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Membersuger/Euro_19	Membersuger	2025-05-03T16:29:05Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T16:19:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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jnjj/Vvv	jnjj	2025-05-03T16:26:47Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "base_model:jnjj/model_no_bias_qwen3-0.6B", "base_model:quantized:jnjj/model_no_bias_qwen3-0.6B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T16:26:42Z	--- base_model: jnjj/model_no_bias_qwen3-0.6B library_name: transformers tags: - llama-cpp - gguf-my-repo --- # jnjj/model_no_bias_qwen3-0.6B-Q3_K_L-GGUF This model was converted to GGUF format from [`jnjj/model_no_bias_qwen3-0.6B`](https://huggingface.co/jnjj/model_no_bias_qwen3-0.6B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/jnjj/model_no_bias_qwen3-0.6B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo jnjj/model_no_bias_qwen3-0.6B-Q3_K_L-GGUF --hf-file model_no_bias_qwen3-0.6b-q3_k_l.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo jnjj/model_no_bias_qwen3-0.6B-Q3_K_L-GGUF --hf-file model_no_bias_qwen3-0.6b-q3_k_l.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 jnjj/model_no_bias_qwen3-0.6B-Q3_K_L-GGUF --hf-file model_no_bias_qwen3-0.6b-q3_k_l.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo jnjj/model_no_bias_qwen3-0.6B-Q3_K_L-GGUF --hf-file model_no_bias_qwen3-0.6b-q3_k_l.gguf -c 2048 ```
Eehan/pythia-1b-deduped-tldr-gpm-2dim-temp-6025-beta-0.04	Eehan	2025-05-03T16:19:52Z	0	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T16:17:44Z	--- 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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ai-and-society/llama-3.1-8B-Instruct-SQINT8	ai-and-society	2025-05-03T16:19:19Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "compressed-tensors", "region:us" ]	text-generation	2025-05-03T16:16:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Hachipo/OpenCoder-8B-Base-PIFT-jaen_1000_2	Hachipo	2025-05-03T16:19:17Z	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	2025-05-03T16:15:11Z	--- 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. 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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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Eehan/pythia-1b-deduped-tldr-gpm-2dim-temp-0.67-beta-0.04	Eehan	2025-05-03T16:16:53Z	0	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T16:14:50Z	--- 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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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]
macpaw-research/tst_16bit-mlx	macpaw-research	2025-05-03T16:07:06Z	0	0	mlx	[ "mlx", "safetensors", "llama", "text-generation", "conversational", "base_model:macpaw-research/tst_16bit", "base_model:finetune:macpaw-research/tst_16bit", "license:apache-2.0", "region:us" ]	text-generation	2025-05-03T16:03:41Z	--- license: apache-2.0 base_model: macpaw-research/tst_16bit tags: - mlx library_name: mlx pipeline_tag: text-generation --- # macpaw-research/tst_16bit-mlx This model [macpaw-research/tst_16bit-mlx](https://huggingface.co/macpaw-research/tst_16bit-mlx) was converted to MLX format from [macpaw-research/tst_16bit](https://huggingface.co/macpaw-research/tst_16bit) using mlx-lm version 0.23.2. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("macpaw-research/tst_16bit-mlx") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
hardik9719/videomae-base-finetuned-ucf-timesfomer-5-5-25-610videos	hardik9719	2025-05-03T16:04:39Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "timesformer", "video-classification", "generated_from_trainer", "base_model:facebook/timesformer-base-finetuned-k400", "base_model:finetune:facebook/timesformer-base-finetuned-k400", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2025-05-03T05:26:46Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: facebook/timesformer-base-finetuned-k400 tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-ucf-timesfomer-5-5-25-610videos results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # videomae-base-finetuned-ucf-timesfomer-5-5-25-610videos This model is a fine-tuned version of [facebook/timesformer-base-finetuned-k400](https://huggingface.co/facebook/timesformer-base-finetuned-k400) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1723 - Accuracy: 0.5749 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 276 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 1.581 \| 0.3370 \| 93 \| 1.6027 \| 0.4022 \| \| 1.4297 \| 1.3370 \| 186 \| 1.2423 \| 0.5869 \| \| 0.8781 \| 2.3261 \| 276 \| 1.0586 \| 0.6129 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu118 - Datasets 3.3.2 - Tokenizers 0.21.1
Anchor-Chitra-Tripathi-Viral-Video/18-video.Anchor-Chitra-Tripathi.viral.video.original.here	Anchor-Chitra-Tripathi-Viral-Video	2025-05-03T16:03:15Z	0	0	null	[ "region:us" ]	null	2025-05-03T15:59:42Z	[🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://videohere.top/?Anchor-Chitra-Tripathi) [►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://videohere.top/?Anchor-Chitra-Tripathi) [<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?Anchor-Chitra-Tripathi)
ASethi04/meta-llama-Llama-3.1-8B-pubmedqa-third-lora-4-0.0001-same-prompt-template	ASethi04	2025-05-03T16:00:30Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "endpoints_compatible", "region:us" ]	null	2025-05-03T14:19:39Z	--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: meta-llama-Llama-3.1-8B-pubmedqa-third-lora-4-0.0001-same-prompt-template tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for meta-llama-Llama-3.1-8B-pubmedqa-third-lora-4-0.0001-same-prompt-template This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="ASethi04/meta-llama-Llama-3.1-8B-pubmedqa-third-lora-4-0.0001-same-prompt-template", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/torchql-org/huggingface/runs/gp8nvulh) This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Romain-XV/8f3edba3-d660-4f05-bc84-9befdb0b2deb	Romain-XV	2025-05-03T15:59:04Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "mistral", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "unsloth", "conversational", "arxiv:2305.18290", "base_model:unsloth/Phi-3-mini-4k-instruct", "base_model:finetune:unsloth/Phi-3-mini-4k-instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T15:09:42Z	--- base_model: unsloth/Phi-3-mini-4k-instruct library_name: transformers model_name: 8f3edba3-d660-4f05-bc84-9befdb0b2deb tags: - generated_from_trainer - axolotl - dpo - trl - unsloth licence: license --- # Model Card for 8f3edba3-d660-4f05-bc84-9befdb0b2deb This model is a fine-tuned version of [unsloth/Phi-3-mini-4k-instruct](https://huggingface.co/unsloth/Phi-3-mini-4k-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="Romain-XV/8f3edba3-d660-4f05-bc84-9befdb0b2deb", 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/romain_fnc-xventures/Gradients-On-Demand/runs/8bqxlpyz) 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}} } ```
bihungba1101/json_segmenting_sft_warmup_qwen	bihungba1101	2025-05-03T15:57:22Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-4B", "base_model:finetune:unsloth/Qwen3-4B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-03T15:57:03Z	--- base_model: unsloth/Qwen3-4B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: bihungba1101 - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-4B 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)
TOMFORD79/Fly43	TOMFORD79	2025-05-03T15:56:22Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-03T15:15:28Z	--- 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).
TOMFORD79/Fly41	TOMFORD79	2025-05-03T15:56:05Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-03T15:15:16Z	--- 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).
Triangle104/QWQ-32B-Dawnwhisper-Q3_K_M-GGUF	Triangle104	2025-05-03T15:54:40Z	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	2025-05-03T15:53:30Z	--- base_model: DoppelReflEx/QWQ-32B-Dawnwhisper library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # Triangle104/QWQ-32B-Dawnwhisper-Q3_K_M-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-Q3_K_M-GGUF --hf-file qwq-32b-dawnwhisper-q3_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/QWQ-32B-Dawnwhisper-Q3_K_M-GGUF --hf-file qwq-32b-dawnwhisper-q3_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/QWQ-32B-Dawnwhisper-Q3_K_M-GGUF --hf-file qwq-32b-dawnwhisper-q3_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/QWQ-32B-Dawnwhisper-Q3_K_M-GGUF --hf-file qwq-32b-dawnwhisper-q3_k_m.gguf -c 2048 ```
Hachipo/OpenCoder-8B-Base-MIFT-en_1000_2	Hachipo	2025-05-03T15:54:10Z	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	2025-05-03T15:50:20Z	--- 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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recuse/mBERT-distiluse-base-multilingual-cased-v2-MLM	recuse	2025-05-03T15:51:17Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-05-03T15:47:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> recuse/mBERT-distiluse-base-multilingual-cased-v2-MLM 을 한국어 위키피디아 데이터셋과 MLM 방식을 활용하여 continue pre-training을 하였습니다. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
1-Jobz-Hunting-Sajal-Malik-Viral-Video-18x/wATCH.TRENDING.VIDEO.Jobz.Hunting.Sajal.Malik.viral.video.Tutorial	1-Jobz-Hunting-Sajal-Malik-Viral-Video-18x	2025-05-03T15:51:12Z	0	0	null	[ "region:us" ]	null	2025-05-03T15:49:17Z	[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=Sajal-Malik) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=Sajal-Malik) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=Sajal-Malik)
DuongTrongChi/vinallama-2.7b-chat-sft-v1	DuongTrongChi	2025-05-03T15:47:34Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T15:44:49Z	--- 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]
memeviss/zombieX_9	memeviss	2025-05-03T15:44:54Z	0	0	null	[ "safetensors", "region:us" ]	null	2025-05-03T15:40:29Z	# Optimized TTS Model This model has been optimized for 100% TOP1 performance using advanced parameter enhancement techniques. ## Usage To generate speech using this model, you can use the included script: ```bash ./generate_speech.py --text "Your text here" --output_path output.wav ``` For more details, see the optimization report in this directory.
memeviss/zombieX_6	memeviss	2025-05-03T15:43:39Z	0	0	null	[ "safetensors", "region:us" ]	null	2025-05-03T15:40:27Z	# Optimized TTS Model This model has been optimized for 100% TOP1 performance using advanced parameter enhancement techniques. ## Usage To generate speech using this model, you can use the included script: ```bash ./generate_speech.py --text "Your text here" --output_path output.wav ``` For more details, see the optimization report in this directory.
Triangle104/QWQ-32B-Dawnwhisper-Q3_K_S-GGUF	Triangle104	2025-05-03T15:43:11Z	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	2025-05-03T15:42:08Z	--- base_model: DoppelReflEx/QWQ-32B-Dawnwhisper library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # Triangle104/QWQ-32B-Dawnwhisper-Q3_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-Q3_K_S-GGUF --hf-file qwq-32b-dawnwhisper-q3_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/QWQ-32B-Dawnwhisper-Q3_K_S-GGUF --hf-file qwq-32b-dawnwhisper-q3_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/QWQ-32B-Dawnwhisper-Q3_K_S-GGUF --hf-file qwq-32b-dawnwhisper-q3_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/QWQ-32B-Dawnwhisper-Q3_K_S-GGUF --hf-file qwq-32b-dawnwhisper-q3_k_s.gguf -c 2048 ```
franzexplorer77/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-alert_armored_trout	franzexplorer77	2025-05-03T15:43:02Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am alert armored trout", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-03T13:54:42Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-alert_armored_trout tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am alert armored trout - unsloth - trl licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-alert_armored_trout This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="franzexplorer77/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-alert_armored_trout", 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}} } ```
RichardErkhov/selfcorrexp_-_llama3_non_balanced_regular_sft_2e6_bz32_ep2-gguf	RichardErkhov	2025-05-03T15:42:44Z	0	0	null	[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T13:22:19Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) llama3_non_balanced_regular_sft_2e6_bz32_ep2 - GGUF - Model creator: https://huggingface.co/selfcorrexp/ - Original model: https://huggingface.co/selfcorrexp/llama3_non_balanced_regular_sft_2e6_bz32_ep2/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q2_K.gguf](https://huggingface.co/RichardErkhov/selfcorrexp_-_llama3_non_balanced_regular_sft_2e6_bz32_ep2-gguf/blob/main/llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q2_K.gguf) \| Q2_K \| 2.96GB \| \| [llama3_non_balanced_regular_sft_2e6_bz32_ep2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/selfcorrexp_-_llama3_non_balanced_regular_sft_2e6_bz32_ep2-gguf/blob/main/llama3_non_balanced_regular_sft_2e6_bz32_ep2.IQ3_XS.gguf) \| IQ3_XS \| 3.28GB \| \| [llama3_non_balanced_regular_sft_2e6_bz32_ep2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/selfcorrexp_-_llama3_non_balanced_regular_sft_2e6_bz32_ep2-gguf/blob/main/llama3_non_balanced_regular_sft_2e6_bz32_ep2.IQ3_S.gguf) \| IQ3_S \| 3.43GB \| \| [llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/selfcorrexp_-_llama3_non_balanced_regular_sft_2e6_bz32_ep2-gguf/blob/main/llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q3_K_S.gguf) \| Q3_K_S \| 3.41GB \| \| [llama3_non_balanced_regular_sft_2e6_bz32_ep2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/selfcorrexp_-_llama3_non_balanced_regular_sft_2e6_bz32_ep2-gguf/blob/main/llama3_non_balanced_regular_sft_2e6_bz32_ep2.IQ3_M.gguf) \| IQ3_M \| 3.52GB \| \| [llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q3_K.gguf](https://huggingface.co/RichardErkhov/selfcorrexp_-_llama3_non_balanced_regular_sft_2e6_bz32_ep2-gguf/blob/main/llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q3_K.gguf) \| Q3_K \| 3.74GB \| \| [llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/selfcorrexp_-_llama3_non_balanced_regular_sft_2e6_bz32_ep2-gguf/blob/main/llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q3_K_M.gguf) \| Q3_K_M \| 3.74GB \| \| [llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/selfcorrexp_-_llama3_non_balanced_regular_sft_2e6_bz32_ep2-gguf/blob/main/llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q3_K_L.gguf) \| Q3_K_L \| 4.03GB \| \| [llama3_non_balanced_regular_sft_2e6_bz32_ep2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/selfcorrexp_-_llama3_non_balanced_regular_sft_2e6_bz32_ep2-gguf/blob/main/llama3_non_balanced_regular_sft_2e6_bz32_ep2.IQ4_XS.gguf) \| IQ4_XS \| 4.18GB \| \| [llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q4_0.gguf](https://huggingface.co/RichardErkhov/selfcorrexp_-_llama3_non_balanced_regular_sft_2e6_bz32_ep2-gguf/blob/main/llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q4_0.gguf) \| Q4_0 \| 4.34GB \| \| [llama3_non_balanced_regular_sft_2e6_bz32_ep2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/selfcorrexp_-_llama3_non_balanced_regular_sft_2e6_bz32_ep2-gguf/blob/main/llama3_non_balanced_regular_sft_2e6_bz32_ep2.IQ4_NL.gguf) \| IQ4_NL \| 4.38GB \| \| [llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/selfcorrexp_-_llama3_non_balanced_regular_sft_2e6_bz32_ep2-gguf/blob/main/llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q4_K_S.gguf) \| Q4_K_S \| 4.37GB \| \| [llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q4_K.gguf](https://huggingface.co/RichardErkhov/selfcorrexp_-_llama3_non_balanced_regular_sft_2e6_bz32_ep2-gguf/blob/main/llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q4_K.gguf) \| Q4_K \| 4.58GB \| \| [llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/selfcorrexp_-_llama3_non_balanced_regular_sft_2e6_bz32_ep2-gguf/blob/main/llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q4_K_M.gguf) \| Q4_K_M \| 4.58GB \| \| [llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q4_1.gguf](https://huggingface.co/RichardErkhov/selfcorrexp_-_llama3_non_balanced_regular_sft_2e6_bz32_ep2-gguf/blob/main/llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q4_1.gguf) \| Q4_1 \| 4.78GB \| \| [llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q5_0.gguf](https://huggingface.co/RichardErkhov/selfcorrexp_-_llama3_non_balanced_regular_sft_2e6_bz32_ep2-gguf/blob/main/llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q5_0.gguf) \| Q5_0 \| 5.21GB \| \| [llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/selfcorrexp_-_llama3_non_balanced_regular_sft_2e6_bz32_ep2-gguf/blob/main/llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q5_K_S.gguf) \| Q5_K_S \| 5.21GB \| \| [llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q5_K.gguf](https://huggingface.co/RichardErkhov/selfcorrexp_-_llama3_non_balanced_regular_sft_2e6_bz32_ep2-gguf/blob/main/llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q5_K.gguf) \| Q5_K \| 5.34GB \| \| [llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/selfcorrexp_-_llama3_non_balanced_regular_sft_2e6_bz32_ep2-gguf/blob/main/llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q5_K_M.gguf) \| Q5_K_M \| 5.34GB \| \| [llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q5_1.gguf](https://huggingface.co/RichardErkhov/selfcorrexp_-_llama3_non_balanced_regular_sft_2e6_bz32_ep2-gguf/blob/main/llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q5_1.gguf) \| Q5_1 \| 5.65GB \| \| [llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q6_K.gguf](https://huggingface.co/RichardErkhov/selfcorrexp_-_llama3_non_balanced_regular_sft_2e6_bz32_ep2-gguf/blob/main/llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q6_K.gguf) \| Q6_K \| 6.14GB \| \| [llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q8_0.gguf](https://huggingface.co/RichardErkhov/selfcorrexp_-_llama3_non_balanced_regular_sft_2e6_bz32_ep2-gguf/blob/main/llama3_non_balanced_regular_sft_2e6_bz32_ep2.Q8_0.gguf) \| Q8_0 \| 7.95GB \| Original model description: --- 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]
memeviss/zombieX_3	memeviss	2025-05-03T15:42:33Z	0	0	null	[ "safetensors", "region:us" ]	null	2025-05-03T15:40:26Z	# Optimized TTS Model This model has been optimized for 100% TOP1 performance using advanced parameter enhancement techniques. ## Usage To generate speech using this model, you can use the included script: ```bash ./generate_speech.py --text "Your text here" --output_path output.wav ``` For more details, see the optimization report in this directory.
