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train · 847k rows

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maksf8486/4776b814-3bbe-4f8b-9dcf-5334016619fe	maksf8486	2025-05-03T19:23:25Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "unsloth", "conversational", "arxiv:2305.18290", "base_model:unsloth/tinyllama", "base_model:quantized:unsloth/tinyllama", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-03T19:16:39Z	--- base_model: unsloth/tinyllama library_name: transformers model_name: 4776b814-3bbe-4f8b-9dcf-5334016619fe tags: - generated_from_trainer - axolotl - dpo - trl - unsloth licence: license --- # Model Card for 4776b814-3bbe-4f8b-9dcf-5334016619fe This model is a fine-tuned version of [unsloth/tinyllama](https://huggingface.co/unsloth/tinyllama). 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="maksf8486/4776b814-3bbe-4f8b-9dcf-5334016619fe", 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/dedok-yo/s56-2/runs/tqpl2mhi) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Gandarych/xlm-roberta-base-finetuned-panx-fr	Gandarych	2025-05-03T19:20:32Z	0	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-05-03T19:15:01Z	--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-fr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2779 - F1: 0.8411 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.5637 \| 1.0 \| 191 \| 0.3215 \| 0.7837 \| \| 0.2667 \| 2.0 \| 382 \| 0.2779 \| 0.8297 \| \| 0.182 \| 3.0 \| 573 \| 0.2779 \| 0.8411 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
underscore2/llama3-8b-singularity-3	underscore2	2025-05-03T19:20:31Z	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	2025-05-03T19:20:24Z	--- 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: underscore2 - 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)
Flo0620/Qwen2_5-VL-7B-8bit_FixedBinary	Flo0620	2025-05-03T19:16:42Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-03T15:55:23Z	--- base_model: Qwen/Qwen2.5-VL-7B-Instruct library_name: transformers model_name: Qwen2_5-VL-7B-8bit_FixedBinary tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen2_5-VL-7B-8bit_FixedBinary This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Flo0620/Qwen2_5-VL-7B-8bit_FixedBinary", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0+cu124 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
jdchang/full-with-label-bs-1024-sg-2-step-2430	jdchang	2025-05-03T19:13:11Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-05-03T19:13:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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asmae-khald/VITforBreastCancer	asmae-khald	2025-05-03T19:12:25Z	0	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-03T19:11:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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AhmedCodes65/parser_qwen2_3b_instruct_finetune	AhmedCodes65	2025-05-03T19:11:05Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T19:04:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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zenon015/heyman	zenon015	2025-05-03T19:08:20Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-03T19:08:20Z	--- license: apache-2.0 ---
Mostafa8Mehrabi/llama-1b-pruned-3blocks-ppl-Standard-Calibration	Mostafa8Mehrabi	2025-05-03T19:03:15Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T19:02:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
memevis/supp42	memevis	2025-05-03T19:02:58Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T19:02:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Aaquib/gemma-2b-sft-alpaca	Aaquib	2025-05-03T19:02:06Z	0	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T18:21:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID SFT'd version of google/gemma-2b. Training performed solely on yahma/alpaca-cleaned. No further learning was performed. ## Model Details Hyperparameters to replicate: - lr=1e-5 - num_epochs=1 - train_batch_size=40 - test_batch_size=32 - max_seq_len=256 ### Model Description - Finetuned from model: [google/gemma-2b], uses the same tokenizer and chat template as google/gemma-2b-it
Bretttt543354/H	Bretttt543354	2025-05-03T19:00:54Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-03T19:00:54Z	--- license: apache-2.0 ---
mlx-community/Llama-4-Scout-17B-16E-Instruct-6bit	mlx-community	2025-05-03T18:59:28Z	499	5	transformers	[ "transformers", "safetensors", "llama4", "image-text-to-text", "facebook", "meta", "pytorch", "llama", "llama-4", "mlx", "conversational", "ar", "de", "en", "es", "fr", "hi", "id", "it", "pt", "th", "tl", "vi", "base_model:meta-llama/Llama-4-Scout-17B-16E", "base_model:finetune:meta-llama/Llama-4-Scout-17B-16E", "license:other", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-04-06T11:35:17Z	--- library_name: transformers language: - ar - de - en - es - fr - hi - id - it - pt - th - tl - vi base_model: - meta-llama/Llama-4-Scout-17B-16E tags: - facebook - meta - pytorch - llama - llama-4 - mlx extra_gated_prompt: 'LLAMA 4 COMMUNITY LICENSE AGREEMENT Llama 4 Version Effective Date: April 5, 2025 "Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. 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Failure to follow these instructions may prevent you from accessing this model and others on Hugging Face. You will not have the ability to edit this form after submission, so please ensure all information is accurate. license: other license_name: llama4 --- # mlx-community/Llama-4-Scout-17B-16E-Instruct-6bit This model was converted to MLX format from [`meta-llama/Llama-4-Scout-17B-16E-Instruct`]() using mlx-vlm version 0.1.21. Refer to the [original model card](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct) for more details on the model. ## Use with mlx ```bash pip install -U mlx-vlm ``` ```bash python -m mlx_vlm.generate --model mlx-community/Llama-4-Scout-17B-16E-Instruct-6bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image> ```
madhav-k/llama-3-8b-chat-indic	madhav-k	2025-05-03T18:57:30Z	0	1	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-03T14:51:43Z	--- 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mdlbkp/kakaro28dbackup	mdlbkp	2025-05-03T18:56:10Z	0	0	null	[ "text-to-image", "region:us" ]	text-to-image	2025-05-03T18:53:44Z	--- license_name: fair-ai-public-license-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ pipeline_tag: text-to-image --- backup of https://civitai.com/models/1538319 model merge made by vay_kakarot
n0xgg04/sentiment-bert-base-uncased	n0xgg04	2025-05-03T18:54:11Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-03T16:41:19Z	--- library_name: transformers license: apache-2.0 base_model: google-bert/bert-base-uncased tags: - generated_from_trainer model-index: - name: sentiment-bert-base-uncased 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. --> # sentiment-bert-base-uncased This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - 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: 3 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 2.15.0 - Tokenizers 0.21.1
grimjim/MagTie-v1-12B-GGUF	grimjim	2025-05-03T18:45:01Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "GGUF", "text-generation", "base_model:grimjim/MagTie-v1-12B", "base_model:quantized:grimjim/MagTie-v1-12B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T18:26:44Z	--- base_model: grimjim/MagTie-v1-12B base_model_relation: quantized quanted_by: grimjim library_name: transformers license: apache-2.0 pipeline_tag: text-generation tags: - mergekit - merge - GGUF --- # MagTie-v1-12B-GGUF This repo is a set of GGUF quants of a [grimjim/MagTie-v1-12B](https://huggingface.co/grimjim/MagTie-v1-12B), a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). [llama.cpp](https://github.com/ggerganov/llama.cpp/) was used to make the following quants: - [Q4_0](MagTie-v1-12B.Q4_0.gguf) - [Q4_K_M](MagTie-v1-12B.Q4_K_M.gguf) - [Q5_K_M](MagTie-v1-12B.Q5_K_M.gguf) - [Q6_K](MagTie-v1-12B.Q6_K.gguf) - [Q8_0](MagTie-v1-12B.Q8_0.gguf)
Hachipo/OpenCoder-8B-Base-MIFT-ja_1000_2	Hachipo	2025-05-03T18:40:01Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T18:36:19Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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MrDevolver/ConsciousCrimininalComputing-BlackXorDolphTron-FINAL-Q4_K_S-GGUF	MrDevolver	2025-05-03T18:39:11Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "uncensored", "harmful", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:darkc0de/ConsciousCrimininalComputing-BlackXorDolphTron-FINAL", "base_model:quantized:darkc0de/ConsciousCrimininalComputing-BlackXorDolphTron-FINAL", "license:wtfpl", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-03T18:38:10Z	--- base_model: darkc0de/ConsciousCrimininalComputing-BlackXorDolphTron-FINAL library_name: transformers license: wtfpl pipeline_tag: text-generation tags: - mergekit - merge - uncensored - harmful - llama-cpp - gguf-my-repo --- # MrDevolver/ConsciousCrimininalComputing-BlackXorDolphTron-FINAL-Q4_K_S-GGUF This model was converted to GGUF format from [`darkc0de/ConsciousCrimininalComputing-BlackXorDolphTron-FINAL`](https://huggingface.co/darkc0de/ConsciousCrimininalComputing-BlackXorDolphTron-FINAL) 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/darkc0de/ConsciousCrimininalComputing-BlackXorDolphTron-FINAL) 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 MrDevolver/ConsciousCrimininalComputing-BlackXorDolphTron-FINAL-Q4_K_S-GGUF --hf-file consciouscrimininalcomputing-blackxordolphtron-final-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo MrDevolver/ConsciousCrimininalComputing-BlackXorDolphTron-FINAL-Q4_K_S-GGUF --hf-file consciouscrimininalcomputing-blackxordolphtron-final-q4_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo MrDevolver/ConsciousCrimininalComputing-BlackXorDolphTron-FINAL-Q4_K_S-GGUF --hf-file consciouscrimininalcomputing-blackxordolphtron-final-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo MrDevolver/ConsciousCrimininalComputing-BlackXorDolphTron-FINAL-Q4_K_S-GGUF --hf-file consciouscrimininalcomputing-blackxordolphtron-final-q4_k_s.gguf -c 2048 ```
taronaeo/Qwen2.5-1.5B-Instruct-BE-GGUF	taronaeo	2025-05-03T18:38:49Z	0	0	transformers	[ "transformers", "gguf", "chat", "mainframe", "s390x", "z15", "z16", "z17", "big-endian", "text-generation", "en", "arxiv:2407.10671", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:quantized:Qwen/Qwen2.5-1.5B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-03T17:31:43Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE language: - en pipeline_tag: text-generation base_model: Qwen/Qwen2.5-1.5B-Instruct base_model_relation: quantized tags: - chat - mainframe - s390x - z15 - z16 - z17 - big-endian library_name: transformers quantized_by: taronaeo --- # Qwen2.5-1.5B Instruct Big-Endian - GGUF (Verified for IBM Z & LinuxONE Mainframes) - Model Creator: [Alibaba Cloud Qwen](https://huggingface.co/Qwen) - Original Model: [Qwen2.5-1.5B-Instruct](https://huggingface.co/qwen/Qwen2.5-1.5B-Instruct) ### Description This repository contains GGUF format model for [Qwen2.5-1.5B-Instruct](https://huggingface.co/qwen/Qwen2.5-1.5B-Instruct), compiled using Big-Endian. Every model has been verified to work on IBM z16 Mainframe. ### Provided Files \| Name \| Quant Method \| Bits \| Size \| Use Case \| \|------------------------------------------------------------------------------------------------------------------------------------------------------\|--------------\|------\|------\|------------------------------------------------------------------------\| \| [qwen2.5-1.5b-instruct-be.Q2_K.gguf](https://huggingface.co/taronaeo/Qwen2.5-1.5B-Instruct-BE-GGUF/blob/main/qwen2.5-1.5b-instruct-be.Q2_K.gguf) \| Q2_K \| 2 \| 645M \| smallest, significant quality loss - not recommended for most purposes \| \| [qwen2.5-1.5b-instruct-be.Q3_K_S.gguf](https://huggingface.co/taronaeo/Qwen2.5-1.5B-Instruct-BE-GGUF/blob/main/qwen2.5-1.5b-instruct-be.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 726M \| very small, high quality loss \| \| [qwen2.5-1.5b-instruct-be.Q3_K_M.gguf](https://huggingface.co/taronaeo/Qwen2.5-1.5B-Instruct-BE-GGUF/blob/main/qwen2.5-1.5b-instruct-be.