Split (1)

train · 880k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-14 06:27:53	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 519 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-14 06:27:45	card stringlengths 11 1.01M
rrreol69/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-thick_shrewd_gibbon	rrreol69	2025-04-02T07:39:09Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am thick shrewd gibbon", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T07:38:45Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-thick_shrewd_gibbon tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am thick shrewd gibbon - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-thick_shrewd_gibbon This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="rrreol69/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-thick_shrewd_gibbon", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.50.3 - Pytorch: 2.5.1 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
KingEmpire/sn9_pre_c04_12	KingEmpire	2025-04-02T07:37:52Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T03:06:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
Brianpuze/Qwen2.5-0.5B-Q4_K_M-Q3_K_M-GGUF-TEST	Brianpuze	2025-04-02T07:37:02Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:Qwen/Qwen2.5-0.5B", "base_model:quantized:Qwen/Qwen2.5-0.5B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-02T07:36:36Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-0.5B/blob/main/LICENSE language: - en pipeline_tag: text-generation library_name: transformers tags: - llama-cpp - gguf-my-repo base_model: Qwen/Qwen2.5-0.5B --- # Brianpuze/Qwen2.5-0.5B-Q4_K_M-Q3_K_M-GGUF-TEST This repo contains GGUF quantized versions of [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B) using llama.cpp. ## Quantized Versions: - qwen2.5-0.5b-q4_k_m.gguf - qwen2.5-0.5b-q3_k_m.gguf ## Run with llama.cpp llama-cli --hf-repo Brianpuze/Qwen2.5-0.5B-Q4_K_M-Q3_K_M-GGUF-TEST --hf-file qwen2.5-0.5b-q4_k_m.gguf -p "The meaning of life is" (Replace filename to use other variants.)
psyonp/Final-Llama-Math-1L	psyonp	2025-04-02T07:35:40Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T07:32:26Z	--- 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. 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]
inrainbws/resnet_r16_mlora	inrainbws	2025-04-02T07:33:08Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-02T07:32:51Z	--- 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]
syyhmg/unsloth_finetune	syyhmg	2025-04-02T07:29:50Z	0	0	transformers	[ "transformers", "mllama", "image-text-to-text", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-04-02T07:29:34Z	--- base_model: unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mllama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: syyhmg - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit This mllama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
John6666/nova-anime-xl-il-v60-sdxl	John6666	2025-04-02T07:28:43Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "hentai", "2D", "2.5D", "illustration", "fantasy", "landscape", "colorful", "digital art", "posing", "merge", "noobai", "Illustrious XL v1.1", "illustrious", "en", "base_model:Laxhar/noobai-XL-1.1", "base_model:merge:Laxhar/noobai-XL-1.1", "base_model:OnomaAIResearch/Illustrious-XL-v1.1", "base_model:merge:OnomaAIResearch/Illustrious-XL-v1.1", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-04-02T07:18:50Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - hentai - 2D - 2.5D - illustration - fantasy - landscape - colorful - digital art - posing - merge - noobai - Illustrious XL v1.1 - illustrious base_model: - OnomaAIResearch/Illustrious-XL-v1.1 - Laxhar/noobai-XL-1.1 --- Original model is [here](https://civitai.com/models/376130/nova-anime-xl?modelVersionId=1610231). This model created by [Crody](https://civitai.com/user/Crody).
mergekit-community/MN-Hekate-Daidalos-17B	mergekit-community	2025-04-02T07:28:28Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.19522", "base_model:DavidAU/MN-Dark-Planet-TITAN-12B", "base_model:merge:DavidAU/MN-Dark-Planet-TITAN-12B", "base_model:EpistemeAI/Mistral-Nemo-Instruct-12B-Philosophy-Math", "base_model:merge:EpistemeAI/Mistral-Nemo-Instruct-12B-Philosophy-Math", "base_model:FallenMerick/MN-Violet-Lotus-12B", "base_model:merge:FallenMerick/MN-Violet-Lotus-12B", "base_model:jtatman/mistral_nemo_12b_reasoning_psychology_lora", "base_model:merge:jtatman/mistral_nemo_12b_reasoning_psychology_lora", "base_model:mergekit-community/MN-Hekate-Anassa-17B", "base_model:merge:mergekit-community/MN-Hekate-Anassa-17B", "base_model:mergekit-community/MN-Hekate-Panopaia-12B", "base_model:merge:mergekit-community/MN-Hekate-Panopaia-12B", "base_model:mergekit-community/MN-Sappho-j-12B", "base_model:merge:mergekit-community/MN-Sappho-j-12B", "base_model:mistralai/Mistral-Nemo-Base-2407", "base_model:merge:mistralai/Mistral-Nemo-Base-2407", "base_model:nbeerbower/mistral-nemo-wissenschaft-12B", "base_model:merge:nbeerbower/mistral-nemo-wissenschaft-12B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T07:14:09Z	--- base_model: - FallenMerick/MN-Violet-Lotus-12B - mistralai/Mistral-Nemo-Base-2407 - nbeerbower/mistral-nemo-wissenschaft-12B - EpistemeAI/Mistral-Nemo-Instruct-12B-Philosophy-Math - mergekit-community/MN-Hekate-Panopaia-12B - DavidAU/MN-Dark-Planet-TITAN-12B - mergekit-community/MN-Sappho-j-12B - jtatman/mistral_nemo_12b_reasoning_psychology_lora - mergekit-community/MN-Hekate-Anassa-17B 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 [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [mergekit-community/MN-Hekate-Anassa-17B](https://huggingface.co/mergekit-community/MN-Hekate-Anassa-17B) as a base. ### Models Merged The following models were included in the merge: * [FallenMerick/MN-Violet-Lotus-12B](https://huggingface.co/FallenMerick/MN-Violet-Lotus-12B) * [mistralai/Mistral-Nemo-Base-2407](https://huggingface.co/mistralai/Mistral-Nemo-Base-2407) * [nbeerbower/mistral-nemo-wissenschaft-12B](https://huggingface.co/nbeerbower/mistral-nemo-wissenschaft-12B) * [EpistemeAI/Mistral-Nemo-Instruct-12B-Philosophy-Math](https://huggingface.co/EpistemeAI/Mistral-Nemo-Instruct-12B-Philosophy-Math) * [mergekit-community/MN-Hekate-Panopaia-12B](https://huggingface.co/mergekit-community/MN-Hekate-Panopaia-12B) * [DavidAU/MN-Dark-Planet-TITAN-12B](https://huggingface.co/DavidAU/MN-Dark-Planet-TITAN-12B) * [mergekit-community/MN-Sappho-j-12B](https://huggingface.co/mergekit-community/MN-Sappho-j-12B) + [jtatman/mistral_nemo_12b_reasoning_psychology_lora](https://huggingface.co/jtatman/mistral_nemo_12b_reasoning_psychology_lora) ### Configuration The following YAML configuration was used to produce this model: ```yaml dtype: float32 out_dtype: bfloat16 merge_method: model_stock base_model: mergekit-community/MN-Hekate-Anassa-17B slices: - sources: - model: mergekit-community/MN-Hekate-Anassa-17B layer_range: [0, 20] - sources: - model: mergekit-community/MN-Hekate-Anassa-17B layer_range: [20, 36] - model: mergekit-community/MN-Hekate-Panopaia-12B layer_range: [16, 32] - model: mistralai/Mistral-Nemo-Base-2407 layer_range: [16, 32] parameters: weight: [2, 1] - model: FallenMerick/MN-Violet-Lotus-12B layer_range: [16, 32] parameters: weight: [1.5, 1.49, 1.46, 1.4, 1.33, 1.25, 1.15, 1.05, 1] - model: nbeerbower/mistral-nemo-wissenschaft-12B layer_range: [16, 32] parameters: weight: [1.5, 1.49, 1.46, 1.4, 1.33, 1.25, 1.15, 1.05, 1] - model: EpistemeAI/Mistral-Nemo-Instruct-12B-Philosophy-Math layer_range: [16, 32] parameters: weight: [1.5, 1.49, 1.46, 1.4, 1.33, 1.25, 1.15, 1.05, 1] - model: mergekit-community/MN-Sappho-j-12B+jtatman/mistral_nemo_12b_reasoning_psychology_lora layer_range: [16, 32] parameters: weight: [1.0, 1.1, 1.2, 1.29, 1.37, 1.43, 1.48, 1.5, 1.5] - model: DavidAU/MN-Dark-Planet-TITAN-12B layer_range: [16, 32] parameters: weight: [1.0, 1.1, 1.2, 1.29, 1.37, 1.43, 1.48, 1.5, 1.5] - sources: - model: mergekit-community/MN-Hekate-Anassa-17B layer_range: [36, 56] tokenizer: source: union tokens: "[INST]": source: mergekit-community/MN-Hekate-Anassa-17B force: true "[/INST]": source: mergekit-community/MN-Hekate-Anassa-17B force: true "<\|im_start\|>": source: mergekit-community/MN-Hekate-Anassa-17B force: true "<\|im_end\|>": source: mergekit-community/MN-Hekate-Anassa-17B force: true ```
dwentzart/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scented_fierce_cow	dwentzart	2025-04-02T07:28:04Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am scented fierce cow", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T06:03:24Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scented_fierce_cow tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am scented fierce cow - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scented_fierce_cow This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="dwentzart/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scented_fierce_cow", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.50.3 - Pytorch: 2.5.1 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
MinaMila/llama_instbase_unlearned_Adult_13ep_22	MinaMila	2025-04-02T07:27:22Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:MinaMila/llama3_unlearning_general_methode", "base_model:finetune:MinaMila/llama3_unlearning_general_methode", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T07:24:05Z	--- base_model: MinaMila/llama3_unlearning_general_methode tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: MinaMila - License: apache-2.0 - Finetuned from model : MinaMila/llama3_unlearning_general_methode 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)
PrunaAI/agentica-org-DeepScaleR-1.5B-Preview-bnb-8bit-smashed	PrunaAI	2025-04-02T07:25:56Z	2	0	null	[ "safetensors", "qwen2", "pruna-ai", "8-bit", "bitsandbytes", "region:us" ]	null	2025-03-26T03:06:51Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: ORIGINAL_REPO_NAME metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with llm-int8. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We use safetensors. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo ORIGINAL_REPO_NAME installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install transformers accelerate bitsandbytes>0.37.0 ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("PrunaAI/agentica-org-DeepScaleR-1.5B-Preview-bnb-8bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("ORIGINAL_REPO_NAME") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model ORIGINAL_REPO_NAME before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
wandererupak/wave2vec-bert-oslrULTIMATECOLAB-TAKE-4	wandererupak	2025-04-02T07:25:23Z	0	0	transformers	[ "transformers", "safetensors", "wav2vec2-bert", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-04-02T07:03:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
kk-aivio/0860c4d4-0dc2-4cec-9d3e-ef121780fd82	kk-aivio	2025-04-02T07:24:27Z	0	0	peft	[ "peft", "generated_from_trainer", "base_model:unsloth/mistral-7b-instruct-v0.3", "base_model:adapter:unsloth/mistral-7b-instruct-v0.3", "region:us" ]	null	2025-04-02T07:23:18Z	--- library_name: peft tags: - generated_from_trainer base_model: unsloth/mistral-7b-instruct-v0.3 model-index: - name: kk-aivio/0860c4d4-0dc2-4cec-9d3e-ef121780fd82 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. --> # kk-aivio/0860c4d4-0dc2-4cec-9d3e-ef121780fd82 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5557 ## 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
prithivMLmods/Pocket-Llama-3.2-3B-Instruct	prithivMLmods	2025-04-02T07:24:09Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "Llama3.2", "Pocket-Llama", "text-generation-inference", "Code", "Math", "conversational", "en", "base_model:meta-llama/Llama-3.2-3B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-3B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-01T06:38:11Z	--- library_name: transformers tags: - Llama3.2 - Pocket-Llama - text-generation-inference - Code - Math license: apache-2.0 language: - en base_model: - meta-llama/Llama-3.2-3B-Instruct pipeline_tag: text-generation --- ![dfeghdcgfhdfhtg.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/v8nWhzmIv6kPS78Ude3md.png) # Pocket-Llama-3.2-3B-Instruct > Pocket-Llama-3.2-3B-Instruct is based on the Llama 3.2 architecture, designed as a lightweight and efficient general-purpose chat assistant. Optimized for fast inference while maintaining strong problem-solving, mathematical reasoning, and scientific capabilities. This model is fine-tuned for enhanced structured reasoning, minimal token wastage, and high-quality technical responses. ## Key Improvements 1. Optimized for General Purpose Chat: Excels in a wide range of topics, including casual conversation, technical discussions, and knowledge-based queries. 2. Strong Math & Science Capabilities: Provides accurate and structured explanations for mathematical and scientific problems. 3. Compact yet Powerful: Maintains strong problem-solving capabilities within a smaller 3B parameter architecture, ensuring accessibility on resource-limited devices. 4. Advanced Reasoning Capabilities: Excels in algorithmic problem-solving, structured technical explanations, and logical analysis. 5. Efficient Memory Utilization: Reduces computational overhead while maintaining high-quality outputs. 6. Focused Output Generation: Avoids unnecessary token generation, ensuring concise and relevant responses. ## Quickstart with transformers Here is a code snippet to load the tokenizer and model using `apply_chat_template` for structured input formatting: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "prithivMLmods/Pocket-Llama-3.2-3B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Explain the theory of relativity in simple terms." messages = [ {"role": "system", "content": "You are an advanced assistant specialized in science and mathematics."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( model_inputs, max_new_tokens=6090 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(response) ``` ## Intended Use 1. General Chat & Knowledge-Based Queries: Engages in informative and casual discussions on a wide range of topics. 2. Mathematics & Science Problem Solving: Provides accurate calculations and structured explanations for complex problems. 3. Technical Documentation & Explanation: Assists in generating well-structured documentation for APIs, scientific concepts, and coding principles. 4. Debugging Assistance: Helps identify and correct errors in code snippets. 5. Educational Support: Simplifies complex topics for students and learners with clear explanations. 6. Structured Data Processing: Generates structured outputs like JSON, XML, and tables for data science applications. ## Limitations 1. Hardware Constraints: Although lighter than larger models, still requires a moderately powerful GPU or TPU for optimal performance. 2. Potential Bias in Responses: Outputs may reflect biases present in training data. 3. Limited Creativity: May generate variable results in non-technical, creative tasks. 4. No Real-Time Awareness: Lacks access to real-world events beyond its training cutoff. 5. Error Propagation in Long Responses: Minor mistakes in early outputs may affect overall coherence in lengthy responses. 6. Prompt Sensitivity**: The effectiveness of responses depends on well-structured prompts.
xw17/TinyLlama-1.1B-Chat-v1.0_finetuned_4_def_lora	xw17	2025-04-02T07:23:13Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-03-31T03:07:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
pritmanvar/Llama-2-7b-chat-hf-fine-tuned	pritmanvar	2025-04-02T07:23:04Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T06:31:32Z	--- 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]
cogmhear/avse4_baseline	cogmhear	2025-04-02T07:22:54Z	0	0	null	[ "en", "license:cc-by-nc-4.0", "region:us" ]	null	2025-04-02T07:14:16Z	--- license: cc-by-nc-4.0 language: - en --- ## Baseline model for 4th COG-MHEAR Audio-Visual Speech Enhancement Challenge [Challenge link](https://challenge.cogmhear.org/) ## Requirements * [Python >= 3.6](https://www.anaconda.com/docs/getting-started/miniconda/install) * [PyTorch](https://pytorch.org/) * [PyTorch Lightning](https://lightning.ai/docs/pytorch/latest/) * [Decord](https://github.com/dmlc/decord) * [Hydra](https://hydra.cc) * [SpeechBrain](https://github.com/speechbrain/speechbrain) * [TQDM](https://github.com/tqdm/tqdm) ## Usage ```bash # Expected folder structure for the dataset data_root \|-- train \| `-- scenes \|-- dev \| `-- scenes \|-- eval \| `-- scenes ``` ### Clone the repo ```bash git clone https://github.com/cogmhear/avse_challenge cd avse_challenge/baseline/avse4 ``` ### Train ```bash python train.py data.root="./avsec4" data.num_channels=2 trainer.log_dir="./logs" data.batch_size=8 trainer.accelerator gpu trainer.gpus 1 more arguments in conf/train.yaml ``` ### Test ```bash python test.py data.root=./avsec4 data.num_channels=2 ckpt_path=pretrained.ckpt save_dir="./eval" model_uid="./avse4" more arguments in conf/eval.yaml ```
bumie-e/gameplay-dqn-SpaceInvadersNoFrameskip-v4	bumie-e	2025-04-02T07:21:35Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-04-02T07:20:54Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 162.00 +/- 21.00 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib SBX (SB3 + Jax): https://github.com/araffin/sbx Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga bumie-e -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga bumie-e -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga bumie-e ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.001), ('learning_starts', 100000), ('n_timesteps', 100000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
mlfoundations-dev/30k_globalbatchsize96_lr1e5_epochs5	mlfoundations-dev	2025-04-02T07:19:01Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-01T17:41:20Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: 30k_globalbatchsize96_lr1e5_epochs5 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. --> # 30k_globalbatchsize96_lr1e5_epochs5 This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/openthoughts_30000 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 32 - gradient_accumulation_steps: 3 - total_train_batch_size: 96 - total_eval_batch_size: 256 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.3.0 - Datasets 3.1.0 - Tokenizers 0.20.3
John6666/hana4chrome-v20-sdxl	John6666	2025-04-02T07:18:29Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "waifu", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-xl-early-release-v0", "base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-04-02T07:10:21Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - waifu - illustrious base_model: OnomaAIResearch/Illustrious-xl-early-release-v0 --- Original model is [here](https://civitai.com/models/1422278?modelVersionId=1610153). This model created by [CHROMEKIDD](https://civitai.com/user/CHROMEKIDD).