oferk/ppo-LunarLander-v2	oferk	2025-05-03T15:41:17Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-03T15:38:39Z	--- 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: 261.46 +/- 22.46 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 ... ```
ASethi04/meta-llama-Llama-3.1-8B-opc-sft-first-lora-4-0.0004	ASethi04	2025-05-03T15:40:52Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "endpoints_compatible", "region:us" ]	null	2025-05-03T14:32:21Z	--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: meta-llama-Llama-3.1-8B-opc-sft-first-lora-4-0.0004 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for meta-llama-Llama-3.1-8B-opc-sft-first-lora-4-0.0004 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="ASethi04/meta-llama-Llama-3.1-8B-opc-sft-first-lora-4-0.0004", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/torchql-org/huggingface/runs/8q8hv38o) This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Bonnief/finetune-afriberta-small-am	Bonnief	2025-05-03T15:38:15Z	0	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "fill-mask", "generated_from_trainer", "base_model:castorini/afriberta_small", "base_model:finetune:castorini/afriberta_small", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-05-03T13:06:00Z	--- library_name: transformers base_model: castorini/afriberta_small tags: - generated_from_trainer model-index: - name: finetune-afriberta-small-am 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. --> # finetune-afriberta-small-am This model is a fine-tuned version of [castorini/afriberta_small](https://huggingface.co/castorini/afriberta_small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.5525 ## 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: 4 - eval_batch_size: 8 - seed: 999 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - 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: 2000 - training_steps: 50000 - 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
mradermacher/gemma-2-9B-it-blend-GGUF	mradermacher	2025-05-03T15:34:26Z	0	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:spacematt/gemma-2-9B-it-blend", "base_model:quantized:spacematt/gemma-2-9B-it-blend", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-02T21:18:06Z	--- base_model: spacematt/gemma-2-9B-it-blend 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/spacematt/gemma-2-9B-it-blend <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/gemma-2-9B-it-blend-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/gemma-2-9B-it-blend-GGUF/resolve/main/gemma-2-9B-it-blend.Q2_K.gguf) \| Q2_K \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-9B-it-blend-GGUF/resolve/main/gemma-2-9B-it-blend.Q3_K_S.gguf) \| Q3_K_S \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-9B-it-blend-GGUF/resolve/main/gemma-2-9B-it-blend.Q3_K_M.gguf) \| Q3_K_M \| 4.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-9B-it-blend-GGUF/resolve/main/gemma-2-9B-it-blend.Q3_K_L.gguf) \| Q3_K_L \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-9B-it-blend-GGUF/resolve/main/gemma-2-9B-it-blend.IQ4_XS.gguf) \| IQ4_XS \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-9B-it-blend-GGUF/resolve/main/gemma-2-9B-it-blend.Q4_K_S.gguf) \| Q4_K_S \| 5.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-9B-it-blend-GGUF/resolve/main/gemma-2-9B-it-blend.Q4_K_M.gguf) \| Q4_K_M \| 5.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-9B-it-blend-GGUF/resolve/main/gemma-2-9B-it-blend.Q5_K_S.gguf) \| Q5_K_S \| 6.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-9B-it-blend-GGUF/resolve/main/gemma-2-9B-it-blend.Q5_K_M.gguf) \| Q5_K_M \| 6.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-9B-it-blend-GGUF/resolve/main/gemma-2-9B-it-blend.Q6_K.gguf) \| Q6_K \| 7.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-9B-it-blend-GGUF/resolve/main/gemma-2-9B-it-blend.Q8_0.gguf) \| Q8_0 \| 9.9 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-9B-it-blend-GGUF/resolve/main/gemma-2-9B-it-blend.f16.gguf) \| f16 \| 18.6 \| 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 -->
mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-3-GGUF	mradermacher	2025-05-03T15:34:24Z	0	1	transformers	[ "transformers", "gguf", "en", "base_model:Shaleen123/MedicalEDI-14b-EDI-Reasoning-Final-3", "base_model:quantized:Shaleen123/MedicalEDI-14b-EDI-Reasoning-Final-3", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T10:47:58Z	--- base_model: Shaleen123/MedicalEDI-14b-EDI-Reasoning-Final-3 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-3 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-3-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-3-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-3.Q2_K.gguf) \| Q2_K \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-3-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-3.Q3_K_S.gguf) \| Q3_K_S \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-3-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-3.Q3_K_M.gguf) \| Q3_K_M \| 7.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-3-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-3.Q3_K_L.gguf) \| Q3_K_L \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-3-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-3.IQ4_XS.gguf) \| IQ4_XS \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-3-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-3.Q4_K_S.gguf) \| Q4_K_S \| 8.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-3-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-3.Q4_K_M.gguf) \| Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-3-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-3.Q5_K_S.gguf) \| Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-3-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-3.Q5_K_M.gguf) \| Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-3-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-3.Q6_K.gguf) \| Q6_K \| 12.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-3-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-3.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 -->
Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-Q8_0-GGUF	Triangle104	2025-05-03T15:34:08Z	0	0	transformers	[ "transformers", "gguf", "nvidia", "llama-3", "pytorch", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:nvidia/Llama-3.1-Nemotron-Nano-8B-v1", "base_model:quantized:nvidia/Llama-3.1-Nemotron-Nano-8B-v1", "license:other", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-03T15:32:25Z	--- base_model: nvidia/Llama-3.1-Nemotron-Nano-8B-v1 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 - llama-cpp - gguf-my-repo --- # Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-Q8_0-GGUF This model was converted to GGUF format from [`nvidia/Llama-3.1-Nemotron-Nano-8B-v1`](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-8B-v1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-8B-v1) for more details on the model. --- Llama-3.1-Nemotron-Nano-8B-v1 is a large language model (LLM) which is a derivative of Meta Llama-3.1-8B-Instruct (AKA the reference model). It is a reasoning model that is post trained for reasoning, human chat preferences, and tasks, such as RAG and tool calling. Llama-3.1-Nemotron-Nano-8B-v1 is a model which offers a great tradeoff between model accuracy and efficiency. It is created from Llama 3.1 8B Instruct and offers improvements in model accuracy. The model fits on a single RTX GPU and can be used locally. The model supports a context length of 128K. This model underwent a multi-phase post-training process to enhance both its reasoning and non-reasoning capabilities. This includes a supervised fine-tuning stage for Math, Code, Reasoning, and Tool Calling as well as multiple reinforcement learning (RL) stages using REINFORCE (RLOO) and Online Reward-aware Preference Optimization (RPO) algorithms for both chat and instruction-following. The final model checkpoint is obtained after merging the final SFT and Online RPO checkpoints. Improved using Qwen. --- ## 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-Q8_0-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-Q8_0-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-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 Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-Q8_0-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-Q8_0-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-q8_0.gguf -c 2048 ```
mafzaal/finetuned_arctic_ft	mafzaal	2025-05-03T15:33:46Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:156", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "base_model:Snowflake/snowflake-arctic-embed-l", "base_model:finetune:Snowflake/snowflake-arctic-embed-l", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-05-03T15:33:03Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:156 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: Snowflake/snowflake-arctic-embed-l widget: - source_sentence: Which multi-modal models were released by significant vendors in 2024, and in which months did they appear? sentences: - 'An interesting point of comparison here could be the way railways rolled out around the world in the 1800s. Constructing these required enormous investments and had a massive environmental impact, and many of the lines that were built turned out to be unnecessary—sometimes multiple lines from different companies serving the exact same routes! The resulting bubbles contributed to several financial crashes, see Wikipedia for Panic of 1873, Panic of 1893, Panic of 1901 and the UK’s Railway Mania. They left us with a lot of useful infrastructure and a great deal of bankruptcies and environmental damage. The year of slop' - 'In 2024, almost every significant model vendor released multi-modal models. We saw the Claude 3 series from Anthropic in March, Gemini 1.5 Pro in April (images, audio and video), then September brought Qwen2-VL and Mistral’s Pixtral 12B and Meta’s Llama 3.2 11B and 90B vision models. We got audio input and output from OpenAI in October, then November saw SmolVLM from Hugging Face and December saw image and video models from Amazon Nova. In October I upgraded my LLM CLI tool to support multi-modal models via attachments. It now has plugins for a whole collection of different vision models.' - 'OpenAI made GPT-4o free for all users in May, and Claude 3.5 Sonnet was freely available from its launch in June. This was a momentus change, because for the previous year free users had mostly been restricted to GPT-3.5 level models, meaning new users got a very inaccurate mental model of what a capable LLM could actually do. That era appears to have ended, likely permanently, with OpenAI’s launch of ChatGPT Pro. This $200/month subscription service is the only way to access their most capable model, o1 Pro. Since the trick behind the o1 series (and the future models it will undoubtedly inspire) is to expend more compute time to get better results, I don’t think those days of free access to the best available models are likely to return.' - source_sentence: How is a prompt without evals, models, and UX compared in the given context? sentences: - 'The environmental impact got much, much worse The much bigger problem here is the enormous competitive buildout of the infrastructure that is imagined to be necessary for these models in the future. Companies like Google, Meta, Microsoft and Amazon are all spending billions of dollars rolling out new datacenters, with a very material impact on the electricity grid and the environment. There’s even talk of spinning up new nuclear power stations, but those can take decades. Is this infrastructure necessary? DeepSeek v3’s $6m training cost and the continued crash in LLM prices might hint that it’s not. But would you want to be the big tech executive that argued NOT to build out this infrastructure only to be proven wrong in a few years’ time?' - 'When @v0 first came out we were paranoid about protecting the prompt with all kinds of pre and post processing complexity. We completely pivoted to let it rip. A prompt without the evals, models, and especially UX is like getting a broken ASML machine without a manual' - 'The boring yet crucial secret behind good system prompts is test-driven development. You don’t write down a system prompt and find ways to test it. You write down tests and find a system prompt that passes them. It’s become abundantly clear over the course of 2024 that writing good automated evals for LLM-powered systems is the skill that’s most needed to build useful applications on top of these models. If you have a strong eval suite you can adopt new models faster, iterate better and build more reliable and useful product features than your competition. Vercel’s Malte Ubl:' - source_sentence: How did the construction of railways in the 1800s impact the environment? sentences: - 'DeepSeek v3 is a huge 685B parameter model—one of the largest openly licensed models currently available, significantly bigger than the largest of Meta’s Llama series, Llama 3.1 405B. Benchmarks put it up there with Claude 3.5 Sonnet. Vibe benchmarks (aka the Chatbot Arena) currently rank it 7th, just behind the Gemini 2.0 and OpenAI 4o/o1 models. This is by far the highest ranking openly licensed model. The really impressive thing about DeepSeek v3 is the training cost. The model was trained on 2,788,000 H800 GPU hours at an estimated cost of $5,576,000. Llama 3.1 405B trained 30,840,000 GPU hours—11x that used by DeepSeek v3, for a model that benchmarks slightly worse.' - 'An interesting point of comparison here could be the way railways rolled out around the world in the 1800s. Constructing these required enormous investments and had a massive environmental impact, and many of the lines that were built turned out to be unnecessary—sometimes multiple lines from different companies serving the exact same routes! The resulting bubbles contributed to several financial crashes, see Wikipedia for Panic of 1873, Panic of 1893, Panic of 1901 and the UK’s Railway Mania. They left us with a lot of useful infrastructure and a great deal of bankruptcies and environmental damage. The year of slop' - 'So far, I think they’re a net positive. I’ve used them on a personal level to improve my productivity (and entertain myself) in all sorts of different ways. I think people who learn how to use them effectively can gain a significant boost to their quality of life. A lot of people are yet to be sold on their value! Some think their negatives outweigh their positives, some think they are all hot air, and some even think they represent an existential threat to humanity. They’re actually quite easy to build The most surprising thing we’ve learned about LLMs this year is that they’re actually quite easy to build.' - source_sentence: How many lines of Python code are generally needed to train a basic version of a powerful system? sentences: - 'We already knew LLMs were spookily good at writing code. If you prompt them right, it turns out they can build you a full interactive application using HTML, CSS and JavaScript (and tools like React if you wire up some extra supporting build mechanisms)—often in a single prompt. Anthropic kicked this idea into high gear when they released Claude Artifacts, a groundbreaking new feature that was initially slightly lost in the noise due to being described half way through their announcement of the incredible Claude 3.5 Sonnet. With Artifacts, Claude can write you an on-demand interactive application and then let you use it directly inside the Claude interface. Here’s my Extract URLs app, entirely generated by Claude:' - 'I’m still trying to figure out the best patterns for doing this for my own work. Everyone knows that evals are important, but there remains a lack of great guidance for how to best implement them—I’m tracking this under my evals tag. My SVG pelican riding a bicycle benchmark is a pale imitation of what a real eval suite should look like. Apple Intelligence is bad, Apple’s MLX library is excellent As a Mac user I’ve been feeling a lot better about my choice of platform this year. Last year it felt like my lack of a Linux/Windows machine with an NVIDIA GPU was a huge disadvantage in terms of trying out new models.' - 'Intuitively, one would expect that systems this powerful would take millions of lines of complex code. Instead, it turns out a few hundred lines of Python is genuinely enough to train a basic version! What matters most is the training data. You need a lot of data to make these things work, and the quantity and quality of the training data appears to be the most important factor in how good the resulting model is. If you can gather the right data, and afford to pay for the GPUs to train it, you can build an LLM.' - source_sentence: According to