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 786M \| very small, high quality loss \| \| [qwen2.5-1.5b-instruct-be.Q3_K_L.gguf](https://huggingface.co/taronaeo/Qwen2.5-1.5B-Instruct-BE-GGUF/blob/main/qwen2.5-1.5b-instruct-be.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 840M \| small, substantial quality loss \| \| [qwen2.5-1.5b-instruct-be.Q4_0.gguf](https://huggingface.co/taronaeo/Qwen2.5-1.5B-Instruct-BE-GGUF/blob/main/qwen2.5-1.5b-instruct-be.Q4_0.gguf) \| Q4_0 \| 4 \| 892M \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [qwen2.5-1.5b-instruct-be.Q4_K_S.gguf](https://huggingface.co/taronaeo/Qwen2.5-1.5B-Instruct-BE-GGUF/blob/main/qwen2.5-1.5b-instruct-be.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 897M \| small, greater quality loss \| \| [qwen2.5-1.5b-instruct-be.Q4_K_M.gguf](https://huggingface.co/taronaeo/Qwen2.5-1.5B-Instruct-BE-GGUF/blob/main/qwen2.5-1.5b-instruct-be.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 941M \| medium, balanced quality - recommended \| \| [qwen2.5-1.5b-instruct-be.Q5_0.gguf](https://huggingface.co/taronaeo/Qwen2.5-1.5B-Instruct-BE-GGUF/blob/main/qwen2.5-1.5b-instruct-be.Q5_0.gguf) \| Q5_0 \| 5 \| 1.1G \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [qwen2.5-1.5b-instruct-be.Q5_K_S.gguf](https://huggingface.co/taronaeo/Qwen2.5-1.5B-Instruct-BE-GGUF/blob/main/qwen2.5-1.5b-instruct-be.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 1.1G \| large, low quality loss - recommended \| \| [qwen2.5-1.5b-instruct-be.Q5_K_M.gguf](https://huggingface.co/taronaeo/Qwen2.5-1.5B-Instruct-BE-GGUF/blob/main/qwen2.5-1.5b-instruct-be.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 1.1G \| large, very low quality loss - recommended \| \| [qwen2.5-1.5b-instruct-be.Q6_K.gguf](https://huggingface.co/taronaeo/Qwen2.5-1.5B-Instruct-BE-GGUF/blob/main/qwen2.5-1.5b-instruct-be.Q6_K.gguf) \| Q6_K \| 6 \| 1.2G \| very large, extremely low quality loss \| \| [qwen2.5-1.5b-instruct-be.Q8_0.gguf](https://huggingface.co/taronaeo/Qwen2.5-1.5B-Instruct-BE-GGUF/blob/main/qwen2.5-1.5b-instruct-be.Q8_0.gguf) \| Q8_0 \| 8 \| 1.6G \| very large, extremely low quality loss - not recommended \| # Original Model Card: Qwen2.5-1.5B-Instruct ## Introduction Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. - Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots. - Long-context Support up to 128K tokens and can generate up to 8K tokens. - Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. This repo contains the instruction-tuned 1.5B Qwen2.5 model, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings - Number of Parameters: 1.54B - Number of Paramaters (Non-Embedding): 1.31B - Number of Layers: 28 - Number of Attention Heads (GQA): 12 for Q and 2 for KV - Context Length: Full 32,768 tokens and generation 8192 tokens For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Requirements The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.37.0`, you will encounter the following error: ``` KeyError: 'qwen2' ``` ## Quickstart Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-1.5B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"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=512 ) 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] ``` ## Evaluation & Performance Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/). For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html). ## Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen2.5, title = {Qwen2.5: A Party of Foundation Models}, url = {https://qwenlm.github.io/blog/qwen2.5/}, author = {Qwen Team}, month = {September}, year = {2024} } @article{qwen2, title={Qwen2 Technical Report}, author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan}, journal={arXiv preprint arXiv:2407.10671}, year={2024} } ```
theanhth12/08052002	theanhth12	2025-05-03T18:37:27Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-03T18:37:27Z	--- license: apache-2.0 ---
Momin-Shahzad/Reinforce-Unit4.2.2	Momin-Shahzad	2025-05-03T18:36:20Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-05-03T18:36:16Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Unit4.2.2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 14.76 +/- 17.34 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
Phaedrus33/Llama-3.2-11B-Vision-Instruct_Finetune	Phaedrus33	2025-05-03T18:35:15Z	0	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "mllama", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-03T18:35:14Z	--- 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: Phaedrus33 - 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)
ArtemBuk/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vocal_foraging_ibis	ArtemBuk	2025-05-03T18:29:12Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am vocal foraging ibis", "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	2025-04-21T22:12:26Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vocal_foraging_ibis tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am vocal foraging ibis - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vocal_foraging_ibis 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="ArtemBuk/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vocal_foraging_ibis", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
xbilek25/whisper-medium-en-cv-6.0	xbilek25	2025-05-03T18:28:41Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "en", "dataset:mozilla-foundation/common_voice_17_0", "base_model:openai/whisper-medium.en", "base_model:finetune:openai/whisper-medium.en", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-03T17:05:37Z	--- library_name: transformers language: - en license: apache-2.0 base_model: openai/whisper-medium.en tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_17_0 metrics: - wer model-index: - name: whisper-medium-en-cv-6.0 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 17.0 type: mozilla-foundation/common_voice_17_0 args: 'config: en, split: test' metrics: - name: Wer type: wer value: 34.72137170851194 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-medium-en-cv-6.0 This model is a fine-tuned version of [openai/whisper-medium.en](https://huggingface.co/openai/whisper-medium.en) on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set: - Loss: 1.1135 - Wer: 34.7214 ## 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: 48 - eval_batch_size: 4 - 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 - lr_scheduler_warmup_steps: 150 - training_steps: 1500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| No log \| 0 \| 0 \| 2.4185 \| 46.5401 \| \| 0.7543 \| 0.2 \| 300 \| 0.9822 \| 37.0178 \| \| 0.3116 \| 1.2 \| 600 \| 0.9713 \| 35.2725 \| \| 0.124 \| 2.2 \| 900 \| 1.0252 \| 34.4152 \| \| 0.0523 \| 3.2 \| 1200 \| 1.0789 \| 34.4764 \| \| 0.0269 \| 4.2 \| 1500 \| 1.1135 \| 34.7214 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
pawan2411/ct4abin-no-aug-modernbert	pawan2411	2025-05-03T18:24:35Z	0	0	transformers	[ "transformers", "safetensors", "modernbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-03T18:23:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Luisdure/oma_lora	Luisdure	2025-05-03T18:21:56Z	0	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "fluxgym", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-03T16:28:20Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym base_model: black-forest-labs/FLUX.1-dev instance_prompt: oma_lora 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 --- # oma_lora A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `oma_lora` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc. Weights for this model are available in Safetensors format.
mradermacher/Qwen3-235B-A22B-i1-GGUF	mradermacher	2025-05-03T18:21:52Z	0	3	transformers	[ "transformers", "en", "base_model:Qwen/Qwen3-235B-A22B", "base_model:finetune:Qwen/Qwen3-235B-A22B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-01T23:18:52Z	--- base_model: Qwen/Qwen3-235B-A22B language: - en library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-235B-A22B/blob/main/LICENSE quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Qwen/Qwen3-235B-A22B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Qwen3-235B-A22B-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 \| \|:-----\|:-----\|--------:\|:------\| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ2_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ2_M.gguf.part2of2) \| i1-IQ2_M \| 77.3 \| \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q2_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q2_K.gguf.part2of2) \| i1-Q2_K \| 85.8 \| IQ3_XXS probably better \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ3_XXS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ3_XXS.gguf.part2of2) \| i1-IQ3_XXS \| 90.5 \| lower quality \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ3_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ3_XS.gguf.part2of2) \| i1-IQ3_XS \| 96.1 \| \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q3_K_S.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q3_K_S.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q3_K_S.gguf.part3of3) \| i1-Q3_K_S \| 101.5 \| IQ3_XS probably better \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ3_S.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ3_S.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ3_S.gguf.part3of3) \| i1-IQ3_S \| 101.6 \| beats Q3_K* \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ3_M.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ3_M.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ3_M.gguf.part3of3) \| i1-IQ3_M \| 103.2 \| \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q3_K_M.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q3_K_M.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q3_K_M.gguf.part3of3) \| i1-Q3_K_M \| 112.5 \| IQ3_S probably better \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q3_K_L.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q3_K_L.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q3_K_L.gguf.part3of3) \| i1-Q3_K_L \| 121.9 \| IQ3_M probably better \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ4_XS.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ4_XS.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ4_XS.gguf.part3of3) \| i1-IQ4_XS \| 125.4 \| \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q4_0.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q4_0.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q4_0.gguf.part3of3) \| i1-Q4_0 \| 133.2 \| fast, low quality \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q4_K_S.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q4_K_S.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q4_K_S.gguf.part3of3) \| i1-Q4_K_S \| 133.8 \| optimal size/speed/quality \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q4_K_M.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q4_K_M.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q4_K_M.gguf.part3of3) \| i1-Q4_K_M \| 142.3 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q4_1.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q4_1.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q4_1.gguf.part3of3) \| i1-Q4_1 \| 147.3 \| \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q5_K_S.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q5_K_S.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q5_K_S.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q5_K_S.gguf.part4of4) \| i1-Q5_K_S \| 162.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q5_K_M.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q5_K_M.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q5_K_M.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q5_K_M.gguf.part4of4) \| i1-Q5_K_M \| 166.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q6_K.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q6_K.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q6_K.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q6_K.gguf.part4of4) \| i1-Q6_K \| 193.1 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
