_model:NousResearch/Yarn-Llama-2-13b-64k", "base_model:adapter:NousResearch/Yarn-Llama-2-13b-64k", "region:us" ]	null	2025-02-25T18:18:19Z	--- library_name: peft base_model: NousResearch/Yarn-Llama-2-13b-64k tags: - axolotl - generated_from_trainer model-index: - name: f2c04e33-ca51-434a-a24b-f2247a2e401e results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: NousResearch/Yarn-Llama-2-13b-64k bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - b61e8732bf90c34c_train_data.json ds_type: json format: custom path: /workspace/input_data/b61e8732bf90c34c_train_data.json type: field_instruction: title field_output: content format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' ddp_timeout: 1800 debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 3 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 150 eval_table_size: null evals_per_epoch: null flash_attention: false fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: true group_by_length: true hub_model_id: leixa/f2c04e33-ca51-434a-a24b-f2247a2e401e hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: 0 logging_steps: 10 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: constant max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 1800 micro_batch_size: 4 mlflow_experiment_name: /tmp/b61e8732bf90c34c_train_data.json model_type: AutoModelForCausalLM num_epochs: 10 optim_args: adam_beta1: 0.9 adam_beta2: 0.999 adam_epsilon: 1e-08 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true relora_prune_ratio: 0.9 resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 150 saves_per_epoch: null sequence_len: 512 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: acopia-grant wandb_mode: online wandb_name: 7a5cf688-1ec2-4add-afd0-6425415d08cf wandb_project: Gradients-On-112 wandb_run: your_name wandb_runid: 7a5cf688-1ec2-4add-afd0-6425415d08cf warmup_steps: 50 weight_decay: 0.0 xformers_attention: null ``` </details><br> # f2c04e33-ca51-434a-a24b-f2247a2e401e This model is a fine-tuned version of [NousResearch/Yarn-Llama-2-13b-64k](https://huggingface.co/NousResearch/Yarn-Llama-2-13b-64k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6537 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.999,adam_epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 50 - training_steps: 1800 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0009 \| 1 \| 1.8179 \| \| 7.0956 \| 0.1282 \| 150 \| 1.7121 \| \| 7.012 \| 0.2565 \| 300 \| 1.6989 \| \| 6.9218 \| 0.3847 \| 450 \| 1.6893 \| \| 7.0598 \| 0.5129 \| 600 \| 1.6829 \| \| 7.0756 \| 0.6412 \| 750 \| 1.6762 \| \| 7.0883 \| 0.7694 \| 900 \| 1.6702 \| \| 7.1086 \| 0.8976 \| 1050 \| 1.6644 \| \| 6.4382 \| 1.0259 \| 1200 \| 1.6637 \| \| 6.2394 \| 1.1541 \| 1350 \| 1.6614 \| \| 6.3278 \| 1.2823 \| 1500 \| 1.6589 \| \| 6.1585 \| 1.4106 \| 1650 \| 1.6571 \| \| 6.3427 \| 1.5388 \| 1800 \| 1.6537 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Free-1-Girl-15-Hands-Original-Viral/FULL.1-Girl-15-Hands.Video.Viral.Video.On.Social.Media.X	Free-1-Girl-15-Hands-Original-Viral	2025-02-25T21:42:20Z	0	0	null	[ "region:us" ]	null	2025-02-25T21:32:04Z	[<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://lekedvideo.xyz/watch/) [🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)](https://lekedvideo.xyz/watch/) [🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://lekedvideo.xyz/watch/)
manjunath99/outputs	manjunath99	2025-02-25T21:42:12Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "unsloth", "trl", "sft", "endpoints_compatible", "region:us" ]	null	2025-02-25T21:42:06Z	--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit library_name: transformers model_name: outputs tags: - generated_from_trainer - unsloth - trl - sft licence: license --- # Model Card for outputs This model is a fine-tuned version of [unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit](https://huggingface.co/unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="manjunath99/outputs", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.1 - Transformers: 4.48.3 - Pytorch: 2.5.1+cu124 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
jfranklin-foundry/task-4-01-ai-Yi-7B-Chat	jfranklin-foundry	2025-02-25T21:39:29Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:01-ai/Yi-1.5-6B-Chat", "base_model:adapter:01-ai/Yi-1.5-6B-Chat", "region:us" ]	null	2025-02-25T21:32:32Z	--- base_model: 01-ai/Yi-1.5-6B-Chat library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.13.2