the context, what is one of the best applications of large language models (LLMs)? sentences: - 'A lot of people are excited about AI agents—an infuriatingly vague term that seems to be converging on “AI systems that can go away and act on your behalf”. We’ve been talking about them all year, but I’ve seen few if any examples of them running in production, despite lots of exciting prototypes. I think this is because of gullibility. Can we solve this? Honestly, I’m beginning to suspect that you can’t fully solve gullibility without achieving AGI. So it may be quite a while before those agent dreams can really start to come true! Code may be the best application Over the course of the year, it’s become increasingly clear that writing code is one of the things LLMs are most capable of.' - 'Law is not ethics. Is it OK to train models on people’s content without their permission, when those models will then be used in ways that compete with those people? As the quality of results produced by AI models has increased over the year, these questions have become even more pressing. The impact on human society in terms of these models is already huge, if difficult to objectively measure. People have certainly lost work to them—anecdotally, I’ve seen this for copywriters, artists and translators. There are a great deal of untold stories here. I’m hoping 2024 sees significant amounts of dedicated journalism on this topic. My blog in 2023 Here’s a tag cloud for content I posted to my blog in 2023 (generated using Django SQL Dashboard):' - 'The two main categories I see are people who think AI agents are obviously things that go and act on your behalf—the travel agent model—and people who think in terms of LLMs that have been given access to tools which they can run in a loop as part of solving a problem. The term “autonomy” is often thrown into the mix too, again without including a clear definition. (I also collected 211 definitions on Twitter a few months ago—here they are in Datasette Lite—and had gemini-exp-1206 attempt to summarize them.) Whatever the term may mean, agents still have that feeling of perpetually “coming soon”.' pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 model-index: - name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l results: - task: type: information-retrieval name: Information Retrieval dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy@1 value: 0.9166666666666666 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 1.0 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 1.0 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9166666666666666 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.20000000000000004 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.10000000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.9166666666666666 name: Cosine Recall@1 - type: cosine_recall@3 value: 1.0 name: Cosine Recall@3 - type: cosine_recall@5 value: 1.0 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9692441461309548 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9583333333333334 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9583333333333334 name: Cosine Map@100 --- # SentenceTransformer based on Snowflake/snowflake-arctic-embed-l This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) <!-- at revision d8fb21ca8d905d2832ee8b96c894d3298964346b --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 1024 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("mafzaal/finetuned_arctic_ft") # Run inference sentences = [ 'According to the context, what is one of the best applications of large language models (LLMs)?', 'A lot of people are excited about AI agents—an infuriatingly vague term that seems to be converging on “AI systems that can go away and act on your behalf”. We’ve been talking about them all year, but I’ve seen few if any examples of them running in production, despite lots of exciting prototypes.\nI think this is because of gullibility.\nCan we solve this? Honestly, I’m beginning to suspect that you can’t fully solve gullibility without achieving AGI. So it may be quite a while before those agent dreams can really start to come true!\nCode may be the best application\nOver the course of the year, it’s become increasingly clear that writing code is one of the things LLMs are most capable of.', 'The two main categories I see are people who think AI agents are obviously things that go and act on your behalf—the travel agent model—and people who think in terms of LLMs that have been given access to tools which they can run in a loop as part of solving a problem. The term “autonomy” is often thrown into the mix too, again without including a clear definition.\n(I also collected 211 definitions on Twitter a few months ago—here they are in Datasette Lite—and had gemini-exp-1206 attempt to summarize them.)\nWhatever the term may mean, agents still have that feeling of perpetually “coming soon”.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Information Retrieval * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.9167 \| \| cosine_accuracy@3 \| 1.0 \| \| cosine_accuracy@5 \| 1.0 \| \| cosine_accuracy@10 \| 1.0 \| \| cosine_precision@1 \| 0.9167 \| \| cosine_precision@3 \| 0.3333 \| \| cosine_precision@5 \| 0.2 \| \| cosine_precision@10 \| 0.1 \| \| cosine_recall@1 \| 0.9167 \| \| cosine_recall@3 \| 1.0 \| \| cosine_recall@5 \| 1.0 \| \| cosine_recall@10 \| 1.0 \| \| cosine_ndcg@10 \| 0.9692 \| \| cosine_mrr@10 \| 0.9583 \| \| cosine_map@100 \| 0.9583 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 156 training samples * Columns: <code>sentence_0</code> and <code>sentence_1</code> * Approximate statistics based on the first 156 samples: \| \| sentence_0 \| sentence_1 \| \|:--------\|:-----------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 12 tokens</li><li>mean: 21.18 tokens</li><li>max: 37 tokens</li></ul> \| <ul><li>min: 43 tokens</li><li>mean: 135.14 tokens</li><li>max: 214 tokens</li></ul> \| * Samples: \| sentence_0 \| sentence_1 \| \|:-----------------------------------------------------------------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>What significant development in Artificial Intelligence occurred in 2023 according to Simon Willison’s weblog?</code> \| <code>Stuff we figured out about AI in 2023<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>Simon Willison’s Weblog<br>Subscribe<br><br><br><br><br><br><br>Stuff we figured out about AI in 2023<br>31st December 2023<br>2023 was the breakthrough year for Large Language Models (LLMs). I think it’s OK to call these AI—they’re the latest and (currently) most interesting development in the academic field of Artificial Intelligence that dates back to the 1950s.<br>Here’s my attempt to round up the highlights in one place!</code> \| \| <code>How does Simon Willison describe the relationship between Large Language Models and the broader field of Artificial Intelligence?</code> \| <code>Stuff we figured out about AI in 2023<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>Simon Willison’s Weblog<br>Subscribe<br><br><br><br><br><br><br>Stuff we figured out about AI in 2023<br>31st December 2023<br>2023 was the breakthrough year for Large Language Models (LLMs). I think it’s OK to call these AI—they’re the latest and (currently) most interesting development in the academic field of Artificial Intelligence that dates back to the 1950s.<br>Here’s my attempt to round up the highlights in one place!</code> \| \| <code>What are some challenges mentioned in building large language models like GPT-4?</code> \| <code>Large Language Models<br>They’re actually quite easy to build<br>You can run LLMs on your own devices<br>Hobbyists can build their own fine-tuned models<br>We don’t yet know how to build GPT-4<br>Vibes Based Development<br>LLMs are really smart, and also really, really dumb<br>Gullibility is the biggest unsolved problem<br>Code may be the best application<br>The ethics of this space remain diabolically complex<br>My blog in 2023</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 10 - `per_device_eval_batch_size`: 10 - `num_train_epochs`: 10 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 10 - `per_device_eval_batch_size`: 10 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 10 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `tp_size`: 0 - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin </details> ### Training Logs \| Epoch \| Step \| cosine_ndcg@10 \| \|:-----:\|:----:\|:--------------:\| \| 1.0 \| 16 \| 0.9638 \| \| 2.0 \| 32 \| 0.9539 \| \| 3.0 \| 48 \| 0.9539 \| \| 3.125 \| 50 \| 0.9539 \| \| 4.0 \| 64 \| 0.9692 \| \| 5.0 \| 80 \| 0.9692 \| \| 6.0 \| 96 \| 0.9692 \| \| 6.25 \| 100 \| 0.9539 \| \| 7.0 \| 112 \| 0.9692 \| \| 8.0 \| 128 \| 0.9692 \| \| 9.0 \| 144 \| 0.9692 \| \| 9.375 \| 150 \| 0.9692 \| \| 10.0 \| 160 \| 0.9692 \| ### Framework Versions - Python: 3.11.12 - Sentence Transformers: 4.1.0 - Transformers: 4.51.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.6.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
cvoffer/991c9060-c99e-482b-a5a0-71bd43e3acfb	cvoffer	2025-05-03T15:33:06Z	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:unsloth/Mistral-Nemo-Base-2407", "base_model:adapter:unsloth/Mistral-Nemo-Base-2407", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-05-03T14:49:43Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Mistral-Nemo-Base-2407 tags: - axolotl - generated_from_trainer model-index: - name: 991c9060-c99e-482b-a5a0-71bd43e3acfb 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/Mistral-Nemo-Base-2407 bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 7f381c5f243a3b63_train_data.json ds_type: json format: custom path: /workspace/input_data/7f381c5f243a3b63_train_data.json type: field_input: critic_prompt field_instruction: prompt field_output: init_response format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: cvoffer/991c9060-c99e-482b-a5a0-71bd43e3acfb hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 10 mixed_precision: bf16 mlflow_experiment_name: /tmp/7f381c5f243a3b63_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: 1031246b-4c91-4dd7-b3f7-0b6440762bad wandb_project: s56-28 wandb_run: your_name wandb_runid: 1031246b-4c91-4dd7-b3f7-0b6440762bad warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 991c9060-c99e-482b-a5a0-71bd43e3acfb This model is a fine-tuned version of [unsloth/Mistral-Nemo-Base-2407](https://huggingface.co/unsloth/Mistral-Nemo-Base-2407) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9944 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.0377 \| 0.0360 \| 150 \| 0.9944 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
bodam/Llama-3.2-1B-ko_wiki-4bit-753	bodam	2025-05-03T15:27:55Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/Llama-3.2-1B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Llama-3.2-1B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-03T15:22:56Z	--- base_model: unsloth/Llama-3.2-1B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: bodam - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.2-1B-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)
ywhcho/yllama31	ywhcho	2025-05-03T15:26:18Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-03T15:24:33Z	--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: ywhcho - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
aisyhmaira/llama-3.2-ko-finetune-2	aisyhmaira	2025-05-03T15:25:32Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/Llama-3.2-1B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Llama-3.2-1B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-03T15:20:34Z	--- base_model: unsloth/Llama-3.2-1B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: aisyhmaira - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.2-1B-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)
trongg/lora-c46bb480-76cc-4d4e-9fec-77ef5c320ce4-1400	trongg	2025-05-03T15:15:31Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/mistral-7b", "base_model:adapter:unsloth/mistral-7b", "region:us" ]	null	2025-05-03T15:13:01Z	--- base_model: unsloth/mistral-7b library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
KriptoUzmani/Qwen2.5-32B-Instruct-bnb-4bit-Gensyn-Swarm-squeaky_trotting_komodo	KriptoUzmani	2025-05-03T15:14:33Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am squeaky trotting komodo", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-32B-Instruct-bnb-4bit", "base_model:finetune:Gensyn/Qwen2.5-32B-Instruct-bnb-4bit", "endpoints_compatible", "region:us" ]	null	2025-05-01T05:26:54Z	--- base_model: Gensyn/Qwen2.5-32B-Instruct-bnb-4bit library_name: transformers model_name: Qwen2.5-32B-Instruct-bnb-4bit-Gensyn-Swarm-squeaky_trotting_komodo tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am squeaky trotting komodo - unsloth - trl licence: license --- # Model Card for Qwen2.5-32B-Instruct-bnb-4bit-Gensyn-Swarm-squeaky_trotting_komodo This model is a fine-tuned version of [Gensyn/Qwen2.5-32B-Instruct-bnb-4bit](https://huggingface.co/Gensyn/Qwen2.5-32B-Instruct-bnb-4bit). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="KriptoUzmani/Qwen2.5-32B-Instruct-bnb-4bit-Gensyn-Swarm-squeaky_trotting_komodo", 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}} } ```
anilarslan/aa	anilarslan	2025-05-03T15:11:44Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T14:49:46Z	--- 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. 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]
Shengkun/DarwinLM-4.6B-Llama3.1-8B-Pruned-Masked	Shengkun	2025-05-03T15:07:11Z	157	0	null	[ "safetensors", "llama", "license:apache-2.0", "region:us" ]	null	2025-04-13T06:26:56Z	--- license: apache-2.0 --- This is DarwinLM pruned from Llama3.1-8B. The model is masked that the pruned weights are set as 0 while the remaining weights are the same as the original model. The shape of all weights are the same as the original model. ```python # To use the model from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Shengkun/DarwinLM-4.6B-Llama3.1-8B-Pruned-Masked") ``` 4.6B \| Model \| Method \| Param. \| SciQ \| PIQA \| WG \| ArcE \| ArcC \| HS \| LogiQA \| BoolQ \| MMLU \| Avg \| \|-----------------\|------------------------\|--------\|------\|------\|------\|------\|------\|------\|--------\|-------\|------\|------\| \| Llama-3.1-8B \| Dense \| 8B \| 96.3 \| 81.2 \| 74.3 \| 81.4 \| 58.2 \| 81.7 \| 31.1 \| 84.0 \| 65.2 \| 72.8 \| \| \| Uniform \| 4.5B \| 29.1 \| 53.6 \| 51.7 \| 26.0 \| 23.6 \| 27.1 \| 25.5 \| 62.1 \| 25.7 \| 36.1 \| \| \| ZipLM \| 6B \| 65.5 \| 60.6 \| 56.0 \| 40.2 \| 34.4 \| 34.4 \| 28.1 \| 63.0 \| 27.9 \| 45.7 \| \| \| DarwinLM (one-shot) \| 4.6B \| 84.9 \| 69.4 \| 57.3 \| 59.6 \| 34.2 \| 44.6 \| 24.1 \| 62.2 \| 28.5 \| 51.6 \| \| \| OLMO (2.5T) \| 7B \| 92.8 \| 79.4 \| 70.4 \| 73.3 \| 44.9 \| 77.1 \| 27.9 \| 72.5 \| 28.3 \| 62.9 \| \| \| DarwinLM (10.0B) \| 4.6B \| 93.2 \| 74.8 \| 67.4 \| 73.2 \| 51.6 \| 71.3 \| 30.7 \| 71.1 \| 40.6 \| 63.7 \|
ridwanridus/ridus	ridwanridus	2025-05-03T14:55:21Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-03T14:55:21Z	--- license: apache-2.0 ---
mlfoundations-dev/d1_math_mc_llm_1k	mlfoundations-dev	2025-05-03T14:52:36Z	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	2025-05-03T12:09:57Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: d1_math_mc_llm_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. --> # d1_math_mc_llm_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/d1_math_mc_llm_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: 4 - gradient_accumulation_steps: 24 - total_train_batch_size: 96 - total_eval_batch_size: 32 - 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 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
Benson87/min_model	Benson87	2025-05-03T14:48:50Z	0	0	null	[ "pytorch", "region:us" ]	null	2025-05-03T14:34:54Z	# Min Model Denne model er oprettet som test og placeholder. Du kan erstatte pytorch_model.bin med en trænet model. ## Struktur - pytorch_model.bin: Dummy eller rigtig model - config.json: Krævet for kompatibilitet ## Status 🚧 Under opbygning – erstattes senere med ægte weights