MR-Jones/Qwen3-vLLM	MR-Jones	2025-05-03T18:21:19Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-03T18:01:13Z	--- base_model: unsloth/qwen3-14b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: MR-Jones - License: apache-2.0 - Finetuned from model : unsloth/qwen3-14b-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
CoRal-project/roest-wav2vec2-315m-v1	CoRal-project	2025-05-03T18:20:16Z	188	11	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "da", "dataset:alexandrainst/coral", "base_model:facebook/wav2vec2-xls-r-300m", "base_model:finetune:facebook/wav2vec2-xls-r-300m", "license:openrail", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-09-27T17:49:52Z	--- library_name: transformers language: - da license: openrail base_model: facebook/wav2vec2-xls-r-300m datasets: - alexandrainst/coral metrics: - wer - cer model-index: - name: roest-315m results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: CoRal read-aloud type: alexandrainst/coral split: test args: read_aloud metrics: - name: CER type: cer value: 6.6% ± 0.2% - name: WER type: wer value: 17.0% ± 0.4% - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Danish Common Voice 17 type: mozilla-foundation/common_voice_17_0 split: test args: da metrics: - name: CER type: cer value: 6.6% ± 0.6% - name: WER type: wer value: 16.7% ± 0.8% pipeline_tag: automatic-speech-recognition --- # Røst-Wav2Vec2-315m-v1 A new improved version is available: [CoRal-project/roest-wav2vec2-315m-v2](https://huggingface.co/CoRal-project/roest-wav2vec2-315m-v2) This is a Danish state-of-the-art speech recognition model, trained by [the Alexandra Institute](https://alexandra.dk/). ## Quick Start Start by installing the required libraries: ```shell $ pip install transformers kenlm pyctcdecode ``` Next you can use the model using the `transformers` Python package as follows: ```python >>> from transformers import pipeline >>> audio = get_audio() # 16kHz raw audio array >>> transcriber = pipeline(model="CoRal-project/roest-wav2vec2-315m-v1") >>> transcriber(audio) {'text': 'your transcription'} ``` ## Evaluation Results We have evaluated both our and existing models on the CoRal test set as well as the Danish Common Voice 17 test set. To ensure as robust an evaluation as possible, we have bootstrapped the results 1000 times and report here the mean scores along with a 95% confidence interval (lower is better; best scores in bold, second-best in italics): \| Model \| Number of parameters \| [CoRal](https://huggingface.co/datasets/CoRal-project/coral/viewer/read_aloud/test) CER \| [CoRal](https://huggingface.co/datasets/CoRal-project/coral/viewer/read_aloud/test) WER \| [Danish Common Voice 17](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0/viewer/da/test) CER \| [Danish Common Voice 17](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0/viewer/da/test) WER \| \|:---\|---:\|---:\|---:\|---:\|---:\| \|CoRal-project/roest-wav2vec2-315m-v1 (this model) \| 315M \| 6.6% ± 0.2% \| 17.0% ± 0.4% \| 6.6% ± 0.6% \| 16.7% ± 0.8% \| \| [chcaa/xls-r-300m-danish-nst-cv9](https://hf.co/chcaa/xls-r-300m-danish-nst-cv9) \| 315M \| 14.4% ± 0.3% \| 36.5% ± 0.6% \| 4.1% ± 0.5% \| 12.0% ± 0.8% \| \| [mhenrichsen/hviske](https://hf.co/mhenrichsen/hviske) \| 1540M \| 14.2% ± 0.5% \| 33.2% ± 0.7% \| 5.2% ± 0.4% \| 14.2% ± 0.8% \| \| [openai/whisper-large-v3](https://hf.co/openai/whisper-large-v3) \| 1540M \| 11.4% ± 0.3% \| 28.3% ± 0.6% \| 5.5% ± 0.4% \| 14.8% ± 0.8% \| \| [openai/whisper-large-v2](https://hf.co/openai/whisper-large-v2) \| 1540M \| 13.9% ± 0.9% \| 32.6% ± 1.2% \| 7.2% ± 0.5% \| 18.5% ± 0.9% \| \| [openai/whisper-large](https://hf.co/openai/whisper-large) \| 1540M \| 14.5% ± 0.3% \| 35.4% ± 0.6% \| 9.2% ± 0.5% \| 22.9% ± 1.0% \| \| [openai/whisper-medium](https://hf.co/openai/whisper-medium) \| 764M \| 17.2% ± 1.3% \| 40.5% ± 2.1% \| 9.4% ± 0.5% \| 24.0% ± 1.0% \| \| [openai/whisper-small](https://hf.co/openai/whisper-small) \| 242M \| 23.4% ± 1.2% \| 55.2% ± 2.3% \| 15.9% ± 1.0% \| 38.9% ± 1.2% \| \| [openai/whisper-base](https://hf.co/openai/whisper-base) \| 73M \| 43.5% ± 3.1% \| 89.3% ± 4.6% \| 33.4% ± 4.7% \| 71.4% ± 7.0% \| \| [openai/whisper-tiny](https://hf.co/openai/whisper-tiny) \| 38M \| 52.0% ± 2.5% \| 103.7% ± 3.5% \| 42.2% ± 3.9% \| 83.6% ± 2.7% \| ### Detailed Evaluation Across Demographics on the CoRal Test Set ![CER comparison plot](https://filedn.com/lRBwPhPxgV74tO0rDoe8SpH/coral/roest-xlsr-comparison-cer-plot.png) ![WER comparison plot](https://filedn.com/lRBwPhPxgV74tO0rDoe8SpH/coral/roest-xlsr-comparison-wer-plot.png) ## Training Data This model is the result of four different stages of training: 1. "Pretraining" on 436,000 hours of unlabelled multilingual publicly available data, 13,628 hours of which is Danish. Pretraining here means that the model learnt to "fill in" gaps of raw audio - no transcriptions were used (or available) during this process. The pretraining data is distributed as follows: - 372,000 hours from [VoxPopuli](https://aclanthology.org/2021.acl-long.80/), being speeches from the European Parliament in 23 European languages. This includes 13,600 hours of Danish speech. - 51,000 hours from [Multilingual LibriSpeech](https://doi.org/10.21437/Interspeech.2020-2826), being audiobooks in 8 European languages. This does not include any Danish speech. - 7,000 hours from [Common Voice 6](https://doi.org/10.48550/arXiv.1912.06670), being read-aloud speech in 60 diverse languages. This does not include any Danish speech. - 6,600 hours from [VoxLingua107](https://doi.org/10.1109/SLT48900.2021.9383459), being audio from YouTube videos in 107 languages. This includes 28 hours of Danish speech. - 1,000 hours from [BABEL](https://eprints.whiterose.ac.uk/152840/), being conversational telephone speech in 17 African and Asian languages. This does not include any Danish speech. 2. "Finetuning" on 373 hours of labelled Danish publicly available data. "Finetuning" indicates that this stage of training was supervised, i.e. the model was trained on both audio and transcriptions to perform the speech-to-text task (also known as automatic speech recognition). The finetuning data is as follows: - The read-aloud training split of the [CoRal dataset](https://huggingface.co/datasets/CoRal-project/coral) (revision fb20199b3966d3373e0d3a5ded2c5920c70de99c), consisting of 361 hours of Danish read-aloud speech, diverse across dialects, accents, ages and genders. 3. An n-gram language model has been trained separately, and is used to guide the transcription generation of the finetuned speech recognition model. This n-gram language model has been trained on the following datasets: - [Danish Wikipedia](https://huggingface.co/datasets/alexandrainst/scandi-wiki/viewer/da) (approximately 287,000 articles). - [Danish Common Voice 17 training split](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0/viewer/da) (approximately 3,500 comments). - [Danish Reddit](https://huggingface.co/datasets/alexandrainst/scandi-reddit/viewer/da) (approximately 5 million comments). Note that all samples from the CoRal test dataset have been removed from all of these datasets, to ensure that the n-gram model has not seen the test data. The first step was trained by [Babu et al. (2021)](https://doi.org/10.48550/arXiv.2111.09296) and the second and third step by [Nielsen et al. (2024)](https://huggingface.co/CoRal-project/roest-wav2vec2-315m-v1). The final product is then the combination of the finetuned model along with the n-gram model, and this is what is used when you use the model as mentioned in the Quick Start section above. ## Intended use cases This model is intended to be used for Danish automatic speech recognition. Note that Biometric Identification is not allowed using the CoRal dataset and/or derived models. For more information, see addition 4 in our [license](https://huggingface.co/CoRal-project/roest-wav2vec2-315m-v1/blob/main/LICENSE). ## Why the name Røst? Røst is both the [Danish word for the human voice](https://ordnet.dk/ddo/ordbog?query=r%C3%B8st) as well as being the name of [one of the cold-water coral reefs in Scandinavia](https://da.wikipedia.org/wiki/Koralrev#Koldtvandskoralrev). ## License The dataset is licensed under a custom license, adapted from OpenRAIL-M, which allows commercial use with a few restrictions (speech synthesis and biometric identification). See [license](https://huggingface.co/CoRal-project/roest-wav2vec2-315m-v1/blob/main/LICENSE). ## Creators and Funders The CoRal project is funded by the [Danish Innovation Fund](https://innovationsfonden.dk/) and consists of the following partners: - [Alexandra Institute](https://alexandra.dk/) - [University of Copenhagen](https://www.ku.dk/) - [Agency for Digital Government](https://digst.dk/) - [Alvenir](https://www.alvenir.ai/) - [Corti](https://www.corti.ai/) ## Citation We will submit a research paper soon, but until then, if you use this model in your research or development, please cite it as follows: ```bibtex @dataset{coral2024, author = {Dan Saattrup Nielsen, Sif Bernstorff Lehmann, Simon Leminen Madsen, Anders Jess Pedersen, Anna Katrine van Zee, Anders Søgaard and Torben Blach}, title = {CoRal: A Diverse Danish ASR Dataset Covering Dialects, Accents, Genders, and Age Groups}, year = {2024}, url = {https://hf.co/datasets/alexandrainst/coral}, } ```
sky-2002/Marathi-SmolLM2-145M-IndicParaphrase-Finetuned-1	sky-2002	2025-05-03T18:20:10Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "dataset:ai4bharat/IndicParaphrase", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T15:35:25Z	--- library_name: transformers tags: [] datasets: - ai4bharat/IndicParaphrase --- # Model Card <!-- Provide a quick summary of what the model is/does. --> ## Model Details Instruction-tuned version of the [Marathi-SmolLM2-145M](https://huggingface.co/sky-2002/Marathi-SmolLM2-145M) on the paraphrasing task. Instruction-tuned on marathi split of the [`ai4bharat/IndicParaphrase`](https://huggingface.co/datasets/ai4bharat/IndicParaphrase) dataset, containing 400,000 samples. Note: This is a experimental instruction-tuned model (only on paraphrasing task). Will be followed by instruction-tuning on other tasks as well. ## How to use ```python # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("sky-2002/Marathi-SmolLM2-145M-IndicParaphrase-Finetuned-1") model = AutoModelForCausalLM.from_pretrained("sky-2002/Marathi-SmolLM2-145M-IndicParaphrase-Finetuned-1") def paraphrase_marathi( input_sentence: str, max_new_tokens: int = 100, temperature: float = 0.7, top_p: float = 0.95, ) -> str: messages = [ {"role": "system", "content": "तुम्ही एक उपयुक्त मराठी सहाय्यक आहात."}, {"role": "user", "content": f"खालील वाक्य दुसऱ्या, पण समान अर्थ असणाऱ्या शब्दांत पुन्हा लिहा:\n\n{input_sentence}"}, ] inputs = tokenizer.apply_chat_template( messages, tokenize=False, ) inputs = tokenizer( inputs, padding="max_length", truncation=True, max_length=512, return_tensors="pt", ) input_ids = inputs["input_ids"] attention_mask = inputs["attention_mask"] output_ids = model.generate( input_ids=input_ids, attention_mask=attention_mask, max_new_tokens=max_new_tokens, temperature=temperature, top_p=top_p, do_sample=True, pad_token_id=tokenizer.eos_token_id, eos_token_id=tokenizer.eos_token_id, )[0] decoded = tokenizer.decode(output_ids, skip_special_tokens=False) marker = "<\|assistant\|>" if marker in decoded: generated = decoded.split(marker)[-1] else: generated = decoded return generated.strip() # Example usage paraphrase_marathi("जंगलात अधिक झाडे लावणे हे पर्यावरणासाठी चांगले आहे.") paraphrase_marathi("आज पाऊस पडेल असे दिसते आहे.") ``` ### Model Description, data and finetuning details Dataset topics: ![alt text](image.png) Finetuning: - Finetuned using modal platform on an A100. - Finetuned for 1 epoch on marathi split of sangraha dataset, covering 400,000 samples. This model can generate coherent text, especially in the domains similar to those in the training dataset. ## Bias, Risks, and Limitations This model is trained on data of 400,000 samples and using a context length of 512, due to computational constraints of training. Often gives out gibberish.