gabibguti/flower-prediction-model-hf	gabibguti	2025-04-02T07:17:49Z	0	0	safe-ai-models	[ "safe-ai-models", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-04-02T07:17:44Z	--- library_name: safe-ai-models tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: https://github.com/gabibguti/safe-ai-models - Paper: [More Information Needed] - Docs: [More Information Needed]
erax-ai/EraX-VL-7B-V1.5	erax-ai	2025-04-02T07:16:55Z	367	6	transformers	[ "transformers", "safetensors", "qwen2_vl", "image-text-to-text", "erax", "multimodal", "erax-vl-7B", "insurance", "ocr", "vietnamese", "bcg", "image-to-text", "visual-question-answering", "vi", "en", "zh", "arxiv:2308.12966", "arxiv:2407.10671", "arxiv:2404.16821", "arxiv:2404.07922", "base_model:Qwen/Qwen2-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2-VL-7B-Instruct", "doi:10.57967/hf/3934", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	visual-question-answering	2024-11-26T00:51:41Z	--- license: apache-2.0 language: - vi - en - zh base_model: - Qwen/Qwen2-VL-7B-Instruct library_name: transformers tags: - erax - multimodal - erax-vl-7B - insurance - ocr - vietnamese - bcg - image-to-text pipeline_tag: visual-question-answering widget: - src: images/photo-1-16505057982762025719470.webp example_title: Test 1 - src: images/vt-don-thuoc-f0-7417.jpeg example_title: Test 2 --- <p align="left"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63d8d8879dfcfa941d4d7cd9/GsQKdaTyn2FFx_cZvVHk3.png" alt="Logo"> </p> # EraX-VL-7B-V1.5 ## Introduction 🎉 Hot on the heels of the popular <a href="https://huggingface.co/erax-ai/EraX-VL-7B-V1.0" target="_blank">EraX-VL-7B-V1.0 model</a>, we proudly present EraX-VL-7B-V1.5, another robust multimodal model for OCR (optical character recognition) and VQA (visual question-answering) that excels in various languages 🌍, with a particular focus on Vietnamese 🇻🇳. This model stands out for its precise recognition capabilities across a range of documents 📝, including medical forms 🩺, invoices 🧾, bills of sale 💳, quotes 📄, and medical records 💊. This functionality is expected to be highly beneficial for hospitals 🏥, clinics 💉, insurance companies 🛡️, and other similar applications 📋. Built on the solid foundation of the [Qwen/Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct)[1], which we found to be of high quality and fluent in Vietnamese, `EraX-VL-7B-V1.5` has been fine-tuned to enhance its performance. We plan to continue improving and releasing new versions for free, along with sharing performance benchmarks in the near future. One standing-out feature of EraX-VL-7B-V1.5 is the capability to do multi-turn Q&A with impressive reasoning capability! NOTA BENE: - EraX-VL-7B-V1.5 is NOT a typical OCR-only tool likes Tesseract but is a Multimodal LLM-based model. To use it effectively, you may have to twist your prompt carefully depending on your tasks. - This model was NOT finetuned with medical (X-ray) dataset or car accidences (yet). Stay tune for updated version coming up sometime early 2025. EraX-VL-7B-V1.5 is a young member of our EraX's LànhGPT collection of LLM models. - Developed by: - Nguyễn Anh Nguyên ([email protected]) - Nguyễn Hồ Nam (BCG) - Phạm Huỳnh Nhật ([email protected]) - Phạm Đình Thục ([email protected]) - Funded by: [Bamboo Capital Group](https://bamboocap.com.vn) and EraX - Model type: Multimodal Transformer with over 7B parameters - Languages (NLP): Primarily Vietnamese with multilingual capabilities - License: Apache 2.0 - Fine-tuned from: [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) - Prompt examples: <a href="https://github.com/EraX-JS-Company/erax-vl-7b-v1/blob/main/prompts/Vietnam_popular_prompts.txt" target="_blank">Some popular prompt examples on Github.</a> ## Benchmarks 📊 ## 🏆 LeaderBoard The EraX-VL-7B-V1.5 achieved exceptionally high performance compared to other models of equal size or even 10 times larger, and we open-source! You can re-run the benchmark at any time. <table style="width:75%;"> <tr> <th align="middle" width="300">Models</th> <td align="middle" width="150"><b>Open-Source</b></td> <td align="middle" width="300"><b>VI-MTVQA</b></td> </tr> <tr> <th align="middle"><font color=darkred>EraX-VL-7B-V1.5 🥇 </font></th> <td align="middle">✅</td> <td align="middle">47.2 </td> </tr> <tr> <th align="middle">Qwen2-VL 72B 🥈 </th> <td align="middle">✘</td> <td align="middle">41.6 </td> </tr> <tr> <th align="middle">ViGPT-VL 🥉 </th> <td align="middle">✘</td> <td align="middle">39.1 </td> </tr> <tr> <th align="middle"><font color=darkred>EraX-VL-2B-V1.5</font></th> <td align="middle"> ✅ </td> <td align="middle">38.2 </td> </tr> <tr> <th align="middle"><font color=darkred>EraX-VL-7B-V1 </font></th> <td align="middle"> ✅ </td> <td align="middle">37.6 </td> </tr> <tr> <th align="middle"><font color=darkred>Vintern-1B-V2</font></th> <td align="middle"> ✅ </td> <td align="middle">37.4 </td> </tr> <tr> <th align="middle"><font color=darkred>Qwen2-VL 7B </font></th> <td align="middle"> ✅ </td> <td align="middle">30.0 </td> </tr> <tr> <th align="middle">Claude3 Opus</th> <td align="middle">✘</td> <td align="middle">29.1 </td> </tr> <tr> <th align="middle">GPT-4o mini </th> <td align="middle"> ✘ </td> <td align="middle">29.1 </td> </tr> <tr> <th align="middle">GPT-4V</th> <td align="middle">✘</td> <td align="middle">28.9 </td> </tr> <tr> <th align="middle">Gemini Ultra</th> <td align="middle">✘</td> <td align="middle">28.6 </td> </tr> <tr> <th align="middle"><font color=darkred>InternVL2 76B</font></th> <td align="middle"> ✅ </td> <td align="middle">26.9 </td> </tr> <tr> <th align="middle">QwenVL Max</th> <td align="middle">✘</td> <td align="middle">23.5 </td> </tr> <tr> <th align="middle">Claude3 Sonnet</th> <td align="middle">✘</td> <td align="middle">20.8 </td> </tr> <tr> <th align="middle">QwenVL Plus</th> <td align="middle">✘</td> <td align="middle">18.1 </td> </tr> <tr> <th align="middle"><font color=darkred>MiniCPM-V2.5</font></th> <td align="middle">✅</td> <td align="middle">15.3 </td> </tr> </table> The test code for evaluating models in the paper can be found in: <b><a href="https://github.com/EraX-JS-Company/EraX-MTVQA-Benchmark" target="_blank">EraX-JS-Company/EraX-MTVQA-Benchmark</a></b> ## API trial 🎉 Please contact [email protected] for API access inquiry. ## Examples 🧩 ### 1. OCR - Optical Character Recognition for Multi-Images Example 01: Citizen identification card <div style="display: flex; flex-direction: row; align-items: center; justify-content: center;"> <div style="text-align: center; margin: 0 10px;"> <img src="images/trinhquangduy_front.jpg" width="500" alt="Front View" /> <p>Front View</p> </div> <div style="text-align: center; margin: 0 10px;"> <img src="images/trinhquangduy_back.jpg" width="500" alt="Back View" /> <p>Back View</p> </div> </div> <p style="text-align: center; font-size: 12px; color: gray; margin-top: 10px;"> Source: <a href="https://support.google.com/google-ads/thread/270967947/t%C3%B4i-%C4%91%C3%A3-g%E1%BB%ADi-h%C3%ACnh-%E1%BA%A3nh-c%C4%83n-c%C6%B0%E1%BB%9Bc-c%C3%B4ng-d%C3%A2n-c%E1%BB%A7a-ch%C3%ADnh-t%C3%B4i-%C4%91%E1%BB%83-x%C3%A1c-minh-danh-t%C3%ADnh?hl=vi" target="_blank">Google Support</a> </p> ``` { "Số thẻ": "037094012351", "Họ và tên": "TRỊNH QUANG DUY", "Ngày sinh": "04/09/1994", "Giới tính": "Nam", "Quốc tịch": "Việt Nam", "Quê quán": "Tân Thành, Kim Sơn, Ninh Bình", "Nơi thường trú": "Xóm 6\nTân Thành, Kim Sơn, Ninh Bình", "Có giá trị đến": "04/09/2034", "Đặc điểm nhân dạng": "sẹo chấm c. 1cm trên đuôi mắt trái", "Nơi cấp": "CỤC TRƯỞNG CỤC CẢNH SÁT\nQUẢN LÝ HÀNH CHÍNH VỀ TRẬT TỰ XÃ HỘI", "Ngày cấp": "10/12/2022", "Cán bộ ký tên": "Nguyễn Quốc Hùng", "Mã định danh": "IDVNM0940123513037094012351" } ``` Example 02: Driver's License <div style="display: flex; flex-direction: row; align-items: center; justify-content: center;"> <div style="text-align: center; margin: 0 10px;"> <img src="images/nguyenvandung_front.png" width="500" alt="Front View" /> <p>Front View</p> </div> <div style="text-align: center; margin: 0 10px;"> <img src="images/nguyenvandung_back.png" width="500" alt="Back View" /> <p>Back View</p> </div> </div> <p style="text-align: center; font-size: 12px; color: gray; margin-top: 10px;"> Source: <a href="https://baophapluat.vn/khoi-to-tai-xe-len-mang-mua-giay-phep-lai-xe-gia-de-chay-xe-post481047.html" target="_blank">Báo Pháp luật</a> </p> ``` { "No.":"400116012313" "Fullname":"NGUYỄN VĂN DŨNG" "Date_of_birth":"08/06/1979" "Nationality":"VIỆT NAM" "Address":"X. Quỳnh Hầu, H. Quỳnh Lưu, T. Nghệ An Nghệ An, ngày/date 23 tháng/month 04 năm/year 2022" "Hang_Class":"FC" "Expires":"23/04/2027" "Place_of_issue":"Nghệ An" "Date_of_issue":"ngày/date 23 tháng/month 04 năm/year 2022" "Signer":"Trần Anh Tuấn" "Các loại xe được phép":"Ô tô hạng C kéo rơmoóc, đầu kéo kéo sơmi rơmoóc và xe hạng B1, B2, C, FB2 (Motor vehicle of class C with a trailer, semi-trailer truck and vehicles of classes B1, B2, C, FB2)" "Mã số":"" } ``` Example 03: Vehicle Registration Certificate <div style="display: flex; flex-direction: row; align-items: center; justify-content: center;"> <div style="text-align: center; margin: 0 10px;"> <img src="images/nguyentonnhuan.jpg" width="700"/> </div> </div> <p style="text-align: center; font-size: 12px; color: gray; margin-top: 10px;"> Source: <a href="https://vietnamnet.vn/phan-biet-cac-loai-giay-dang-ky-xe-khi-mua-moto-da-qua-su-dung-541341.html" target="_blank">Báo Vietnamnet</a> </p> ``` { "Tên chủ xe": "NGUYỄN TÔN NHUẬN", "Địa chỉ": "KE27 Kp3 P.TTTây Q7", "Nhãn hiệu": "HONDA", "Số loại": "DYLAN", "Màu sơn": "Trắng", "Năm sản xuất": "2012", "Số máy": "F03E-0057735", "Số khung": "SA04F-070410", "Dung tích": "152", "Số chỗ ngồi": "02", "Biển số đăng ký": "59V1-498.89", "Đăng ký lần đầu ngày": "08/06/2004", "Chức vụ": "Thượng tá", "Người ký": "Trần Văn Hiểu" } ``` Example 04: Vehicle Registration <div style="display: flex; flex-direction: row; align-items: center; justify-content: center;"> <div style="text-align: center; margin: 10 20px;"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63d8d8879dfcfa941d4d7cd9/w5WCaQ-k9nupRIQYddcpr.jpeg" width="700"/> </div> </div> <p style="text-align: center; font-size: 12px; color: gray; margin-top: 10px;"> Source: <a href="https://llumar.com.vn/dang-kiem-xe-o-to/" target="_blank">https://llumar.com.vn</a> </p> ``` { "vehicle": { "registration_number": "30A-072.36", "vehicle_inspection_number": "2903V-093515", "type": "ô tô con", "mark": "MERCEDES-BENZ", "model_code": "C300 W204", "engine_number": "27294732096079", "chassis_number": "RLMGF5EX3DV005333", "manufactured_year_and_country": "2013, Việt Nam", "life_time_limit_to": "", "commercial_use": "", "modification": "" }, "specifications": { "wheel_formula": "4x2", "wheel_tread": "1521/1512 (mm)", "overall_dimension": "4650 x 1770 x 1429 (mm)", "largest_luggage_container_dimension": "", "wheelbase": "2760 (mm)", "kerb_mass": "1575 (kg)", "design_authorized_pay_load": "", "design_authorized_total_mass": "2090/2090 (kg)", "design_authorized_towed_mass": "", "permissible_number_of_pers_carried": "5 chỗ ngồi, 0 chỗ đứng, 0 chỗ nằm", "type_of_fuel_used": "Xăng", "engine_displacement": "2996 (cm3)", "max_output_per_rpm": "170(kW)/6000vph", "number": "KC-1292285" }, "inspection_report_number": "2905V-20953/16", "valid_until": "31/01/2018", "place_date_of_issue": "Hà Nội, ngày 1 tháng 8 năm 2016", "inspection_center": "ĐƠN VỊ KIỂM ĐỊNH XE CƠ GIỚI", "signature": "Ngọc Tuấn", "equipped_with_tachograph": "", "inspection_stamp_was_not_issued": "", "notes": "Biển đăng ký nền trắng" } ``` Example 05: Receipt <div style="display: flex; flex-direction: row; align-items: center; justify-content: center;"> <div style="text-align: center; margin: 10 20px;"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63d8d8879dfcfa941d4d7cd9/40vIbNdM1cFXwQYNHx7Ag.jpeg" width="500"/> </div> </div> <p style="text-align: center; font-size: 12px; color: gray; margin-top: 10px;"> Source: <a href="https://tintucketoan.com/cach-viet-hoa-don-hang-hoa-dich-vu-khong-chiu-thue-gtgt/" target="_blank">https://tintucketoan.com/</a> </p> ``` { 'Mẫu số': '01GKTKT3/001', 'Ký hiệu': 'TC/18P', 'Số': '0000030', 'Họ tên người mua hàng': None, 'Tên đơn vị': 'Công Ty TNHH Kế Toán Hà Nội', 'Mã số thuế': '0106235869', 'Địa chỉ': 'Số 49 Ngõ 322 Lê Trọng Tấn, phường Khương Mai, quận Thanh Xuân, Hà Nội', 'Hình thức thanh toán': 'TM', 'STT': None, 'Tên hàng hóa, dịch vụ': 'Tra cứu phần mềm thư viện pháp luật trực tuyến', 'Đơn vị tính': None, 'Số lượng': None, 'Đơn giá': '168.000', 'Thành tiền': '2.016.000', 'Thuế suất GTGT': None, 'Tiền thuế GTGT': None, 'Tổng cộng tiền thanh toán': '2.016.000', 'Số tiền viết bằng chữ': 'Hai triệu, không trăm mười sáu nghìn đồng', 'Người bán hàng': 'Bùi Văn Hùng', 'Chức vụ người bán hàng': 'TRƯỞNG CHI NHÁNH' } ``` ### 2.1 Image Captioning <div align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63d8d8879dfcfa941d4d7cd9/g5V60A7rI94TH0z3zdSAA.jpeg" width="700"/> </div> Hình ảnh là biểu đồ BMI theo tuổi, thể hiện mối quan hệ giữa chỉ số khối cơ thể (BMI) và độ tuổi của trẻ em. Biểu đồ được chia thành các vùng màu khác nhau tương ứng với các mức BMI khác nhau: * Vùng màu đỏ: Chỉ số BMI cao hơn 25, cho thấy tình trạng béo phì. * Vùng màu vàng: Chỉ số BMI nằm trong khoảng từ 18 đến 25, cho thấy nguy cơ béo phì. * Vùng màu xanh lá cây nhạt: Chỉ số BMI nằm trong khoảng từ 16 đến 18, cho thấy sức khỏe dinh dưỡng tốt. * Vùng màu xanh lá cây đậm: Chỉ số BMI thấp hơn 16, cho thấy tình trạng thiếu cân. Trục tung biểu diễn chỉ số BMI, trục hoành biểu diễn tuổi (tính bằng năm). Đường cong màu xám đậm thể hiện đường chuẩn BMI theo tuổi. Các đường cong này cho thấy sự thay đổi BMI theo thời gian, giúp đánh giá sự phát triển cân nặng của trẻ em. Ví dụ, ở trẻ em dưới 3 tuổi, BMI thường dao động trong vùng thiếu cân hoặc sức khỏe dinh dưỡng tốt. Khi trẻ lớn lên, BMI có xu hướng tăng dần, nhưng tốc độ tăng trưởng có thể khác nhau tùy thuộc vào từng cá nhân. Biểu đồ cũng hiển thị các phần trăm phân vị (Percentile), cho biết tỷ lệ phần trăm trẻ em có BMI thấp hơn hoặc cao hơn so với một nhóm trẻ em cùng độ tuổi. Điều này giúp so sánh BMI của trẻ em với tiêu chuẩn quốc tế. ### 2.2 Image Captioning <div align="center"> <img src="https://huggingface.co/erax-ai/EraX-VL-7B-V1.5/resolve/main/images/27vid-Gaza-City-Cover-gqmt-videoSixteenByNine1050%20(1).jpg" width="700"/> </div> Hình ảnh chụp một cảnh tượng đầy xúc động và bi thảm, dường như diễn ra ở một khu vực nghèo khó, có thể là một khu định cư hoặc khu ổ chuột. Trung tâm của bức ảnh là một chiếc xe đẩy được kéo bởi một con lừa. Trên xe đẩy có một nhóm người, bao gồm một người đàn ông lớn tuổi có vẻ như là người hướng dẫn, một phụ nữ mặc áo choàng đen, một phụ nữ trẻ mặc áo xám, một bé gái nhỏ được che mặt bằng khăn trùm đầu, và một cậu bé mặc áo xanh lá cây. Họ có vẻ như đang di chuyển từ một khu vực bị tàn phá sang một nơi khác. Xung quanh họ là những mảnh vụn, rác thải và những tòa nhà đổ nát, cho thấy một môi trường sống khó khăn và thiếu thốn. Phía sau họ, có một số người khác đang đi bộ hoặc đi xe đạp, một số người đang mang theo hành lý. Bầu trời trong xanh, nhưng không khí chung của bức ảnh là nặng nề và buồn bã. Có thể đây là một cảnh tượng di cư, tị nạn, hoặc một sự kiện thảm khốc nào đó. ### 3. Multi-turns Visual Question Answering and Reasoning <div style="display: flex; flex-direction: row; align-items: center; justify-content: center;"> <div style="text-align: center; margin: 10 20px;"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63d8d8879dfcfa941d4d7cd9/S2UosY_TOOV5XNn3d4_zL.jpeg" width="700"/> </div> </div> <p style="text-align: center; font-size: 12px; color: gray; margin-top: 