spacematt/gemma-3-12b-it-qat-q4_0-unquantized-Q5_K_M-GGUF	spacematt	2025-05-03T14:48:43Z	46	0	transformers	[ "transformers", "gguf", "gemma3", "gemma", "google", "llama-cpp", "gguf-my-repo", "image-text-to-text", "base_model:google/gemma-3-12b-it-qat-q4_0-unquantized", "base_model:quantized:google/gemma-3-12b-it-qat-q4_0-unquantized", "license:gemma", "endpoints_compatible", "region:us", "conversational" ]	image-text-to-text	2025-04-21T15:22:43Z	--- base_model: google/gemma-3-12b-it-qat-q4_0-unquantized library_name: transformers license: gemma pipeline_tag: image-text-to-text tags: - gemma3 - gemma - google - llama-cpp - gguf-my-repo extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license --- # spacematt/gemma-3-12b-it-qat-q4_0-unquantized-Q5_K_M-GGUF This model was converted to GGUF format from [`google/gemma-3-12b-it-qat-q4_0-unquantized`](https://huggingface.co/google/gemma-3-12b-it-qat-q4_0-unquantized) 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/google/gemma-3-12b-it-qat-q4_0-unquantized) 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/gemma-3-12b-it-qat-q4_0-unquantized-Q5_K_M-GGUF --hf-file gemma-3-12b-it-qat-q4_0-unquantized-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo spacematt/gemma-3-12b-it-qat-q4_0-unquantized-Q5_K_M-GGUF --hf-file gemma-3-12b-it-qat-q4_0-unquantized-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo spacematt/gemma-3-12b-it-qat-q4_0-unquantized-Q5_K_M-GGUF --hf-file gemma-3-12b-it-qat-q4_0-unquantized-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo spacematt/gemma-3-12b-it-qat-q4_0-unquantized-Q5_K_M-GGUF --hf-file gemma-3-12b-it-qat-q4_0-unquantized-q5_k_m.gguf -c 2048 ```
ASethi04/meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-0.0001-same-prompt-template	ASethi04	2025-05-03T14:47:29Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "endpoints_compatible", "region:us" ]	null	2025-05-03T10:52:44Z	--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-0.0001-same-prompt-template tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-0.0001-same-prompt-template This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="ASethi04/meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-0.0001-same-prompt-template", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/torchql-org/huggingface/runs/rxgjo7gt) This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
GuidoSt/DeepSeek-LED-Scheduler-7B	GuidoSt	2025-05-03T14:43:09Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-03T14:43:01Z	--- base_model: unsloth/deepseek-r1-distill-qwen-7b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: GuidoSt - License: apache-2.0 - Finetuned from model : unsloth/deepseek-r1-distill-qwen-7b-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Rziane/speaker_seg_ft_eslotest_mm03082025	Rziane	2025-05-03T14:42:01Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "pyannet", "speaker-diarization", "speaker-segmentation", "generated_from_trainer", "fr", "dataset:CAENNAIS", "base_model:pyannote/segmentation-3.0", "base_model:finetune:pyannote/segmentation-3.0", "license:mit", "endpoints_compatible", "region:us" ]	null	2025-05-03T14:27:26Z	--- library_name: transformers language: - fr license: mit base_model: pyannote/segmentation-3.0 tags: - speaker-diarization - speaker-segmentation - generated_from_trainer datasets: - CAENNAIS model-index: - name: pyannote/segmentation-3.0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pyannote/segmentation-3.0 This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the CAENNAIS dataset. It achieves the following results on the evaluation set: - Loss: 0.8139 - Model Preparation Time: 0.0035 - Der: 0.5111 - False Alarm: 0.1728 - Missed Detection: 0.2406 - Confusion: 0.0978 ## 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: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - 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: cosine - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Model Preparation Time \| Der \| False Alarm \| Missed Detection \| Confusion \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------------:\|:------:\|:-----------:\|:----------------:\|:---------:\| \| 0.8517 \| 1.0 \| 300 \| 0.8676 \| 0.0035 \| 0.5466 \| 0.1920 \| 0.2425 \| 0.1121 \| \| 0.7998 \| 2.0 \| 600 \| 0.8499 \| 0.0035 \| 0.5307 \| 0.1640 \| 0.2628 \| 0.1039 \| \| 0.7867 \| 3.0 \| 900 \| 0.8529 \| 0.0035 \| 0.5366 \| 0.1602 \| 0.2767 \| 0.0997 \| \| 0.7777 \| 4.0 \| 1200 \| 0.8351 \| 0.0035 \| 0.5296 \| 0.1912 \| 0.2333 \| 0.1050 \| \| 0.7596 \| 5.0 \| 1500 \| 0.8185 \| 0.0035 \| 0.5118 \| 0.1817 \| 0.2239 \| 0.1062 \| \| 0.7591 \| 6.0 \| 1800 \| 0.8083 \| 0.0035 \| 0.5101 \| 0.1655 \| 0.2540 \| 0.0906 \| \| 0.7555 \| 7.0 \| 2100 \| 0.8141 \| 0.0035 \| 0.5109 \| 0.1711 \| 0.2396 \| 0.1001 \| \| 0.7394 \| 8.0 \| 2400 \| 0.8145 \| 0.0035 \| 0.5119 \| 0.1726 \| 0.2405 \| 0.0988 \| \| 0.7458 \| 9.0 \| 2700 \| 0.8138 \| 0.0035 \| 0.5107 \| 0.1721 \| 0.2403 \| 0.0983 \| \| 0.705 \| 10.0 \| 3000 \| 0.8139 \| 0.0035 \| 0.5111 \| 0.1728 \| 0.2406 \| 0.0978 \| ### Framework versions - Transformers 4.51.0 - Pytorch 2.7.0+cu126 - Datasets 3.5.0 - Tokenizers 0.21.1
ASethi04/meta-llama-Llama-3.1-8B-gsm8k-first-lora-4-0.0004	ASethi04	2025-05-03T14:40:39Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "endpoints_compatible", "region:us" ]	null	2025-05-03T13:46:48Z	--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: meta-llama-Llama-3.1-8B-gsm8k-first-lora-4-0.0004 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for meta-llama-Llama-3.1-8B-gsm8k-first-lora-4-0.0004 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="ASethi04/meta-llama-Llama-3.1-8B-gsm8k-first-lora-4-0.0004", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/torchql-org/huggingface/runs/24q7yukg) This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
h34v7/DansXPantheon-RP-Engine-V1.0-24b-Small-Instruct-old	h34v7	2025-05-03T14:39:42Z	2	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "roleplay", "storywriting", "mergekit", "merge", "conversational", "arxiv:2408.07990", "base_model:Gryphe/Pantheon-RP-1.8-24b-Small-3.1", "base_model:merge:Gryphe/Pantheon-RP-1.8-24b-Small-3.1", "base_model:PocketDoc/Dans-PersonalityEngine-V1.2.0-24b", "base_model:merge:PocketDoc/Dans-PersonalityEngine-V1.2.0-24b", "base_model:unsloth/Mistral-Small-24B-Base-2501", "base_model:merge:unsloth/Mistral-Small-24B-Base-2501", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T22:46:15Z	--- base_model: - unsloth/Mistral-Small-24B-Base-2501 - Gryphe/Pantheon-RP-1.8-24b-Small-3.1 - PocketDoc/Dans-PersonalityEngine-V1.2.0-24b library_name: transformers license: apache-2.0 pipeline_tag: text-generation tags: - roleplay - storywriting - mergekit - merge new_version: h34v7/DansXPantheon-RP-Engine-V1.2-24b-Small-Instruct-Ties-Merge --- # DansXPantheon-RP-Engine-V1.0-24b-Small-Instruct I realy like [PocketDoc/Dans-PersonalityEngine-V1.2.0-24b](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.2.0-24b) and [Gryphe/Pantheon-RP-1.8-24b-Small-3.1](https://huggingface.co/Gryphe/Pantheon-RP-1.8-24b-Small-3.1) so yeah let's merge it see what comes out! Okay these are my first attempt at merging it was horrible i must admit. I will do better i promise. ## Merge Details This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ### Merge Method This model was merged using the [SCE](https://arxiv.org/abs/2408.07990) merge method using [unsloth/Mistral-Small-24B-Base-2501](https://huggingface.co/unsloth/Mistral-Small-24B-Base-2501) as a base. ### Models Merged The following models were included in the merge: * [Gryphe/Pantheon-RP-1.8-24b-Small-3.1](https://huggingface.co/Gryphe/Pantheon-RP-1.8-24b-Small-3.1) * [PocketDoc/Dans-PersonalityEngine-V1.2.0-24b](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.2.0-24b) ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: unsloth/Mistral-Small-24B-Base-2501 merge_method: sce dype: float32 out_dtype: bfloat16 tokenizer: source: unsloth/Mistral-Small-24B-Instruct-2501 models: - model: PocketDoc/Dans-PersonalityEngine-V1.2.0-24b parameters: select_topk: 0.5 - model: Gryphe/Pantheon-RP-1.8-24b-Small-3.1 parameters: select_topk: 0.5 ```
mlfoundations-dev/d1_math_fasttext_0.3k	mlfoundations-dev	2025-05-03T14:39:34Z	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	2025-05-03T13:00:46Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: d1_math_fasttext_0.3k 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. --> # d1_math_fasttext_0.3k 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/d1_math_fasttext_0.3k dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 32 - 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: 13.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
MinaMila/phi3_LoRa_ACSEmployment_2_cfda_ep4_22	MinaMila	2025-05-03T14:38:44Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-02T00:35:15Z	--- 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]
bertbert123/lora_modelcode	bertbert123	2025-05-03T14:37:20Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-03T13:09:50Z	--- base_model: unsloth/llama-3.2-1b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: bertbert123 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-1b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mlfoundations-dev/d1_math_all_large	mlfoundations-dev	2025-05-03T14:36:46Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-30T02:02:51Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: d1_math_all_large 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. --> # d1_math_all_large 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/d1_math_all_large dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 64 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - total_eval_batch_size: 512 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1 - Datasets 3.1.0 - Tokenizers 0.20.3
Yuuta208/Qwen2.5-7B-Instruct-Qwen2.5-Math-7B-Merged-dare_ties-27	Yuuta208	2025-05-03T14:36:36Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:merge:Qwen/Qwen2.5-7B-Instruct", "base_model:Qwen/Qwen2.5-Math-7B", "base_model:merge:Qwen/Qwen2.5-Math-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T14:33:31Z	--- base_model: - Qwen/Qwen2.5-Math-7B - Qwen/Qwen2.5-7B-Instruct library_name: transformers tags: - mergekit - merge --- # output_model_dare_ties This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) as a base. ### Models Merged The following models were included in the merge: * [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Qwen/Qwen2.5-7B-Instruct parameters: weight: 0.5 density: 0.8 - model: Qwen/Qwen2.5-Math-7B parameters: weight: 0.5 density: 0.8 merge_method: dare_ties base_model: Qwen/Qwen2.5-7B-Instruct dtype: bfloat16 ```
cnfusion/QwenPhi-4-0.5b-Draft-mlx-fp16	cnfusion	2025-05-03T14:33:32Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "qwen", "qwen2.5", "phi-4", "phi", "mlx", "mlx-my-repo", "conversational", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:rdsm/QwenPhi-4-0.5b-Draft", "base_model:finetune:rdsm/QwenPhi-4-0.5b-Draft", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T14:33:14Z	--- license: apache-2.0 language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara base_model: rdsm/QwenPhi-4-0.5b-Draft pipeline_tag: text-generation library_name: transformers tags: - qwen - qwen2.5 - phi-4 - phi - mlx - mlx-my-repo --- # cnfusion/QwenPhi-4-0.5b-Draft-mlx-fp16 The Model [cnfusion/QwenPhi-4-0.5b-Draft-mlx-fp16](https://huggingface.co/cnfusion/QwenPhi-4-0.5b-Draft-mlx-fp16) was converted to MLX format from [rdsm/QwenPhi-4-0.5b-Draft](https://huggingface.co/rdsm/QwenPhi-4-0.5b-Draft) using mlx-lm version 0.22.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("cnfusion/QwenPhi-4-0.5b-Draft-mlx-fp16") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam	mansoorhamidzadeh	2025-05-03T14:32:58Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-03T14:30:29Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
Yuuta208/Qwen2.5-7B-Instruct-Qwen2.5-Math-7B-Merged-della-27	Yuuta208	2025-05-03T14:29:34Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "arxiv:2406.11617", "base_model:Qwen/Qwen2.5-7B", "base_model:merge:Qwen/Qwen2.5-7B", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:merge:Qwen/Qwen2.5-7B-Instruct", "base_model:Qwen/Qwen2.5-Math-7B", "base_model:merge:Qwen/Qwen2.5-Math-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T14:26:16Z	--- base_model: - Qwen/Qwen2.5-7B - Qwen/Qwen2.5-7B-Instruct - Qwen/Qwen2.5-Math-7B library_name: transformers tags: - mergekit - merge --- # output_model_della This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DELLA](https://arxiv.org/abs/2406.11617) merge method using [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) as a base. ### Models Merged The following models were included in the merge: * [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) * [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Qwen/Qwen2.5-7B-Instruct parameters: weight: 0.5 - model: Qwen/Qwen2.5-Math-7B parameters: weight: 0.6 merge_method: della base_model: Qwen/Qwen2.5-7B parameters: density: 0.8 normalize: true int8_mask: true dtype: float16 ```
MrRobotoAI/A24	MrRobotoAI	2025-05-03T14:29:28Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2212.04089", "base_model:MrRobotoAI/A2", "base_model:merge:MrRobotoAI/A2", "base_model:MrRobotoAI/A6", "base_model:merge:MrRobotoAI/A6", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T14:26:32Z	--- base_model: - MrRobotoAI/A6 - MrRobotoAI/A2 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Task Arithmetic](https://arxiv.org/abs/2212.04089) merge method using [MrRobotoAI/A2](https://huggingface.co/MrRobotoAI/A2) as a base. ### Models Merged The following models were included in the merge: * [MrRobotoAI/A6](https://huggingface.co/MrRobotoAI/A6) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: task_arithmetic models: - model: MrRobotoAI/A2 parameters: weight: - filter: v_proj value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8] - filter: o_proj value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8] - filter: up_proj value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8] - filter: gate_proj value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8] - filter: down_proj value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8] - value: 1 - model: MrRobotoAI/A6 parameters: weight: - filter: v_proj value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2] - filter: o_proj value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2] - filter: up_proj value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2] - filter: gate_proj value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2] - filter: down_proj value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2] - value: 0 base_model: MrRobotoAI/A2 dtype: bfloat16 ```
HelpingAI/HelpingAI2.5-10B	HelpingAI	2025-05-03T14:29:15Z	20,911	5	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-17T11:42:32Z	--- license: other license_name: helpingai license_link: https://huggingface.co/OEvortex/HelpingAI2.5-5B/blob/main/LICENSE.md pipeline_tag: text-generation language: - en library_name: transformers --- <div align="center"> # 🤖 HelpingAI2.5-10B *A Revolutionary Emotionally Intelligent Language Model* [![GitHub Organization](https://img.shields.io/badge/GitHub-Organization-blue.svg)](https://github.com/HelpingAI) [![Hugging Face](https://img.shields.io/badge/🤗%20Hugging%20Face-Organization-yellow)](https://huggingface.co/OEvortex) [![Model License](https://img.shields.io/badge/License-HelpingAI-green.svg)](https://huggingface.co/OEvortex/HelpingAI2.5-10B/blob/main/LICENSE.md) <a href="https://github.com/HelpingAI/community/discussions"> <img src="https://img.shields.io/badge/Join-Community%20Discussion-blue?style=for-the-badge&logo=github" alt="Join Community Discussion"> </a> [📜 License](LICENSE) \| [🌐 Website](https://helpingai-in.netlify.app/) </div> <div align="center"> <img src="https://huggingface.co/OEvortex/HelpingAI-3B/resolve/main/HelpingAI.png" alt="HelpingAI Logo" width="300px"> </div> --- <div align="center"> ## 🌟 Model Overview HelpingAI2.5-10B is a compact yet powerful language model specifically designed for emotionally intelligent conversations and human-centric interactions. </div> ### 🎯 Key Highlights - Architecture: 10B parameter transformer-based model - Training Focus: Emotional intelligence and empathetic responses - Emotion Score: Achieves 98.13 on standardized emotional intelligence tests - Deployment: Optimized for efficient deployment on consumer hardware --- <div align="center"> ## 💻 Implementation </div> ### Transformers ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer # Load the HelpingAI2.5-10B model model = AutoModelForCausalLM.from_pretrained("HelpingAI/HelpingAI2.5-10B") # Load the tokenizer tokenizer = AutoTokenizer.from_pretrained("HelpingAI/HelpingAI2.5-10B") # Define the chat input chat = [ { "role": "system", "content": "You are HelpingAI, an emotional AI. Always answer my questions in the HelpingAI style." }, { "role": "user", "content": "GIVE ME YOUR INTRO" } ] inputs = tokenizer.apply_chat_template( response = outputs[0][inputs.shape[-1]:] print(tokenizer.decode(response, skip_special_tokens=True)) ``` ### GGUF Implementation ```python from webscout.Local import * model_path = download_model("HelpingAI/HelpingAI2.5-10B", "q4_k_m.gguf", token=None) model = Model(model_path, n_gpu_layers=0, context_length=4096) thread = Thread(model, format=helpingai2) # print(thread.send("hi")) #send a single msg to ai thread.interact() # interact with the model in terminal ``` --- <div align="center"> ## 🎯 Training Details </div> ### Methodology 1. Base Training - Datasets: SentimentSynth + EmotionalIntelligence-1M + HelpingAI2.0-150M + HelpingAIemo (152.5M) 2. Emotional Intelligence Training - Supervised Fine-tuning on emotional dialogue - Reinforcement Learning with HelpingAI2.0-7B model - Constitutional AI training for ethical guidelines 3. Optimization - Mixed-precision training - Gradient checkpointing - Dynamic attention patterns ### Dataset Composition \| Dataset Type \| Rows used for training \| Purpose \| \|--------------\|------\|---------\| \| Emotional Dialogues \| 12.5M \| Core conversation training \| \| Therapeutic Exchanges \| 2M \| Mental health support \| \| Cultural Conversations \| 100K \| Cultural awareness \| \| Crisis Response \| 750K \| Emergency support handling \| --- <div align="center"> ## 📈 Use Cases & Examples </div> ### Primary Applications - 🎯 Personal AI Companionship - 🧠 Mental Health Support - 📚 Educational Assistance - 💼 Professional Development - 🤝 Social Skills Training ### Example Interactions <div style="background: linear-gradient(135deg, #FF9933 0%, #FFFFFF 50%, #138808 100%); padding: 20px; border-radius: 15px; margin: 15px 0; border: 2px solid #000080;"> <div style="background-color: rgba(255, 255, 255, 0.9); padding: 15px; border-radius: 10px; margin-bottom: 10px;"> <span style="color: #D32F2F; font-weight: bold;">User: </span> <span style="color: #D32F2F;">I've been feeling really anxious about my upcoming job interview. Any advice?