mlx-community/Qwen2.5-VL-3B-Instruct-4bit	mlx-community	2025-02-25T21:37:41Z	561	1	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "multimodal", "mlx", "conversational", "en", "base_model:Qwen/Qwen2.5-VL-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-01-29T01:56:02Z	--- license_name: qwen-research license_link: https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct/blob/main/LICENSE language: - en pipeline_tag: image-text-to-text tags: - multimodal - mlx library_name: transformers base_model: - Qwen/Qwen2.5-VL-3B-Instruct --- # mlx-community/Qwen2.5-VL-3B-Instruct-4bit This model was converted to MLX format from [`Qwen/Qwen2.5-VL-3B-Instruct`]() using mlx-vlm version 0.1.11. Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) for more details on the model. ## Use with mlx ```bash pip install -U mlx-vlm ``` ```bash python -m mlx_vlm.generate --model mlx-community/Qwen2.5-VL-3B-Instruct-4bit --max-tokens 100 --temp 0.0 --prompt "Describe this image." --image <path_to_image> ```
Keltezaa/danielle-rose-russell-sololora	Keltezaa	2025-02-25T21:36:49Z	21	1	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "migrated", "photorealistic", "woman", "celebrity", "realistic", "danielle rose russell", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-02-24T08:24:23Z	--- license: other license_name: bespoke-lora-trained-license license_link: https://multimodal.art/civitai-licenses?allowNoCredit=True&allowCommercialUse=Image&allowDerivatives=False&allowDifferentLicense=False tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora - migrated - photorealistic - woman - celebrity - realistic - danielle rose russell base_model: black-forest-labs/FLUX.1-dev instance_prompt: widget: - text: 'a beautiful woman. long hair, pink lips, in a shirt,' output: url: >- 34773489.jpeg - text: 'close-up portrait of a woman, captured with a telephoto lens for a candid effect. long, flowing brown hair, slightly tousled by the wind, minimal makeup with soft pink lips and natural blush, radiant skin. wearing a light, flowy dress, delicate necklace visible, casual yet elegant. caught mid-moment, looking off into the distance with a thoughtful expression, sunlight softly illuminating her face, blurred background of trees and greenery for a dreamy bokeh effect. soft, natural tones, warm lighting, intimate, spontaneous, serene, reminiscent of a candid fashion editorial.' output: url: >- 34773492.jpeg - text: 'close-up portrait of a woman, indoor setting with professional lighting. long, wavy brown hair, casually loose, fresh makeup with pink lips, natural blush, light eyeliner. wearing a casual pastel-colored hoodie, small silver stud earrings, relaxed and youthful. gazing playfully into the camera, bright smile, soft light highlighting her features, set against a simple, softly blurred studio backdrop. bright tones, soft focus, lively, energetic, modern, effortlessly chic, Instagram-ready.' output: url: >- 34773498.jpeg - text: 'close-up portrait of a woman, indoor setting with professional lighting. long, wavy brown hair, casually loose, fresh makeup with pink lips, natural blush, light eyeliner. wearing a casual pastel-colored hoodie, small silver stud earrings, relaxed and youthful. gazing playfully into the camera, bright smile, soft light highlighting her features, set against a simple, softly blurred studio backdrop. bright tones, soft focus, lively, energetic, modern, effortlessly chic, Instagram-ready.' output: url: >- 34773488.jpeg - text: 'half-body portrait of a female blacksmith, in the midst of intense work in a messy forge. long, dark hair tied back in a loose ponytail, face streaked with soot and sweat, fierce and focused expression, lips slightly parted in concentration. wearing a rugged leather apron over a sleeveless tunic, strong arms exposed, one hand gripping a heavy iron hammer mid-swing, the other firmly holding a glowing sword blade, still in the forging process. surrounded by the clutter of a dirty blacksmith shop—scattered tools, piles of scrap metal, unfinished weapons, and a roaring furnace casting a warm glow over the scene, with sparks flying from the heated metal. dark, earthy tones, harsh lighting, gritty atmosphere, intense, raw, full of strength and craftsmanship, dynamic fantasy artwork quality.' output: url: >- 34773500.jpeg - text: 