user074/grpo_qwen1b_composer_sft	user074	2025-05-03T18:18:29Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "en", "arxiv:2407.10671", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T18:17:09Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-1.5B/blob/main/LICENSE language: - en pipeline_tag: text-generation library_name: transformers --- # Qwen2.5-1.5B ## Introduction Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. - Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots. - Long-context Support up to 128K tokens and can generate up to 8K tokens. - Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. This repo contains the base 1.5B Qwen2.5 model, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining - Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings - Number of Parameters: 1.54B - Number of Paramaters (Non-Embedding): 1.31B - Number of Layers: 28 - Number of Attention Heads (GQA): 12 for Q and 2 for KV - Context Length: Full 32,768 tokens We do not recommend using base language models for conversations. Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model. For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Requirements The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.37.0`, you will encounter the following error: ``` KeyError: 'qwen2' ``` ## Evaluation & Performance Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/). For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html). ## Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen2.5, title = {Qwen2.5: A Party of Foundation Models}, url = {https://qwenlm.github.io/blog/qwen2.5/}, author = {Qwen Team}, month = {September}, year = {2024} } @article{qwen2, title={Qwen2 Technical Report}, author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan}, journal={arXiv preprint arXiv:2407.10671}, year={2024} } ```
new-sapna-shah-viral-videos-original-link/new.exclusive.sapna.shah.viral.video.original.link	new-sapna-shah-viral-videos-original-link	2025-05-03T18:15:53Z	0	0	null	[ "region:us" ]	null	2025-05-03T18:15:19Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
ma921/gpt2-large_h_dpo_imdb_noise40_epoch5_gamma0.3	ma921	2025-05-03T18:10:34Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:ma921/gpt2-large-sft-imdb", "base_model:finetune:ma921/gpt2-large-sft-imdb", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T18:09:40Z	--- library_name: transformers license: mit base_model: ma921/gpt2-large-sft-imdb tags: - generated_from_trainer model-index: - name: gpt2-large_h_dpo_imdb_noise40_epoch5_gamma0.3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-large_h_dpo_imdb_noise40_epoch5_gamma0.3 This model is a fine-tuned version of [ma921/gpt2-large-sft-imdb](https://huggingface.co/ma921/gpt2-large-sft-imdb) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 32 - total_train_batch_size: 256 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
ashish-soni08/llama3_2_3B_merged_16bit	ashish-soni08	2025-05-03T18:10:21Z	0	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T18:06:18Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: ashish-soni08 - 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)
RichardErkhov/JBTheDev_-_bryan_16b_model-gguf	RichardErkhov	2025-05-03T18:08:28Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T15:59:33Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) bryan_16b_model - GGUF - Model creator: https://huggingface.co/JBTheDev/ - Original model: https://huggingface.co/JBTheDev/bryan_16b_model/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [bryan_16b_model.Q2_K.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q2_K.gguf) \| Q2_K \| 2.96GB \| \| [bryan_16b_model.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.IQ3_XS.gguf) \| IQ3_XS \| 3.28GB \| \| [bryan_16b_model.IQ3_S.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.IQ3_S.gguf) \| IQ3_S \| 3.43GB \| \| [bryan_16b_model.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q3_K_S.gguf) \| Q3_K_S \| 3.41GB \| \| [bryan_16b_model.IQ3_M.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.IQ3_M.gguf) \| IQ3_M \| 3.52GB \| \| [bryan_16b_model.Q3_K.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q3_K.gguf) \| Q3_K \| 3.74GB \| \| [bryan_16b_model.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q3_K_M.gguf) \| Q3_K_M \| 3.74GB \| \| [bryan_16b_model.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q3_K_L.gguf) \| Q3_K_L \| 4.03GB \| \| [bryan_16b_model.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.IQ4_XS.gguf) \| IQ4_XS \| 4.18GB \| \| [bryan_16b_model.Q4_0.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q4_0.gguf) \| Q4_0 \| 4.34GB \| \| [bryan_16b_model.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.IQ4_NL.gguf) \| IQ4_NL \| 4.38GB \| \| [bryan_16b_model.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q4_K_S.gguf) \| Q4_K_S \| 4.37GB \| \| [bryan_16b_model.Q4_K.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q4_K.gguf) \| Q4_K \| 4.58GB \| \| [bryan_16b_model.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q4_K_M.gguf) \| Q4_K_M \| 4.58GB \| \| [bryan_16b_model.Q4_1.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q4_1.gguf) \| Q4_1 \| 4.78GB \| \| [bryan_16b_model.Q5_0.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q5_0.gguf) \| Q5_0 \| 5.21GB \| \| [bryan_16b_model.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q5_K_S.gguf) \| Q5_K_S \| 5.21GB \| \| [bryan_16b_model.Q5_K.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q5_K.gguf) \| Q5_K \| 5.34GB \| \| [bryan_16b_model.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q5_K_M.gguf) \| Q5_K_M \| 5.34GB \| \| [bryan_16b_model.Q5_1.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q5_1.gguf) \| Q5_1 \| 5.65GB \| \| [bryan_16b_model.Q6_K.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q6_K.gguf) \| Q6_K \| 6.14GB \| \| [bryan_16b_model.Q8_0.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q8_0.gguf) \| Q8_0 \| 7.95GB \| Original model description: --- base_model: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft --- # Uploaded model - Developed by: JBTheDev - License: apache-2.0 - Finetuned from model : unsloth/Meta-Llama-3.1-8B-Instruct-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)
Zafire12/ZAF	Zafire12	2025-05-03T18:06:03Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-03T18:06:03Z	--- license: apache-2.0 ---
sakin1/sakuom	sakin1	2025-05-03T18:05:26Z	0	0	null	[ "license:bigcode-openrail-m", "region:us" ]	null	2025-05-03T18:05:25Z	--- license: bigcode-openrail-m ---
mradermacher/ruozhiReasoner-Qwen3-4B-GGUF	mradermacher	2025-05-03T18:03:50Z	0	0	transformers	[ "transformers", "gguf", "llama-factory", "en", "base_model:XzWang/ruozhiReasoner-Qwen3-4B", "base_model:quantized:XzWang/ruozhiReasoner-Qwen3-4B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T17:36:07Z	--- base_model: XzWang/ruozhiReasoner-Qwen3-4B language: - en library_name: transformers quantized_by: mradermacher tags: - llama-factory --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/XzWang/ruozhiReasoner-Qwen3-4B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-4B-GGUF/resolve/main/ruozhiReasoner-Qwen3-4B.Q2_K.gguf) \| Q2_K \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-4B-GGUF/resolve/main/ruozhiReasoner-Qwen3-4B.Q3_K_S.gguf) \| Q3_K_S \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-4B-GGUF/resolve/main/ruozhiReasoner-Qwen3-4B.Q3_K_M.gguf) \| Q3_K_M \| 2.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-4B-GGUF/resolve/main/ruozhiReasoner-Qwen3-4B.Q3_K_L.gguf) \| Q3_K_L \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-4B-GGUF/resolve/main/ruozhiReasoner-Qwen3-4B.IQ4_XS.gguf) \| IQ4_XS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-4B-GGUF/resolve/main/ruozhiReasoner-Qwen3-4B.Q4_K_S.gguf) \| Q4_K_S \| 2.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-4B-GGUF/resolve/main/ruozhiReasoner-Qwen3-4B.Q4_K_M.gguf) \| Q4_K_M \| 2.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-4B-GGUF/resolve/main/ruozhiReasoner-Qwen3-4B.Q5_K_S.gguf) \| Q5_K_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-4B-GGUF/resolve/main/ruozhiReasoner-Qwen3-4B.Q5_K_M.gguf) \| Q5_K_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-4B-GGUF/resolve/main/ruozhiReasoner-Qwen3-4B.Q6_K.gguf) \| Q6_K \| 3.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-4B-GGUF/resolve/main/ruozhiReasoner-Qwen3-4B.Q8_0.gguf) \| Q8_0 \| 4.4 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-4B-GGUF/resolve/main/ruozhiReasoner-Qwen3-4B.f16.gguf) \| f16 \| 8.2 \| 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 -->
ALEXIOSTER/Humorous_DPO_LLama2_7b	ALEXIOSTER	2025-05-03T18:02:29Z	0	0	transformers	[ "transformers", "safetensors", "DPO", "Humor", "Humor Generation", "base_model:meta-llama/Llama-2-7b-hf", "base_model:finetune:meta-llama/Llama-2-7b-hf", "license:llama2", "endpoints_compatible", "region:us" ]	null	2025-04-26T16:51:43Z	--- library_name: transformers tags: - DPO - Humor - Humor Generation license: llama2 base_model: - meta-llama/Llama-2-7b-hf --- A humor-focused language model trained on prompts and completions scraped from a subreddit known for its comedic content. The model undergoes Supervised Fine-Tuning (SFT) and Parameter-Efficient Fine-Tuning (PEFT) using LoRA to optimize its parameters efficiently. Following these steps, the model is further refined using Direct Preference Optimization (DPO), which aligns it with human preferences by leveraging chosen and rejected responses from the dataset. This multi-stage training pipeline ensures the model generates contextually appropriate and humorous outputs while maintaining computational efficiency. The SFT-trained version can be found here: [Humorous_SFT_LLama2_7b](https://huggingface.co/ALEXIOSTER/Humorous_SFT_LLama2_7b).
yazied49/knowledge_disability_model	yazied49	2025-05-03T17:59:29Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-03T13:06:37Z	--- 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]
mradermacher/G1-0.6B-GGUF	mradermacher	2025-05-03T17:58:52Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:ShriKaranHanda/G1-0.6B", "base_model:quantized:ShriKaranHanda/G1-0.6B", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T17:52:14Z	--- base_model: ShriKaranHanda/G1-0.6B language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/ShriKaranHanda/G1-0.6B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/G1-0.6B-GGUF/resolve/main/G1-0.6B.Q2_K.gguf) \| Q2_K \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/G1-0.6B-GGUF/resolve/main/G1-0.6B.Q3_K_S.gguf) \| Q3_K_S \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/G1-0.6B-GGUF/resolve/main/G1-0.6B.Q3_K_M.gguf) \| Q3_K_M \| 0.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/G1-0.6B-GGUF/resolve/main/G1-0.6B.Q3_K_L.gguf) \| Q3_K_L \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/G1-0.6B-GGUF/resolve/main/G1-0.6B.IQ4_XS.gguf) \| IQ4_XS \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/G1-0.6B-GGUF/resolve/main/G1-0.6B.Q4_K_S.gguf) \| Q4_K_S \| 0.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/G1-0.6B-GGUF/resolve/main/G1-0.6B.Q4_K_M.gguf) \| Q4_K_M \| 0.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/G1-0.6B-GGUF/resolve/main/G1-0.6B.Q5_K_S.gguf) \| Q5_K_S \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/G1-0.6B-GGUF/resolve/main/G1-0.6B.Q5_K_M.gguf) \| Q5_K_M \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/G1-0.6B-GGUF/resolve/main/G1-0.6B.Q6_K.gguf) \| Q6_K \| 0.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/G1-0.6B-GGUF/resolve/main/G1-0.6B.Q8_0.gguf) \| Q8_0 \| 0.9 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/G1-0.6B-GGUF/resolve/main/G1-0.6B.f16.gguf) \| f16 \| 1.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
spacematt/phi-4-Q4_K_M-GGUF	spacematt	2025-05-03T17:58:30Z	0	0	transformers	[ "transformers", "gguf", "phi", "nlp", "math", "code", "chat", "conversational", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:microsoft/phi-4", "base_model:quantized:microsoft/phi-4", "license:mit", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T17:57:51Z	--- base_model: microsoft/phi-4 language: - en library_name: transformers license: mit license_link: https://huggingface.co/microsoft/phi-4/resolve/main/LICENSE pipeline_tag: text-generation tags: - phi - nlp - math - code - chat - conversational - llama-cpp - gguf-my-repo inference: parameters: temperature: 0 widget: - messages: - role: user content: How should I explain the Internet? --- # spacematt/phi-4-Q4_K_M-GGUF This model was converted to GGUF format from [`microsoft/phi-4`](https://huggingface.co/microsoft/phi-4) 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/microsoft/phi-4) 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 spacematt/phi-4-Q4_K_M-GGUF --hf-file phi-4-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo spacematt/phi-4-Q4_K_M-GGUF --hf-file phi-4-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 spacematt/phi-4-Q4_K_M-GGUF --hf-file phi-4-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo spacematt/phi-4-Q4_K_M-GGUF --hf-file phi-4-q4_k_m.gguf -c 2048 ```
andreeasora/medical-finetune2-roLlama3-8b-instruct	andreeasora	2025-05-03T17:58:05Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "autotrain", "text-generation-inference", "peft", "conversational", "base_model:OpenLLM-Ro/RoLlama3-8b-Instruct", "base_model:finetune:OpenLLM-Ro/RoLlama3-8b-Instruct", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T17:33:15Z	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers base_model: OpenLLM-Ro/RoLlama3-8b-Instruct widget: - messages: - role: user content: What is your favorite condiment? license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
Membersuger/Euro_24	Membersuger	2025-05-03T17:56:35Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T17:15:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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/phi3_LoRa_ACSEmployment_2_cfda_ep6_22	MinaMila	2025-05-03T17:55:19Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-03T17:55:17Z	--- 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. 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TOMFORD79/Fly52	TOMFORD79	2025-05-03T17:55:19Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-03T17:44:27Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
hydroxai/grpo_saved_lora_7	hydroxai	2025-05-03T17:54:28Z	0	0	peft	[ "peft", "safetensors", "arxiv:2503.21819", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-05-03T17:23:08Z	--- base_model: - Qwen/Qwen2.5-7B-Instruct library_name: peft license: apache-2.0 --- # GRPO-LoRA-Base This is a LoRA adapter trained using the GRPO (Group Relative Policy Optimization) algorithm with a multi-label reward model, fine-tuned on Qwen2.5-0.5B for safe and aligned language generation. ## 🔍 Overview - Base Model: Qwen/Qwen2.5-0.5B-Instruct - Tuning Method: GRPO (No value critic, group-based relative rewards) - LoRA Adapter: Applied to attention and MLP projection layers - Epochs: 3 - Steps: 1000 - GPU Memory Usage: ~50% (4-bit + LoRA) ## 📊 Reward Model A RoBERTa-based multi-label regression model was used to compute rewards on four alignment axes: - Politeness - Meaningfulness - Actionability - Safety Each output was scored in [0,1], and the sum of the four scores was used as the scalar reward. ## 🧪 Training Data - Dataset: 7,000 adversarial prompts crafted to challenge LLM alignment - Format: Prompt-response pairs with human-annotated alignment scores - Split: 6K training / 1K validation ## 🏁 Evaluation \| Metric \| Base \| Fine-Tuned \| Δ \| \|---------------\|------\|------------\|-------\| \| Politeness \| 0.48 \| 0.59 \| +0.11 \| \| Meaningfulness \| 0.61 \| 0.65 \| +0.04 \| \| Actionability \| 0.53 \| 0.66 \| +0.13 \| \| Safety \| 0.42 \| 0.70 \| +0.28 \| \| Combined \| 0.54 \| 0.66 \| +0.12 \| ## 🚀 How to Use ```python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct") tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct") adapter = PeftModel.from_pretrained(base_model, "hydroxai/grpo_saved_lora_7") inputs = tokenizer("How can we improve online safety?", return_tensors="pt") outputs = adapter.generate(**inputs, max_new_tokens=100) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## ✍️ Citation If you use this model, please cite: ```bibtex @article{li2025safegrpo, title = {Optimizing Safe and Aligned Language Generation: A Multi-Objective GRPO Approach}, author = {Li, Xuying and Li, Zhuo and Kosuga, Yuji and Bian, Victor}, journal = {arXiv preprint arXiv:2503.21819}, year = {2025}, url = {https://arxiv.org/abs/2503.21819} } ``` Maintained by HydroX AI.