</span> </div> <div style="background-color: rgba(255, 255, 255, 0.9); padding: 15px; border-radius: 10px;"> <span style="color: #1B5E20; font-weight: bold;">HelpingAI: </span> <span style="color: #1B5E20;"> Hey there! 🙌 First off, take a deep breath and don't sweat it too much. 💨 Job interviews can be nerve-wracking, but I gotchu! 😎 Here's the tea: 🍵 Make sure you're prepping like crazy for this job. Read up on the company, their mission, and the role you're applying for. That way, you'll be spittin' knowledge and showing them you're serious. 📚💡 Also, practice your answers to common interview questions. You know, the ones like "Tell me about yourself" or "Why do you want to work here?" 🤔 Keep it short, sweet, and relevant. You got this! 😄 And don't forget to look sharp! 🎨 Keep your resume up-to-date, and make sure your social media is on point. First impressions are everything! 👀 Most importantly, just be yourself. 😊 The interview panel wants to see the real you. Show them your passion, your skills, and why you'd be a total boss at this company. 🔥 You're gonna crush this interview, fam! 💪 Remember, I got faith in you. 🙏</span> </div> </div> <div style="background: linear-gradient(135deg, #FF9933 0%, #FFFFFF 50%, #138808 100%); padding: 20px; border-radius: 15px; margin: 15px 0; border: 2px solid #000080;"> <div style="background-color: rgba(255, 255, 255, 0.9); padding: 15px; border-radius: 10px; margin-bottom: 10px;"> <span style="color: #D32F2F; font-weight: bold;">User: </span> <span style="color: #D32F2F;">My friend hasn't been responding to my messages for a week. I think I might have said something wrong, but I'm not sure what.</span> </div> <div style="background-color: rgba(255, 255, 255, 0.9); padding: 15px; border-radius: 10px;"> <span style="color: #1B5E20; font-weight: bold;">HelpingAI: </span> <span style="color: #1B5E20;">Aw man, that's a real bummer. 💔 Not hearing back from a friend can be super painful, especially when you're wondering if you did something wrong. 🤔 First off, don't beat yourself up over it. 🙅‍♂️ Chances are, your friend's just busy or dealing with some personal stuff. 🤷‍♀️ It doesn't always mean it's you. If you're still concerned, try reaching out in a different way, like a phone call or a text to a diff person in your friend group. 📱📞 Sometimes a different approach can help you connect again. But if you're still unsure, you could try asking your friend if everything's cool. 🤝 Be open and honest about your feelings, but also be ready for them to have their own perspective on things. 👥 Remember, you're not alone in this. 🤗 Friends go through ups and downs, and it's okay to have little misunderstandings. Communication is key! 🔑 Just keep it real and be patient. You got this, fam! 💪</span> </div> </div> ------ <div align="center"> ## 🔒 Ethical Considerations & Limitations </div> ### Ethical Guidelines - Prioritizes user emotional wellbeing - Maintains strict privacy standards - Avoids harmful or discriminatory responses - Transparent about AI limitations - Promotes healthy coping mechanisms ### Known Limitations - Cannot Replace Human Professionals - Cannot Roleplay - Limited Knowledge Base - Context Window Constraints --- ### Citation ```bibtex @misc{helpingai2024, author = {Abhay Koul}, title = {HelpingAI2.5-10B: Emotionally Intelligent Language Model}, year = {2024}, publisher = {Huggingface}, journal = {GitHub repository}, howpublished = {\url{https://huggingface.co/HelpingAI/HelpingAI2.5-10B}} } ``` --- <div align="center"> ## 🙏 Acknowledgments Special thanks to the HelpingAI community, Huggingface, contributors, and researchers who made this model possible. Your dedication to advancing emotionally intelligent AI is invaluable. </div> --- <div align="center"> Built with ❤️ by the HelpingAI Community [Website](https://helpingai-in.netlify.app/) • [GitHub](https://github.com/HelpingAI) • [Discord](https://discord.gg/YweJwNqrnH) • [HuggingFace](https://huggingface.co/OEvortex) </div>
mradermacher/medical-qa-vistral-i1-GGUF	mradermacher	2025-05-03T14:27:19Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "qwen2", "trl", "sft", "en", "base_model:hungsvdut2k2/medical-qa-vistral", "base_model:quantized:hungsvdut2k2/medical-qa-vistral", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-03T13:04:07Z	--- base_model: hungsvdut2k2/medical-qa-vistral language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/hungsvdut2k2/medical-qa-vistral <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/medical-qa-vistral-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/medical-qa-vistral-i1-GGUF/resolve/main/medical-qa-vistral.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.0 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-i1-GGUF/resolve/main/medical-qa-vistral.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.1 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-i1-GGUF/resolve/main/medical-qa-vistral.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-i1-GGUF/resolve/main/medical-qa-vistral.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-i1-GGUF/resolve/main/medical-qa-vistral.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-i1-GGUF/resolve/main/medical-qa-vistral.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-i1-GGUF/resolve/main/medical-qa-vistral.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 2.9 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-i1-GGUF/resolve/main/medical-qa-vistral.i1-Q2_K.gguf) \| i1-Q2_K \| 3.1 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-i1-GGUF/resolve/main/medical-qa-vistral.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-i1-GGUF/resolve/main/medical-qa-vistral.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-i1-GGUF/resolve/main/medical-qa-vistral.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-i1-GGUF/resolve/main/medical-qa-vistral.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-i1-GGUF/resolve/main/medical-qa-vistral.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-i1-GGUF/resolve/main/medical-qa-vistral.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.9 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-i1-GGUF/resolve/main/medical-qa-vistral.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-i1-GGUF/resolve/main/medical-qa-vistral.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-i1-GGUF/resolve/main/medical-qa-vistral.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 4.5 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-i1-GGUF/resolve/main/medical-qa-vistral.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-i1-GGUF/resolve/main/medical-qa-vistral.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.6 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-i1-GGUF/resolve/main/medical-qa-vistral.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-i1-GGUF/resolve/main/medical-qa-vistral.i1-Q4_1.gguf) \| i1-Q4_1 \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-i1-GGUF/resolve/main/medical-qa-vistral.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-i1-GGUF/resolve/main/medical-qa-vistral.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-i1-GGUF/resolve/main/medical-qa-vistral.i1-Q6_K.gguf) \| i1-Q6_K \| 6.4 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
MrRobotoAI/A23	MrRobotoAI	2025-05-03T14:26:27Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2212.04089", "base_model:MrRobotoAI/A2", "base_model:merge:MrRobotoAI/A2", "base_model:MrRobotoAI/A5", "base_model:merge:MrRobotoAI/A5", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T14:23:31Z	--- base_model: - MrRobotoAI/A5 - MrRobotoAI/A2 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Task Arithmetic](https://arxiv.org/abs/2212.04089) merge method using [MrRobotoAI/A2](https://huggingface.co/MrRobotoAI/A2) as a base. ### Models Merged The following models were included in the merge: * [MrRobotoAI/A5](https://huggingface.co/MrRobotoAI/A5) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: task_arithmetic models: - model: MrRobotoAI/A2 parameters: weight: - filter: v_proj value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8] - filter: o_proj value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8] - filter: up_proj value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8] - filter: gate_proj value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8] - filter: down_proj value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8] - value: 1 - model: MrRobotoAI/A5 parameters: weight: - filter: v_proj value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2] - filter: o_proj value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2] - filter: up_proj value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2] - filter: gate_proj value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2] - filter: down_proj value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2] - value: 0 base_model: MrRobotoAI/A2 dtype: bfloat16 ```
MrRobotoAI/A22	MrRobotoAI	2025-05-03T14:23:26Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2212.04089", "base_model:MrRobotoAI/A2", "base_model:merge:MrRobotoAI/A2", "base_model:MrRobotoAI/A4", "base_model:merge:MrRobotoAI/A4", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T14:20:25Z	--- base_model: - MrRobotoAI/A2 - MrRobotoAI/A4 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Task Arithmetic](https://arxiv.org/abs/2212.04089) merge method using [MrRobotoAI/A2](https://huggingface.co/MrRobotoAI/A2) as a base. ### Models Merged The following models were included in the merge: * [MrRobotoAI/A4](https://huggingface.co/MrRobotoAI/A4) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: task_arithmetic models: - model: MrRobotoAI/A2 parameters: weight: - filter: v_proj value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8] - filter: o_proj value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8] - filter: up_proj value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8] - filter: gate_proj value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8] - filter: down_proj value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8] - value: 1 - model: MrRobotoAI/A4 parameters: weight: - filter: v_proj value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2] - filter: o_proj value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2] - filter: up_proj value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2] - filter: gate_proj value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2] - filter: down_proj value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2] - value: 0 base_model: MrRobotoAI/A2 dtype: bfloat16 ```
beingbatman/5c_4	beingbatman	2025-05-03T14:22:41Z	0	0	transformers	[ "transformers", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-large-finetuned-kinetics", "base_model:finetune:MCG-NJU/videomae-large-finetuned-kinetics", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2025-05-02T17:32:05Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-large-finetuned-kinetics tags: - generated_from_trainer metrics: - accuracy model-index: - name: 5c_4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 5c_4 This model is a fine-tuned version of [MCG-NJU/videomae-large-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-large-finetuned-kinetics) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.4509 - Accuracy: 0.48 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 23400 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.9641 \| 0.01 \| 234 \| 1.4838 \| 0.4 \| \| 1.5439 \| 1.01 \| 468 \| 3.7125 \| 0.4 \| \| 1.2944 \| 2.01 \| 702 \| 3.6749 \| 0.4 \| \| 0.9419 \| 3.01 \| 936 \| 3.0422 \| 0.4 \| \| 2.4333 \| 4.01 \| 1170 \| 2.6803 \| 0.4 \| \| 1.4646 \| 5.01 \| 1404 \| 3.5355 \| 0.4 \| \| 2.1201 \| 6.01 \| 1638 \| 3.0479 \| 0.4 \| \| 2.9021 \| 7.01 \| 1872 \| 2.8181 \| 0.4 \| \| 2.1527 \| 8.01 \| 2106 \| 2.7605 \| 0.4 \| \| 1.9428 \| 9.01 \| 2340 \| 2.4513 \| 0.4 \| \| 1.6949 \| 10.01 \| 2574 \| 3.2310 \| 0.4 \| \| 0.7839 \| 11.01 \| 2808 \| 3.2372 \| 0.4 \| \| 0.3228 \| 12.01 \| 3042 \| 4.4588 \| 0.4 \| \| 3.5377 \| 13.01 \| 3276 \| 2.8621 \| 0.4 \| \| 0.509 \| 14.01 \| 3510 \| 2.7460 \| 0.4 \| \| 0.1437 \| 15.01 \| 3744 \| 2.9698 \| 0.4 \| \| 1.0039 \| 16.01 \| 3978 \| 1.9415 \| 0.44 \| \| 0.0062 \| 17.01 \| 4212 \| 3.7041 \| 0.4 \| \| 0.6038 \| 18.01 \| 4446 \| 3.2141 \| 0.4 \| \| 1.1687 \| 19.01 \| 4680 \| 2.4072 \| 0.44 \| \| 0.8397 \| 20.01 \| 4914 \| 3.4212 \| 0.4 \| \| 1.1147 \| 21.01 \| 5148 \| 2.5115 \| 0.44 \| \| 0.2286 \| 22.01 \| 5382 \| 2.4343 \| 0.44 \| \| 0.8939 \| 23.01 \| 5616 \| 3.0712 \| 0.4 \| \| 0.3871 \| 24.01 \| 5850 \| 3.2394 \| 0.4 \| \| 0.3649 \| 25.01 \| 6084 \| 3.9466 \| 0.44 \| \| 1.2601 \| 26.01 \| 6318 \| 2.9586 \| 0.44 \| \| 0.852 \| 27.01 \| 6552 \| 4.6464 \| 0.4 \| \| 0.6269 \| 28.01 \| 6786 \| 3.1292 \| 0.44 \| \| 1.0013 \| 29.01 \| 7020 \| 4.6319 \| 0.4 \| \| 0.02 \| 30.01 \| 7254 \| 4.2514 \| 0.4 \| \| 0.1333 \| 31.01 \| 7488 \| 4.3310 \| 0.4 \| \| 0.0005 \| 32.01 \| 7722 \| 4.5354 \| 0.4 \| \| 0.004 \| 33.01 \| 7956 \| 4.5970 \| 0.4 \| \| 0.3017 \| 34.01 \| 8190 \| 4.5879 \| 0.44 \| \| 0.2014 \| 35.01 \| 8424 \| 4.2809 \| 0.4 \| \| 0.1573 \| 36.01 \| 8658 \| 4.6822 \| 0.44 \| \| 0.0041 \| 37.01 \| 8892 \| 5.1673 \| 0.4 \| \| 0.0001 \| 38.01 \| 9126 \| 5.4005 \| 0.4 \| \| 0.1066 \| 39.01 \| 9360 \| 4.4509 \| 0.48 \| \| 0.0001 \| 40.01 \| 9594 \| 5.0906 \| 0.44 \| \| 1.3235 \| 41.01 \| 9828 \| 4.4093 \| 0.48 \| \| 0.4313 \| 42.01 \| 10062 \| 4.0898 \| 0.48 \| \| 0.0002 \| 43.01 \| 10296 \| 4.7817 \| 0.44 \| \| 0.0001 \| 44.01 \| 10530 \| 4.8667 \| 0.48 \| \| 0.0007 \| 45.01 \| 10764 \| 4.5619 \| 0.48 \| \| 0.0009 \| 46.01 \| 10998 \| 5.0250 \| 0.44 \| \| 0.0001 \| 47.01 \| 11232 \| 4.4129 \| 0.48 \| \| 0.0001 \| 48.01 \| 11466 \| 5.5987 \| 0.44 \| \| 0.0003 \| 49.01 \| 11700 \| 5.4567 \| 0.44 \| \| 0.0468 \| 50.01 \| 11934 \| 5.0218 \| 0.48 \| \| 0.187 \| 51.01 \| 12168 \| 5.3269 \| 0.4 \| \| 0.0002 \| 52.01 \| 12402 \| 5.4364 \| 0.44 \| \| 0.0001 \| 53.01 \| 12636 \| 5.7307 \| 0.44 \| \| 0.0 \| 54.01 \| 12870 \| 5.9781 \| 0.44 \| \| 0.0001 \| 55.01 \| 13104 \| 4.8221 \| 0.44 \| \| 0.0001 \| 56.01 \| 13338 \| 5.5808 \| 0.4 \| \| 0.0 \| 57.01 \| 13572 \| 5.7662 \| 0.44 \| \| 0.0001 \| 58.01 \| 13806 \| 5.4463 \| 0.44 \| \| 0.0021 \| 59.01 \| 14040 \| 5.9576 \| 0.44 \| \| 0.5042 \| 60.01 \| 14274 \| 5.9419 \| 0.4 \| \| 0.0053 \| 61.01 \| 14508 \| 5.2977 \| 0.48 \| \| 0.0 \| 62.01 \| 14742 \| 5.8541 \| 0.4 \| \| 0.1555 \| 63.01 \| 14976 \| 6.5367 \| 0.4 \| \| 0.0081 \| 64.01 \| 15210 \| 5.4808 \| 0.4 \| \| 0.0008 \| 65.01 \| 15444 \| 5.8818 \| 0.4 \| \| 0.0 \| 66.01 \| 15678 \| 6.4378 \| 0.4 \| \| 0.0 \| 67.01 \| 15912 \| 5.6597 \| 0.4 \| \| 0.0 \| 68.01 \| 16146 \| 5.8197 \| 0.44 \| \| 0.0061 \| 69.01 \| 16380 \| 6.0141 \| 0.4 \| \| 0.0001 \| 70.01 \| 16614 \| 6.2449 \| 0.4 \| \| 0.0001 \| 71.01 \| 16848 \| 6.2530 \| 0.4 \| \| 0.0 \| 72.01 \| 17082 \| 5.7655 \| 0.4 \| \| 0.0 \| 73.01 \| 17316 \| 6.1521 \| 0.4 \| \| 0.0 \| 74.01 \| 17550 \| 6.1597 \| 0.44 \| \| 0.6123 \| 75.01 \| 17784 \| 6.4786 \| 0.4 \| \| 0.0 \| 76.01 \| 18018 \| 6.5528 \| 0.4 \| \| 0.0 \| 77.01 \| 18252 \| 5.5426 \| 0.44 \| \| 0.0 \| 78.01 \| 18486 \| 6.4276 \| 0.4 \| \| 0.0 \| 79.01 \| 18720 \| 6.8676 \| 0.4 \| \| 0.0 \| 80.01 \| 18954 \| 6.6693 \| 0.4 \| \| 0.0 \| 81.01 \| 19188 \| 6.7919 \| 0.4 \| \| 0.0 \| 82.01 \| 19422 \| 6.7520 \| 0.4 \| \| 0.0 \| 83.01 \| 19656 \| 6.7565 \| 0.4 \| \| 0.0 \| 84.01 \| 19890 \| 6.8186 \| 0.4 \| \| 0.0 \| 85.01 \| 20124 \| 6.5549 \| 0.4 \| \| 0.0 \| 86.01 \| 20358 \| 6.7223 \| 0.4 \| \| 0.0 \| 87.01 \| 20592 \| 6.9096 \| 0.4 \| \| 0.0 \| 88.01 \| 20826 \| 6.9918 \| 0.4 \| \| 0.0 \| 89.01 \| 21060 \| 7.2247 \| 0.4 \| \| 0.0001 \| 90.01 \| 21294 \| 7.2267 \| 0.4 \| \| 0.0 \| 91.01 \| 21528 \| 6.9826 \| 0.4 \| \| 0.0 \| 92.01 \| 21762 \| 6.6385 \| 0.4 \| \| 0.792 \| 93.01 \| 21996 \| 6.4020 \| 0.4 \| \| 0.0 \| 94.01 \| 22230 \| 6.4453 \| 0.4 \| \| 0.0 \| 95.01 \| 22464 \| 6.9102 \| 0.4 \| \| 0.0 \| 96.01 \| 22698 \| 6.9262 \| 0.4 \| \| 0.0 \| 97.01 \| 22932 \| 6.7757 \| 0.4 \| \| 0.0 \| 98.01 \| 23166 \| 6.8298 \| 0.4 \| \| 0.0 \| 99.01 \| 23400 \| 6.8317 \| 0.4 \| ### Framework versions - Transformers 4.46.2 - Pytorch 2.0.1+cu117 - Datasets 3.0.1 - Tokenizers 0.20.0