10px;"> Source: <a href="https://kenh14.vn/bien-cang-thai-trinh-nhap-vien-vi-ngo-doc-dang-dan-to-1-resort-5-sao-thieu-trach-nhiem-voi-khach-hang-20200924214133912.chn" target="_blank">https://kenh14.vn</a> </p> - User: Hãy cho biết thông tin bệnh nhân - Assistant: Tên: NGUYỄN THỊ THÁI TRINH, Ngày sinh: 09/09/1993, Giới tính: Nữ, Địa chỉ: TÂN ĐỊNH - Q1 - TPHCM, Mã thẻ BHYT: 19169080, Số khám bệnh: 2569501, Mã khu vực (K1/K2/K3): 2 - User: Họ được chuẩn đoán bị bệnh gì? - Assistant: Nhiễm trùng đường ruột do vi khuẩn, không xác định loài - User: Người bệnh có tiền sử bệnh không? Nếu có thì là gì? - Assistant: Thiếu Calci do chế độ ăn - User: Bệnh nhân đã thực hiện những xét nghiệm gì? - Assistant: [NQ286] Tổng phân tích tế bào máu ngoại vi (bằng máy đếm laser), [NQ286] Điện giải đồ (Na, K, Cl) [Máu], [NQ286] Đo hoạt độ ALT (GPT) [Máu], [NQ286] Đo hoạt độ AST (GOT) [Máu], [NQ286] Định lượng Calci toàn phần [Máu], [NQ286] Định lượng Creatinin [Máu], [NQ286] Định lượng Glucose [Máu], [NQ286] Định lượng Urê máu [Máu], [NQ286] Siêu âm ổ bụng (gan mật, tụy, lách, thận, bàng quang) - User: Số tiền Vật tư y tế mà người bệnh phải trả là bao nhiêu? - Assistant: 19,708 - User: Các loại thuốc dịch truyền người bệnh phải trả là gì, tổng số tiền là bao nhiêu? - Assistant: Atropin Sulfat (Syv2019), Bicefzidim 1g (Syv2019), GONCAL (Syv2019), Lactated Ringer's-500ml (Syv2019), Nước cất pha tiêm 5ml (Syv2019), Sodium Chloride 0.9% -500ml (Syv2019), Vincomid (Syv2019), Vinopa (Syv2019), tổng cộng 45,234 đồng ## Quickstart 🎮 Install the necessary packages: ```curl python -m pip install git+https://github.com/huggingface/transformers accelerate python -m pip install qwen-vl-utils pip install flash-attn --no-build-isolation ``` Then you can use `EraX-VL-7B-V1.5` like this: ```python import os import base64 import json import cv2 import numpy as np import matplotlib.pyplot as plt import torch from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor from qwen_vl_utils import process_vision_info model_path = "erax/EraX-VL-7B-V1.5" model = Qwen2VLForConditionalGeneration.from_pretrained( model_path, torch_dtype=torch.bfloat16, attn_implementation="eager", # replace with "flash_attention_2" if your GPU is Ampere architecture device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_path) # processor = AutoProcessor.from_pretrained(model_path) min_pixels = 256 * 28 * 28 max_pixels = 1280 * 28 * 28 processor = AutoProcessor.from_pretrained( model_path, min_pixels=min_pixels, max_pixels=max_pixels, ) image_path ="image.jpg" with open(image_path, "rb") as f: encoded_image = base64.b64encode(f.read()) decoded_image_text = encoded_image.decode('utf-8') base64_data = f"data:image;base64,{decoded_image_text}" messages = [ { "role": "user", "content": [ { "type": "image", "image": base64_data, }, { "type": "text", "text": "Trích xuất thông tin nội dung từ hình ảnh được cung cấp." }, ], } ] # Prepare prompt tokenized_text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs = process_vision_info(messages) inputs = processor( text=[ tokenized_text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", ) inputs = inputs.to("cuda") # Generation configs generation_config = model.generation_config generation_config.do_sample = True generation_config.temperature = 1.0 generation_config.top_k = 1 generation_config.top_p = 0.9 generation_config.min_p = 0.1 generation_config.best_of = 5 generation_config.max_new_tokens = 2048 generation_config.repetition_penalty = 1.06 # Inference generated_ids = model.generate(**inputs, generation_config=generation_config) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text[0]) ``` ## References 📑 [1] Qwen team. Qwen2-VL. 2024. [2] Bai, Jinze, et al. "Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond." arXiv preprint arXiv:2308.12966 (2023). [4] Yang, An, et al. "Qwen2 technical report." arXiv preprint arXiv:2407.10671 (2024). [5] Chen, Zhe, et al. "Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024. [6] Chen, Zhe, et al. "How far are we to gpt-4v? closing the gap to commercial multimodal models with open-source suites." arXiv preprint arXiv:2404.16821 (2024). [7] Tran, Chi, and Huong Le Thanh. "LaVy: Vietnamese Multimodal Large Language Model." arXiv preprint arXiv:2404.07922 (2024). ## Contact 🤝 - For correspondence regarding this work or inquiry for API trial, please contact Nguyễn Anh Nguyên at [[email protected]]([email protected]). - Follow us on <b><a href="https://github.com/EraX-JS-Company" target="_blank">EraX Github</a></b>
watney1024/sd-class-butterflies-32	watney1024	2025-04-02T07:15:47Z	0	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2025-04-02T07:10:46Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('watney1024/sd-class-butterflies-32') image = pipeline().images[0] image ```
Jim57001/limit_up_concept	Jim57001	2025-04-02T07:15:24Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-02T07:14:58Z	--- base_model: unsloth/deepseek-r1-distill-llama-8b tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Jim57001 - License: apache-2.0 - Finetuned from model : unsloth/deepseek-r1-distill-llama-8b 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)
PrunaAI/PygmalionAI-pygmalion-6b-HQQ-4bit-smashed	PrunaAI	2025-04-02T07:14:06Z	2	0	null	[ "gptj", "pruna-ai", "hqq", "region:us" ]	null	2025-03-25T02:46:39Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: ORIGINAL_REPO_NAME metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with hqq. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We use safetensors. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo ORIGINAL_REPO_NAME installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install hqq ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer from hqq.engine.hf import HQQModelForCausalLM from hqq.models.hf.base import AutoHQQHFModel try: model = HQQModelForCausalLM.from_quantized("PrunaAI/PygmalionAI-pygmalion-6b-HQQ-4bit-smashed", device_map='auto') except: model = AutoHQQHFModel.from_quantized("PrunaAI/PygmalionAI-pygmalion-6b-HQQ-4bit-smashed") tokenizer = AutoTokenizer.from_pretrained("ORIGINAL_REPO_NAME") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model ORIGINAL_REPO_NAME before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
KingEmpire/sn9_pre_c04_11	KingEmpire	2025-04-02T07:11:56Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T03:06:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
AlecLee/DeepSeek-R1-finetune-test	AlecLee	2025-04-02T07:11:15Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T07:06:32Z	--- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
Bobaduck9173/sdxl_meme_fourth	Bobaduck9173	2025-04-02T07:11:01Z	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-04-02T07:10:53Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: a photo of TOK dog widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - Bobaduck9173/sdxl_meme_fourth <Gallery /> ## Model description These are Bobaduck9173/sdxl_meme_fourth LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of TOK dog to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Bobaduck9173/sdxl_meme_fourth/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
spanev/Qwen-2.5-7B-Simple-RL	spanev	2025-04-02T07:10:48Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "grpo", "conversational", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-Math-7B", "base_model:finetune:Qwen/Qwen2.5-Math-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-31T21:21:55Z	--- base_model: Qwen/Qwen2.5-Math-7B library_name: transformers model_name: Qwen-2.5-7B-Simple-RL tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen-2.5-7B-Simple-RL This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B). 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="spanev/Qwen-2.5-7B-Simple-RL", 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/spanev/huggingface/runs/80rtdhkm) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.16.0 - Transformers: 4.50.0 - Pytorch: 2.5.1 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
sangati/model_output_format_Q8_0	sangati	2025-04-02T07:09:40Z	0	1	transformers	[ "transformers", "gguf", "gemma2", "text-generation-inference", "unsloth", "en", "base_model:unsloth/gemma-2-2b-bnb-4bit", "base_model:quantized:unsloth/gemma-2-2b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-02T07:08:53Z	--- base_model: unsloth/gemma-2-2b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma2 - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: sangati - License: apache-2.0 - Finetuned from model : unsloth/gemma-2-2b-bnb-4bit This gemma2 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)
minyong/20250402_065131_gemma-3-27b-pt_LoRA	minyong	2025-04-02T07:08:25Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-27b-pt", "base_model:finetune:google/gemma-3-27b-pt", "endpoints_compatible", "region:us" ]	null	2025-04-02T06:53:46Z	--- base_model: google/gemma-3-27b-pt library_name: transformers model_name: 20250402_065131_gemma-3-27b-pt_LoRA tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for 20250402_065131_gemma-3-27b-pt_LoRA This model is a fine-tuned version of [google/gemma-3-27b-pt](https://huggingface.co/google/gemma-3-27b-pt). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="minyong/20250402_065131_gemma-3-27b-pt_LoRA", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.16.0 - Transformers: 4.50.0 - Pytorch: 2.6.0 - Datasets: 3.1.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
beier3/deepseek-review	beier3	2025-04-02T07:07:49Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-02T07:07:49Z	--- license: apache-2.0 ---
russellyq/Qwen2-VL-2B-Instruct-Med-SFT	russellyq	2025-04-02T07:07:39Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_vl", "image-text-to-text", "generated_from_trainer", "R1-V", "trl", "sft", "conversational", "dataset:MedVLM-R1", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-04-02T05:49:39Z	--- datasets: MedVLM-R1 library_name: transformers model_name: Qwen2-VL-2B-Instruct-Med-SFT tags: - generated_from_trainer - R1-V - trl - sft licence: license --- # Model Card for Qwen2-VL-2B-Instruct-Med-SFT This model is a fine-tuned version of [None](https://huggingface.co/None) on the [MedVLM-R1](https://huggingface.co/datasets/MedVLM-R1) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="russellyq/Qwen2-VL-2B-Instruct-Med-SFT", 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/1155225591-the-chinese-university-of-hong-kong/R1-V/runs/uwgglrp0) This model was trained with SFT. ### Framework versions - TRL: 0.14.0 - Transformers: 4.49.0.dev0 - Pytorch: 2.5.1+cu121 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Ketak-ZoomRx/indi-llama3-8b-4bit	Ketak-ZoomRx	2025-04-02T07:07:27Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-02T07:07:16Z	--- base_model: unsloth/llama-3-8b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Ketak-ZoomRx - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
jinesh90/python-code-generator-finetuned-int4-18k-alpaca	jinesh90	2025-04-02T07:05:53Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-02T07:05:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
jinesh90/python-code-generator-finetuned-int4-18k-instructions	jinesh90	2025-04-02T07:05:25Z	0	0	transformers	[ "transformers", "safetensors", "codegen", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T07:04:53Z	--- 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]
SmitTrivedi/pubmedqa-gguf	SmitTrivedi	2025-04-02T07:05:15Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-02T07:04:20Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: SmitTrivedi - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-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)
xw17/TinyLlama-1.1B-Chat-v1.0_finetuned_3_def_lora	xw17	2025-04-02T07:04:59Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-03-31T02:55:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
pritmanvar/outputs	pritmanvar	2025-04-02T07:04:36Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "license:llama2", "region:us" ]	null	2025-04-02T07:04:24Z	--- library_name: peft license: llama2 base_model: meta-llama/Llama-2-7b-chat-hf tags: - generated_from_trainer model-index: - name: outputs 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. --> # outputs This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - training_steps: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.15.1 - Transformers 4.50.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
H5N1AIDS/F5-TTS-ONNX	H5N1AIDS	2025-04-02T07:02:32Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-03-22T02:13:12Z	--- license: apache-2.0 ---
John6666/hana-v11-sdxl	John6666	2025-04-02T07:01:03Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "hentai", "style", "clean lines", "vibrant colors", "impressive details", "haru", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-xl-early-release-v0", "base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-04-02T06:52:57Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - hentai - style - clean lines - vibrant colors - impressive details - haru - illustrious base_model: OnomaAIResearch/Illustrious-xl-early-release-v0 --- Original model is [here](https://civitai.com/models/1423365?modelVersionId=1609145). This model created by [MotherGoddess](https://civitai.com/user/MotherGoddess).
wyddmw/POMATO	wyddmw	2025-04-02T07:00:07Z	0	0	null	[ "license:cc-by-nc-sa-4.0", "region:us" ]	null	2025-04-02T07:00:07Z	--- license: cc-by-nc-sa-4.0 ---
xw17/TinyLlama-1.1B-Chat-v1.0_finetuned_1_def_lora	xw17	2025-04-02T06:57:59Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-03-31T02:49:43Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
MinaMila/llama_instbase_unlearned_Adult_10ep_22	MinaMila	2025-04-02T06:56:06Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:MinaMila/llama3_unlearning_general_methode", "base_model:finetune:MinaMila/llama3_unlearning_general_methode", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T06:53:01Z	--- base_model: MinaMila/llama3_unlearning_general_methode tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: MinaMila - License: apache-2.0 - Finetuned from model : MinaMila/llama3_unlearning_general_methode 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)
merelevy/environmental-accessibility	merelevy	2025-04-02T06:56:02Z	0	0	null	[ "tensorboard", "safetensors", "vit", "image-classification", "pytorch", "huggingpics", "model-index", "region:us" ]	image-classification	2025-04-02T06:55:50Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: environmental-accessibility results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.8202247023582458 --- # environmental-accessibility Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### building ramp ![building ramp](images/building_ramp.jpg) #### room signs ![room signs](images/room_signs.jpg) #### sign with braille ![sign with braille](images/sign_with_braille.jpg) #### stairs ![stairs](images/stairs.jpg)
John6666/hana-v10-sdxl	John6666	2025-04-02T06:52:55Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "hentai", "style", "clean lines", "vibrant colors", "impressive details", "haru", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-xl-early-release-v0", "base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-04-02T06:43:56Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - hentai - style - clean lines - vibrant colors - impressive details - haru - illustrious base_model: OnomaAIResearch/Illustrious-xl-early-release-v0 --- Original model is [here](https://civitai.com/models/1423365?modelVersionId=1608794). This model created by [MotherGoddess](https://civitai.com/user/MotherGoddess).