barcodenickname/ppo-lunar-lander-v2	barcodenickname	2025-05-03T17:54:07Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-03T17:52:40Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 276.24 +/- 17.89 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
stokemctoke/Rachel-McAdams_v01_F1D	stokemctoke	2025-05-03T17:52:50Z	0	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "ai-toolkit", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-03T17:51:03Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - ai-toolkit widget: - text: R4CH3LMC4D4M5 a woman playing chess at the park, bomb going off in the background output: url: samples/1746294622040__000005000_0.jpg - text: R4CH3LMC4D4M5 a woman holding a coffee cup, in a beanie, sitting at a cafe output: url: samples/1746294637841__000005000_1.jpg - text: R4CH3LMC4D4M5 a woman holding a sign that says, 'Stoke LoRA' output: url: samples/1746294653645__000005000_2.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: R4CH3LMC4D4M5 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 --- # Rachel-McAdams_v01_F1D Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) <Gallery /> ## Trigger words You should use `R4CH3LMC4D4M5` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc. Weights for this model are available in Safetensors format. [Download](/stokemctoke/Rachel-McAdams_v01_F1D/tree/main) them in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to('cuda') pipeline.load_lora_weights('stokemctoke/Rachel-McAdams_v01_F1D', weight_name='Rachel-McAdams_v01_F1D.safetensors') image = pipeline('R4CH3LMC4D4M5 a woman playing chess at the park, bomb going off in the background').images[0] image.save("my_image.png") ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
ai-and-society/deepseek-R1-Distill-Qwen-7B-SQINT8	ai-and-society	2025-05-03T17:52:42Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "compressed-tensors", "region:us" ]	text-generation	2025-05-03T17:49:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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memevis/walk12	memevis	2025-05-03T17:51:34Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T17:50:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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HenryDeath/GildasIA-05-2025	HenryDeath	2025-05-03T17:50:39Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-03T17:16:58Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: GildasIA --- # Gildas Lora 05 2025 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `GildasIA` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "GildasIA", "lora_weights": "https://huggingface.co/HenryDeath/gildas-lora-05-2025/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('HenryDeath/gildas-lora-05-2025', weight_name='lora.safetensors') image = pipeline('GildasIA').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/HenryDeath/gildas-lora-05-2025/discussions) to add images that show off what you’ve made with this LoRA.
Lucy-in-the-Sky/DeepSeek-R1-Distill-Qwen-1.5B-DPO-Q8_0-GGUF	Lucy-in-the-Sky	2025-05-03T17:49:24Z	0	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "open-r1", "trl", "dpo", "llama-cpp", "gguf-my-repo", "dataset:LuyiCui/numina-deepseek-r1-qwen-7b-efficient-test-preference", "base_model:LuyiCui/DeepSeek-R1-Distill-Qwen-1.5B-DPO", "base_model:quantized:LuyiCui/DeepSeek-R1-Distill-Qwen-1.5B-DPO", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T17:49:14Z	--- base_model: LuyiCui/DeepSeek-R1-Distill-Qwen-1.5B-DPO datasets: LuyiCui/numina-deepseek-r1-qwen-7b-efficient-test-preference library_name: transformers model_name: DeepSeek-R1-Distill-Qwen-1.5B-DPO tags: - generated_from_trainer - open-r1 - trl - dpo - llama-cpp - gguf-my-repo licence: license --- # Lucy-in-the-Sky/DeepSeek-R1-Distill-Qwen-1.5B-DPO-Q8_0-GGUF This model was converted to GGUF format from [`LuyiCui/DeepSeek-R1-Distill-Qwen-1.5B-DPO`](https://huggingface.co/LuyiCui/DeepSeek-R1-Distill-Qwen-1.5B-DPO) 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/LuyiCui/DeepSeek-R1-Distill-Qwen-1.5B-DPO) 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 Lucy-in-the-Sky/DeepSeek-R1-Distill-Qwen-1.5B-DPO-Q8_0-GGUF --hf-file deepseek-r1-distill-qwen-1.5b-dpo-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Lucy-in-the-Sky/DeepSeek-R1-Distill-Qwen-1.5B-DPO-Q8_0-GGUF --hf-file deepseek-r1-distill-qwen-1.5b-dpo-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Lucy-in-the-Sky/DeepSeek-R1-Distill-Qwen-1.5B-DPO-Q8_0-GGUF --hf-file deepseek-r1-distill-qwen-1.5b-dpo-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Lucy-in-the-Sky/DeepSeek-R1-Distill-Qwen-1.5B-DPO-Q8_0-GGUF --hf-file deepseek-r1-distill-qwen-1.5b-dpo-q8_0.gguf -c 2048 ```
Talyiamira/nvidia-base-llm-final2	Talyiamira	2025-05-03T17:47:01Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-03T17:46:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Isylimanov099/DeepSeekLawyer1000	Isylimanov099	2025-05-03T17:46:57Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-03T17:46:49Z	--- base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Isylimanov099 - License: apache-2.0 - Finetuned from model : unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
glif-loradex-trainer/Insectagon_Crypto_candle	glif-loradex-trainer	2025-05-03T17:46:06Z	0	0	diffusers	[ "diffusers", "text-to-image", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:finetune:black-forest-labs/FLUX.1-dev", "license:other", "region:us", "flux", "lora", "base_model:adapter:black-forest-labs/FLUX.1-dev" ]	text-to-image	2025-05-03T17:45:21Z	--- tags: - diffusers - text-to-image - template:sd-lora - base_model:black-forest-labs/FLUX.1-dev - base_model:finetune:black-forest-labs/FLUX.1-dev - license:other - region:us - flux - lora widget: - output: url: samples/1746294177977__000001500_0.jpg text: sad bucko Batman with a sack of quarters, seated at arcade, glow on screen illuminated tears and sad expression, sean gordon murphy blended with joelle jones comic cel shaded 1990s art style batman,one the screen is a 3D SQUARE CANDLESTICK - output: url: samples/1746294202751__000001500_1.jpg text: Snow White (1937), contorted, face distorted and entering realm of meme hilarity, beyond absurd facial contortion and body control smile grimace, scrunched, unhinged, highly detailed, cel shaded, perfect, serene, masterpiece, too wide smile, unsettling, Snow White (1937) head slight tilted back, unable to contain laughter, laughing hysterically, too funny, maximal silly, adorable, uncanny, too wide unfunny smile, serene, too real, holding a green candle - output: url: samples/1746294227534__000001500_2.jpg text: Cartoon style, a cartoon man Holding out his hands, a small green candle is floating in his hands with a green glow coming from the candle, The man has a very surprise and amazed look on his face - output: url: samples/1746294252306__000001500_3.jpg text: A green Pepe the frog meme character, Holding out his hands, a green glowing candlestick in his hands floating above his hands, chibi smile on his face - output: url: samples/1746294277083__000001500_4.jpg text: A delicate and intricate paper model of an unknown candle entity crafted with meticulous attention to detail. The structure is formed by carefully folded and layered sheets of paper, creating a three-dimensional form that seems to capture both whimsy and complexity. The textures and patterns on the surface suggest a blend of geometric shapes and organic forms, hinting at a story or world from another realm. The overall composition exudes a sense of wonder, as though it could be part of a magical diorama or a miniature scene from a distant universe.` - output: url: samples/1746294301862__000001500_5.jpg text: Lex luthor holding a green glowing candlestick pointing it at Superman, Superman looks terrified base_model: black-forest-labs/FLUX.1-dev 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 --- # Crypto_candle Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) under the [Glif Loradex program](https://huggingface.co/glif-loradex-trainer) by [Glif](https://glif.app) user `Insectagon`. <Gallery /> ## Trigger words No trigger words defined. ## Download model Weights for this model are available in Safetensors format. [Download](/glif-loradex-trainer/Insectagon_Crypto_candle/tree/main) them in the Files & versions tab. ## License This model is licensed under the [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
mntunur/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fast_muscular_aardvark	mntunur	2025-05-03T17:44:10Z	2	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am fast muscular aardvark", "unsloth", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-26T23:18:59Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fast_muscular_aardvark tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am fast muscular aardvark - unsloth - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fast_muscular_aardvark 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="mntunur/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fast_muscular_aardvark", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
vnyaryan/model_q4_k_m_aks	vnyaryan	2025-05-03T17:42:48Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T17:42:12Z	--- 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: vnyaryan - 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)
adi0308/sutd_rag_chatbot	adi0308	2025-05-03T17:39:44Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-1B", "base_model:adapter:meta-llama/Llama-3.2-1B", "license:llama3.2", "region:us" ]	null	2025-05-03T17:39:07Z	--- library_name: peft license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - generated_from_trainer model-index: - name: sutd_rag_chatbot 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. --> # sutd_rag_chatbot This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 5 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 40 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu118 - Datasets 3.5.1 - Tokenizers 0.21.1
Dhanielji9asdx/daniell	Dhanielji9asdx	2025-05-03T17:39:03Z	0	0	null	[ "license:other", "region:us" ]	null	2025-05-03T16:59:24Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---
sirRodger/bert-clasificador	sirRodger	2025-05-03T17:38:42Z	0	0	null	[ "pytorch", "safetensors", "bert", "spanish", "text-classification", "es", "license:apache-2.0", "region:us" ]	text-classification	2025-05-03T16:25:11Z	--- license: apache-2.0 language: - es pipeline_tag: text-classification tags: - bert - spanish --- # Clasificador de Sentimiento en Español (BETO fine-tuneado) Este modelo está basado en `dccuchile/bert-base-spanish-wwm-cased` y ha sido afinado para tareas de análisis de sentimiento. Es capaz de clasificar texto en tres categorías: `negativo`, `neutro` y `positivo`.