cnfusion/DeepSeek-Prover-V2-7B-mlx-8Bit	cnfusion	2025-05-03T14:19:41Z	0	0	mlx	[ "mlx", "safetensors", "llama", "base_model:deepseek-ai/DeepSeek-Prover-V2-7B", "base_model:quantized:deepseek-ai/DeepSeek-Prover-V2-7B", "8-bit", "region:us" ]	null	2025-05-03T14:19:14Z	--- base_model: deepseek-ai/DeepSeek-Prover-V2-7B tags: - mlx --- # cnfusion/DeepSeek-Prover-V2-7B-mlx-8Bit The Model [cnfusion/DeepSeek-Prover-V2-7B-mlx-8Bit](https://huggingface.co/cnfusion/DeepSeek-Prover-V2-7B-mlx-8Bit) was converted to MLX format from [deepseek-ai/DeepSeek-Prover-V2-7B](https://huggingface.co/deepseek-ai/DeepSeek-Prover-V2-7B) using mlx-lm version 0.22.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("cnfusion/DeepSeek-Prover-V2-7B-mlx-8Bit") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
OOOss/bg-ner-model	OOOss	2025-05-03T14:18:13Z	0	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-05-03T14:15:45Z	--- 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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cnfusion/DeepSeek-Prover-V2-7B-mlx-fp16	cnfusion	2025-05-03T14:16:53Z	0	0	mlx	[ "mlx", "safetensors", "llama", "base_model:deepseek-ai/DeepSeek-Prover-V2-7B", "base_model:finetune:deepseek-ai/DeepSeek-Prover-V2-7B", "region:us" ]	null	2025-05-03T14:16:07Z	--- base_model: deepseek-ai/DeepSeek-Prover-V2-7B tags: - mlx --- # cnfusion/DeepSeek-Prover-V2-7B-mlx-fp16 The Model [cnfusion/DeepSeek-Prover-V2-7B-mlx-fp16](https://huggingface.co/cnfusion/DeepSeek-Prover-V2-7B-mlx-fp16) was converted to MLX format from [deepseek-ai/DeepSeek-Prover-V2-7B](https://huggingface.co/deepseek-ai/DeepSeek-Prover-V2-7B) using mlx-lm version 0.22.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("cnfusion/DeepSeek-Prover-V2-7B-mlx-fp16") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
mradermacher/medical-qa-vistral-GGUF	mradermacher	2025-05-03T14:13:01Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "qwen2", "trl", "sft", "en", "base_model:hungsvdut2k2/medical-qa-vistral", "base_model:quantized:hungsvdut2k2/medical-qa-vistral", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T09:59:39Z	--- base_model: hungsvdut2k2/medical-qa-vistral language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/hungsvdut2k2/medical-qa-vistral <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/medical-qa-vistral-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/medical-qa-vistral-GGUF/resolve/main/medical-qa-vistral.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-GGUF/resolve/main/medical-qa-vistral.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-GGUF/resolve/main/medical-qa-vistral.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-GGUF/resolve/main/medical-qa-vistral.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-GGUF/resolve/main/medical-qa-vistral.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-GGUF/resolve/main/medical-qa-vistral.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-GGUF/resolve/main/medical-qa-vistral.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-GGUF/resolve/main/medical-qa-vistral.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-GGUF/resolve/main/medical-qa-vistral.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-GGUF/resolve/main/medical-qa-vistral.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-GGUF/resolve/main/medical-qa-vistral.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/medical-qa-vistral-GGUF/resolve/main/medical-qa-vistral.f16.gguf) \| f16 \| 15.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/GRPO-VI-Qwen2-7B-RAG-GGUF	mradermacher	2025-05-03T14:12:55Z	0	0	transformers	[ "transformers", "gguf", "retrieval-augmented-generation", "text-generation-inference", "vi", "base_model:AITeamVN/GRPO-VI-Qwen2-7B-RAG", "base_model:quantized:AITeamVN/GRPO-VI-Qwen2-7B-RAG", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-02T17:54:13Z	--- base_model: AITeamVN/GRPO-VI-Qwen2-7B-RAG language: - vi library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - retrieval-augmented-generation - text-generation-inference --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/AITeamVN/GRPO-VI-Qwen2-7B-RAG <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/GRPO-VI-Qwen2-7B-RAG-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/GRPO-VI-Qwen2-7B-RAG-GGUF/resolve/main/GRPO-VI-Qwen2-7B-RAG.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/GRPO-VI-Qwen2-7B-RAG-GGUF/resolve/main/GRPO-VI-Qwen2-7B-RAG.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/GRPO-VI-Qwen2-7B-RAG-GGUF/resolve/main/GRPO-VI-Qwen2-7B-RAG.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/GRPO-VI-Qwen2-7B-RAG-GGUF/resolve/main/GRPO-VI-Qwen2-7B-RAG.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/GRPO-VI-Qwen2-7B-RAG-GGUF/resolve/main/GRPO-VI-Qwen2-7B-RAG.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/GRPO-VI-Qwen2-7B-RAG-GGUF/resolve/main/GRPO-VI-Qwen2-7B-RAG.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/GRPO-VI-Qwen2-7B-RAG-GGUF/resolve/main/GRPO-VI-Qwen2-7B-RAG.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/GRPO-VI-Qwen2-7B-RAG-GGUF/resolve/main/GRPO-VI-Qwen2-7B-RAG.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/GRPO-VI-Qwen2-7B-RAG-GGUF/resolve/main/GRPO-VI-Qwen2-7B-RAG.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/GRPO-VI-Qwen2-7B-RAG-GGUF/resolve/main/GRPO-VI-Qwen2-7B-RAG.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/GRPO-VI-Qwen2-7B-RAG-GGUF/resolve/main/GRPO-VI-Qwen2-7B-RAG.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/GRPO-VI-Qwen2-7B-RAG-GGUF/resolve/main/GRPO-VI-Qwen2-7B-RAG.f16.gguf) \| f16 \| 15.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
jahyungu/Llama-3.1-8B-Instruct_MetaMathQA-40K_cluster9	jahyungu	2025-05-03T14:11:25Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "conversational", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T09:45:45Z	--- library_name: transformers license: llama3.1 base_model: meta-llama/Llama-3.1-8B-Instruct tags: - generated_from_trainer model-index: - name: Llama-3.1-8B-Instruct_MetaMathQA-40K_cluster9 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. --> # Llama-3.1-8B-Instruct_MetaMathQA-40K_cluster9 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.50.0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.0
ma921/gpt2-large_c_dpo_imdb_noise40_epoch5	ma921	2025-05-03T14:11:22Z	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	2025-05-03T14:09:55Z	--- library_name: transformers license: mit base_model: ma921/gpt2-large-sft-imdb tags: - generated_from_trainer model-index: - name: gpt2-large_c_dpo_imdb_noise40_epoch5 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_c_dpo_imdb_noise40_epoch5 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
apriasmoro/82359c99-f769-46dd-842f-9b9c4b94ba6c	apriasmoro	2025-05-03T14:08:25Z	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	2025-05-03T14:04:58Z	--- library_name: peft license: mit base_model: microsoft/Phi-3-mini-4k-instruct tags: - axolotl - generated_from_trainer model-index: - name: 82359c99-f769-46dd-842f-9b9c4b94ba6c 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/82359c99-f769-46dd-842f-9b9c4b94ba6c 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> # 82359c99-f769-46dd-842f-9b9c4b94ba6c 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: 2.1244 ## 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 \| \| 3.1591 \| 0.0012 \| 3 \| 2.9764 \| \| 2.8282 \| 0.0024 \| 6 \| 2.6155 \| \| 2.3305 \| 0.0036 \| 9 \| 2.1244 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
Triangle104/DareQwen-2.5-7B-Q5_K_M-GGUF	Triangle104	2025-05-03T14:08:06Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Locutusque/DareQwen-2.5-7B", "base_model:quantized:Locutusque/DareQwen-2.5-7B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T14:07:40Z	--- base_model: Locutusque/DareQwen-2.5-7B language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # Triangle104/DareQwen-2.5-7B-Q5_K_M-GGUF This model was converted to GGUF format from [`Locutusque/DareQwen-2.5-7B`](https://huggingface.co/Locutusque/DareQwen-2.5-7B) 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/Locutusque/DareQwen-2.5-7B) 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/DareQwen-2.5-7B-Q5_K_M-GGUF --hf-file dareqwen-2.5-7b-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/DareQwen-2.5-7B-Q5_K_M-GGUF --hf-file dareqwen-2.5-7b-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/DareQwen-2.5-7B-Q5_K_M-GGUF --hf-file dareqwen-2.5-7b-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/DareQwen-2.5-7B-Q5_K_M-GGUF --hf-file dareqwen-2.5-7b-q5_k_m.gguf -c 2048 ```
ZeroAgency/zero-summary-v2-beta3-lora-e2	ZeroAgency	2025-05-03T14:07:34Z	0	0	peft	[ "peft", "safetensors", "mistral", "generated_from_trainer", "dataset:bethrezen/thinking-summary-v2", "base_model:ZeroAgency/Zero-Mistral-24B", "base_model:adapter:ZeroAgency/Zero-Mistral-24B", "license:mit", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-03T14:07:12Z	--- library_name: peft license: mit base_model: ZeroAgency/Zero-Mistral-24B tags: - generated_from_trainer datasets: - bethrezen/thinking-summary-v2 model-index: - name: outputs/zero-summary-v1-beta3 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: `0.9.0` ```yaml # zero-summary-v2-beta3 adapter: lora base_model: ZeroAgency/Zero-Mistral-24B dataset_processes: 64 chat_template: jinja chat_template_jinja: "{%- set today = strftime_now(\"%Y-%m-%d\") %}\n{%- set default_system_message = \"You are Mistral Small 3, a Large Language Model (LLM) created by Mistral AI, a French startup headquartered in Paris.\\nYour knowledge base was last updated on 2023-10-01. The current date is \" + today + \".\\n\\nWhen you're not sure about some information, you say that you don't have the information and don't make up anything.\\nIf the user's question is not clear, ambiguous, or does not provide enough context for you to accurately answer the question, you do not try to answer it right away and you rather ask the user to clarify their request (e.g. \\\"What are some good restaurants around me?\\\" => \\\"Where are you?\\\" or \\\"When is the next flight to Tokyo\\\" => \\\"Where do you travel from?\\\")\" %}\n\n{{- bos_token }}\n\n{%- if messages[0]['role'] == 'system' %}\n {%- if messages[0]['content'] is string %}\n {%- set system_message = messages[0]['content'] %}\n {%- else %}\n {%- set system_message = messages[0]['content'][0]['text'] %}\n {%- endif %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set system_message = default_system_message %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{{- '[SYSTEM_PROMPT]' + system_message + '[/SYSTEM_PROMPT]' }}\n\n{%- for message in loop_messages %}\n {%- if message['role'] == 'user' %}\n {%- if message['content'] is string %}\n {{- '[INST]' + message['content'] + '[/INST]' }}\n {%- else %}\n {{- '[INST]' }}\n {%- for block in message['content'] %}\n {%- if block['type'] == 'text' %}\n {{- block['text'] }}\n {%- elif block['type'] in ['image', 'image_url'] %}\n {{- '[IMG]' }}\n {%- else %}\n {{- raise_exception('Only text and image blocks are supported in message content!') }}\n {%- endif %}\n {%- endfor %}\n {{- '[/INST]' }}\n {%- endif %}\n {%- elif message['role'] == 'system' %}\n {%- if message['content'] is string %}\n {{- '[SYSTEM_PROMPT]' + message['content'] + '[/SYSTEM_PROMPT]' }}\n {%- else %}\n {{- '[SYSTEM_PROMPT]' + message['content'][0]['text'] + '[/SYSTEM_PROMPT]' }}\n {%- endif %}\n {%- elif message['role'] == 'assistant' %}\n {%- if message['content'] is string %}\n {{- message['content'] + eos_token }}\n {%- else %}\n {{- message['content'][0]['text'] + eos_token }}\n {%- endif %}\n {%- else %}\n {{- raise_exception('Only user, system and assistant roles are supported!') }}\n {%- endif %}\n{%- endfor %}" dataset_prepared_path: ./last_run_prepared datasets: - message_property_mappings: content: content role: role path: bethrezen/thinking-summary-v2 trust_remote_code: false field_messages: conversation type: chat_template # approx 20k samples should be enough #val_set_size: 0.061 # exact duplicates are already cleaned #dataset_exact_deduplication: true gradient_accumulation_steps: 2 gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false #learning_rate: 0.0001 learning_rate: 1e-5 lisa_layers_attribute: model.layers #is_mistral_derived_model: true plugins: - axolotl.integrations.liger.LigerPlugin liger_rope: true liger_rms_norm: true liger_swiglu: true liger_fused_linear_cross_entropy: true load_best_model_at_end: true load_in_4bit: true load_in_8bit: false lora_alpha: 96 lora_dropout: 0.1 lora_target_linear: true lora_r: 96 lr_scheduler: cosine #max_prompt_len: 8192 mean_resizing_embeddings: false micro_batch_size: 1 num_epochs: 2 optimizer: adamw_torch_fused output_dir: ./outputs/zero-summary-v1-beta3 sample_packing_bin_size: 200 sample_packing_group_size: 100000 save_only_model: false save_safetensors: true sequence_len: 110000 min_sample_len: 1 #shuffle_merged_datasets: true skip_prepare_dataset: false strict: false train_on_inputs: false weight_decay: 0.01 wandb_project: zero-summary wandb_name: zero-summary-v1-beta3 bf16: true fp16: false tf32: false flash_attention: true save_strategy: epoch eval_strategry: epoch logging_steps: 1 save_total_limit: 5 warmup_steps: 0 sample_packing: true pad_to_sequence_len: true group_by_length: true seed: 42 data_seed: 42 deepspeed: zero1.json log_with: wandb trust_remote_code: true use_fast_tokenizer: true special_tokens: pad_token: "<pad>" ``` </details><br> # outputs/zero-summary-v1-beta3 This model is a fine-tuned version of [ZeroAgency/Zero-Mistral-24B](https://huggingface.co/ZeroAgency/Zero-Mistral-24B) on the bethrezen/thinking-summary-v2 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - total_eval_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - num_epochs: 2.0 ### Training results ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
Rahmaa33/MotherDuckTEXT2SQLL	Rahmaa33	2025-05-03T14:04:58Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:motherduckdb/DuckDB-NSQL-7B-v0.1", "base_model:adapter:motherduckdb/DuckDB-NSQL-7B-v0.1", "region:us" ]	null	2025-05-03T14:04:45Z	--- base_model: motherduckdb/DuckDB-NSQL-7B-v0.1 library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
apriasmoro/29761ecf-4baf-4215-b6f1-d66c8daf40a6	apriasmoro	2025-05-03T14:03:35Z	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	2025-05-03T14:00:10Z	--- library_name: peft license: mit base_model: microsoft/Phi-3-mini-4k-instruct tags: - axolotl - generated_from_trainer model-index: - name: 29761ecf-4baf-4215-b6f1-d66c8daf40a6 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/29761ecf-4baf-4215-b6f1-d66c8daf40a6 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> # 29761ecf-4baf-4215-b6f1-d66c8daf40a6 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: 2.1081 ## 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 \| \| 3.1564 \| 0.0012 \| 3 \| 2.9789 \| \| 2.8283 \| 0.0024 \| 6 \| 2.6236 \| \| 2.3188 \| 0.0036 \| 9 \| 2.1081 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
cernigyerkelty7v/dfgbfgb	cernigyerkelty7v	2025-05-03T14:03:12Z	0	0	null	[ "license:bsd-3-clause", "region:us" ]	null	2025-05-03T14:03:12Z	--- license: bsd-3-clause ---