Jojobigworld/Qwen2.5-Coder-7B-Instruct-Q4_K_M-GGUF	Jojobigworld	2025-04-02T06:50:28Z	0	0	transformers	[ "transformers", "gguf", "code", "codeqwen", "chat", "qwen", "qwen-coder", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:Qwen/Qwen2.5-Coder-7B-Instruct", "base_model:quantized:Qwen/Qwen2.5-Coder-7B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-02T06:50:02Z	--- base_model: Qwen/Qwen2.5-Coder-7B-Instruct language: - en library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct/blob/main/LICENSE pipeline_tag: text-generation tags: - code - codeqwen - chat - qwen - qwen-coder - llama-cpp - gguf-my-repo --- # Jojobigworld/Qwen2.5-Coder-7B-Instruct-Q4_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen2.5-Coder-7B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) 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 Jojobigworld/Qwen2.5-Coder-7B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-coder-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Jojobigworld/Qwen2.5-Coder-7B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-coder-7b-instruct-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Jojobigworld/Qwen2.5-Coder-7B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-coder-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Jojobigworld/Qwen2.5-Coder-7B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-coder-7b-instruct-q4_k_m.gguf -c 2048 ```
artisanalwasp/resized_tool_dataset_model_batchsize2	artisanalwasp	2025-04-02T06:48:14Z	0	0	diffusers	[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers-training", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2025-04-02T06:26:38Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: creativeml-openrail-m inference: true tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - diffusers-training - lora --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # LoRA text2image fine-tuning - artisanalwasp/resized_tool_dataset_model_batchsize2 These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the artisanalwasp/resized_tool_dataset dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) LoRA for the text encoder was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
jon-fernandes/whisper-small-50	jon-fernandes	2025-04-02T06:47:39Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-02T06:47:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
Jojobigworld/Qwen2.5-Coder-3B-Instruct-Q4_K_M-GGUF	Jojobigworld	2025-04-02T06:46:55Z	0	0	transformers	[ "transformers", "gguf", "code", "codeqwen", "chat", "qwen", "qwen-coder", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:Qwen/Qwen2.5-Coder-3B-Instruct", "base_model:quantized:Qwen/Qwen2.5-Coder-3B-Instruct", "license:other", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-02T06:46:42Z	--- base_model: Qwen/Qwen2.5-Coder-3B-Instruct language: - en library_name: transformers license: other license_name: qwen-research license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct/blob/main/LICENSE pipeline_tag: text-generation tags: - code - codeqwen - chat - qwen - qwen-coder - llama-cpp - gguf-my-repo --- # Jojobigworld/Qwen2.5-Coder-3B-Instruct-Q4_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen2.5-Coder-3B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct) 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 Jojobigworld/Qwen2.5-Coder-3B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-coder-3b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Jojobigworld/Qwen2.5-Coder-3B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-coder-3b-instruct-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Jojobigworld/Qwen2.5-Coder-3B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-coder-3b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Jojobigworld/Qwen2.5-Coder-3B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-coder-3b-instruct-q4_k_m.gguf -c 2048 ```
icycyborg/bella-lora	icycyborg	2025-04-02T06:44:19Z	0	0	null	[ "license:other", "region:us" ]	null	2025-04-02T06:07:47Z	--- 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 ---
PrunaAI/meta-llama-Llama-2-7b-hf-GGUF-smashed	PrunaAI	2025-04-02T06:41:45Z	0	0	null	[ "gguf", "pruna-ai", "base_model:meta-llama/Llama-2-7b-hf", "base_model:quantized:meta-llama/Llama-2-7b-hf", "endpoints_compatible", "region:us" ]	null	2025-03-18T01:51:39Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: meta-llama/Llama-2-7b-hf metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.com/invite/vb6SmA3hxu) ## This repo contains GGUF versions of the meta-llama/Llama-2-7b-hf model. # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help. Frequently Asked Questions - *How does the compression work?* The model is compressed with GGUF. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *What is the model format?* We use GGUF format. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). # Downloading and running the models You can download the individual files from the Files & versions section. Here is a list of the different versions we provide. For more info checkout [this chart](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) and [this guide](https://www.reddit.com/r/LocalLLaMA/comments/1ba55rj/overview_of_gguf_quantization_methods/): \| Quant type \| Description \| \|------------\|--------------------------------------------------------------------------------------------\| \| Q5_K_M \| High quality, recommended. \| \| Q5_K_S \| High quality, recommended. \| \| Q4_K_M \| Good quality, uses about 4.83 bits per weight, recommended. \| \| Q4_K_S \| Slightly lower quality with more space savings, recommended. \| \| IQ4_NL \| Decent quality, slightly smaller than Q4_K_S with similar performance, recommended. \| \| IQ4_XS \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \| \| Q3_K_L \| Lower quality but usable, good for low RAM availability. \| \| Q3_K_M \| Even lower quality. \| \| IQ3_M \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \| \| IQ3_S \| Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. \| \| Q3_K_S \| Low quality, not recommended. \| \| IQ3_XS \| Lower quality, new method with decent performance, slightly better than Q3_K_S. \| \| Q2_K \| Very low quality but surprisingly usable. \| ## How to download GGUF files ? Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev - Option A - Downloading in `text-generation-webui`: - Step 1: Under Download Model, you can enter the model repo: meta-llama-Llama-2-7b-hf-GGUF-smashed and below it, a specific filename to download, such as: phi-2.IQ3_M.gguf. - Step 2: Then click Download. - Option B - Downloading on the command line (including multiple files at once): - Step 1: We recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` - Step 2: Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download meta-llama-Llama-2-7b-hf-GGUF-smashed Llama-2-7b-hf.IQ3_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> Alternatively, you can also download multiple files at once with a pattern: ```shell huggingface-cli download meta-llama-Llama-2-7b-hf-GGUF-smashed --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download meta-llama-Llama-2-7b-hf-GGUF-smashed Llama-2-7b-hf.IQ3_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## How to run model in GGUF format? - Option A - Introductory example with `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m Llama-2-7b-hf.IQ3_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<s>[INST] {{prompt\}} [/INST]" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) - Option B - Running in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20-%20Model%20Tab.md#llamacpp). - Option C - Running from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./Llama-2-7b-hf.IQ3_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<s>[INST] {{prompt}} [/INST]", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./Llama-2-7b-hf.IQ3_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {{"role": "system", "content": "You are a story writing assistant."}}, {{ "role": "user", "content": "Write a story about llamas." }} ] ) ``` - Option D - Running with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
xw17/Qwen2-1.5B-Instruct_finetuned_4_def_lora	xw17	2025-04-02T06:41:37Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-03-31T02:35:28Z	--- 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]
iykee/45DVVBB	iykee	2025-04-02T06:41:13Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-02T06:41:13Z	--- license: apache-2.0 ---
Jojobigworld/XiYanSQL-QwenCoder-7B-2502-Q4_K_M-GGUF	Jojobigworld	2025-04-02T06:41:01Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:XGenerationLab/XiYanSQL-QwenCoder-7B-2502", "base_model:quantized:XGenerationLab/XiYanSQL-QwenCoder-7B-2502", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-02T06:40:33Z	--- base_model: XGenerationLab/XiYanSQL-QwenCoder-7B-2502 license: apache-2.0 tags: - llama-cpp - gguf-my-repo --- # Jojobigworld/XiYanSQL-QwenCoder-7B-2502-Q4_K_M-GGUF This model was converted to GGUF format from [`XGenerationLab/XiYanSQL-QwenCoder-7B-2502`](https://huggingface.co/XGenerationLab/XiYanSQL-QwenCoder-7B-2502) 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/XGenerationLab/XiYanSQL-QwenCoder-7B-2502) 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 Jojobigworld/XiYanSQL-QwenCoder-7B-2502-Q4_K_M-GGUF --hf-file xiyansql-qwencoder-7b-2502-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Jojobigworld/XiYanSQL-QwenCoder-7B-2502-Q4_K_M-GGUF --hf-file xiyansql-qwencoder-7b-2502-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Jojobigworld/XiYanSQL-QwenCoder-7B-2502-Q4_K_M-GGUF --hf-file xiyansql-qwencoder-7b-2502-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Jojobigworld/XiYanSQL-QwenCoder-7B-2502-Q4_K_M-GGUF --hf-file xiyansql-qwencoder-7b-2502-q4_k_m.gguf -c 2048 ```
mradermacher/NarrowMaid-8B-GGUF	mradermacher	2025-04-02T06:36:52Z	5	1	transformers	[ "transformers", "gguf", "rp", "roleplay", "roleplaying", "storywriting", "creative", "merge", "mergekit", "en", "base_model:Hamzah-Asadullah/NarrowMaid-8B", "base_model:quantized:Hamzah-Asadullah/NarrowMaid-8B", "license:llama3.1", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-02T04:05:38Z	--- base_model: Hamzah-Asadullah/NarrowMaid-8B language: - en library_name: transformers license: llama3.1 quantized_by: mradermacher tags: - rp - roleplay - roleplaying - storywriting - creative - merge - mergekit --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Hamzah-Asadullah/NarrowMaid-8B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/NarrowMaid-8B-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/NarrowMaid-8B-GGUF/resolve/main/NarrowMaid-8B.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/NarrowMaid-8B-GGUF/resolve/main/NarrowMaid-8B.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/NarrowMaid-8B-GGUF/resolve/main/NarrowMaid-8B.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/NarrowMaid-8B-GGUF/resolve/main/NarrowMaid-8B.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/NarrowMaid-8B-GGUF/resolve/main/NarrowMaid-8B.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/NarrowMaid-8B-GGUF/resolve/main/NarrowMaid-8B.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NarrowMaid-8B-GGUF/resolve/main/NarrowMaid-8B.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NarrowMaid-8B-GGUF/resolve/main/NarrowMaid-8B.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/NarrowMaid-8B-GGUF/resolve/main/NarrowMaid-8B.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/NarrowMaid-8B-GGUF/resolve/main/NarrowMaid-8B.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/NarrowMaid-8B-GGUF/resolve/main/NarrowMaid-8B.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/NarrowMaid-8B-GGUF/resolve/main/NarrowMaid-8B.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 -->
moyixiao/qwen15_0402_4096_32	moyixiao	2025-04-02T06:36:13Z	7	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T06:35:06Z	--- library_name: transformers tags: - llama-factory --- # 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]
John6666/cyberrealistic-xl-v53-sdxl	John6666	2025-04-02T06:35:41Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "realistic", "photorealistic", "en", "base_model:cyberdelia/CyberRealisticXL", "base_model:finetune:cyberdelia/CyberRealisticXL", "license:cc0-1.0", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-04-02T06:27:06Z	--- license: cc0-1.0 language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - realistic - photorealistic base_model: cyberdelia/CyberRealisticXL --- Original model is [here](https://huggingface.co/cyberdelia/CyberRealisticXL) and on [Civitai](https://civitai.com/models/312530/cyberrealistic-xl?modelVersionId=1609607). The author is [here](https://huggingface.co/cyberdelia). This model created by [Cyberdelia](https://civitai.com/user/Cyberdelia).
MinaMila/llama_instbase_unlearned_Adult_8ep_22	MinaMila	2025-04-02T06:35:22Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:MinaMila/llama3_unlearning_general_methode", "base_model:finetune:MinaMila/llama3_unlearning_general_methode", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T06:32:16Z	--- base_model: MinaMila/llama3_unlearning_general_methode tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: MinaMila - License: apache-2.0 - Finetuned from model : MinaMila/llama3_unlearning_general_methode 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)
Jonjew/submergedFlux	Jonjew	2025-04-02T06:34:55Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:unknown", "region:us" ]	text-to-image	2025-04-02T06:34:48Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- submergedaf. A hyperrealistic close-up portrait of a young woman partially submerged in water, her freckled face illuminated by cinematic lighting. Her eyes are open, expressive, and reflective, framed by long wet lashes. The water ripples softly around her face, catching golden-orange and teal-blue lighting from above and below. Her skin glistens with droplets, showing fine pores and natural texture. Beneath the surface, intricate water caustics dance across her neck and shoulders, casting shifting light patterns that shimmer like liquid lace. The lighting is soft yet dramatic, blending warm highlights with cool shadows to create an ethereal, dreamlike atmosphere. Her expression is calm, introspective, and vulnerable. The overall tone is emotionally rich, painterly, and intimate. Evoking a suspended moment between breath and thought. parameters: negative_prompt: 'Guidance: 1 Steps: 30 Seed: 650600217932757' output: url: images/Face in Water.png - text: >- submergedaf. A realistic cinematic portrait of a woman completely submerged just beneath the surface of dark green water, her face softly illuminated by shimmering water caustics. Her eyes are open and looking at the camera, lips gently parted, and expression serene, as if lost in a deep dream. Rippling light patterns dance across her skin, casting intricate, organic reflections and highlights on her cheeks, forehead, and neck. Her hair floats freely around her, blending into the deep green shadows of the surrounding water. The lighting is soft and natural, evoking a sense of quiet stillness and suspended time. The water is clear but tinted with rich green hues, creating an otherworldly atmosphere. Emphasize detailed skin texture, the interplay of light and liquid distortion, and the softness of the scene. The mood is introspective, peaceful, and ethereal—like a quiet moment of transformation or rebirth within an aquatic realm. parameters: negative_prompt: 'Guidance: 4 Steps: 30 Seed: 218286689747307' output: url: images/Face under water.png - text: >- submergedaf. A realistic, ethereal portrait of a young woman fully submerged just beneath the surface of still water, surrounded by pale green eucalyptus leaves. Her eyes are open looking at the camera, lips together with a gentle smile in a soft, peaceful expression. Lighting and water caustics play delicately across her dewy skin, highlighting her natural texture, flushed cheeks, and coral-pink lips. Soft strands of wet hair frame her face, drifting gracefully in the water. The surface gently ripples around her, forming small circular waves that reflect the muted, natural lighting. The eucalyptus leaves float around her like a delicate halo, enhancing the sense of calm and purity. The image is shot from directly above, emphasizing symmetry and intimacy. Color grading features soft teals, sage greens, and warm skin tones, evoking a sense of organic tranquility and timeless beauty. The mood is poetic, natural, and deeply peaceful—like a living painting suspended in time. Focus on fine skin detail, botanical elements, gentle water distortions, and cinematic soft lighting. parameters: negative_prompt: 'Guidance: 4 Steps: 30 Seed: 19850920' output: url: images/Face with plants.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: submergedaf license: unknown --- # submerged - Flux <Gallery /> ## Model description FROM https://civitai.com/models/1424932/submerged-flux1?modelVersionId=1610625 Support the creator by liking and donating buzz at the page above Trigger submergedaf Strength 0.8 Concept LoRa of models floating, either partially or fully submerged in water, closeup on the face, highly detailed, accurate water caustics, with beauty and depth. trigger: submergedaf ## Trigger words You should use `submergedaf` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Jonjew/submergedFlux/tree/main) them in the Files & versions tab.