'half-body portrait of a woman, in a captivating fantasy setting. long, flowing raven-black hair, slightly windswept, glowing amber eyes, with soft golden makeup accentuating her features, and warm, glossy lips. wearing an ornate crimson gown with intricate gold detailing, shimmering jewelry, and a delicate tiara. holding a glowing ball of fire in her hands, fingers gently cradling the flames, with a mesmerizing smile, set against a dark, enchanted forest illuminated by the fire''s warm glow and distant, glowing runes. rich colors, dynamic lighting, magical atmosphere, alluring, powerful, otherworldly, cinematic fantasy art quality.' output: url: >- 34773503.jpeg - text: 'half-body portrait of a woman, in a sporty and energetic style. long, sleek black hair tied up in a high ponytail, light makeup with a natural, glowing look, subtly defined brows, and soft pink lips. wearing a fitted athletic crop top, accentuating her toned physique, paired with form-fitting black leggings, highlighting her curves, minimal accessories with a sporty watch. standing confidently, one hand resting on her hip, the other slightly raised, with a determined yet friendly smile, set against a bright, modern gym backdrop, with workout equipment subtly blurred in the background. bright tones, sharp lighting, dynamic, athletic, energetic, and fashionable, exuding strength and confidence.' output: url: >- 34773502.jpeg - text: 'half-body portrait of a woman, in a dynamic and stylish setting. long, sleek black hair, styled straight with a few loose strands framing her face, light makeup with a natural blush, soft pink lips, and striking, defined eyes. wearing a fitted black tank top, fingerless gloves, and a white cropped undershirt, accentuating her athletic figure, with red arm guards and a utility belt. standing confidently, one hand resting on her hip, the other slightly raised, a determined yet warm smile on her face, with a futuristic cityscape softly blurred in the background. cool tones, sharp contrast, dynamic lighting, strong and feminine, cinematic, iconic, and video game-inspired quality.' output: url: >- 34773510.jpeg - text: 'half-body portrait of a female model, in an art studio setting. long, straight black hair, neatly styled, light makeup with rosy lips and soft eye makeup. crisp white button-up shirt, tucked into a flowy pastel pleated skirt, delicate bracelet on wrist. striking a poised pose, one hand gently resting on her hip, the other holding a paintbrush, serene expression, surrounded by canvases and art supplies in the background. soft lighting, warm colors, artistic ambiance, elegant, creative, sophisticated, gallery-quality.' output: url: >- 34773512.jpeg - text: 'half-body portrait of a female model, indoor setting, under warm lighting. long, wavy brown hair, casually tousled, natural makeup with soft pink lips and subtle highlighter. sleek white camisole, fitted and elegant, paired with high-waisted denim shorts, minimal jewelry. striking a confident pose, one hand resting on her hip, the other gently brushing her hair back, inviting smile, in a softly lit studio with neutral-colored walls. soft tones, gentle shadows, relaxed atmosphere, contemporary, chic, fashion-forward, editorial quality.' output: url: >- 34773515.jpeg - text: 'half-body painting of a woman, stylized portrait, contemporary art. short blonde hair, curled, bright red lips, dark eyeliner, subtle blush. black leather jacket, white t-shirt, silver bracelet. standing, arms crossed, serious expression, in front of a graffiti wall, daytime. bold colors, strong contrast, dramatic lighting, expressive, avant-garde, vibrant, gallery-quality.' output: url: >- 34773622.jpeg - text: 'half-body painting of a woman, stylized portrait, contemporary art. short blonde hair, curled, bright red lips, dark eyeliner, subtle blush. black leather jacket, white t-shirt, silver bracelet. standing, arms crossed, serious expression, in front of a graffiti wall, daytime. bold colors, strong contrast, dramatic lighting, expressive, avant-garde, vibrant, gallery-quality.' output: url: >- 34773627.jpeg - text: 'half-body portrait of a woman, styled in a high school uniform theme, with a sultry edge. long, wavy black hair, loosely styled, bold red lips with subtle smokey eye makeup for a seductive look. wearing a fitted white blouse, unbuttoned at the top, paired with an ultra-short