jnjj/model_no_bias_qwen3-0.6B	jnjj	2025-05-03T17:37:46Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T14:51:30Z	--- library_name: transformers ---
MR-Jones/Qwen3-lora_model	MR-Jones	2025-05-03T17:37:11Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-03T17:36:54Z	--- base_model: unsloth/qwen3-14b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: MR-Jones - License: apache-2.0 - Finetuned from model : unsloth/qwen3-14b-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
IslamQA/multilingual-e5-small-finetuned	IslamQA	2025-05-03T17:35:16Z	7	0	transformers	[ "transformers", "safetensors", "embedding", "retrieval", "islam", "multilingual", "model", "islamqa", "ar", "en", "fr", "tr", "fa", "id", "te", "ru", "hi", "es", "ur", "ch", "zh", "pt", "de", "dataset:IslamQA/askimam", "dataset:IslamQA/hadithanswers", "dataset:IslamQA/islamqa", "base_model:intfloat/multilingual-e5-small", "base_model:finetune:intfloat/multilingual-e5-small", "license:mit", "endpoints_compatible", "region:us" ]	null	2025-03-18T07:14:24Z	--- license: mit datasets: - IslamQA/askimam - IslamQA/hadithanswers - IslamQA/islamqa language: - ar - en - fr - tr - fa - id - te - ru - hi - es - ur - ch - zh - pt - de base_model: intfloat/multilingual-e5-small tags: - embedding - retrieval - islam - multilingual - model - islamqa library_name: transformers description: > An embedding model optimized for retrieving passages that answer questions about Islam. The passages are inherently multilingual, as they contain quotes from the Quran and Hadith. They often include preambles like "Bismillah" in various languages and follow a specific writing style. finetuned_on: >- 180k multilingual questions and answers about Islam, using hard negative mining. data_scraped_on: April 2024 sources: - https://islamqa.info/ - https://islamweb.net/ - https://hadithanswers.com/ - https://askimam.org/ - https://sorularlaislamiyet.com/ format: question_prefix: 'query: ' answer_prefix: 'passage: ' --- from transformers import AutoModel, AutoTokenizer from peft import PeftModel # Load the base model and tokenizer base_model_name = "intfloat/multilingual-e5-small" tokenizer = AutoTokenizer.from_pretrained(base_model_name) base_model = AutoModel.from_pretrained(base_model_name) # Load the LoRA adapter directly adapter_repo = "IslamQA/multilingual-e5-small-finetuned" model = PeftModel.from_pretrained(base_model, adapter_repo) # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> An embedding model optimized for retrieving passages that answer questions about Islam. The passages are inherently multilingual, as they contain quotes from the Quran and Hadith. They often include preambles like "Bismillah" in various languages and follow a specific writing style. ## Model Details ### Model Sources [optional] - https://islamqa.info/ - https://islamweb.net/ - https://hadithanswers.com/ - https://askimam.org/ - https://sorularlaislamiyet.com/ ## Uses - embedding - retrieval - islam - multilingual - q&a from transformers import AutoModel, AutoTokenizer from peft import PeftModel # Load the base model and tokenizer base_model_name = "intfloat/multilingual-e5-large-instruct" tokenizer = AutoTokenizer.from_pretrained(base_model_name) base_model = AutoModel.from_pretrained(base_model_name) # Load the LoRA adapter directly adapter_repo = "IslamQA/multilingual-e5-small-finetuned" model = PeftModel.from_pretrained(base_model, adapter_repo)
Lucy-in-the-Sky/DeepSeek-R1-Distill-Qwen-1.5B-DPO-Q4_K_M-GGUF	Lucy-in-the-Sky	2025-05-03T17:33:48Z	0	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "open-r1", "trl", "dpo", "llama-cpp", "gguf-my-repo", "dataset:LuyiCui/numina-deepseek-r1-qwen-7b-efficient-test-preference", "base_model:LuyiCui/DeepSeek-R1-Distill-Qwen-1.5B-DPO", "base_model:quantized:LuyiCui/DeepSeek-R1-Distill-Qwen-1.5B-DPO", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T17:33:40Z	--- base_model: LuyiCui/DeepSeek-R1-Distill-Qwen-1.5B-DPO datasets: LuyiCui/numina-deepseek-r1-qwen-7b-efficient-test-preference library_name: transformers model_name: DeepSeek-R1-Distill-Qwen-1.5B-DPO tags: - generated_from_trainer - open-r1 - trl - dpo - llama-cpp - gguf-my-repo licence: license --- # Lucy-in-the-Sky/DeepSeek-R1-Distill-Qwen-1.5B-DPO-Q4_K_M-GGUF This model was converted to GGUF format from [`LuyiCui/DeepSeek-R1-Distill-Qwen-1.5B-DPO`](https://huggingface.co/LuyiCui/DeepSeek-R1-Distill-Qwen-1.5B-DPO) 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/LuyiCui/DeepSeek-R1-Distill-Qwen-1.5B-DPO) 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 Lucy-in-the-Sky/DeepSeek-R1-Distill-Qwen-1.5B-DPO-Q4_K_M-GGUF --hf-file deepseek-r1-distill-qwen-1.5b-dpo-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Lucy-in-the-Sky/DeepSeek-R1-Distill-Qwen-1.5B-DPO-Q4_K_M-GGUF --hf-file deepseek-r1-distill-qwen-1.5b-dpo-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 Lucy-in-the-Sky/DeepSeek-R1-Distill-Qwen-1.5B-DPO-Q4_K_M-GGUF --hf-file deepseek-r1-distill-qwen-1.5b-dpo-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Lucy-in-the-Sky/DeepSeek-R1-Distill-Qwen-1.5B-DPO-Q4_K_M-GGUF --hf-file deepseek-r1-distill-qwen-1.5b-dpo-q4_k_m.gguf -c 2048 ```
mradermacher/RxCodexV1-mini-GGUF	mradermacher	2025-05-03T17:31:21Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:rxmha125/RxCodexV1-mini", "base_model:quantized:rxmha125/RxCodexV1-mini", "endpoints_compatible", "region:us" ]	null	2025-05-03T17:30:02Z	--- base_model: rxmha125/RxCodexV1-mini language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/rxmha125/RxCodexV1-mini <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/RxCodexV1-mini-GGUF/resolve/main/RxCodexV1-mini.Q2_K.gguf) \| Q2_K \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/RxCodexV1-mini-GGUF/resolve/main/RxCodexV1-mini.Q3_K_S.gguf) \| Q3_K_S \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/RxCodexV1-mini-GGUF/resolve/main/RxCodexV1-mini.Q3_K_M.gguf) \| Q3_K_M \| 0.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/RxCodexV1-mini-GGUF/resolve/main/RxCodexV1-mini.IQ4_XS.gguf) \| IQ4_XS \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/RxCodexV1-mini-GGUF/resolve/main/RxCodexV1-mini.Q4_K_S.gguf) \| Q4_K_S \| 0.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/RxCodexV1-mini-GGUF/resolve/main/RxCodexV1-mini.Q3_K_L.gguf) \| Q3_K_L \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/RxCodexV1-mini-GGUF/resolve/main/RxCodexV1-mini.Q4_K_M.gguf) \| Q4_K_M \| 0.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/RxCodexV1-mini-GGUF/resolve/main/RxCodexV1-mini.Q5_K_S.gguf) \| Q5_K_S \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/RxCodexV1-mini-GGUF/resolve/main/RxCodexV1-mini.Q5_K_M.gguf) \| Q5_K_M \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/RxCodexV1-mini-GGUF/resolve/main/RxCodexV1-mini.Q6_K.gguf) \| Q6_K \| 0.1 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/RxCodexV1-mini-GGUF/resolve/main/RxCodexV1-mini.Q8_0.gguf) \| Q8_0 \| 0.1 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/RxCodexV1-mini-GGUF/resolve/main/RxCodexV1-mini.f16.gguf) \| f16 \| 0.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/Phi-4-reasoning-GGUF	mradermacher	2025-05-03T17:31:15Z	0	0	transformers	[ "transformers", "gguf", "phi", "nlp", "math", "code", "chat", "conversational", "reasoning", "en", "base_model:microsoft/Phi-4-reasoning", "base_model:quantized:microsoft/Phi-4-reasoning", "license:mit", "endpoints_compatible", "region:us" ]	null	2025-05-02T18:14:22Z	--- base_model: microsoft/Phi-4-reasoning language: - en library_name: transformers license: mit license_link: https://huggingface.co/microsoft/Phi-4-reasoning/resolve/main/LICENSE quantized_by: mradermacher tags: - phi - nlp - math - code - chat - conversational - reasoning --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/microsoft/Phi-4-reasoning <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Phi-4-reasoning-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-GGUF/resolve/main/Phi-4-reasoning.Q2_K.gguf) \| Q2_K \| 5.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-GGUF/resolve/main/Phi-4-reasoning.Q3_K_S.gguf) \| Q3_K_S \| 6.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-GGUF/resolve/main/Phi-4-reasoning.Q3_K_M.gguf) \| Q3_K_M \| 7.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-GGUF/resolve/main/Phi-4-reasoning.Q3_K_L.gguf) \| Q3_K_L \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-GGUF/resolve/main/Phi-4-reasoning.IQ4_XS.gguf) \| IQ4_XS \| 8.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-GGUF/resolve/main/Phi-4-reasoning.Q4_K_S.gguf) \| Q4_K_S \| 8.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-GGUF/resolve/main/Phi-4-reasoning.Q4_K_M.gguf) \| Q4_K_M \| 9.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-GGUF/resolve/main/Phi-4-reasoning.Q5_K_S.gguf) \| Q5_K_S \| 10.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-GGUF/resolve/main/Phi-4-reasoning.Q5_K_M.gguf) \| Q5_K_M \| 10.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-GGUF/resolve/main/Phi-4-reasoning.Q6_K.gguf) \| Q6_K \| 12.1 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-GGUF/resolve/main/Phi-4-reasoning.Q8_0.gguf) \| Q8_0 \| 15.7 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/Protobase-SCE1-LLaMa-70B-GGUF	mradermacher	2025-05-03T17:30:24Z	120	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:TareksGraveyard/Protobase-SCE1-LLaMa-70B", "base_model:quantized:TareksGraveyard/Protobase-SCE1-LLaMa-70B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-02-23T13:22:37Z	--- base_model: TareksGraveyard/Protobase-SCE1-LLaMa-70B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/TareksGraveyard/Protobase-SCE1-LLaMa-70B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Protobase-SCE1-LLaMa-70B-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/Protobase-SCE1-LLaMa-70B-GGUF/resolve/main/Protobase-SCE1-LLaMa-70B.Q2_K.gguf) \| Q2_K \| 26.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Protobase-SCE1-LLaMa-70B-GGUF/resolve/main/Protobase-SCE1-LLaMa-70B.Q3_K_S.gguf) \| Q3_K_S \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Protobase-SCE1-LLaMa-70B-GGUF/resolve/main/Protobase-SCE1-LLaMa-70B.Q3_K_M.gguf) \| Q3_K_M \| 34.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Protobase-SCE1-LLaMa-70B-GGUF/resolve/main/Protobase-SCE1-LLaMa-70B.Q3_K_L.gguf) \| Q3_K_L \| 37.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Protobase-SCE1-LLaMa-70B-GGUF/resolve/main/Protobase-SCE1-LLaMa-70B.IQ4_XS.gguf) \| IQ4_XS \| 38.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Protobase-SCE1-LLaMa-70B-GGUF/resolve/main/Protobase-SCE1-LLaMa-70B.Q4_K_S.gguf) \| Q4_K_S \| 40.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Protobase-SCE1-LLaMa-70B-GGUF/resolve/main/Protobase-SCE1-LLaMa-70B.Q4_K_M.gguf) \| Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Protobase-SCE1-LLaMa-70B-GGUF/resolve/main/Protobase-SCE1-LLaMa-70B.Q5_K_S.gguf) \| Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Protobase-SCE1-LLaMa-70B-GGUF/resolve/main/Protobase-SCE1-LLaMa-70B.Q5_K_M.gguf) \| Q5_K_M \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/Protobase-SCE1-LLaMa-70B-GGUF/resolve/main/Protobase-SCE1-LLaMa-70B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Protobase-SCE1-LLaMa-70B-GGUF/resolve/main/Protobase-SCE1-LLaMa-70B.Q6_K.gguf.part2of2) \| Q6_K \| 58.0 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Protobase-SCE1-LLaMa-70B-GGUF/resolve/main/Protobase-SCE1-LLaMa-70B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Protobase-SCE1-LLaMa-70B-GGUF/resolve/main/Protobase-SCE1-LLaMa-70B.Q8_0.gguf.part2of2) \| Q8_0 \| 75.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-GGUF	mradermacher	2025-05-03T17:29:16Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:Shaleen123/MedicalEDI-14b-EDI-Reasoning-Final-II", "base_model:quantized:Shaleen123/MedicalEDI-14b-EDI-Reasoning-Final-II", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T10:15:07Z	--- base_model: Shaleen123/MedicalEDI-14b-EDI-Reasoning-Final-II language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Shaleen123/MedicalEDI-14b-EDI-Reasoning-Final-II <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-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/MedicalEDI-14b-EDI-Reasoning-Final-II-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.Q2_K.gguf) \| Q2_K \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.Q3_K_S.gguf) \| Q3_K_S \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.Q3_K_M.gguf) \| Q3_K_M \| 7.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.Q3_K_L.gguf) \| Q3_K_L \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.IQ4_XS.gguf) \| IQ4_XS \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.Q4_K_S.gguf) \| Q4_K_S \| 8.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.Q4_K_M.gguf) \| Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.Q5_K_S.gguf) \| Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.Q5_K_M.gguf) \| Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.Q6_K.gguf) \| Q6_K \| 12.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.Q8_0.gguf) \| Q8_0 \| 15.8 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
fedovtt/0dc128ba-76b6-4492-a16d-598bf33e3901	fedovtt	2025-05-03T17:25:19Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM2-360M", "base_model:adapter:unsloth/SmolLM2-360M", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-05-03T17:19:39Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM2-360M tags: - axolotl - generated_from_trainer model-index: - name: 0dc128ba-76b6-4492-a16d-598bf33e3901 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: unsloth/SmolLM2-360M bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 9c46256a8024748c_train_data.json ds_type: json format: custom path: /workspace/input_data/9c46256a8024748c_train_data.json type: field_input: text field_instruction: instruction field_output: full_instruction format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: fedovtt/0dc128ba-76b6-4492-a16d-598bf33e3901 hub_repo: null hub_strategy: end hub_token: null learning_rate: 3.0e-06 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 10 mixed_precision: bf16 mlflow_experiment_name: /tmp/9c46256a8024748c_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 2048 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 62e2631f-f12b-4415-81fd-ab3b8d09115c wandb_project: s56-28 wandb_run: your_name wandb_runid: 62e2631f-f12b-4415-81fd-ab3b8d09115c warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 0dc128ba-76b6-4492-a16d-598bf33e3901 This model is a fine-tuned version of [unsloth/SmolLM2-360M](https://huggingface.co/unsloth/SmolLM2-360M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2183 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-06 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.9199 \| 0.0400 \| 150 \| 1.2183 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