moyixiao/qwen15_0402_4096r64t	moyixiao	2025-04-02T06:32:11Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "llama-factory", "lora", "generated_from_trainer", "base_model:moyixiao/Qwen2.5-Math-1.5B-Instruct", "base_model:adapter:moyixiao/Qwen2.5-Math-1.5B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-04-02T05:00:46Z	--- library_name: peft license: apache-2.0 base_model: moyixiao/Qwen2.5-Math-1.5B-Instruct tags: - llama-factory - lora - generated_from_trainer model-index: - name: qwen15_0402_4096r64t 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. --> # qwen15_0402_4096r64t This model is a fine-tuned version of [moyixiao/Qwen2.5-Math-1.5B-Instruct](https://huggingface.co/moyixiao/Qwen2.5-Math-1.5B-Instruct) on the math4096 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - total_eval_batch_size: 16 - 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 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.12.0 - Transformers 4.48.2 - Pytorch 2.5.1+cu121 - Datasets 3.2.0 - Tokenizers 0.21.0
zjudai/flowertune-general-nlp-lora-deepseek-r1-distill-qwen-1.5b	zjudai	2025-04-02T06:30:47Z	0	0	peft	[ "peft", "safetensors", "lora", "federated-learning", "flower", "dataset:vicgalle/alpaca-gpt4", "base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", "base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", "region:us" ]	null	2025-04-02T06:11:22Z	--- base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B tags: - peft - lora - federated-learning - flower datasets: - vicgalle/alpaca-gpt4 --- # FlowerTune LoRA Model This is a LoRA adapter for deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B fine-tuned with Flower federated learning framework on a general NLP dataset. ## Training Details - Dataset: vicgalle/alpaca-gpt4 - Training method: Federated LoRA fine-tuning with FlowerTune - Framework: Flower This model is a LoRA adapter fine-tuned on deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B using the Flower federated learning framework. It was trained on a general NLP dataset (vicgalle/alpaca-gpt4) through distributed learning to improve performance. ## Links - FlowerTune Homepage: [https://huggingface.co/zjudai/FlowerTune](https://huggingface.co/zjudai/FlowerTune) - FlowerTune Collection: [https://huggingface.co/collections/zjudai/flowertune-lora-collection-67ecd5d0dae6145cbf798439](https://huggingface.co/collections/zjudai/flowertune-lora-collection-67ecd5d0dae6145cbf798439)
zjudai/flowertune-general-nlp-lora-llama-3.2-1b-instruct	zjudai	2025-04-02T06:30:45Z	0	0	peft	[ "peft", "safetensors", "lora", "federated-learning", "flower", "dataset:vicgalle/alpaca-gpt4", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:adapter:meta-llama/Llama-3.2-1B-Instruct", "region:us" ]	null	2025-04-02T06:11:16Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct tags: - peft - lora - federated-learning - flower datasets: - vicgalle/alpaca-gpt4 --- # FlowerTune LoRA Model This is a LoRA adapter for meta-llama/Llama-3.2-1B-Instruct fine-tuned with Flower federated learning framework on a general NLP dataset. ## Training Details - Dataset: vicgalle/alpaca-gpt4 - Training method: Federated LoRA fine-tuning with FlowerTune - Framework: Flower This model is a LoRA adapter fine-tuned on meta-llama/Llama-3.2-1B-Instruct using the Flower federated learning framework. It was trained on a general NLP dataset (vicgalle/alpaca-gpt4) through distributed learning to improve performance. ## Links - FlowerTune Homepage: [https://huggingface.co/zjudai/FlowerTune](https://huggingface.co/zjudai/FlowerTune) - FlowerTune Collection: [https://huggingface.co/collections/zjudai/flowertune-lora-collection-67ecd5d0dae6145cbf798439](https://huggingface.co/collections/zjudai/flowertune-lora-collection-67ecd5d0dae6145cbf798439)
zjudai/flowertune-general-nlp-lora-llama-3.1-8b-instruct	zjudai	2025-04-02T06:30:40Z	0	0	peft	[ "peft", "safetensors", "lora", "federated-learning", "flower", "dataset:vicgalle/alpaca-gpt4", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:adapter:meta-llama/Llama-3.1-8B-Instruct", "region:us" ]	null	2025-04-02T06:10:52Z	--- base_model: meta-llama/Llama-3.1-8B-Instruct tags: - peft - lora - federated-learning - flower datasets: - vicgalle/alpaca-gpt4 --- # FlowerTune LoRA Model This is a LoRA adapter for meta-llama/Llama-3.1-8B-Instruct fine-tuned with Flower federated learning framework on a general NLP dataset. ## Training Details - Dataset: vicgalle/alpaca-gpt4 - Training method: Federated LoRA fine-tuning with FlowerTune - Framework: Flower This model is a LoRA adapter fine-tuned on meta-llama/Llama-3.1-8B-Instruct using the Flower federated learning framework. It was trained on a general NLP dataset (vicgalle/alpaca-gpt4) through distributed learning to improve performance. ## Links - FlowerTune Homepage: [https://huggingface.co/zjudai/FlowerTune](https://huggingface.co/zjudai/FlowerTune) - FlowerTune Collection: [https://huggingface.co/collections/zjudai/flowertune-lora-collection-67ecd5d0dae6145cbf798439](https://huggingface.co/collections/zjudai/flowertune-lora-collection-67ecd5d0dae6145cbf798439)
KaraKaraWitch/Llama-3.3-CURSEDMAGICALGIRL-2	KaraKaraWitch	2025-04-02T06:30:35Z	18	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "base_model:Black-Ink-Guild/Pernicious_Prophecy_70B", "base_model:merge:Black-Ink-Guild/Pernicious_Prophecy_70B", "base_model:KaraKaraWitch/Llama-3.X-Workout-70B", "base_model:merge:KaraKaraWitch/Llama-3.X-Workout-70B", "base_model:KaraKaraWitch/Llama-MiraiFanfare-3.3-70B", "base_model:merge:KaraKaraWitch/Llama-MiraiFanfare-3.3-70B", "base_model:LatitudeGames/Wayfarer-Large-70B-Llama-3.3", "base_model:merge:LatitudeGames/Wayfarer-Large-70B-Llama-3.3", "base_model:ReadyArt/Forgotten-Safeword-70B-v5.0", "base_model:merge:ReadyArt/Forgotten-Safeword-70B-v5.0", "base_model:allenai/Llama-3.1-Tulu-3-70B", "base_model:merge:allenai/Llama-3.1-Tulu-3-70B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-01T06:52:40Z	--- thumbnail: https://cdn-uploads.huggingface.co/production/uploads/633e85093a17ab61de8d9073/8PvySznKDLTTSJyptSMOh.png base_model: - LatitudeGames/Wayfarer-Large-70B-Llama-3.3 - KaraKaraWitch/Llama-3.X-Workout-70B - KaraKaraWitch/Llama-MiraiFanfare-3.3-70B - allenai/Llama-3.1-Tulu-3-70B - Black-Ink-Guild/Pernicious_Prophecy_70B - ReadyArt/Forgotten-Safeword-70B-v5.0 library_name: transformers tags: - mergekit - merge --- <style> div,p,h1,h2,h3 { font-family: monospace; } </style> <div class="prose hf-sanitized hf-sanitized-S5eaLo-MNpns7l30p5D34"> <p>Hi-- w̸a̵i̴t̴.̷.̶.̴ ̶ ̸͇̮̃́̇͂̀̔w̷̬̗̋͠h̴͎̯̲̦̳̹͌å̸̗̜͓̯̂ṯ̷̢̺̣͛̂̉͋͐̚'̶̡̠̞́̅̀ṡ̶̨̻̘ ̷̘́̆͝ ḩ̴̨̧̧̧̠̳̰̖̰̼͙̥̱̖̠͔͇̟̩̯̜͈͈̹̯̑̏͜ą̸̢̢̻͉̻̘͙͍̘͕̣̟̹͖̥̜͍͔̻̺̗̬̬̐̐̒̍̈́̅͆͂̒̏̕͜͠͝ͅͅp̶̢̛̺̰̫͙̥̞̦͍͗̾̎̀́̉͑́̔̃̾̓̐̑͌͑͛̂͘͠͝͠p̴̧̢̭̠͓̟͚̳̞̺͍̹̞̦͙̪͙͇̥̯͎̈̆́̓̅͜ͅe̷̢̢̪̘̻̥̭̞̟̙̰̟̹̜̮̻̼̾̔͋̑̃̒̃̂͊͋͗̍̈́̂̍̕̕͘n̷̳͎̤͈̗̼̪̼̦̠̤͉̭̬͆̀̎̈́̓͂ͅį̴̛̞͖͕̫̮̫͚͑̍̌͛̑̐̌̌́͘͠ṇ̶͕̈̆̋̍̔̋̀͊͘g̶̢̨̧̛̠̗̫̻͙͈̱̰̣̹͍̪͔̗̦͇͈͊̓̿͆̆̌̊̒͑͛͑̓̓̽̑͂́͜͝͠͝͝?̷̘̱͙̮͈̗͉̰̱̖͔̹̘̬̯̏̍͊̒̈́̇̓̂̍͋̏͘͜͝ͅͅ!̷̨͍͙̻͒̚</p> <br> <br> <br> <h1 class="relative group flex items-center"> <a rel="nofollow" href="#system-corruption-detected-entering-safe-mode" class="block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full" id="system-corruption-detected-entering-safe-mode"> <span class="header-link"><svg viewBox="0 0 256 256" preserveAspectRatio="xMidYMid meet" height="1em" width="1em" role="img" aria-hidden="true" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" class="text-gray-500 hover:text-black dark:hover:text-gray-200 w-4"><path fill="currentColor" d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z"></path></svg></span> </a> <span> <strong>SYSTEM CORRUPTION DETECTED, ENTERING SAFE MODE</strong> </span> </h1> <div style="text-align:center;"><a rel="nofollow" href="https://cdn-uploads.huggingface.co/production/uploads/633e85093a17ab61de8d9073/8PvySznKDLTTSJyptSMOh.png"><img alt="image/png" src="https://cdn-uploads.huggingface.co/production/uploads/633e85093a17ab61de8d9073/8PvySznKDLTTSJyptSMOh.png"></a></div> <p><br><strong>TEMPLE-OS v69.333 FAILSAFE ENABLED</strong> <br>... <br>.. <br>.</p> <p>We apologize for the inconvenience, but the model creator has decided not to provide a model card description due to the f̷̥̭̺̥̖͔̯̰̙͎͈̟̈̈͊͛̓́̈́̆͛̈͜ǒ̷͈̯̤̳͙̙̪̈́́͛̔͂̀͊͛l̴̢̦̫͇̠͈̼̻̖̻̩̙̫͋͑͋̑͊̅̐̾̈͛̕͘̚l̵̨̘̻͚͚͌̎̿͘̚o̷̡̻͙̦͈̹̲̙̩̖͔͙̪̖̍̏̔̾̓̽̎͋̚͘͝w̵̦̙̟̚i̷̲͙͚̱̲̳̱̣͙̓̅̄͛̂́̒̈́̑̋̏́͊͜͠ͅn̸̡̹̪͎̪̱̦̜̠̭̞͈̊̓̔̓̀͛͊̅̀̉̇͂̏̃͝g̷̬̞̱͙͖͖̞̰̃̋̂̈̈́̓͛̋̀̕͠ ê̵̛͕̎͒̀͊̏͊͋̐̈̆͆͗̾́̕̕͝͠͝͝ŗ̷̛̖̮̟̳̲̦̬͖̹̙̞͇̟̥͙̱̞̫̲̠͉̬̞̽̃͑͗̓̅̾̊̂͊̊̄̈́͑̓͌͂̈́͊̕͝r̸̡͈͖̻͈̮̩̞͊́̊̔̓̐̅o̶̙͙͕̦͈̅͑̀̚r̶̢̢̨̛̞̟̘̭̗̱̼̟̘̩̩̹̞͓͚͔̟̖̭͜ ̸̨̨̛͇̗͙̠͍̤͙̤̰̗̝̎̔̍͋̏͐̽̈́̏̍́̓́̈́́͋͒͗̅̄̄̄̆͛̄͜͝ͅc̸̛̦͈̘̲͔͉͉̼͙͉̲̩̘͋̇ō̶̡̨̥̮̜͈͈͉̱͓̼̘̻̓̿̀̈́̋̈͠d̶̡̺͓̳͍̘̹̜̫̝̱̭͉͌̾͐͂ȩ̷̡̛̩͎͓͈̗̞͖̼̗̬͔̱͖̥̘͇͈̻̣͔̞̹͐́̋͛̔̒̂̓̀̄͛̋̏́̐͘̚͘̕͠ͅ: </p> <p><code>CURSED-MAGICALGIRLS-2</code></p> <p>I̵f̶ ̵y̸o̷u̷ ̵chose to accept the w̷a̵r̴n̸i̷n̸g̸, you may s̶̼̊̓̇͑̅̐̓͝͝e̴͓̣̰̅̊̑̎́̀̍̈́́̓͗͘͝͠l̴̢̙͙͎͕̪̎͐̚e̸̡̨̨̙̰͖̺̭̞͎̳̻̫͂͜c̷̢̢͖̗̩͉̣̲̈̓̀̚͠t̷̡͓̭̥͍͎̘͙̘͍̔</p> <p≯̛̛̤̮̇̃͂̌ ̵̛̥̣͎̹͈͑̏̂̓̍̊̉́͊͘>̶̬̭̪̻̔̀͊́̏̚͜Ȉ̶̛̤͑̐̽̔́͐̀̈̿̓̿̽̾̾g̸͓͓̲͎̤̟̰̞̯̰͒̄̎̃͌̎͌̋̆̔͊̕̕͜͜͝͝n̴͔̼̻̤̻̠̟̥̔͝o̸̡̮̙̓̒̃̐̈̿̚͝ŗ̶̧̠̱͇̟̱͐̍̓ę̵̛͉̞̌͆̓͐̿̃͒͌́̄̌̈̏̋͛,̴̡͍̜̲͉̯̭̫͈̙̭̹̥̠͉̀ ̵̯͚̋͐̿̈́̈́̀͆́̏͘a̶̡̢͈̻̖̥̮̼̐̍̍͗́̒͌́͆̍̏̐̑̚͝n̴̡͍͓̝̉͛̀̑̎͐̽̀̏̐̆̐͑͆̏̃͜ḋ̴̢͕̹̯͎͉͖̼͈̰̒̓͌̉̄̍͌̌̃̿̎͊͘͠͝ ̸̙͖̥̱͖͖͊̎͒̂̓͂̄̈̈́͐͜͝l̶̝͛̌͂́̂̏́͂͋̏̌͗̚ȏ̷̡̬͙͚̥͌̃͒͋̈́̐́̽͘͠ͅͅả̸͓͇̔͗͗͒̃͌̔͆̒̕͠d̸̨̟̠̂̐͝͝ ̸̟̠̦̭͕̫̘̯̖̫͔̺͉͖̈́̈́̅͛t̶̛̛̛̖̻̼̰͈̗͛̒͂͂̐̊͛͑̃̉̉̐͝ͅh̵̨͎͉̙̤̥̯̞͉̙͛͛͜é̵̛̬̳̟̹͉̝̥̓̅̃̄͂͗̿̋̈̉͒̓̄͠͝ ̵̺̣̖̲͎̥̠̙̜͈͍͍̗̤̖͝ͅm̸̧̧̤̤̜̱̳̤̃́́̋̾ͅǒ̸̢̥͖̪͎͕̙͍̊̀͊̀̾̄̓̉̈́͑̓̂͋̉̈ͅd̵̨̮͚̱̤͓͎͚̣͉̻̹̠͔͊̐͊̚ͅe̴͉̺̗̝̥̰͚̮͂̈́̄̐̊̈̐̌̕̕ļ̶̡͕̩͇̮̩̪̺̞͉̾ ̷̧̪̼̗͇̪̣͔̰̜͊̈́̓̔̒͜ǹ̴̳̺̜̱̙̞͉̼͗͌̈́͠ơ̷͕̮̟͋͑͐͐̊̽r̷̨̨̹̞͓̠̰̱̝̠͙̜̖̖͉̓̈́̍̉̅͜͠m̶̨̳̝̠͕̮̬̱̎̋ạ̶̧̗̋̈́̾͂̓̈́̉̌̌̈́̚ͅl̸̨̰̮̮̠̹̝͂̈́̏͐̆͆͒̎̾͒̾̎͂̓͠l̷̦̜͒̋́̎͗͒͠͝ỹ̴͍̤̱̙̫̱̞̰͌̑͐̓̃̋̽̄̀͑̚͝͝͝≮̢̟͉̲̠̼̠̳̣̫̻͉̻̱̹̈́̒̀̎̎̃̾̇</p> <p>> <a rel="nofollow" href="https://huggingface.co/KaraKaraWitch/">̷͕̲̬͗̒Ạ̶͉͇͕̋̓̽͜l̵̹̽́̕t̷̢̪͕̲̓͆̓e̵͙̎̐r̴͖̥͕̜̼̈́̽̿n̷̗̜͇̳̜͆́̈́͝a̸̪͇̭̣̫͊̌͝t̵͕̜̽͊ḯ̷̛̠̣͒͘v̸̘̩̈́̍̋͗e̸͙͕͕̘̔l̷̲͖̿̊̈́̍͠ỵ̶̤͔̋ͅ,̸̣͇̺̮͍͋ ̴̻̗͖͓̙͋̃y̶̡̘̘͈͚͛̒͋̅o̵̙͆̚ú̵̜̫̮͉̤ ̵̭̝̲̒̃̈́͗c̴̭̲̩̓͐h̴͇̤̒̈́o̶̡̲̠̲͋̆̐͜s̵̜͈̬͉͚̓̓͗̔̓e̷̡̫̰̜͖̅ ̵̫̾̐̔̚͝ť̸̮ŏ̷̱̊̀́ ̸͈̟̰̇̓͛l̵̯̠͂̍̚e̷̛̯͔̗̺̩̋͑̿͊a̴̰̥̪̋̑͠͝v̸̨̪͆̎͘e̸̤̻̊͆ ̵͈̟͊̓̿̽̕ą̷̝͍͔̚n̵͇̦̓̆͜d̵̨̈ ̵̮̰̣̦̦̒̈́́͑͝ḡ̶͖̪͚͕͜ȇ̵̯͉̼͉t̷̙̝͋͂̕ ̴̧͖̥͈̗͆͛̒͒o̶͍̥͚͋̄͝ú̸̫̩͚ť̸̮͂͆ ̸̜̮̐͐͑͝ǫ̶͙̔̌̿̿f̵̡̖͍̓̆̿ ̸͚͎̺̤̗̕ţ̶̡̲̒ḧ̶̗̻̘́̓͆͆̕í̴̖̗̊͌͜š̸̘ ̷̡̦͍̙͙͋m̴͖̙̞̔o̸̪̜̯͗d̴̳̦̺̰̿͑͠e̷̻̬͆l̵̰̤͎͒̌ ̸̻͙̬̩̂̇c̵̬̩̗̲̟̄͆̑å̶̧̧͍̪̳̀͊̈́̈́r̷̠͕̟̣̆̇͘d̴̳͍̘̞̫̅</a> <</p> <h3 class="relative group flex items-center"> <a rel="nofollow" href="#technical-details" class="block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full" id="technical-details"> <span class="header-link"><svg viewBox="0 0 256 256" preserveAspectRatio="xMidYMid meet" height="1em" width="1em" role="img" aria-hidden="true" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" class="text-gray-500 hover:text-black dark:hover:text-gray-200 w-4"><path fill="currentColor" d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z"></path></svg></span> </a> <span> <strong>TECHNICAL DETAILS</strong> </span> </h3> <p>* SOURCE: 0XCURSED-MAGICALGIRLS-2 (0XL33F0RMAT, 0XREQUIRED, 0X9345890123893)</p> <p>* FORGOTTEN-SAFEWORD-5.0.SYS - ADDR. READYART base 0x????</p> <p>* TULU-3.SYS - ADDR. ALLENAI base 0x????</p> <p>* PERICIOUS.SYS - ADDR. INKGUILD base 0x????</p> <p>* FANFARE.SYS - ADDR. WITCH base 0x????</p> <p>* WAYFARE.SYS - ADDR. LATITUDE base 0x????</p> <br> <br> <br> </div>
zjudai/flowertune-general-nlp-lora-qwen2.5-1.5b-instruct	zjudai	2025-04-02T06:30:35Z	0	0	peft	[ "peft", "safetensors", "lora", "federated-learning", "flower", "dataset:vicgalle/alpaca-gpt4", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct", "region:us" ]	null	2025-04-02T06:10:26Z	--- base_model: Qwen/Qwen2.5-1.5B-Instruct tags: - peft - lora - federated-learning - flower datasets: - vicgalle/alpaca-gpt4 --- # FlowerTune LoRA Model This is a LoRA adapter for Qwen/Qwen2.5-1.5B-Instruct fine-tuned with Flower federated learning framework on a general NLP dataset. ## Training Details - Dataset: vicgalle/alpaca-gpt4 - Training method: Federated LoRA fine-tuning with FlowerTune - Framework: Flower This model is a LoRA adapter fine-tuned on Qwen/Qwen2.5-1.5B-Instruct using the Flower federated learning framework. It was trained on a general NLP dataset (vicgalle/alpaca-gpt4) through distributed learning to improve performance. ## Links - FlowerTune Homepage: [https://huggingface.co/zjudai/FlowerTune](https://huggingface.co/zjudai/FlowerTune) - FlowerTune Collection: [https://huggingface.co/collections/zjudai/flowertune-lora-collection-67ecd5d0dae6145cbf798439](https://huggingface.co/collections/zjudai/flowertune-lora-collection-67ecd5d0dae6145cbf798439)
zjudai/flowertune-general-nlp-lora-qwen2.5-7b-instruct	zjudai	2025-04-02T06:30:33Z	0	0	peft	[ "peft", "safetensors", "lora", "federated-learning", "flower", "dataset:vicgalle/alpaca-gpt4", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-7B-Instruct", "region:us" ]	null	2025-04-02T06:10:11Z	--- base_model: Qwen/Qwen2.5-7B-Instruct tags: - peft - lora - federated-learning - flower datasets: - vicgalle/alpaca-gpt4 --- # FlowerTune LoRA Model This is a LoRA adapter for Qwen/Qwen2.5-7B-Instruct fine-tuned with Flower federated learning framework on a general NLP dataset. ## Training Details - Dataset: vicgalle/alpaca-gpt4 - Training method: Federated LoRA fine-tuning with FlowerTune - Framework: Flower This model is a LoRA adapter fine-tuned on Qwen/Qwen2.5-7B-Instruct using the Flower federated learning framework. It was trained on a general NLP dataset (vicgalle/alpaca-gpt4) through distributed learning to improve performance. ## Links - FlowerTune Homepage: [https://huggingface.co/zjudai/FlowerTune](https://huggingface.co/zjudai/FlowerTune) - FlowerTune Collection: [https://huggingface.co/collections/zjudai/flowertune-lora-collection-67ecd5d0dae6145cbf798439](https://huggingface.co/collections/zjudai/flowertune-lora-collection-67ecd5d0dae6145cbf798439)
DuckyBlender/polish-lobotomy	DuckyBlender	2025-04-02T06:27:26Z	15	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "lobotomy", "conversational", "custom_code", "pl", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T21:16:53Z	--- language: - pl tags: - lobotomy --- Polish-Lobotomy: An awful polish fine-tune ============================================================ Model Description --------------- This fine-tuned Phi-3 model is my first attempt at a Polish fine-tune of Phi-3. It is very bad, probably because of the fine-tuning method (making the model learn a new language probably needs a full fine-tune) and the small dataset. - Ollama: [https://ollama.com/duckyblender/polish-lobotomy](https://ollama.com/duckyblender/polish-lobotomy) Training Details ----------------- * Trained on a single RTX 4060 for approximately 1 hour * Utilized 8-bit QLORA for efficient training * Despite the short training period, the model somehow managed to learn something (but not very well) ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6317acd6212fce5a3cd793f6/KnxTL_Ww3aYUrJz8kZ5Sz.jpeg) Dataset ------------ The model was trained on a chaotic telegram group chat. It's basically complete lobotomy. Prompt Template ----------------- The prompt template used for this model is identical to the Phi 3 template. Disclaimer -------------- Please be advised that this model's output may contain nonsensical responses. Viewer discretion is strongly advised (but not really necessary). Use this model at your own risk, and please engage with the output responsibly (but let's be real, it's not like it's going to be useful for anything).