plaid skirt, black tie loosely hanging, and knee-high white socks with black heels. standing confidently, one hand on her hip, the other playfully tugging at the tie, alluring smile, set against a dimly lit hallway with lockers in the background. moody lighting, high contrast, bold, edgy, provocative, fashion-forward, editorial quality.' output: url: >- 34773632.jpeg - text: 'half-body portrait of a woman, indoor classroom setting. long, straight black hair, casually styled, natural makeup with a light pink lip tint and soft blush. wearing a cropped sweater, ultra-short plaid pleated skirt, white knee-high socks, paired with casual sneakers. sitting confidently on a desk, one leg slightly bent, hands resting on the desk''s edge, playful smile, surrounded by books and school supplies, with sunlight streaming through the classroom windows. warm tones, soft lighting, casual, youthful, carefree, vibrant, schoolgirl-chic, snapshot-worthy.' output: url: >- 34773619.jpeg - text: 'half-body portrait of an outstanding woman, award-winning photograph. sleek, elegant hairstyle with soft waves cascading over her shoulders, perfectly styled, radiant makeup with flawless foundation, bold red lips, and subtly defined eyes. wearing a sophisticated evening gown, shimmering fabric with delicate embroidery, accessorized with statement earrings, exuding grace and poise. captured mid-smile, warm and captivating, with her gaze slightly off-camera, set against a minimalist background that enhances her presence, soft light highlighting her features and creating a refined, polished effect. rich tones, exquisite lighting, luxurious atmosphere, timeless elegance, masterfully composed, high-end, professional photography quality.' output: url: >- 34773629.jpeg - text: 'half-body portrait of an outstanding woman, award-winning photograph. sleek, elegant hairstyle with soft waves cascading over her shoulders, perfectly styled, radiant makeup with flawless foundation, bold red lips, and subtly defined eyes. wearing a sophisticated evening gown, shimmering fabric with delicate embroidery, accessorized with statement earrings, exuding grace and poise. captured mid-smile, warm and captivating, with her gaze slightly off-camera, set against a minimalist background that enhances her presence, soft light highlighting her features and creating a refined, polished effect. rich tones, exquisite lighting, luxurious atmosphere, timeless elegance, masterfully composed, high-end, professional photography quality.' output: url: >- 34773630.jpeg - text: 'close-up portrait of a woman, indoor setting with professional lighting. long, straight black hair, neatly styled, light makeup with soft pink lips, natural blush, subtle eyeliner. wearing a cropped sweater, ultra-short plaid pleated skirt, paired with white knee-high socks, youthful and trendy. sitting casually with legs crossed, playful smile, soft light emphasizing her fresh and lively look, set against a modern, minimalist studio backdrop. bright tones, soft lighting, youthful, energetic, playful, fashion-forward, social media-ready.' output: url: >- 34773639.jpeg - text: 'half-body portrait of a woman, luxury fashion, high-end advertising. sleek black hair, slicked back, bold red lips, flawless skin, subtle smokey eye makeup. elegant black strapless dress, featuring a Cartier diamond necklace, matching bracelet, and statement ring. standing in a minimalistic, sophisticated studio setting, one hand gently touching the necklace, poised expression, glamorous yet confident. sharp contrasts, soft spotlight, luxurious, refined, timeless, editorial, high-gloss magazine quality.' output: url: >- 34773641.jpeg - text: 'half-body portrait of a woman, in a romantic fantasy setting. chin-length blonde bob cut, softly styled, with gentle waves framing her face, natural makeup with soft pink lips and a dreamy glow. wearing a delicate pastel-colored gown, adorned with lace and subtle shimmer, complemented by a sparkling crystal necklace. standing in an enchanted garden, surrounded by glowing flowers and floating lanterns, hands lightly clasped, gazing upward with a serene smile, as soft light reflects off her hair. muted pastel tones, ethereal lighting, magical atmosphere, enchanting, whimsical, and cinematic fantasy quality.' output: url: >- 34773646.jpeg - text: 'half-body portrait of a woman, in a romantic fantasy