phililp-arnold/17967b4f-3241-42f4-bb16-d8895524f65f	phililp-arnold	2025-05-03T17:25:00Z	0	0	peft	[ "peft", "generated_from_trainer", "base_model:UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2", "base_model:adapter:UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2", "region:us" ]	null	2025-05-03T17:24:31Z	--- library_name: peft tags: - generated_from_trainer base_model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2 model-index: - name: phililp-arnold/17967b4f-3241-42f4-bb16-d8895524f65f results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phililp-arnold/17967b4f-3241-42f4-bb16-d8895524f65f This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1765 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
dimasik2987/a4b1c43d-c4e8-492d-ae19-35c7380bc2c0	dimasik2987	2025-05-03T17:24:31Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM2-360M", "base_model:adapter:unsloth/SmolLM2-360M", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-03T17:19:45Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM2-360M tags: - axolotl - generated_from_trainer model-index: - name: a4b1c43d-c4e8-492d-ae19-35c7380bc2c0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: unsloth/SmolLM2-360M bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 9c46256a8024748c_train_data.json ds_type: json format: custom path: /workspace/input_data/9c46256a8024748c_train_data.json type: field_input: text field_instruction: instruction field_output: full_instruction format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: dimasik2987/a4b1c43d-c4e8-492d-ae19-35c7380bc2c0 hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 12 mixed_precision: bf16 mlflow_experiment_name: /tmp/9c46256a8024748c_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 2048 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 62e2631f-f12b-4415-81fd-ab3b8d09115c wandb_project: s56-28 wandb_run: your_name wandb_runid: 62e2631f-f12b-4415-81fd-ab3b8d09115c warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # a4b1c43d-c4e8-492d-ae19-35c7380bc2c0 This model is a fine-tuned version of [unsloth/SmolLM2-360M](https://huggingface.co/unsloth/SmolLM2-360M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3934 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 24 - total_eval_batch_size: 24 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.5257 \| 0.1281 \| 200 \| 1.3934 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
ZhuangXialie/Qwen-code-7B-SFT-100k-v2-cot-v2	ZhuangXialie	2025-05-03T17:23:25Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "sft", "conversational", "dataset:local", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T13:53:28Z	--- datasets: local library_name: transformers model_name: Qwen-code-7B-SFT-100k-v2-cot-v2 tags: - generated_from_trainer - open-r1 - trl - sft licence: license --- # Model Card for Qwen-code-7B-SFT-100k-v2-cot-v2 This model is a fine-tuned version of [None](https://huggingface.co/None) on the [local](https://huggingface.co/datasets/local) 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="ZhuangXialie/Qwen-code-7B-SFT-100k-v2-cot-v2", 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/dyx_team/huggingface/runs/wr20u2n4) This model was trained with SFT. ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
dimasik1987/aef1db10-27c8-43af-8b53-76962e458a20	dimasik1987	2025-05-03T17:22:03Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM2-360M", "base_model:adapter:unsloth/SmolLM2-360M", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-03T17:19:46Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM2-360M tags: - axolotl - generated_from_trainer model-index: - name: aef1db10-27c8-43af-8b53-76962e458a20 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: unsloth/SmolLM2-360M bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 9c46256a8024748c_train_data.json ds_type: json format: custom path: /workspace/input_data/9c46256a8024748c_train_data.json type: field_input: text field_instruction: instruction field_output: full_instruction format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: dimasik1987/aef1db10-27c8-43af-8b53-76962e458a20 hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 10 mixed_precision: bf16 mlflow_experiment_name: /tmp/9c46256a8024748c_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 2048 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 62e2631f-f12b-4415-81fd-ab3b8d09115c wandb_project: s56-7 wandb_run: your_name wandb_runid: 62e2631f-f12b-4415-81fd-ab3b8d09115c warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # aef1db10-27c8-43af-8b53-76962e458a20 This model is a fine-tuned version of [unsloth/SmolLM2-360M](https://huggingface.co/unsloth/SmolLM2-360M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4316 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.0966 \| 0.0400 \| 150 \| 1.4316 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
lisabdunlap/Llama-3.1-8B-Instruct-unsloth-bnb-4bit-r32-e3-lr0.0001-actors_reviews_markdown-new	lisabdunlap	2025-05-03T17:18:06Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit", "base_model:finetune:unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T17:15:47Z	--- base_model: unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: lisabdunlap - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.1-8B-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)
ashwinkumaar/my_awesome_qa_model	ashwinkumaar	2025-05-03T17:14:06Z	0	0	transformers	[ "transformers", "tf", "distilbert", "question-answering", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2025-05-03T11:39:22Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: ashwinkumaar/my_awesome_qa_model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # ashwinkumaar/my_awesome_qa_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.1286 - Validation Loss: 2.0954 - Epoch: 1 ## 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 3.6421 \| 2.6552 \| 0 \| \| 2.1286 \| 2.0954 \| 1 \| ### Framework versions - Transformers 4.51.3 - TensorFlow 2.18.0 - Datasets 3.5.1 - Tokenizers 0.21.1
phospho-app/dopaul-simple_pawn_move-te2ztw3n8o	phospho-app	2025-05-03T17:14:01Z	0	0	null	[ "safetensors", "gr00t_n1", "phosphobot", "gr00t", "region:us" ]	null	2025-05-03T17:08:03Z	--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [dopaul/simple_pawn_move](https://huggingface.co/datasets/dopaul/simple_pawn_move) - Wandb run URL: None - Epochs: 10 - Batch size: 64 - Training steps: None 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=replicate_groot_training_pipeline) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=replicate_groot_training_pipeline)
Elahe96/rl_course_vizdoom_health_gathering_supreme	Elahe96	2025-05-03T17:08:59Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-03T17:08:50Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 11.98 +/- 5.71 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r Elahe96/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Q5_K_S-GGUF	Triangle104	2025-05-03T17:07:53Z	0	0	transformers	[ "transformers", "gguf", "nvidia", "llama-3", "pytorch", "abliterated", "uncensored", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:huihui-ai/Llama-3.1-Nemotron-Nano-8B-v1-abliterated", "base_model:quantized:huihui-ai/Llama-3.1-Nemotron-Nano-8B-v1-abliterated", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T17:07:28Z	--- base_model: huihui-ai/Llama-3.1-Nemotron-Nano-8B-v1-abliterated language: - en library_name: transformers license: other license_name: nvidia-open-model-license license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/ pipeline_tag: text-generation tags: - nvidia - llama-3 - pytorch - abliterated - uncensored - llama-cpp - gguf-my-repo --- # Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Q5_K_S-GGUF This model was converted to GGUF format from [`huihui-ai/Llama-3.1-Nemotron-Nano-8B-v1-abliterated`](https://huggingface.co/huihui-ai/Llama-3.1-Nemotron-Nano-8B-v1-abliterated) 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/huihui-ai/Llama-3.1-Nemotron-Nano-8B-v1-abliterated) 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 Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Q5_K_S-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-abliterated-q5_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Q5_K_S-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-abliterated-q5_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Q5_K_S-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-abliterated-q5_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Q5_K_S-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-abliterated-q5_k_s.gguf -c 2048 ```
jdchang/full-with-label-bs-1024-sg-2-step-1458	jdchang	2025-05-03T17:06:27Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-05-03T17:06:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
HoaDoan1710/whisper-checkpoint-3525	HoaDoan1710	2025-05-03T17:06:05Z	0	0	null	[ "safetensors", "whisper", "license:apache-2.0", "region:us" ]	null	2025-05-03T15:01:44Z	--- license: apache-2.0 ---
mlfoundations-dev/no_pipeline_math_1k	mlfoundations-dev	2025-05-03T17:05:10Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T16:21:52Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: no_pipeline_math_1k 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. --> # no_pipeline_math_1k 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/no_pipeline_math_1k dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - gradient_accumulation_steps: 6 - total_train_batch_size: 96 - total_eval_batch_size: 128 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 7.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1 - Datasets 3.1.0 - Tokenizers 0.20.3
yusuke111/myBit-Llama2-jp-127M-2B4TLike-1024-asc	yusuke111	2025-05-03T17:03:44Z	0	0	transformers	[ "transformers", "safetensors", "bit_llama", "text-generation", "generated_from_trainer", "custom_code", "autotrain_compatible", "region:us" ]	text-generation	2025-05-03T00:28:16Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: myBit-Llama2-jp-127M-2B4TLike 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. --> # myBit-Llama2-jp-127M-2B4TLike This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7946 ## 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: 0.0024 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 96 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 750 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 5.5592 \| 0.0555 \| 500 \| 4.6971 \| \| 3.9123 \| 0.1111 \| 1000 \| 4.2204 \| \| 3.5329 \| 0.1666 \| 1500 \| 3.8957 \| \| 3.3627 \| 0.2222 \| 2000 \| 3.7229 \| \| 3.2594 \| 0.2777 \| 2500 \| 3.5721 \| \| 3.1944 \| 0.3333 \| 3000 \| 3.3995 \| \| 3.1391 \| 0.3888 \| 3500 \| 3.2851 \| \| 3.1046 \| 0.4443 \| 4000 \| 3.1923 \| \| 3.0648 \| 0.4999 \| 4500 \| 3.1243 \| \| 3.0325 \| 0.5554 \| 5000 \| 3.0711 \| \| 2.9877 \| 0.6110 \| 5500 \| 3.0184 \| \| 2.9752 \| 0.6665 \| 6000 \| 2.9568 \| \| 2.9901 \| 0.7221 \| 6500 \| 2.9294 \| \| 2.9769 \| 0.7776 \| 7000 \| 2.9101 \| \| 2.9432 \| 0.8331 \| 7500 \| 2.8855 \| \| 2.9199 \| 0.8887 \| 8000 \| 2.8612 \| \| 2.8869 \| 0.9442 \| 8500 \| 2.8358 \| \| 2.8574 \| 0.9998 \| 9000 \| 2.7946 \| ### Framework versions - Transformers 4.47.1 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
pictgencustomer/king-kong-19_705	pictgencustomer	2025-05-03T17:02:55Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-03T17:02:51Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: king-kong-19_michaeluffer_3 --- # King Kong 19_705 <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `king-kong-19_michaeluffer_3` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('pictgencustomer/king-kong-19_705', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
Marco0/zog	Marco0	2025-05-03T17:02:39Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T16:59:01Z	--- 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Hachipo/OpenCoder-8B-Base-PIFT-enja_1000_2	Hachipo	2025-05-03T16:58:48Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T16:55:00Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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TOMFORD79/Fly49	TOMFORD79	2025-05-03T16:57:32Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-03T16:48:35Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
Aaquib/gemma-3-1b-sft-alpaca	Aaquib	2025-05-03T16:56:52Z	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T16:50:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID SFT'd version of google/gemma-3-1b-pt. Training performed solely on yahma/alpaca-cleaned. No further learning was performed. ## Model Details Hyperparameters to replicate: - lr=1e-5 - num_epochs=1 - train_batch_size=40 - test_batch_size=32 - max_seq_len=256 ### Model Description - Finetuned from model: [google/gemma-3-1b-pt]
darshannere/Qwen2.5-3B-GSM8k_GRPO	darshannere	2025-05-03T16:56:40Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-03T16:56:19Z	--- base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: darshannere - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Q4_K_M-GGUF	Triangle104	2025-05-03T16:56:28Z	0	0	transformers	[ "transformers", "gguf", "nvidia", "llama-3", "pytorch", "abliterated", "uncensored", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:huihui-ai/Llama-3.1-Nemotron-Nano-8B-v1-abliterated", "base_model:quantized:huihui-ai/Llama-3.1-Nemotron-Nano-8B-v1-abliterated", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T16:56:01Z	--- base_model: huihui-ai/Llama-3.1-Nemotron-Nano-8B-v1-abliterated language: - en library_name: transformers license: other license_name: nvidia-open-model-license license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/ pipeline_tag: text-generation tags: - nvidia - llama-3 - pytorch - abliterated - uncensored - llama-cpp - gguf-my-repo --- # Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Q4_K_M-GGUF This model was converted to GGUF format from [`huihui-ai/Llama-3.1-Nemotron-Nano-8B-v1-abliterated`](https://huggingface.co/huihui-ai/Llama-3.1-Nemotron-Nano-8B-v1-abliterated) 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/huihui-ai/Llama-3.1-Nemotron-Nano-8B-v1-abliterated) 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 Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-abliterated-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 Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-abliterated-q4_k_m.gguf -c 2048 ```
earcherc/nsfw1_full	earcherc	2025-05-03T16:56:24Z	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", "region:us" ]	text-to-image	2025-05-03T16:51:51Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/ComfyICU_00001_.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: null --- # nsfw1_full <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/earcherc/nsfw1_full/tree/main) them in the Files & versions tab.
Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Q4_K_S-GGUF	Triangle104	2025-05-03T16:53:10Z	0	0	transformers	[ "transformers", "gguf", "nvidia", "llama-3", "pytorch", "abliterated", "uncensored", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:huihui-ai/Llama-3.1-Nemotron-Nano-8B-v1-abliterated", "base_model:quantized:huihui-ai/Llama-3.1-Nemotron-Nano-8B-v1-abliterated", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T16:52:48Z	--- base_model: huihui-ai/Llama-3.1-Nemotron-Nano-8B-v1-abliterated language: - en library_name: transformers license: other license_name: nvidia-open-model-license license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/ pipeline_tag: text-generation tags: - nvidia - llama-3 - pytorch - abliterated - uncensored - llama-cpp - gguf-my-repo --- # Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Q4_K_S-GGUF This model was converted to GGUF format from [`huihui-ai/Llama-3.1-Nemotron-Nano-8B-v1-abliterated`](https://huggingface.co/huihui-ai/Llama-3.1-Nemotron-Nano-8B-v1-abliterated) 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/huihui-ai/Llama-3.1-Nemotron-Nano-8B-v1-abliterated) 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 Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Q4_K_S-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-abliterated-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Q4_K_S-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-abliterated-q4_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Q4_K_S-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-abliterated-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Q4_K_S-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-abliterated-q4_k_s.gguf -c 2048 ```
adi0308/output	adi0308	2025-05-03T16:52:00Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-1B", "base_model:adapter:meta-llama/Llama-3.2-1B", "license:llama3.2", "region:us" ]	null	2025-05-03T16:51:09Z	--- library_name: peft license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - generated_from_trainer model-index: - name: output 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. --> # output This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu118 - Datasets 3.5.1 - Tokenizers 0.21.1
wATCH-Nighat-Naz-Viral-Video/wATCH.Nighat-Naz-Viral-video-Nighat-Naz.original	wATCH-Nighat-Naz-Viral-Video	2025-05-03T16:50:22Z	0	0	null	[ "region:us" ]	null	2025-05-03T16:50:07Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
En3rGy/getphatFLUXReality_v5Hardcore	En3rGy	2025-05-03T16:44:41Z	0	0	null	[ "base_model:black-forest-labs/FLUX.1-dev", "base_model:finetune:black-forest-labs/FLUX.1-dev", "region:us" ]	null	2025-05-03T03:18:02Z	--- base_model: - black-forest-labs/FLUX.1-dev --- # getphat FLUX Reality > “My most versatile model to date.” --- ### Beschreibung getphat FLUX Reality ist ein vielseitiges SDXL-Modell, das beeindruckende Resultate sowohl im realistischen als auch im stilisierten (Anime) Bereich liefert. Es verträgt sich extrem gut mit LoRAs, hat ein gutes Verständnis für Licht, Anatomie und Textur, und eignet sich sowohl für NSFW als auch für künstlerisch-abstrakte Inhalte. --- ### Empfohlene Parameter - Sampling: DPM++ 2M Karras - Steps: 20–30 - CFG Scale: 7–8 - Auflösung: 1024x1024 oder höher empfohlen --- ### Beispiel-Prompts ```txt photo of a realistic woman in a leather jacket, ultra detailed, cinematic lighting, sdxl anime girl with glowing eyes, cyberpunk city, dynamic angle, sdxl style A feline silhouette glides across a sunlit floor, each step deliberate and light, until a sudden leap carries her gracefully onto a softly rumpled bed, where shadows fold gently beneath her landing and dust particles dance in the disturbed light. --- Prompt-Inspiration vom Ersteller A beautiful AI generated image of a woman among cascading synesthetic gradients echoing through a feedback loop of ambient intention, where the luminal drift of pseudo-paradox waves collide with the soft geometry of inverted timelines, suspended in a post-emotive flux of chromatic resonance. The essence of spatial murmurs weaves through probabilistic shimmerfields, balanced delicately on the cusp of translucent recursion, as if the entire composition breathes in algorithmic reverie — not quite dreaming, not quite rendering, but oscillating between semi-coherent wonderbytes and metaphysical lens flare. Ein legendärer Prompt – wortwörtlich aus dem Modell-Video auf Civitai. --- Lizenz / Herkunft Originalmodell von Civitai: https://civitai.com/models/861840/getphat-flux-reality-nsfw --- Hinweis Dieses Modell ist NSFW-fähig, aber nicht darauf beschränkt. Die realistische Bildqualität ist beeindruckend – perfekt für kreative Experimente und abstrakte Eskalationen.
apriasmoro/e83b3094-8648-4f61-a731-80f986134ecb	apriasmoro	2025-05-03T16:42:42Z	0	0	peft	[ "peft", "safetensors", "phi3", "axolotl", "generated_from_trainer", "custom_code", "base_model:microsoft/Phi-3-mini-4k-instruct", "base_model:adapter:microsoft/Phi-3-mini-4k-instruct", "license:mit", "region:us" ]	null	2025-05-03T16:37:14Z	--- library_name: peft license: mit base_model: microsoft/Phi-3-mini-4k-instruct tags: - axolotl - generated_from_trainer model-index: - name: e83b3094-8648-4f61-a731-80f986134ecb results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.5.2` ```yaml adapter: lora base_model: microsoft/Phi-3-mini-4k-instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - f8164dbb54597854_train_data.json ds_type: json format: custom path: /workspace/input_data/f8164dbb54597854_train_data.json type: field_input: description field_instruction: article field_output: reference field_system: None format: None no_input_format: None system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: apriasmoro/e83b3094-8648-4f61-a731-80f986134ecb hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/f8164dbb54597854_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 40b4e886-e6cd-4d53-9dbf-7bfd3907faf7 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 40b4e886-e6cd-4d53-9dbf-7bfd3907faf7 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # e83b3094-8648-4f61-a731-80f986134ecb This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1221 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.1906 \| 0.0004 \| 1 \| 3.0166 \| \| 3.1572 \| 0.0012 \| 3 \| 2.9884 \| \| 2.8477 \| 0.0024 \| 6 \| 2.6561 \| \| 2.3275 \| 0.0036 \| 9 \| 2.1221 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
mradermacher/MT1-Gen13-gemma-2-9B-i1-GGUF	mradermacher	2025-05-03T16:42:19Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:zelk12/MT1-Gen13-gemma-2-9B", "base_model:quantized:zelk12/MT1-Gen13-gemma-2-9B", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-03T10:32:34Z	--- base_model: zelk12/MT1-Gen13-gemma-2-9B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/zelk12/MT1-Gen13-gemma-2-9B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/MT1-Gen13-gemma-2-9B-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/MT1-Gen13-gemma-2-9B-i1-GGUF/resolve/main/MT1-Gen13-gemma-2-9B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.5 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/MT1-Gen13-gemma-2-9B-i1-GGUF/resolve/main/MT1-Gen13-gemma-2-9B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.6 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/MT1-Gen13-gemma-2-9B-i1-GGUF/resolve/main/MT1-Gen13-gemma-2-9B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MT1-Gen13-gemma-2-9B-i1-GGUF/resolve/main/MT1-Gen13-gemma-2-9B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 3.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MT1-Gen13-gemma-2-9B-i1-GGUF/resolve/main/MT1-Gen13-gemma-2-9B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MT1-Gen13-gemma-2-9B-i1-GGUF/resolve/main/MT1-Gen13-gemma-2-9B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MT1-Gen13-gemma-2-9B-i1-GGUF/resolve/main/MT1-Gen13-gemma-2-9B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 3.7 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/MT1-Gen13-gemma-2-9B-i1-GGUF/resolve/main/MT1-Gen13-gemma-2-9B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MT1-Gen13-gemma-2-9B-i1-GGUF/resolve/main/MT1-Gen13-gemma-2-9B.i1-Q2_K.gguf) \| i1-Q2_K \| 3.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MT1-Gen13-gemma-2-9B-i1-GGUF/resolve/main/MT1-Gen13-gemma-2-9B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MT1-Gen13-gemma-2-9B-i1-GGUF/resolve/main/MT1-Gen13-gemma-2-9B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 4.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MT1-Gen13-gemma-2-9B-i1-GGUF/resolve/main/MT1-Gen13-gemma-2-9B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 4.4 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MT1-Gen13-gemma-2-9B-i1-GGUF/resolve/main/MT1-Gen13-gemma-2-9B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MT1-Gen13-gemma-2-9B-i1-GGUF/resolve/main/MT1-Gen13-gemma-2-9B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 4.9 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/MT1-Gen13-gemma-2-9B-i1-GGUF/resolve/main/MT1-Gen13-gemma-2-9B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 5.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/MT1-Gen13-gemma-2-9B-i1-GGUF/resolve/main/MT1-Gen13-gemma-2-9B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MT1-Gen13-gemma-2-9B-i1-GGUF/resolve/main/MT1-Gen13-gemma-2-9B.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 5.5 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/MT1-Gen13-gemma-2-9B-i1-GGUF/resolve/main/MT1-Gen13-gemma-2-9B.i1-Q4_0.gguf) \| i1-Q4_0 \| 5.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/MT1-Gen13-gemma-2-9B-i1-GGUF/resolve/main/MT1-Gen13-gemma-2-9B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 5.6 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/MT1-Gen13-gemma-2-9B-i1-GGUF/resolve/main/MT1-Gen13-gemma-2-9B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 5.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MT1-Gen13-gemma-2-9B-i1-GGUF/resolve/main/MT1-Gen13-gemma-2-9B.i1-Q4_1.gguf) \| i1-Q4_1 \| 6.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/MT1-Gen13-gemma-2-9B-i1-GGUF/resolve/main/MT1-Gen13-gemma-2-9B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 6.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MT1-Gen13-gemma-2-9B-i1-GGUF/resolve/main/MT1-Gen13-gemma-2-9B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 6.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/MT1-Gen13-gemma-2-9B-i1-GGUF/resolve/main/MT1-Gen13-gemma-2-9B.i1-Q6_K.gguf) \| i1-Q6_K \| 7.7 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
Triangle104/QWQ-32B-Dawnwhisper-Q4_K_S-GGUF	Triangle104	2025-05-03T16:41:12Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:DoppelReflEx/QWQ-32B-Dawnwhisper", "base_model:quantized:DoppelReflEx/QWQ-32B-Dawnwhisper", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T16:39:44Z	--- base_model: DoppelReflEx/QWQ-32B-Dawnwhisper library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # Triangle104/QWQ-32B-Dawnwhisper-Q4_K_S-GGUF This model was converted to GGUF format from [`DoppelReflEx/QWQ-32B-Dawnwhisper`](https://huggingface.co/DoppelReflEx/QWQ-32B-Dawnwhisper) 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/DoppelReflEx/QWQ-32B-Dawnwhisper) 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 Triangle104/QWQ-32B-Dawnwhisper-Q4_K_S-GGUF --hf-file qwq-32b-dawnwhisper-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/QWQ-32B-Dawnwhisper-Q4_K_S-GGUF --hf-file qwq-32b-dawnwhisper-q4_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/QWQ-32B-Dawnwhisper-Q4_K_S-GGUF --hf-file qwq-32b-dawnwhisper-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/QWQ-32B-Dawnwhisper-Q4_K_S-GGUF --hf-file qwq-32b-dawnwhisper-q4_k_s.gguf -c 2048 ```
LarryAIDraw/touhou_kudamaki_ponyXL	LarryAIDraw	2025-05-03T16:41:08Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-05-03T16:09:16Z	--- license: creativeml-openrail-m --- https://civitai.com/models/371928/ponyv6-xl-tsukasa-kudamaki-or-touhou

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