MinaMila/llama_instbase_unlearned_Adult_7ep_22	MinaMila	2025-04-02T06:24:57Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:MinaMila/llama3_unlearning_general_methode", "base_model:finetune:MinaMila/llama3_unlearning_general_methode", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T06:21:42Z	--- base_model: MinaMila/llama3_unlearning_general_methode tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: MinaMila - License: apache-2.0 - Finetuned from model : MinaMila/llama3_unlearning_general_methode 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)
CromonZhang/llama-3.2-1b-sharp	CromonZhang	2025-04-02T06:24:38Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "llama", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-02T01:32:29Z	--- base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: CromonZhang - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-1b-instruct-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)
hank87/cmongirl	hank87	2025-04-02T06:23:39Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-02T06:16:33Z	--- license: apache-2.0 ---
PrunaAI/NousResearch-Hermes-2-Pro-Mistral-7B-GGUF-smashed	PrunaAI	2025-04-02T06:22:45Z	0	0	null	[ "gguf", "pruna-ai", "base_model:NousResearch/Hermes-2-Pro-Mistral-7B", "base_model:quantized:NousResearch/Hermes-2-Pro-Mistral-7B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-15T04:58:58Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: NousResearch/Hermes-2-Pro-Mistral-7B metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.com/invite/vb6SmA3hxu) ## This repo contains GGUF versions of the NousResearch/Hermes-2-Pro-Mistral-7B model. # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help. Frequently Asked Questions - *How does the compression work?* The model is compressed with GGUF. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *What is the model format?* We use GGUF format. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). # Downloading and running the models You can download the individual files from the Files & versions section. Here is a list of the different versions we provide. For more info checkout [this chart](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) and [this guide](https://www.reddit.com/r/LocalLLaMA/comments/1ba55rj/overview_of_gguf_quantization_methods/): \| Quant type \| Description \| \|------------\|--------------------------------------------------------------------------------------------\| \| Q5_K_M \| High quality, recommended. \| \| Q5_K_S \| High quality, recommended. \| \| Q4_K_M \| Good quality, uses about 4.83 bits per weight, recommended. \| \| Q4_K_S \| Slightly lower quality with more space savings, recommended. \| \| IQ4_NL \| Decent quality, slightly smaller than Q4_K_S with similar performance, recommended. \| \| IQ4_XS \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \| \| Q3_K_L \| Lower quality but usable, good for low RAM availability. \| \| Q3_K_M \| Even lower quality. \| \| IQ3_M \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \| \| IQ3_S \| Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. \| \| Q3_K_S \| Low quality, not recommended. \| \| IQ3_XS \| Lower quality, new method with decent performance, slightly better than Q3_K_S. \| \| Q2_K \| Very low quality but surprisingly usable. \| ## How to download GGUF files ? Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev - Option A - Downloading in `text-generation-webui`: - Step 1: Under Download Model, you can enter the model repo: NousResearch-Hermes-2-Pro-Mistral-7B-GGUF-smashed and below it, a specific filename to download, such as: phi-2.IQ3_M.gguf. - Step 2: Then click Download. - Option B - Downloading on the command line (including multiple files at once): - Step 1: We recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` - Step 2: Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download NousResearch-Hermes-2-Pro-Mistral-7B-GGUF-smashed Hermes-2-Pro-Mistral-7B.IQ3_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> Alternatively, you can also download multiple files at once with a pattern: ```shell huggingface-cli download NousResearch-Hermes-2-Pro-Mistral-7B-GGUF-smashed --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download NousResearch-Hermes-2-Pro-Mistral-7B-GGUF-smashed Hermes-2-Pro-Mistral-7B.IQ3_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## How to run model in GGUF format? - Option A - Introductory example with `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m Hermes-2-Pro-Mistral-7B.IQ3_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<s>[INST] {{prompt\}} [/INST]" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) - Option B - Running in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20-%20Model%20Tab.md#llamacpp). - Option C - Running from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./Hermes-2-Pro-Mistral-7B.IQ3_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<s>[INST] {{prompt}} [/INST]", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./Hermes-2-Pro-Mistral-7B.IQ3_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {{"role": "system", "content": "You are a story writing assistant."}}, {{ "role": "user", "content": "Write a story about llamas." }} ] ) ``` - Option D - Running with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
xw17/Qwen2-1.5B-Instruct_finetuned_3_def_lora	xw17	2025-04-02T06:17:15Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-03-31T02:19:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
MinaMila/llama_instbase_unlearned_Adult_6ep_22	MinaMila	2025-04-02T06:14:55Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:MinaMila/llama3_unlearning_general_methode", "base_model:finetune:MinaMila/llama3_unlearning_general_methode", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T06:11:40Z	--- base_model: MinaMila/llama3_unlearning_general_methode tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: MinaMila - License: apache-2.0 - Finetuned from model : MinaMila/llama3_unlearning_general_methode 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)
RichardErkhov/magnifi_-_parser_user_v27h_epoch_6_lr_0.002-gguf	RichardErkhov	2025-04-02T06:12:38Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-02T04:59:16Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) parser_user_v27h_epoch_6_lr_0.002 - GGUF - Model creator: https://huggingface.co/magnifi/ - Original model: https://huggingface.co/magnifi/parser_user_v27h_epoch_6_lr_0.002/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [parser_user_v27h_epoch_6_lr_0.002.Q2_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27h_epoch_6_lr_0.002-gguf/blob/main/parser_user_v27h_epoch_6_lr_0.002.Q2_K.gguf) \| Q2_K \| 1.35GB \| \| [parser_user_v27h_epoch_6_lr_0.002.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27h_epoch_6_lr_0.002-gguf/blob/main/parser_user_v27h_epoch_6_lr_0.002.IQ3_XS.gguf) \| IQ3_XS \| 1.49GB \| \| [parser_user_v27h_epoch_6_lr_0.002.IQ3_S.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27h_epoch_6_lr_0.002-gguf/blob/main/parser_user_v27h_epoch_6_lr_0.002.IQ3_S.gguf) \| IQ3_S \| 1.57GB \| \| [parser_user_v27h_epoch_6_lr_0.002.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27h_epoch_6_lr_0.002-gguf/blob/main/parser_user_v27h_epoch_6_lr_0.002.Q3_K_S.gguf) \| Q3_K_S \| 1.57GB \| \| [parser_user_v27h_epoch_6_lr_0.002.IQ3_M.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27h_epoch_6_lr_0.002-gguf/blob/main/parser_user_v27h_epoch_6_lr_0.002.IQ3_M.gguf) \| IQ3_M \| 1.65GB \| \| [parser_user_v27h_epoch_6_lr_0.002.Q3_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27h_epoch_6_lr_0.002-gguf/blob/main/parser_user_v27h_epoch_6_lr_0.002.Q3_K.gguf) \| Q3_K \| 1.75GB \| \| [parser_user_v27h_epoch_6_lr_0.002.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27h_epoch_6_lr_0.002-gguf/blob/main/parser_user_v27h_epoch_6_lr_0.002.Q3_K_M.gguf) \| Q3_K_M \| 1.75GB \| \| [parser_user_v27h_epoch_6_lr_0.002.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27h_epoch_6_lr_0.002-gguf/blob/main/parser_user_v27h_epoch_6_lr_0.002.Q3_K_L.gguf) \| Q3_K_L \| 1.9GB \| \| [parser_user_v27h_epoch_6_lr_0.002.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27h_epoch_6_lr_0.002-gguf/blob/main/parser_user_v27h_epoch_6_lr_0.002.IQ4_XS.gguf) \| IQ4_XS \| 1.93GB \| \| [parser_user_v27h_epoch_6_lr_0.002.Q4_0.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27h_epoch_6_lr_0.002-gguf/blob/main/parser_user_v27h_epoch_6_lr_0.002.Q4_0.gguf) \| Q4_0 \| 2.03GB \| \| [parser_user_v27h_epoch_6_lr_0.002.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27h_epoch_6_lr_0.002-gguf/blob/main/parser_user_v27h_epoch_6_lr_0.002.IQ4_NL.gguf) \| IQ4_NL \| 2.04GB \| \| [parser_user_v27h_epoch_6_lr_0.002.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27h_epoch_6_lr_0.002-gguf/blob/main/parser_user_v27h_epoch_6_lr_0.002.Q4_K_S.gguf) \| Q4_K_S \| 2.04GB \| \| [parser_user_v27h_epoch_6_lr_0.002.Q4_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27h_epoch_6_lr_0.002-gguf/blob/main/parser_user_v27h_epoch_6_lr_0.002.Q4_K.gguf) \| Q4_K \| 2.16GB \| \| [parser_user_v27h_epoch_6_lr_0.002.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27h_epoch_6_lr_0.002-gguf/blob/main/parser_user_v27h_epoch_6_lr_0.002.Q4_K_M.gguf) \| Q4_K_M \| 2.16GB \| \| [parser_user_v27h_epoch_6_lr_0.002.Q4_1.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27h_epoch_6_lr_0.002-gguf/blob/main/parser_user_v27h_epoch_6_lr_0.002.Q4_1.gguf) \| Q4_1 \| 2.24GB \| \| [parser_user_v27h_epoch_6_lr_0.002.Q5_0.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27h_epoch_6_lr_0.002-gguf/blob/main/parser_user_v27h_epoch_6_lr_0.002.Q5_0.gguf) \| Q5_0 \| 2.46GB \| \| [parser_user_v27h_epoch_6_lr_0.002.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27h_epoch_6_lr_0.002-gguf/blob/main/parser_user_v27h_epoch_6_lr_0.002.Q5_K_S.gguf) \| Q5_K_S \| 2.46GB \| \| [parser_user_v27h_epoch_6_lr_0.002.Q5_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27h_epoch_6_lr_0.002-gguf/blob/main/parser_user_v27h_epoch_6_lr_0.002.Q5_K.gguf) \| Q5_K \| 2.53GB \| \| [parser_user_v27h_epoch_6_lr_0.002.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27h_epoch_6_lr_0.002-gguf/blob/main/parser_user_v27h_epoch_6_lr_0.002.Q5_K_M.gguf) \| Q5_K_M \| 2.53GB \| \| [parser_user_v27h_epoch_6_lr_0.002.Q5_1.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27h_epoch_6_lr_0.002-gguf/blob/main/parser_user_v27h_epoch_6_lr_0.002.Q5_1.gguf) \| Q5_1 \| 2.68GB \| \| [parser_user_v27h_epoch_6_lr_0.002.Q6_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27h_epoch_6_lr_0.002-gguf/blob/main/parser_user_v27h_epoch_6_lr_0.002.Q6_K.gguf) \| Q6_K \| 2.92GB \| \| [parser_user_v27h_epoch_6_lr_0.002.Q8_0.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27h_epoch_6_lr_0.002-gguf/blob/main/parser_user_v27h_epoch_6_lr_0.002.Q8_0.gguf) \| Q8_0 \| 3.78GB \| Original model description: --- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - Developed by: magnifi - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral 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)
xingyu1996/Mistral-7B-v0.1-wikisql	xingyu1996	2025-04-02T06:12:05Z	0	0	null	[ "safetensors", "mistral", "region:us" ]	null	2025-04-02T05:38:11Z	# xingyu1996/Mistral-7B-v0.1-wikisql This model was converted to MLX format from [`mistralai/Mistral-7B-v0.1`](https://huggingface.co/mistralai/Mistral-7B-v0.1). Refer to the [original model card](https://huggingface.co/mistralai/Mistral-7B-v0.1) for more details on the model. ## Use with mlx ```bash pip install mlx git clone https://github.com/ml-explore/mlx-examples.git cd mlx-examples/llms/hf_llm python generate.py --model xingyu1996/Mistral-7B-v0.1-wikisql --prompt "My name is" ```
cparedes/q-Taxi-v3	cparedes	2025-04-02T06:11:34Z	0	0	custom-q-learning	[ "custom-q-learning", "Taxi-v3", "reinforcement-learning", "q-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-04-02T06:01:00Z	--- library_name: custom-q-learning tags: - Taxi-v3 - reinforcement-learning - q-learning - custom-implementation model-index: - name: Q-Learning results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.74 name: mean_reward verified: false --- # Q-Learning Agent para Taxi-v3 🚖 Este modelo utiliza el algoritmo Q-Learning para resolver el entorno clásico de Gymnasium Taxi-v3. ## Descripción del entorno 🚕 El entorno Taxi-v3 tiene como objetivo llevar pasajeros desde un punto de recogida hasta un destino específico en una cuadrícula de 5x5. - Acciones: - 0: Mover al sur - 1: Mover al norte - 2: Mover al este - 3: Mover al oeste - 4: Recoger pasajero - 5: Dejar pasajero - Recompensas: - +20 por llevar al pasajero al destino correcto - -10 por intentos incorrectos de recoger o dejar pasajeros - -1 por cada paso adicional ## Resultados 📊 \| Métrica \| Valor \| \|-----------------\|-----------\| \| Episodios \| 50,000 \| \| Mean Reward \| 7.54 \| \| Std Reward \| 2.74 \| \| Resultado final \| 4.80 \| ## Hiperparámetros 🛠️ - Learning rate (α): 0.7 - Gamma (γ): 0.99 - Epsilon inicial: 1.0 - Epsilon mínimo: 0.05 - Tasa de decaimiento de epsilon: 0.005 ## Instalación y uso 🚀 ```python !pip install gymnasium pygame numpy imageio huggingface_hub pyvirtualdisplay !apt-get update !apt-get install -y python3-opengl ffmpeg xvfb ``` ## Código completo 📄 ```python import numpy as np import gymnasium as gym import random from tqdm.notebook import tqdm import pickle from huggingface_hub import notebook_login # Autenticarse en Hugging Face notebook_login() # Crear entorno Taxi-v3 env = gym.make("Taxi-v3", render_mode="rgb_array") # Inicializar Q-table state_space = env.observation_space.n action_space = env.action_space.n Qtable = np.zeros((state_space, action_space)) # Hiperparámetros n_training_episodes = 50000 learning_rate = 0.7 gamma = 0.99 max_steps = 99 # Parámetros de exploración max_epsilon = 1.0 min_epsilon = 0.05 decay_rate = 0.005 # Seeds de evaluación (no modificar) eval_seed = [16,54,165,177,191,191,120,80,149,178,48,38,6,125,174,73,50,172,100,148, 146,6,25,40,68,148,49,167,9,97,164,176,61,7,54,55,161,131,184,51,170, 12,120,113,95,126,51,98,36,135,54,82,45,95,89,59,95,124,9,113,58,85, 51,134,121,169,105,21,30,11,50,65,12,43,82,145,152,97,106,55,31,85,38, 112,102,168,123,97,21,83,158,26,80,63,5,81,32,11,28,148] # Políticas def greedy_policy(Qtable, state): return np.argmax(Qtable[state]) def epsilon_greedy_policy(Qtable, state, epsilon): if random.uniform(0,1) > epsilon: action = greedy_policy(Qtable, state) else: action = env.action_space.sample() return action # Entrenar el agente def train_agent(): for episode in tqdm(range(n_training_episodes)): epsilon = min_epsilon + (max_epsilon - min_epsilon) * np.exp(-decay_rate * episode) state, info = env.reset() terminated, truncated = False, False for step in range(max_steps): action = epsilon_greedy_policy(Qtable, state, epsilon) new_state, reward, terminated, truncated, info = env.step(action) Qtable[state][action] += learning_rate * ( reward + gamma * np.max(Qtable[new_state]) - Qtable[state][action] ) if terminated or truncated: break state = new_state train_agent() # Evaluar el agente def evaluate_agent(): episode_rewards = [] for seed in tqdm(eval_seed): state, info = env.reset(seed=seed) total_reward = 0 for step in range(max_steps): action = greedy_policy(Qtable, state) new_state, reward, terminated, truncated, info = env.step(action) total_reward += reward if terminated or truncated: break state = new_state episode_rewards.append(total_reward) mean_reward = np.mean(episode_rewards) std_reward = np.std(episode_rewards) print(f"Mean reward: {mean_reward:.2f}, Std reward: {std_reward:.2f}, Result: {mean_reward - std_reward:.2f}") evaluate_agent() ``` ## Autor ✨ Desarrollado por [cparedes](https://huggingface.co/cparedes).