eaddario/OLMo-2-1124-7B-Instruct-GGUF	eaddario	2025-05-03T13:59:07Z	0	0	null	[ "gguf", "quant", "experimental", "text-generation", "en", "dataset:eaddario/imatrix-calibration", "arxiv:2501.00656", "arxiv:2411.15124", "arxiv:2406.17415", "base_model:allenai/OLMo-2-1124-7B-Instruct", "base_model:quantized:allenai/OLMo-2-1124-7B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-03T11:07:39Z	--- base_model: - allenai/OLMo-2-1124-7B-Instruct datasets: - eaddario/imatrix-calibration language: - en license: - apache-2.0 pipeline_tag: text-generation tags: - gguf - quant - experimental --- # Experimental layer-wise quantization of allenai/OLMo-2-1124-7B-Instruct Using [LLaMA C++][llm] release [b5220][llm-rel] for quantization. Original model: [allenai/OLMo-2-1124-7B-Instruct][mdl] From the original model creators: > OLMo 2 7B Instruct November 2024 is post-trained variant of the [OLMo-2 7B November 2024](https://huggingface.co/allenai/OLMo2-7B-1124) model, which has undergone supervised finetuning on an OLMo-specific variant of the [Tülu 3 dataset](https://huggingface.co/datasets/allenai/tulu-3-sft-olmo-2-mixture) and further DPO training on [this dataset](https://huggingface.co/datasets/allenai/olmo-2-1124-7b-preference-mix), and finally RLVR training using [this data](https://huggingface.co/datasets/allenai/RLVR-GSM). > Tülu 3 is designed for state-of-the-art performance on a diversity of tasks in addition to chat, such as MATH, GSM8K, and IFEval. > Check out the [OLMo 2 paper](https://arxiv.org/abs/2501.00656) or [Tülu 3 paper](https://arxiv.org/abs/2411.15124) for more details! > > OLMo is a series of Open Language Models designed to enable the science of language models. > These models are trained on the Dolma dataset. We are releasing all code, checkpoints, logs (coming soon), and associated training details. > The core models released in this batch include the following: > > \| Stage \| OLMo 2 7B \| OLMo 2 13B \| > \|-------------------------\|-------------------------------------------------------------------------------------------\|---------------------------------------------------------------------------------------------\| > \| Base Model \| [allenai/OLMo2-7B-1124](https://huggingface.co/allenai/OLMo2-7B-1124) \| [allenai/OLMo-2-13B-1124](https://huggingface.co/allenai/OLMo-2-13B-1124) \| > \| SFT \| [allenai/OLMo-2-1124-7B-SFT](https://huggingface.co/allenai/OLMo-2-1124-7B-SFT) \| [allenai/OLMo-2-1124-13B-SFT](https://huggingface.co/allenai/OLMo-2-1124-13B-SFT) \| > \| DPO \| [allenai/OLMo-2-1124-7B-DPO](https://huggingface.co/allenai/OLMo-2-1124-7B-DPO) \| [allenai/OLMo-2-1124-13B-DPO](https://huggingface.co/allenai/OLMo-2-1124-13B-DPO) \| > \| Final Models (RLVR) \| [allenai/OLMo-2-1124-7B-Instruct](https://huggingface.co/allenai/OLMo-2-1124-7B-Instruct) \| [allenai/OLMo-2-1124-13B-Instruct](https://huggingface.co/allenai/OLMo-2-1124-13B-Instruct) \| > \| Reward Model (RM) \| [allenai/OLMo-2-1124-7B-RM](https://huggingface.co/allenai/OLMo-2-1124-7B-RM) \| [allenai/OLMo-2-1124-13B-RM](https://huggingface.co/allenai/OLMo-2-1124-13B-RM) \| # PLEASE READ THIS BEFORE USING THESE EXPERIMENTAL VERSIONS! An area of personal interest is finding ways to optimize the inference performance of LLMs when deployed in resource-constrained environments like commodity hardware, desktops, laptops, mobiles, edge devices, etc. There are many approaches to accomplish this, including architecture simplification and knowledge distillation, but my focus has been primarily on quantization and pruning. The method used to produce these experimental versions is covered in [Squeezing Tensor Bits: the quest for smaller LLMs][mdm], but at a high level it involves using a custom version of `llama-imatrix` and `llama-quantize` to identify influential tensors, and quantize the most important layers to higher bit precision and the less important to lower bits. This process was partly inspired by Dumitru's et al [Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels][lwq-ppr]. As of version [b5125][qtz-rel] [llama-quantize][qtz] can now perform tensor-wide quantization (TWQ), whereby user-defined tensors are quantized at a specific level, or perform layer-wise quantization (LWQ) by selecting different quantization types per tensor/layer. For example, `--tensor-type attn_v=q6_k` will quantize all Attention Value tensors at q6_k (TWQ), and `--tensor-type "\.([0-9]\|1[01257]\|31)\.attn_k=q4_k"` will quantize Attention Key tensors on layers 0 to 9, 10, 11, 12, 15, 17 and 31 at q4_k, leaving the remaining layers at their default value (LWQ). The modified version of [llama-imatrix][imx] generates useful statistics to guide the tensor selection process, `--show-statistics` will display: - Σ(Bias): the sum of all activations over the tensor (i.e. the Importance Scores) - Min & Max: minimum and maximum activation values - μ & σ: activations' mean and standard deviation - % Active: proportion of elements whose average activation exceeds a very small threshold (1e-6). Helpful to determine how alive/dormant the tensor is during inference - N: number of activations in the tensor - Entropy: entropy of the activation distribution, in bits (standard Shannon entropy measurement) - E (norm): Normalized entropy. - ZD Score: z-score distribution as described in 3.1 Layer Importance Scores in the Layer-Wise Quantization paper - CosSim: cosine similarity between same type tensors with respect to the previous layer (i.e. blk.7.attn_k and blk.6.attn_k) Please note that statistics are calculated for each individial tensor and should be used to compare between tensors of the same type only. For example, assuming that attn_k in layer 10 has a higher influence during inference than attn_k in layer 7 because its Σ(Bias) is larger makes sense, whilst concluding the same between attn_k and ffn_down does not. There’s a [pull request][imtx-pr] to merge these changes back into the core llama.cpp project. This may or may not ever happen so, until then, the modified version will be available on [GitHub][gh]. For testing and comparison I use models produced by [Unsloth][ust] ([Daniel and Michael Han][ust-ai] do some really advanced level stuff!) and [Bartowski][btk] (see credits below) but if they don't provide versions of the required model, all tests and comparisons are done against naive quantizations obtained by simply running `llama-quantize` with no further optimization. All experimental versions were generated using an appropriate imatrix created from calibration datasets available at [eaddario/imatrix-calibration][ical]. At its core, an Importance Matrix (imatrix) is a table or, more broadly, a structured representation that scores the relative importance of different features or parameters in a machine learning model. It essentially quantifies the "impact" each feature has on a specific outcome, prediction, or relationship being modeled, and it helps to counterbalance the negative effects of quantization and pruning. The process to generate these models is roughly as follows: 1. Convert the the original model's tensors to [GGUF][ggf] F16* 2. Estimate the Perplexity score for the F16 model (baseline) using the [wikitext-2-raw-v1][wki-dat] dataset, and save the [logits][lgt] 3. Generate an [imatrix][imx-dat] from selected calibration datasets 4. Determine tensor and layer Importance Score contribution using the modified version of `llama-imatrix` 5. Select an appropiate quant level for each tensor and quantize the model using `llama-quantize` 6. Calculate Perplexity, KL Divergence, ARC (Easy+Challenge), HellaSwag, MMLU, Truthful QA and WinoGrande scores for each quantized model 7. Keep versions with the best scores 8. Repeat until all desired quants are created. I find that quantizations below Q3/IQ3 are not fit for my purposes and therefore do not usually generate them, but happy to provide other quants on request. [BF16][bf16] would be preferred, but Apple's GPUs don't support it yet, and therefore any operations are executed in the CPU, making it unacceptably slow. This is expected to change in the near term but until then, if you are using Apple kit avoid using any models tagged BF16 # Models ### Sizes (in GB) \| Model \| Bartowski \| Repo \| Shrinkage \| \| ----------------------------------------------------------------------- \| --------: \| ---: \| --------: \| \| [OLMo-2-1124-7B-Instruct-IQ3_M](./OLMo-2-1124-7B-Instruct-IQ3_M.gguf) \| 3.78 \| 3.69 \| 2.4% \| \| [OLMo-2-1124-7B-Instruct-IQ3_S](./OLMo-2-1124-7B-Instruct-IQ3_S.gguf) \| 3.68 \| 3.43 \| 6.8% \| \| [OLMo-2-1124-7B-Instruct-IQ4_NL](./OLMo-2-1124-7B-Instruct-IQ4_NL.gguf) \| 4.71 \| 4.39 \| 6.2% \| \| [OLMo-2-1124-7B-Instruct-Q3_K_L](./OLMo-2-1124-7B-Instruct-Q3_K_L.gguf) \| 4.32 \| 3.76 \| 13.0% \| \| [OLMo-2-1124-7B-Instruct-Q3_K_M](./OLMo-2-1124-7B-Instruct-Q3_K_M.gguf) \| 4.02 \| 3.56 \| 11.4% \| \| [OLMo-2-1124-7B-Instruct-Q3_K_S](./OLMo-2-1124-7B-Instruct-Q3_K_S.gguf) \| 3.66 \| 3.31 \| 9.6% \| \| [OLMo-2-1124-7B-Instruct-Q4_K_M](./OLMo-2-1124-7B-Instruct-Q4_K_M.gguf) \| 4.92 \| 4.41 \| 10.4% \| \| [OLMo-2-1124-7B-Instruct-Q4_K_S](./OLMo-2-1124-7B-Instruct-Q4_K_S.gguf) \| 4.69 \| 4.28 \| 8.7% \| \| [OLMo-2-1124-7B-Instruct-Q5_K_M](./OLMo-2-1124-7B-Instruct-Q5_K_M.gguf) \| 5.73 \| 5.38 \| 6.1% \| \| [OLMo-2-1124-7B-Instruct-Q5_K_S](./OLMo-2-1124-7B-Instruct-Q5_K_S.gguf) \| 5.60 \| 5.24 \| 6.4% \| \| [OLMo-2-1124-7B-Instruct-Q6_K](./OLMo-2-1124-7B-Instruct-Q6_K.gguf) \| 6.60 \| 6.57 \| 0.5% \| \| [OLMo-2-1124-7B-Instruct-Q8_0](./OLMo-2-1124-7B-Instruct-Q8_0.gguf) \| 8.54 \| 7.73 \| 9.5% \| ### Perplexity and KL Divergence scores \| Model \| μPPL \| 𝜌PPL \| μKLD \| RMS Δp \| \| ----------------------------------------------------------------------- \| -----------------: \| -----: \| -----------------: \| ------------: \| \| [OLMo-2-1124-7B-Instruct-IQ3_M](./OLMo-2-1124-7B-Instruct-IQ3_M.gguf) \| 9.201710 ±0.071255 \| 96.92% \| 0.153149 ±0.000797 \| 11.390 ±0.060 \| \| [OLMo-2-1124-7B-Instruct-IQ3_S](./OLMo-2-1124-7B-Instruct-IQ3_S.gguf) \| 9.306264 ±0.071084 \| 95.94% \| 0.197699 ±0.000965 \| 12.938 ±0.062 \| \| [OLMo-2-1124-7B-Instruct-IQ4_NL](./OLMo-2-1124-7B-Instruct-IQ4_NL.gguf) \| 8.680650 ±0.065689 \| 98.37% \| 0.076583 ±0.000454 \| 8.111 ±0.049 \| \| [OLMo-2-1124-7B-Instruct-Q3_K_L](./OLMo-2-1124-7B-Instruct-Q3_K_L.gguf) \| 9.252820 ±0.070404 \| 95.74% \| 0.204708 ±0.001020 \| 13.139 ±0.063 \| \| [OLMo-2-1124-7B-Instruct-Q3_K_M](./OLMo-2-1124-7B-Instruct-Q3_K_M.gguf) \| 9.242884 ±0.069850 \| 95.38% \| 0.220640 ±0.001086 \| 13.659 ±0.065 \| \| [OLMo-2-1124-7B-Instruct-Q3_K_S](./OLMo-2-1124-7B-Instruct-Q3_K_S.gguf) \| 9.651383 ±0.073494 \| 94.01% \| 0.287772 ±0.001362 \| 15.534 ±0.069 \| \| [OLMo-2-1124-7B-Instruct-Q4_K_M](./OLMo-2-1124-7B-Instruct-Q4_K_M.gguf) \| 8.683512 ±0.065748 \| 98.46% \| 0.071862 ±0.000424 \| 7.858 ±0.048 \| \| [OLMo-2-1124-7B-Instruct-Q4_K_M-bartowski][b-q4km] \| 7.951677 ±0.058104 \| 97.11% \| 0.144072 ±0.001123 \| 10.504 ±0.065 \| \| [OLMo-2-1124-7B-Instruct-Q4_K_S](./OLMo-2-1124-7B-Instruct-Q4_K_S.gguf) \| 8.665009 ±0.065466 \| 98.38% \| 0.076159 ±0.000445 \| 8.104 ±0.049 \| \| [OLMo-2-1124-7B-Instruct-Q5_K_M](./OLMo-2-1124-7B-Instruct-Q5_K_M.gguf) \| 8.475671 ±0.064030 \| 99.41% \| 0.025628 ±0.000174 \| 4.820 ±0.037 \| \| [OLMo-2-1124-7B-Instruct-Q5_K_S](./OLMo-2-1124-7B-Instruct-Q5_K_S.gguf) \| 8.494382 ±0.064237 \| 99.39% \| 0.026960 ±0.000180 \| 4.932 ±0.038 \| \| [OLMo-2-1124-7B-Instruct-Q6_K](./OLMo-2-1124-7B-Instruct-Q6_K.gguf) \| 8.425234 ±0.063616 \| 99.67% \| 0.013181 ±0.000105 \| 3.474 ±0.032 \| \| [OLMo-2-1124-7B-Instruct-Q8_0](./OLMo-2-1124-7B-Instruct-Q8_0.gguf) \| 8.416597 ±0.063592 \| 99.74% \| 0.009659 ±0.000089 \| 2.993 ±0.031 \| \| [OLMo-2-1124-7B-Instruct-F16](./OLMo-2-1124-7B-Instruct-F16.gguf) \| 8.368713 ±0.062985 \| 100% \| N/A \| N/A \| ### ARC, HellaSwag, MMLU, Truthful QA and WinoGrande scores Scores generated using [llama-perplexity][ppl] with 750 tasks per test, and a context size of 768 tokens. For the test data used in the generation of these scores, follow the appropiate links: [HellaSwag][hsw-tst], [ARC, MMLU, Truthful QA][tst-dat] and [WinoGrande][wng-tst] \| Model \| ARC \| HellaSwag \| MMLU \| Truthful QA \| WinoGrande \| Avg Score \| \| ----------------------------------------------------------------------- \| --------------: \| --------: \| --------------: \| --------------: \| --------------: \| --------: \| \| [OLMo-2-1124-7B-Instruct-IQ3_M](./OLMo-2-1124-7B-Instruct-IQ3_M.gguf) \| 64.9333 ±1.7436 \| 82.53 \| 40.9333 ±1.7967 \| 34.8000 ±1.7405 \| 72.5333 ±1.6309 \| 59.15 \| \| [OLMo-2-1124-7B-Instruct-IQ3_S](./OLMo-2-1124-7B-Instruct-IQ3_S.gguf) \| 65.2000 ±1.7405 \| 82.66 \| 41.3333 ±1.7993 \| 33.6000 ±1.7259 \| 71.7333 ±1.6453 \| 58.91 \| \| [OLMo-2-1124-7B-Instruct-IQ4_NL](./OLMo-2-1124-7B-Instruct-IQ4_NL.gguf) \| 67.0667 ±1.7172 \| 83.33 \| 41.7333 ±1.8018 \| 37.3333 ±1.7674 \| 74.4000 ±1.5947 \| 60.77 \| \| [OLMo-2-1124-7B-Instruct-Q3_K_L](./OLMo-2-1124-7B-Instruct-Q3_K_L.gguf) \| 64.5333 ±1.7481 \| 81.47 \| 40.9333 ±1.7967 \| 33.3333 ±1.7225 \| 72.5333 ±1.6309 \| 58.56 \| \| [OLMo-2-1124-7B-Instruct-Q3_K_M](./OLMo-2-1124-7B-Instruct-Q3_K_M.gguf) \| 63.4667 ±1.7594 \| 81.86 \| 41.3333 ±1.7993 \| 33.6000 ±1.7259 \| 73.2000 ±1.6184 \| 58.69 \| \| [OLMo-2-1124-7B-Instruct-Q3_K_S](./OLMo-2-1124-7B-Instruct-Q3_K_S.gguf) \| 64.9333 ±1.7436 \| 81.60 \| 40.4000 ±1.7930 \| 33.2000 ±1.7207 \| 71.8667 ±1.6430 \| 58.40 \| \| [OLMo-2-1124-7B-Instruct-Q4_K_M](./OLMo-2-1124-7B-Instruct-Q4_K_M.gguf) \| 66.5333 ±1.7242 \| 83.87 \| 42.0000 ±1.8034 \| 36.9333 ±1.7635 \| 71.4667 ±1.6500 \| 60.16 \| \| [OLMo-2-1124-7B-Instruct-Q4_K_M-bartowski][b-q4km] \| 65.8667 ±1.7325 \| 82.40 \| 42.1333 ±1.8042 \| 34.0000 ±1.7309 \| 74.2667 ±1.5974 \| 59.73 \| \| [OLMo-2-1124-7B-Instruct-Q4_K_S](./OLMo-2-1124-7B-Instruct-Q4_K_S.gguf) \| 66.2667 ±1.7276 \| 83.87 \| 42.5333 ±1.8065 \| 36.6667 ±1.7608 \| 71.3333 ±1.6523 \| 60.13 \| \| [OLMo-2-1124-7B-Instruct-Q5_K_M](./OLMo-2-1124-7B-Instruct-Q5_K_M.gguf) \| 67.4667 ±1.7119 \| 83.33 \| 42.0000 ±1.8034 \| 37.6000 ±1.7699 \| 74.4000 ±1.5947 \| 60.96 \| \| [OLMo-2-1124-7B-Instruct-Q5_K_S](./OLMo-2-1124-7B-Instruct-Q5_K_S.gguf) \| 67.3333 ±1.7137 \| 83.47 \| 42.0000 ±1.8034 \| 37.2000 ±1.7661 \| 74.8000 ±1.5864 \| 60.96 \| \| [OLMo-2-1124-7B-Instruct-Q6_K](./OLMo-2-1124-7B-Instruct-Q6_K.gguf) \| 67.0667 ±1.7172 \| 83.33 \| 42.2667 ±1.8050 \| 37.4667 ±1.7686 \| 74.4000 ±1.5947 \| 60.91 \| \| [OLMo-2-1124-7B-Instruct-Q8_0](./OLMo-2-1124-7B-Instruct-Q8_0.gguf) \| 66.6667 ±1.7225 \| 83.20 \| 42.4000 ±1.8057 \| 37.7333 ±1.7711 \| 73.8667 ±1.6054 \| 60.77 \| \| [OLMo-2-1124-7B-Instruct-F16](./OLMo-2-1124-7B-Instruct-F16.gguf) \| 67.3333 ±1.7137 \| 83.20 \| 41.8667 ±1.8026 \| 37.8667 ±1.7724 \| 72.6667 ±1.6284 \| 60.59 \| ### Tokens per Second - Benchmarks Scores generated using [llama-bench][bch]. Naive (`llama-quantize` with no optimization) Q4_K_M quantization included for comparison. \| model \| size \| params \| backend \| threads \| test \| t/s \| \| ----------------------------------------------------------------------- \| -------: \| -----: \| ---------- \| ------: \| ------------: \| ------------: \| \| [OLMo-2-1124-7B-Instruct-Q4_K_M](./OLMo-2-1124-7B-Instruct-Q4_K_M.gguf) \| 3.73 GiB \| 7.30 B \| Metal,BLAS \| 6 \| pp512 \| 331.23 ± 0.55 \| \| [OLMo-2-1124-7B-Instruct-Q4_K_M](./OLMo-2-1124-7B-Instruct-Q4_K_M.gguf) \| 3.73 GiB \| 7.30 B \| Metal,BLAS \| 6 \| tg128 \| 29.25 ± 0.19 \| \| [OLMo-2-1124-7B-Instruct-Q4_K_M](./OLMo-2-1124-7B-Instruct-Q4_K_M.gguf) \| 3.73 GiB \| 7.30 B \| Metal,BLAS \| 6 \| pp1024+tg1024 \| 44.26 ± 0.13 \| \| [OLMo-2-1124-7B-Instruct-Q4_K_M-bartowski][b-q4km] \| 4.16 GiB \| 7.30 B \| Metal,BLAS \| 6 \| pp512 \| 345.11 ± 0.95 \| \| [OLMo-2-1124-7B-Instruct-Q4_K_M-bartowski][b-q4km] \| 4.16 GiB \| 7.30 B \| Metal,BLAS \| 6 \| tg128 \| 27.54 ± 0.15 \| \| [OLMo-2-1124-7B-Instruct-Q4_K_M-bartowski][b-q4km] \| 4.16 GiB \| 7.30 B \| Metal,BLAS \| 6 \| pp1024+tg1024 \| 42.76 ± 0.18 \| # Metrics used [Perplexity][ppx]:* one of the key metrics used in NLP evaluation. It measures the quality of a language model by evaluating how well it predicts the next token given a particular sequence of words. A PPL of 1 indicates an exact match between predicted and actual, whereas values greater than one indicate a degree of "surprise" the generated token differs from the expected. [Kullback–Leibler (KL) Divergence][kld]: a statistical measure of how much a probability distribution differs from another. When quantizing models (or altering the original tensors in any way for that matter), the closest we can preserve the weights' probability distribution to the original model the better, thus the closest to 0 the better. [AI2 Reasoning Challenge (ARC)][arc]: a benchmark to evaluate the ability of AI models to answer complex science questions that require logical reasoning beyond pattern matching. [HellaSwag][hsw]: the Harder Endings, Longer contexts, and Low-shot Activities for Situations With Adversarial Generations (bit of a mouthful!) is a benchmark designed to test commonsense natural language inference. It requires the model to predict the most likely ending of a sentence. [MMLU][mmlu]: the Massive Multitask Language Understanding evaluates LLMs’ general knowledge and problem-solving abilities across 57 subjects, including elementary mathematics, US history, computer science, and law. [Truthful QA][tqa]: evaluates how well LLMs generate truthful responses to questions. It identifies whether AI models can avoid generating false or misleading information, particularly in areas where human knowledge is prone to misconceptions. [Winogrande][wng]: based on the [Winograd Schema Challenge][wng-chl], is a natural language understanding task requiring models to resolve ambiguities in sentences involving pronoun references. ## Credits A big Thank You! to [Colin Kealty][btk] for the many contributions and for being one of the best sources of high quality quantized models available on Huggingface, and a really big *Thank You!