setting. chin-length blonde bob cut, softly styled, with gentle waves framing her face, natural makeup with soft pink lips and a dreamy glow. wearing a delicate pastel-colored gown, adorned with lace and subtle shimmer, complemented by a sparkling crystal necklace. standing in an enchanted garden, surrounded by glowing flowers and floating lanterns, hands lightly clasped, gazing upward with a serene smile, as soft light reflects off her hair. muted pastel tones, ethereal lighting, magical atmosphere, enchanting, whimsical, and cinematic fantasy quality.' output: url: >- 34773645.jpeg --- # Danielle Rose Russell SoloLoRA <Gallery /> ## Model description <h3 id="danielle-rose-russell-an-american-actress.-co7hq7ti1">Danielle Rose Russell, an American actress.</h3><p></p><p><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/1355e708-bd89-4be2-8789-63b82f6e0d73/width=525/1355e708-bd89-4be2-8789-63b82f6e0d73.jpeg" /><span style="color:rgb(250, 176, 5)">TI(Embedding) version: </span><a target="_blank" rel="ugc" href="https://civitai.com/models/782089/danielle-rose-russell-soloti">https://civitai.com/models/782089/danielle-rose-russell-soloti</a></p> ## Download model Weights for this model are available in Safetensors format. [Download](/Keltezaa/danielle-rose-russell-sololora/tree/main) them in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch device = "cuda" if torch.cuda.is_available() else "cpu" pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to(device) pipeline.load_lora_weights('Keltezaa/danielle-rose-russell-sololora', weight_name='DanielleRR_SoloLoRA_F1V1.safetensors') image = pipeline('half-body portrait of a woman, in a romantic fantasy setting. chin-length blonde bob cut, softly styled, with gentle waves framing her face, natural makeup with soft pink lips and a dreamy glow. wearing a delicate pastel-colored gown, adorned with lace and subtle shimmer, complemented by a sparkling crystal necklace. standing in an enchanted garden, surrounded by glowing flowers and floating lanterns, hands lightly clasped, gazing upward with a serene smile, as soft light reflects off her hair. muted pastel tones, ethereal lighting, magical atmosphere, enchanting, whimsical, and cinematic fantasy quality.').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)
locuslab/base-smollm2-1.7b-all_raw_folders_metadata-600B	locuslab	2025-02-25T21:35:55Z	0	0	null	[ "pytorch", "llama", "model", "transformer", "smollm2", "license:mit", "region:us" ]	null	2025-02-25T21:30:44Z	--- version: main family: smollm2-1.7b model_name: all_raw_folders_metadata-600B license: mit tags: - model - transformer - smollm2 --- # SmolLM2 all_raw_folders_metadata-600B (Version: main) ## Model Details - Architecture: SmolLM2 - Parameters: 1.7B ## Training Configuration ```yaml optimizer: class_path: torch.optim.AdamW init_args: lr: 0.0005 weight_decay: 0.01 precision: bf16-mixed seed: 42 train: global_batch_size: 1024 max_seq_length: 2048 max_tokens: 600000000000 micro_batch_size: 8 ``` ## Model Loading and Revision System This repository hosts multiple revisions of the model. To load a specific revision, use the `revision` parameter. For example: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("locuslab/all_raw_folders_metadata-600B", revision="final") tokenizer = AutoTokenizer.from_pretrained("locuslab/all_raw_folders_metadata-600B", revision="final") ``` Replace `"final"` with the desired revision.
robiulawaldev/871d18cd-5289-4306-91b7-289196f4e217	robiulawaldev	2025-02-25T21:33:30Z	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:HuggingFaceH4/zephyr-7b-beta", "base_model:adapter:HuggingFaceH4/zephyr-7b-beta", "license:mit", "region:us" ]	null	2025-02-25T19:08:52Z	--- library_name: peft license: mit base_model: HuggingFaceH4/zephyr-7b-beta tags: - axolotl - generated_from_trainer model-index: - name: 871d18cd-5289-4306-91b7-289196f4e217 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. --> # 871d18cd-5289-4306-91b7-289196f4e217 This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0762 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
valoomba/FuseO1-Tool-Support	valoomba	2025-02-25T21:33:23Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "unsloth", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-02-25T20:19:22Z	--- 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]

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