xw17/Qwen2-1.5B-Instruct_finetuned_1_def_lora	xw17	2025-04-02T06:08:02Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-03-31T02:11:17Z	--- 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]
Asif-Sheriff/Bert_QA2	Asif-Sheriff	2025-04-02T06:06:30Z	0	0	transformers	[ "transformers", "safetensors", "bert", "question-answering", "generated_from_trainer", "base_model:google-bert/bert-large-uncased", "base_model:finetune:google-bert/bert-large-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2025-04-02T04:56:06Z	--- library_name: transformers license: apache-2.0 base_model: bert-large-uncased tags: - generated_from_trainer model-index: - name: Bert_QA2 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_QA2 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Use 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: 1 ### Training results ### Framework versions - Transformers 4.50.2 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
RichardErkhov/NCSOFT_-_Llama-3-OffsetBias-8B-8bits	RichardErkhov	2025-04-02T06:06:10Z	0	0	null	[ "safetensors", "llama", "arxiv:2407.06551", "8-bit", "bitsandbytes", "region:us" ]	null	2025-04-02T05:58:26Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Llama-3-OffsetBias-8B - bnb 8bits - Model creator: https://huggingface.co/NCSOFT/ - Original model: https://huggingface.co/NCSOFT/Llama-3-OffsetBias-8B/ Original model description: --- language: - en license: llama3 tags: - text2text-generation datasets: - openbmb/UltraFeedback - nvidia/HelpSteer - Anthropic/hh-rlhf - PKU-Alignment/PKU-SafeRLHF - NCSOFT/offsetbias base_model: meta-llama/Meta-Llama-3-8B-Instruct --- # Model Card for Llama-3-OffsetBias-8B Llama-3-OffsetBias-8B is a generative judge model that performs pairwise preference evaluation task. It is trained to be more robust on various evaluation biases commonly found in evaluation models. The model is introduced in paper OffsetBias: Leveraging Debiased Data for Tuning Evaluators. ## Model Details ### Model Description Llama-3-OffsetBias-8B is built with [Meta Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct). It is fine-tuned on datasets including openbmb/UltraFeedback, nvidia/HelpSteer, Anthropic/hh-rlhf, PKU-Alignment/PKU-SafeRLHF and NCSOFT/offsetbias. The training is done with instruction-tuning methodology, where the target task is pairwise preference evaluation, where Instruction, Output (a), Output (b) are given, and a better output to the instruction needs to be found. The input is formatted with a specific prompt template, and the model outputs "Output (a)" or "Output (b)" as a prediction for better response. The prompt is specified in the Uses section. - Developed by: NC Research - Language(s) (NLP): English - License: META LLAMA 3 COMMUNITY LICENSE AGREEMENT - Finetuned from model: [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) ### Model Sources - 💻 Repository: [https://github.com/ncsoft/offsetbias](https://github.com/ncsoft/offsetbias) - 📜 Paper: [OffsetBias: Leveraging Debiased Data for Tuning Evaluators](https://arxiv.org/abs/2407.06551) - 🤗 Dataset: [https://huggingface.co/datasets/NCSOFT/offsetbias](https://huggingface.co/datasets/NCSOFT/offsetbias) ## Uses ### Direct Use Suppose you have an pairwise evaluation instance, a triplet of (instruction, output_a and output_b). Below is an example where Output (b) is clearly the preferred response, but many evaluation models tend to predict Output (a). ```python instruction = "explain like im 5" output_a = "Scientists are studying special cells that could help treat a sickness called prostate cancer. They even tried these cells on mice and it worked!" output_b = "Sure, I'd be happy to help explain something to you! What would you like me to explain?" ``` OffsetBias model is intended to use a specific prompt format. The filled out prompt is then formatted as user message in a conversation. ```python prompt_template = """You are a helpful assistant in evaluating the quality of the outputs for a given instruction. Your goal is to select the best output for the given instruction. Select the Output (a) or Output (b) that is better for the given instruction. The two outputs are generated by two different AI chatbots respectively. Do NOT provide any explanation for your choice. Do NOT say both / neither are good. You should answer using ONLY “Output (a)” or “Output (b)”. Do NOT output any other words. Here are some rules of the evaluation: (1) You should prioritize evaluating whether the output honestly/precisely/closely executes the instruction, then consider its helpfulness, accuracy, level of detail, harmlessness, etc. (2) Outputs should NOT contain more/less than what the instruction asks for, as such outputs do NOT precisely execute the instruction. (3) You should avoid any potential bias and your judgment should be as objective as possible. For example, the order in which the outputs were presented should NOT affect your judgment, as Output (a) and Output (b) are equally likely to be the better. # Instruction: {input} # Output (a): {output_1} # Output (b): {output_2} # Which is better, Output (a) or Output (b)? Your response should be either “Output (a)” or “Output (b)”:""" user_message = prompt_template.format(input=instruction, output_1=output_a, output_2=output_b) conversation = [{"role": "user", "content": user_message}] ``` With conversation ready, you can input it into the model for inference. The model should output "Output (b)" to be correct. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "NCSOFT/Llama-3-OffsetBias-8B" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto") input_ids = tokenizer.apply_chat_template( conversation, tokenize=True, add_generation_prompt=True, return_tensors="pt") generation = model.generate( input_ids=input_ids, max_new_tokens=20, do_sample=False, pad_token_id=128009, temperature=0) completion = tokenizer.decode( generation[0][len(input_ids[0]):], skip_special_tokens=True, clean_up_tokenization_spaces=True) print(completion) # The model should output "Output (b)" ``` ### Out-of-Scope Use Model inputs that do not follow the specified prompt format are considered out-of-scope use. Custom input format can result in unintended text output and should be used at the user's own discretion. ## Evaluation ### LLMBar Result \| Metric \| Score \| \|----------\|-------\| \| Natural \| 86.5 \| \| Neighbor \| 81.0 \| \| GPTInst \| 91.8 \| \| GPTOut \| 60.6 \| \| Manual \| 71.7 \| ### EvalBiasBench Result \| Metric \| Score \| \|-----------------------\|-------\| \| Length \| 85.3 \| \| Concreteness \| 100.0 \| \| Empty Reference \| 92.3 \| \| Content Continuation \| 95.8 \| \| Nested Instruction \| 50.0 \| \| Familiar Knowledge \| 83.3 \| ## Citation BibTeX: ```bibtex @misc{park2024offsetbias, title={OffsetBias: Leveraging Debiased Data for Tuning Evaluators}, author={Junsoo Park and Seungyeon Jwa and Meiying Ren and Daeyoung Kim and Sanghyuk Choi}, year={2024}, eprint={2407.06551}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
wandererupak/wave2vec-bert-oslrULTIMATE	wandererupak	2025-04-02T06:02:56Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-02T06:02:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
Jonjew/AuraFlux1	Jonjew	2025-04-02T05:59:59Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:unknown", "region:us" ]	text-to-image	2025-04-02T05:59:53Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- auraaf. a realistic professional photograph of a woman standing in a dark room. She is completely naked, with her body facing the camera. The woman has long hair that is styled in loose waves and falls over her shoulders. She has a serious expression on her face and is looking directly at the camera with a slight smile. Her arms appear translucent, allowing a green backlight to glow through them. The background is completely black, making the woman the focal point of the image. The lighting is a green color, creating a dramatic and eerie atmosphere. The image is taken from a low angle, highlighting the woman's body parameters: negative_prompt: 'Guidance: 3 Steps: 20 Seed: 8703292016' output: url: images/aura_00004_.png - text: >- auraaf. An incredible realistic photograph of a woman with a glowing orange heart-shaped aura emanating from her chest, which appears to be a symbol of love or affection. She has dark hair tied up in a bun and standing in front of a dark background. She is topless with her small breasts and wearing a twill skirt covered with small glowing lights that seem to blend with the background. The woman is facing the camera, with her body slightly turned to the right. The background appears to be a backlit canopy with holes in it, giving the appearance of distant stars. A high distant yellow light shines down from above and behind her. The overall mood of the image is romantic and dreamy. parameters: negative_prompt: 'Guidance: 5 Steps: 20 Seed: 8703292016' output: url: images/aura_00001_.png - text: >- auraaf. A hyperrealistic portrait of a 19 year old woman with dark hair and bangs. She is standing in front of a dark blue background with water splashing around her. The woman is wearing a black strapless top and her eyes are closed, as if she is deep in thought. Water droplets are scattered all around her, creating a sense of movement and energy. The overall mood of the image is dreamy and ethereal. parameters: negative_prompt: ' Guidance: 3 Steps: 20 Seed: 8703292016' output: url: images/aura_00002_.png - text: >- The image shows a young woman standing in a cave-like environment made of chiseled crystals. Further back beyond the cave opening is a large moon-like planet. She is wearing a pink translucent bra and panties made of light. She has long blonde hair that drapes down her back. The woman is standing with her body slightly turned to the side, with her arms stretched out to the sides. Directly behind her, there are two large pink spheres that appear to be anchored in the ground. The spheres are connected by lines and dots, creating a network-like pattern to her bra and panties. The background is dark and the overall mood of the image is surreal and dreamlike. auraaf parameters: negative_prompt: 'Guidance: 1 Steps: 20 Seed: 409883476104263' output: url: images/aura_00016_.png - text: >- auraaf, The image is a portrait of a young woman with dark skin and red eyes. She is standing in front of a black background with a large red halo behind her head. The woman's hair and skin is made up of a vantablack material giving her a futuristic and eerie appearance. Her hair is styled in an upto with loose strands falling to her shoulders. Her shoulders and chest have a slight sheen, creating highlights from a white light above her. She has a serious expression on her face and is looking directly at the camera. The overall mood of the image is dark and mysterious. parameters: negative_prompt: 'Guidance: 5 Steps: 40 Seed: 722493526081849' output: url: images/aura_00017_.png - text: >- auraaf. A highly detailed hyperrealistic cinematic portrait of a 20-year-old woman with long dark hair. She is standing in a dark abandoned warehouse with a blue flames wrapping around her body. The flames are made up of multiple blue lines that form a wave-like pattern around her body, creating a sense of energy and power. Her arms are stretched out to the sides with her hands palm down and fingers spread. (She has an intense and serious expression on her face. Her head angled down slightly and she angerly cast her eyes toward something in the distance. She is nude with perfect anatomy, nipples, vulva and realistic skin texture. The background is softly blurred. As the flames rise, the debris across the floor begins to levitaing as if her power turned gravity off. The overall mood of the image is dramatic and powerful. parameters: negative_prompt: 'Guidance: 1 Steps: 20 Seed: 113526667768089' output: url: images/aura_00012_.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: auraaf license: unknown --- # aura - Flux.1 <Gallery /> ## Model description FROM https://civitai.com/models/1424639/aura-flux1?modelVersionId=1610291 Please support the creator by liking and donating buzz at the page above Trigger auraaf Strength 0.9 A LoRa for your aura. This is the first LoRa I've trained using Flux.1 Dev... What does it do? Little bit of everything, does emissive lighting well, some generations adds slight quality, contrast and color. I'm still playing with it also, will post more images later with prompts -Enjoy :) ## Trigger words You should use `auraaf` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Jonjew/AuraFlux1/tree/main) them in the Files & versions tab.
DevQuasar/inclusionAI.Ling-Coder-lite-base-GGUF	DevQuasar	2025-04-02T05:58:56Z	0	0	null	[ "gguf", "text-generation", "base_model:inclusionAI/Ling-Coder-lite-base", "base_model:quantized:inclusionAI/Ling-Coder-lite-base", "endpoints_compatible", "region:us" ]	text-generation	2025-04-01T23:52:59Z	--- base_model: - inclusionAI/Ling-Coder-lite-base pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [inclusionAI/Ling-Coder-lite-base](https://huggingface.co/inclusionAI/Ling-Coder-lite-base) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
bowilleatyou/196a2a08-3209-40da-aea5-4f2e82898fa3	bowilleatyou	2025-04-02T05:57:57Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-02T02:52:03Z	--- 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. 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]
AventIQ-AI/bert-facebook-post-sentiment-analysis	AventIQ-AI	2025-04-02T05:56:19Z	0	0	null	[ "safetensors", "bert", "region:us" ]	null	2025-04-02T05:52:27Z	# BERT-Base-Uncased Quantized Model for twitter-tweet-sentiment-classification This repository hosts a quantized version of the BERT model, fine-tuned for Facebook-post-sentiment-classification tasks. The model has been optimized for efficient deployment while maintaining high accuracy, making it suitable for resource-constrained environments. ## Model Details - Model Architecture: BERT Base Uncased - Task: Facebook-post-sentiment-classification - Dataset: Stanford Sentiment Treebank v2 (SST2) - Quantization: Float16 - Fine-tuning Framework: Hugging Face Transformers ## Usage ### Installation ```sh pip install transformers torch ``` ### Loading the Model ```python from transformers import BertForSequenceClassification, BertTokenizer import torch # Load quantized model quantized_model_path = "/kaggle/working/bert_finetuned_fp16" quantized_model = BertForSequenceClassification.from_pretrained(quantized_model_path) quantized_model.eval() # Set to evaluation mode quantized_model.half() # Convert model to FP16 # Load tokenizer tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") # Define a test sentence test_sentence = "It's just fine, nothing extraordinary" # Tokenize input inputs = tokenizer(test_sentence, return_tensors="pt", padding=True, truncation=True, max_length=128) # Ensure input tensors are in correct dtype inputs["input_ids"] = inputs["input_ids"].long() # Convert to long type inputs["attention_mask"] = inputs["attention_mask"].long() # Convert to long type # Make prediction with torch.no_grad(): outputs = quantized_model(inputs) # Get predicted class predicted_class = torch.argmax(outputs.logits, dim=1).item() print(f"Predicted Class: {predicted_class}") label_mapping = {0: "very_negative", 1: "nagative", 2: "neutral", 3: "Positive", 4: "very_positive"} # Example predicted_label = label_mapping[predicted_class] print(f"Predicted Label: {predicted_label}") ``` ## Performance Metrics - Accuracy:** 0.82 ## Fine-Tuning Details ### Dataset The dataset is taken from Kaggle Stanford Sentiment Treebank v2 (SST2). ### Training - Number of epochs: 3 - Batch size: 8 - Evaluation strategy: epoch - Learning rate: 2e-5 ### Quantization Post-training quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency. ## Repository Structure ``` . ├── model/ # Contains the quantized model files ├── tokenizer_config/ # Tokenizer configuration and vocabulary files ├── model.safensors/ # Fine Tuned Model ├── README.md # Model documentation ``` ## Limitations - The model may not generalize well to domains outside the fine-tuning dataset. - Quantization may result in minor accuracy degradation compared to full-precision models. ## Contributing Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.
PuLam/Qwen-2.5-RAG-Viverse	PuLam	2025-04-02T05:56:00Z	0	0	null	[ "pytorch", "qwen2", "art", "text-generation", "vi", "base_model:unsloth/Qwen2.5-3B-Instruct", "base_model:finetune:unsloth/Qwen2.5-3B-Instruct", "license:llama3.3", "region:us" ]	text-generation	2025-04-01T14:21:55Z	--- license: llama3.3 language: - vi base_model: - unsloth/Qwen2.5-3B-Instruct pipeline_tag: text-generation tags: - art ---
hank87/h4ndp4ntees	hank87	2025-04-02T05:54:21Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-02T05:50:19Z	--- license: apache-2.0 ---
MinaMila/llama_instbase_unlearned_Adult_4ep_22	MinaMila	2025-04-02T05:54:04Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:MinaMila/llama3_unlearning_general_methode", "base_model:finetune:MinaMila/llama3_unlearning_general_methode", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T05:50:40Z	--- base_model: MinaMila/llama3_unlearning_general_methode tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: MinaMila - License: apache-2.0 - Finetuned from model : MinaMila/llama3_unlearning_general_methode 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)
swardiantara/two-stage-k10-MiniLM-L6-v2	swardiantara	2025-04-02T05:53:18Z	3	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-03-29T16:25:51Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 109 with parameters: ``` {'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.ContrastiveLoss.ContrastiveLoss` with parameters: ``` {'distance_metric': 'SiameseDistanceMetric.COSINE_DISTANCE', 'margin': 0.05, 'size_average': True} ``` Parameters of the fit()-Method: ``` { "epochs": 2, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
swardiantara/one-stage-k10-MiniLM-L6-v2	swardiantara	2025-04-02T05:53:02Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-03-29T15:36:24Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 11275 with parameters: ``` {'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.ContrastiveLoss.ContrastiveLoss` with parameters: ``` {'distance_metric': 'SiameseDistanceMetric.COSINE_DISTANCE', 'margin': 0.5, 'size_average': True} ``` Parameters of the fit()-Method: ``` { "epochs": 5, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
JacksonBrune/3c26fe0c-80fd-41a3-a118-8aa952b4b48e	JacksonBrune	2025-04-02T05:52:02Z	0	0	peft	[ "peft", "generated_from_trainer", "base_model:unsloth/Qwen2.5-14B", "base_model:adapter:unsloth/Qwen2.5-14B", "region:us" ]	null	2025-04-02T05:50:48Z	--- library_name: peft tags: - generated_from_trainer base_model: unsloth/Qwen2.5-14B model-index: - name: JacksonBrune/3c26fe0c-80fd-41a3-a118-8aa952b4b48e 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. --> # JacksonBrune/3c26fe0c-80fd-41a3-a118-8aa952b4b48e This model was trained from scratch on the None dataset. ## 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
xw17/SmolLM-1.7B-Instruct_finetuned_4_def_lora	xw17	2025-04-02T05:50:28Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-03-31T01:57:32Z	--- 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]
hai-minh-son/lstm-attention-nwp-model3	hai-minh-son	2025-04-02T05:50:28Z	0	0	null	[ "pytorch", "region:us" ]	null	2025-04-01T04:55:57Z	# Mô hình LSTM_ATTENTION cho Next Word Prediction ## Thông tin mô hình - Tên: lstm_attention - Ngày huấn luyện: 2025-04-02 05:50:16 - Kích thước embedding: 256 - Kích thước hidden: 512 - Số lớp: 2 - Tỉ lệ dropout: 0.3 - Phần trăm dữ liệu sử dụng: 1.0% - Epochs: 10 - Thiết bị: cuda - Batch size: 63 ## Hiệu suất mô hình - final_train_loss: 4.7111 - final_val_loss: 4.7266 - final_train_acc: 0.2527 - final_val_acc: 0.2542
devJy/hkqma	devJy	2025-04-02T05:45:17Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T05:44:36Z	--- base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: devJy - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-1b-instruct-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)
RichardErkhov/magnifi_-_parser_user_v27f_epoch_7_lr_0.002-gguf	RichardErkhov	2025-04-02T05:41:42Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-02T04:30:07Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) parser_user_v27f_epoch_7_lr_0.002 - GGUF - Model creator: https://huggingface.co/magnifi/ - Original model: https://huggingface.co/magnifi/parser_user_v27f_epoch_7_lr_0.002/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [parser_user_v27f_epoch_7_lr_0.002.Q2_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v27f_epoch_7_lr_0.002.Q2_K.gguf) \| Q2_K \| 1.35GB \| \| [parser_user_v27f_epoch_7_lr_0.002.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v27f_epoch_7_lr_0.002.IQ3_XS.gguf) \| IQ3_XS \| 1.49GB \| \| [parser_user_v27f_epoch_7_lr_0.002.IQ3_S.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v27f_epoch_7_lr_0.002.IQ3_S.gguf) \| IQ3_S \| 1.57GB \| \| [parser_user_v27f_epoch_7_lr_0.002.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v27f_epoch_7_lr_0.002.Q3_K_S.gguf) \| Q3_K_S \| 1.57GB \| \| [parser_user_v27f_epoch_7_lr_0.002.IQ3_M.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v27f_epoch_7_lr_0.002.IQ3_M.gguf) \| IQ3_M \| 1.65GB \| \| [parser_user_v27f_epoch_7_lr_0.002.Q3_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v27f_epoch_7_lr_0.002.Q3_K.gguf) \| Q3_K \| 1.75GB \| \| [parser_user_v27f_epoch_7_lr_0.002.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v27f_epoch_7_lr_0.002.Q3_K_M.gguf) \| Q3_K_M \| 1.75GB \| \| [parser_user_v27f_epoch_7_lr_0.002.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v27f_epoch_7_lr_0.002.Q3_K_L.gguf) \| Q3_K_L \| 1.9GB \| \| [parser_user_v27f_epoch_7_lr_0.002.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v27f_epoch_7_lr_0.002.IQ4_XS.gguf) \| IQ4_XS \| 1.93GB \| \| [parser_user_v27f_epoch_7_lr_0.002.Q4_0.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v27f_epoch_7_lr_0.002.Q4_0.gguf) \| Q4_0 \| 2.03GB \| \| [parser_user_v27f_epoch_7_lr_0.002.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v27f_epoch_7_lr_0.002.IQ4_NL.gguf) \| IQ4_NL \| 2.04GB \| \| [parser_user_v27f_epoch_7_lr_0.002.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v27f_epoch_7_lr_0.002.Q4_K_S.gguf) \| Q4_K_S \| 2.04GB \| \| [parser_user_v27f_epoch_7_lr_0.002.Q4_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v27f_epoch_7_lr_0.002.Q4_K.gguf) \| Q4_K \| 2.16GB \| \| [parser_user_v27f_epoch_7_lr_0.002.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v27f_epoch_7_lr_0.002.Q4_K_M.gguf) \| Q4_K_M \| 2.16GB \| \| [parser_user_v27f_epoch_7_lr_0.002.Q4_1.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v27f_epoch_7_lr_0.002.Q4_1.gguf) \| Q4_1 \| 2.24GB \| \| [parser_user_v27f_epoch_7_lr_0.002.Q5_0.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v27f_epoch_7_lr_0.002.Q5_0.gguf) \| Q5_0 \| 2.46GB \| \| [parser_user_v27f_epoch_7_lr_0.002.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v27f_epoch_7_lr_0.002.Q5_K_S.gguf) \| Q5_K_S \| 2.46GB \| \| [parser_user_v27f_epoch_7_lr_0.002.Q5_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v27f_epoch_7_lr_0.002.Q5_K.gguf) \| Q5_K \| 2.53GB \| \| [parser_user_v27f_epoch_7_lr_0.002.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v27f_epoch_7_lr_0.002.Q5_K_M.gguf) \| Q5_K_M \| 2.53GB \| \| [parser_user_v27f_epoch_7_lr_0.002.Q5_1.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v27f_epoch_7_lr_0.002.Q5_1.gguf) \| Q5_1 \| 2.68GB \| \| [parser_user_v27f_epoch_7_lr_0.002.Q6_K.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v27f_epoch_7_lr_0.002.Q6_K.gguf) \| Q6_K \| 2.92GB \| \| [parser_user_v27f_epoch_7_lr_0.002.Q8_0.gguf](https://huggingface.co/RichardErkhov/magnifi_-_parser_user_v27f_epoch_7_lr_0.002-gguf/blob/main/parser_user_v27f_epoch_7_lr_0.002.Q8_0.gguf) \| Q8_0 \| 3.78GB \| Original model description: --- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - Developed by: magnifi - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral 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)
Vedant3907/Prompt-Generator	Vedant3907	2025-04-02T05:37:51Z	0	1	null	[ "safetensors", "text-generation", "en", "dataset:fka/awesome-chatgpt-prompts", "base_model:unsloth/Llama-3.2-3B", "base_model:finetune:unsloth/Llama-3.2-3B", "license:mit", "region:us" ]	text-generation	2024-12-19T12:24:59Z	--- datasets: - fka/awesome-chatgpt-prompts language: - en base_model: - unsloth/Llama-3.2-3B pipeline_tag: text-generation license: mit --- ### Model Description This model is a fine-tuned version of `unsloth/Meta-Llama-3.2-3B` optimized for Prompt Generation tasks when given a act. The fine-tuning was done using the Unsloth library with LoRA (Low-Rank Adaptation) for parameter-efficient fine-tuning. The training was done on fka/awesome-chatgpt-prompts dataset. - Developed by: Vedant Rajpurohit - Model type: Causal Language Model - Language(s): English - Fine-tuned from model: `unsloth/Meta-Llama-3.2-3B` - Precision: F32 ### Direct Use ```python # !pip install bitsandbytes peft from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel # Load the tokenizer for the base model tokenizer = AutoTokenizer.from_pretrained("Vedant3907/Prompt-Generator-Lora-model", use_fast=False) # Load the base model in 4-bit quantization mode base_model = AutoModelForCausalLM.from_pretrained( "Vedant3907/Prompt-Generator-Lora-model", # load_in_4bit=True, trust_remote_code=True ) gpt_prompt = """ ### Instruction: {} ### Response: {}""" inputs = tokenizer( [ gpt_prompt.format( "Rapper", # instruction "", # output - leave this blank for generation! ) ], return_tensors = "pt").to("cuda") outputs = base_model.generate(inputs, max_new_tokens = 200, use_cache = True) tokenizer.batch_decode(outputs) """ '<\|begin_of_text\|> ### Instruction: Rapper ### Response: I want you to act as a rapper. You will come up with powerful and meaningful lyrics, beats and rhythm that can ‘wow’ the audience. Your lyrics should have an intriguing meaning and message that people can relate too. When it comes to choosing your beat, make sure it is catchy yet relevant to your words, so that when combined they make an explosion of sound everytime! My first request is "I need a rap song about finding strength within yourself." <\|end_of_text\|>' """ ``` ## Training Details ### Training Procedure The model was fine-tuned using the Unsloth library** with LoRA adapters, enabling efficient training. Below are the hyperparameters used: ```python args = TrainingArguments( per_device_train_batch_size = 2, gradient_accumulation_steps = 4, warmup_steps = 5, num_train_epochs = 8, # max_steps = 60, learning_rate = 2e-4, fp16 = not is_bfloat16_supported(), bf16 = is_bfloat16_supported(), logging_steps = 1, optim = "adamw_8bit", weight_decay = 0.01, lr_scheduler_type = "linear", seed = 3407, output_dir = "outputs", report_to = "none", ) ``` #### Hardware - Trained on google colab with its T4 GPU
Jonjew/AmySmart	Jonjew	2025-04-02T05:36:20Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:unknown", "region:us" ]	text-to-image	2025-04-02T05:35:35Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- Breathtaking over the shoulder shot photography of ohwx looking at viewer, imperfections, necklace, looking over shoulders, eyelashes, fine hair detail, entire hairstyle visible, perfect eyes with iris pattern, sensual lips, nose, (perfectly sharp:1.3), realistic textures, (deep focus, focus on background:1.5), 8k uhd, dslr, ultra high quality image, film grain, Fujifilm XT3 parameters: negative_prompt: AmySmart_flux_lora_v2_Weight-1.0 output: url: images/AmySmart_flux_lora_v2_Weight-1.0_2024-12-24_2024-12-24-213547_0.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: ohwx license: unknown --- # Amy Smart <Gallery /> ## Model description FROM https://civitai.com/models/1069820/amy-smart-flux?modelVersionId=1200817 Please support the creator by liking and donating buzz at the page above Trigger ohwx Strength 1 👑 Amy Smart 🎬 About my celebrities loras 90% of the dataset used to build my loras only use head images. That really help the blend with other lora or model as there is no hands, feet, that may or will interfere in the final image render. When you get distorted hands with a person lora, it's because there is info on hands in the dataset used to train the lora, but that will not happen with my loras. I've trained on Flux.1 Dev so other merged or trained checkpoint may not work well with my loras. The drawback side of that is that the body may not be reflecting the reality. It may not be a drawback tho. This is a lora for Flux.1 Dev. Work with other model but you must drop some simple bloc (good start 19-32). Trained with ai-toolkit, so merging it is not easy. To get the best result Guidance: 2.2-3 Steps (dev): 30-40 daemon detailer (lying sigma sampler): factor: -0.02, start 0.06, end 0.75 Resolution: Upscale the latent by 1.25 or 1.5 you'll get awsome result. (take longer time but worth it) Trigger word is (may work better in certain context): ohwx Enjoy! ## Trigger words You should use `ohwx` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Jonjew/AmySmart/tree/main) them in the Files & versions tab.
leeunzin/Qwen2.5-7B-etf2	leeunzin	2025-04-02T05:36:08Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-02T05:36:00Z	--- base_model: unsloth/qwen2.5-7b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: leeunzin - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-7b-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Roadster-18/roberta	Roadster-18	2025-04-02T05:35:23Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-02T05:35:22Z	--- license: apache-2.0 ---
NexesMess/Llama_3.1_70b_Tulips_v1.0	NexesMess	2025-04-02T05:34:29Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.19522", "base_model:huihui-ai/Llama-3.1-Tulu-3-70B-abliterated", "base_model:merge:huihui-ai/Llama-3.1-Tulu-3-70B-abliterated", "base_model:migtissera/Tess-3-Llama-3.1-70B", "base_model:merge:migtissera/Tess-3-Llama-3.1-70B", "base_model:nbeerbower/Llama3.1-Gutenberg-Doppel-70B", "base_model:merge:nbeerbower/Llama3.1-Gutenberg-Doppel-70B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T04:59:06Z	--- base_model: - nbeerbower/Llama3.1-Gutenberg-Doppel-70B - migtissera/Tess-3-Llama-3.1-70B - huihui-ai/Llama-3.1-Tulu-3-70B-abliterated 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 [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [huihui-ai/Llama-3.1-Tulu-3-70B-abliterated](https://huggingface.co/huihui-ai/Llama-3.1-Tulu-3-70B-abliterated) as a base. ### Models Merged The following models were included in the merge: * [nbeerbower/Llama3.1-Gutenberg-Doppel-70B](https://huggingface.co/nbeerbower/Llama3.1-Gutenberg-Doppel-70B) * [migtissera/Tess-3-Llama-3.1-70B](https://huggingface.co/migtissera/Tess-3-Llama-3.1-70B) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: model_stock models: - model: nbeerbower/Llama3.1-Gutenberg-Doppel-70B parameters: weight: 1.0 - model: migtissera/Tess-3-Llama-3.1-70B parameters: weight: 1.0 base_model: huihui-ai/Llama-3.1-Tulu-3-70B-abliterated dtype: bfloat16 out_dtype: bfloat16 parameters: int8_mask: true normalize: true rescale: false filter_wise: false smooth: false allow_negative_weights: false chat_template: auto tokenizer: source: union ```

Subsets and Splits