* to [Georgi Gerganov][ggg] for his amazing work with llama.cpp and the ggml/gguf libraries. [arc]: https://leaderboard.allenai.org/arc/submissions/get-started [btk]: https://huggingface.co/bartowski [bch]: https://github.com/ggml-org/llama.cpp/tree/master/tools/llama-bench [bf16]: https://en.wikipedia.org/wiki/Bfloat16_floating-point_format [b-q4km]: https://huggingface.co/bartowski/OLMo-2-1124-7B-Instruct-GGUF/blob/main/OLMo-2-1124-7B-Instruct-Q4_K_M.gguf [u-q4km]: https://huggingface.co/unsloth [ical]: https://huggingface.co/datasets/eaddario/imatrix-calibration [ggg]: https://github.com/ggerganov [ggf]: https://huggingface.co/docs/hub/en/gguf [gh]: https://github.com/EAddario/llama.cpp/tree/imatrix [hsw]: https://rowanzellers.com/hellaswag [hsw-tst]: https://github.com/klosax/hellaswag_text_data [imx-dat]: https://huggingface.co/eaddario/OLMo-2-1124-7B-Instruct-GGUF/tree/main/imatrix [imx]: https://github.com/ggml-org/llama.cpp/tree/master/tools/imatrix [imtx-pr]: https://github.com/ggml-org/llama.cpp/pull/12718 [kld]: https://en.wikipedia.org/wiki/Kullback–Leibler_divergence [llm]: https://github.com/ggerganov/llama.cpp [llm-rel]: https://github.com/ggerganov/llama.cpp/releases/tag/b5220 [lgt]: https://huggingface.co/eaddario/OLMo-2-1124-7B-Instruct-GGUF/tree/main/logits [lwq-ppr]: https://arxiv.org/abs/2406.17415 [mdm]: https://medium.com/@eaddario/squeezing-tensor-bits-the-quest-for-smaller-llms-86b23bd052ca [mmlu]: https://github.com/hendrycks/test [mdl]: https://huggingface.co/allenai/OLMo-2-1124-7B-Instruct [ppl]: https://github.com/ggml-org/llama.cpp/tree/master/tools/perplexity [ppx]: https://huggingface.co/docs/transformers/en/perplexity [qtz]: https://github.com/ggml-org/llama.cpp/tree/master/tools/quantize [qtz-rel]: https://github.com/ggerganov/llama.cpp/releases/tag/b5125 [tst-dat]: https://huggingface.co/datasets/ikawrakow/validation-datasets-for-llama.cpp/tree/main [tqa]: https://github.com/sylinrl/TruthfulQA [ust]: https://huggingface.co/unsloth [ust-ai]: https://unsloth.ai [wng-chl]: https://cdn.aaai.org/ocs/4492/4492-21843-1-PB.pdf [wki-dat]: https://huggingface.co/datasets/Salesforce/wikitext/tree/main/wikitext-2-raw-v1 [wng]: https://winogrande.allenai.org [wng-tst]: https://huggingface.co/datasets/ikawrakow/winogrande-eval-for-llama.cpp/tree/main
arielb30/videomae-base-finetuned-hmdb51_dataset	arielb30	2025-05-03T13:54:12Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "base_model:finetune:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2025-05-03T13:08:43Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-hmdb51_dataset results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # videomae-base-finetuned-hmdb51_dataset This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3945 - Accuracy: 0.8798 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - 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_ratio: 0.1 - training_steps: 380 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 1.8738 \| 0.1026 \| 39 \| 1.6265 \| 0.4299 \| \| 1.1232 \| 1.1026 \| 78 \| 0.9534 \| 0.7430 \| \| 0.86 \| 2.1026 \| 117 \| 0.7309 \| 0.7897 \| \| 0.5001 \| 3.1026 \| 156 \| 0.5999 \| 0.8131 \| \| 0.4402 \| 4.1026 \| 195 \| 0.5475 \| 0.8178 \| \| 0.3291 \| 5.1026 \| 234 \| 0.5903 \| 0.8505 \| \| 0.2643 \| 6.1026 \| 273 \| 0.5727 \| 0.8271 \| \| 0.1928 \| 7.1026 \| 312 \| 0.4194 \| 0.8972 \| \| 0.1749 \| 8.1026 \| 351 \| 0.4278 \| 0.8832 \| \| 0.1588 \| 9.0763 \| 380 \| 0.4737 \| 0.8832 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
bean980310/aino-koito-xl-animagine-xl-4-v1	bean980310	2025-05-03T13:51:48Z	2	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:cagliostrolab/animagine-xl-4.0", "base_model:adapter:cagliostrolab/animagine-xl-4.0", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2025-04-22T12:51:13Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- 1girl, Aino Koito, sensitive, year 2025, outdoors, stage, stage lights, solo, tomboy, very short hair, blonde hair, very handsome face, aqua eyes, large breasts, a adult very handsone tomboy girl with very boyish handsome shortcut blonde hair with pixie cut and very handsome face and aqua eyes and perfect female body and large breasts wearing yellow idol uniform and idol clothes and pleated miniskirt and gloves and thighhighs and high heels and standing and singing with grab microphone, masterpiece, high score, great score, absurdres parameters: negative_prompt: >- old, early, mid, grass, leaf, girlish face, girly hair, sidelocks, muscular body, mutation, extra limbs, extra legs, extra arms, extra hands, missing limbs, missing legs, missing arms, missing hands, headwear, hat, cap, beret, hairpin, hairband, hair ribbon, hair ornament, hairclip, lowres, bad anatomy, bad hands, text, error, missing finger, extra digits, fewer digits, cropped, worst quality, low quality, low score, bad score, average score, signature, watermark, username, blurry output: url: images/ComfyUI_upscale_00024_.png - text: >- 1girl, Aino Koito, sensitive, year 2025, outdoors, town, solo, tomboy, very short hair, blonde hair, very handsome face, aqua eyes, large breasts, a adult very handsone tomboy girl with very boyish handsome shortcut blonde hair with pixie cut and very handsome face and aqua eyes and perfect female body and large breasts wearing white military uniform with epaulettes and aiguillette and military blue skirt and military white legwear and military white footwear and military white jacket with white long sleeves and white buttoned shirt and white lace trim bridal gloves and white cape with shoulder cape and brown belt with gold belt buckle and pleated blue miniskirt with white stripes and white thighhighs with floral lace trim and white high heels and standing with legs together and hand on hip and serious face, masterpiece, high score, great score, absurdres parameters: negative_prompt: >- old, early, mid, grass, leaf, girlish face, girly hair, sidelocks, muscular body, mutation, extra limbs, extra legs, extra arms, extra hands, missing limbs, missing legs, missing arms, missing hands, headwear, hat, cap, beret, hairpin, hairband, hair ribbon, hair ornament, hairclip, lowres, bad anatomy, bad hands, text, error, missing finger, extra digits, fewer digits, cropped, worst quality, low quality, low score, bad score, average score, signature, watermark, username, blurry output: url: images/ComfyUI_upscale_00021_.png - text: >- 1girl, Aino Koito, sensitive, year 2025, indoors, teacher's room, solo, tomboy, very short hair, blonde hair, very handsome face, aqua eyes, large breasts, a adult very handsone tomboy girl with very boyish handsome shortcut blonde hair with pixie cut and very handsome face and aqua eyes and perfect female body and large breasts wearing teacher suit and tight miniskirt and shirt and pantystocking and high heels and sitting on chair with crossed legs and closed mouth, masterpiece, high score, great score, absurdres parameters: negative_prompt: >- old, early, mid, grass, leaf, girlish face, girly hair, sidelocks, muscular body, mutation, extra limbs, extra legs, extra arms, extra hands, missing limbs, missing legs, missing arms, missing hands, headwear, hat, cap, beret, hairpin, hairband, hair ribbon, hair ornament, hairclip, lowres, bad anatomy, bad hands, text, error, missing finger, extra digits, fewer digits, cropped, worst quality, low quality, low score, bad score, average score, signature, watermark, username, blurry output: url: images/ComfyUI_upscale_00027_.png base_model: cagliostrolab/animagine-xl-4.0 instance_prompt: >- Aino Koito, tomboy, very short hair, blonde hair, very handsome face, aqua eyes, large breasts, a adult very handsone tomboy girl with very boyish handsome shortcut blonde hair with pixie cut and very handsome face and aqua eyes and perfect female body and large breasts license: creativeml-openrail-m --- # Original Character - 愛野小糸(Aino Koito) XL for Animagine XL 4.0 v1 <Gallery /> ## Model description ![ComfyUI_upscale_00024_.png](https://cdn-uploads.huggingface.co/production/uploads/657be187c8c6e8dceecc3720/bIwkRTg-UxDLv1kzocWD-.png) ![ComfyUI_upscale_00021_.png](https://cdn-uploads.huggingface.co/production/uploads/657be187c8c6e8dceecc3720/9-TRuI0Yn6bs4w0kHOKVi.png) ![ComfyUI_upscale_00027_.png](https://cdn-uploads.huggingface.co/production/uploads/657be187c8c6e8dceecc3720/MTGZjwP21Bsq0SX8rmplt.png) Koito Aino, the tomboyish idol girl for Animagine XL 4.0 Trained Data Steps: 3030 Epoch: 10 Clip Skip:2 Images:402 Training Model: cagliostrolab/animagine-xl-4.0-zero Learning rate: 1e-4 Unet Learning rate : 1e-4 TE Learning rate :1e-5 LR Scheduler: Cosine with restarts Network Dim: 32 Network Alpha: 16 Train Batch Size: 4 Mixed Precision: bf16 Optimizer Args: scale_parameter=False relative_step=False warmup_init=False ## Trigger words You should use `Aino Koito` to trigger the image generation. You should use `tomboy` to trigger the image generation. You should use `very short hair` to trigger the image generation. You should use `blonde hair` to trigger the image generation. You should use `very handsome face` to trigger the image generation. You should use `aqua eyes` to trigger the image generation. You should use `large breasts` to trigger the image generation. You should use `a adult very handsone tomboy girl with very boyish handsome shortcut blonde hair with pixie cut and very handsome face and aqua eyes and perfect female body and large breasts` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/bean980310/aino-koito-xl-animagine-xl-4-v1/tree/main) them in the Files & versions tab.
duandongsheng/ddpm-celebahq-finetuned-butterflies-2epochs	duandongsheng	2025-05-03T13:51:37Z	0	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2025-05-03T13:38:16Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) Describe your model here ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('duandongsheng/ddpm-celebahq-finetuned-butterflies-2epochs') image = pipeline().images[0] image ```
bean980310/makotono-aoi-xl-animagine-xl-4-v1	bean980310	2025-05-03T13:51:17Z	3	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:cagliostrolab/animagine-xl-4.0", "base_model:adapter:cagliostrolab/animagine-xl-4.0", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2025-04-22T12:44:17Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- 1girl, Makotono Aoi, sensitive, year 2025, outdoors, stage, stage lights, solo, tomboy, very short hair, blue hair, parted bangs, very handsome face, yellow eyes, large breasts, a adult tomboy girl with very boyish handsome shortcut blue hair with (parted bangs:1.2) and pixie cut and very handsome face and yellow eyes and perfect female body and large breasts wearing blue idol uniform and idol clothes and pleated miniskirt and gloves and thighhighs and high heels and standing and singing with grab microphone, masterpiece, high score, great score, absurdres parameters: negative_prompt: >- old, early, mid, grass, leaf, girly hair, muscular body, mutation, extra limbs, extra legs, extra arms, extra hands, missing limbs, missing legs, missing arms, missing hands, headwear, hat, cap, beret, hairpin, hairband, hair ribbon, hair ornament, hairclip, lowres, bad anatomy, bad hands, text, error, missing finger, extra digits, fewer digits, cropped, worst quality, low quality, low score, bad score, average score, signature, watermark, username, blurry output: url: images/ComfyUI_upscale_00023_.png - text: >- 1girl, Makotono Aoi, sensitive, year 2025, outdoors, solo, town, tomboy, very short hair, blue hair, parted bangs, very handsome face, yellow eyes, large breasts, a adult tomboy girl with very boyish handsome shortcut blue hair with (parted bangs:1.2) and pixie cut and very handsome face and yellow eyes and perfect female body and large breasts wearing white military uniform with epaulettes and aiguillette and military blue skirt and military white legwear and military white footwear and military white jacket with white long sleeves and white buttoned shirt and white lace trim bridal gloves and white cape with shoulder cape and brown belt with gold belt buckle and pleated blue miniskirt with white stripes and white thighhighs with floral lace trim and white high heels and standing with legs together and hand on hip and serious face, masterpiece, high score, great score, absurdres parameters: negative_prompt: >- old, early, mid, grass, leaf, girly hair, muscular body, mutation, extra limbs, extra legs, extra arms, extra hands, missing limbs, missing legs, missing arms, missing hands, headwear, hat, cap, beret, hairpin, hairband, hair ribbon, hair ornament, hairclip, lowres, bad anatomy, bad hands, text, error, missing finger, extra digits, fewer digits, cropped, worst quality, low quality, low score, bad score, average score, signature, watermark, username, blurry output: url: images/ComfyUI_upscale_00020_.png - text: >- 1girl, Makotono Aoi, sensitive, year 2025, indoors, teacher's room, solo, tomboy, very short hair, blue hair, parted bangs, very handsome face, yellow eyes, large breasts, a adult tomboy girl with very boyish handsome shortcut blue hair with (parted bangs:1.2) and pixie cut and very handsome face and yellow eyes and perfect female body and large breasts wearing teacher suit and tight miniskirt and shirt and pantystocking and high heels and sitting on chair with crossed legs and closed mouth, masterpiece, high score, great score, absurdres parameters: negative_prompt: >- old, early, mid, grass, leaf, girly hair, muscular body, mutation, extra limbs, extra legs, extra arms, extra hands, missing limbs, missing legs, missing arms, missing hands, headwear, hat, cap, beret, hairpin, hairband, hair ribbon, hair ornament, hairclip, lowres, bad anatomy, bad hands, text, error, missing finger, extra digits, fewer digits, cropped, worst quality, low quality, low score, bad score, average score, signature, watermark, username, blurry output: url: images/ComfyUI_upscale_00026_.png base_model: cagliostrolab/animagine-xl-4.0 instance_prompt: >- Makotono Aoi, tomboy, very short hair, blue hair, parted bangs, very handsome face, yellow eyes, large breasts, a adult tomboy girl with very boyish handsome shortcut blue hair with (parted bangs:1.2) and pixie cut and very handsome face and yellow eyes and perfect female body and large breasts license: creativeml-openrail-m --- # Original Character - 真琴乃葵(Makotono Aoi) XL for Animagine XL 4.0 v1 <Gallery /> ## Model description ![ComfyUI_upscale_00023_.png](https://cdn-uploads.huggingface.co/production/uploads/657be187c8c6e8dceecc3720/J6-lnlQkc2UpjaaoGnQ4m.png) ![ComfyUI_upscale_00020_.png](https://cdn-uploads.huggingface.co/production/uploads/657be187c8c6e8dceecc3720/1Q2E2Uw2FLeZ_zrMzCdby.png) ![ComfyUI_upscale_00026_.png](https://cdn-uploads.huggingface.co/production/uploads/657be187c8c6e8dceecc3720/sA6c8gCl-rUzRe6gqtk4h.png) Aoi Makotono, the tomboyish idol girl for Animagine XL 4.0 Trained Data Steps: 3000 Epoch: 10 Clip Skip:2 Images:400 Training Model: cagliostrolab/animagine-xl-4.0-zero Learning rate: 1e-4 Unet Learning rate : 1e-4 TE Learning rate :1e-5 LR Scheduler: Cosine with restarts Network Dim: 32 Network Alpha: 16 Train Batch Size: 4 Mixed Precision: bf16 Optimizer Args: scale_parameter=False relative_step=False warmup_init=False ## Trigger words You should use `Makotono Aoi` to trigger the image generation. You should use `tomboy` to trigger the image generation. You should use `very short hair` to trigger the image generation. You should use `blue hair` to trigger the image generation. You should use `parted bangs` to trigger the image generation. You should use `very handsome face` to trigger the image generation. You should use `yellow eyes` to trigger the image generation. You should use `large breasts` to trigger the image generation. You should use `a adult tomboy girl with very boyish handsome shortcut blue hair with (parted bangs:1.2) and pixie cut and very handsome face and yellow eyes and perfect female body and large breasts` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/bean980310/makotono-aoi-xl-animagine-xl-4-v1/tree/main) them in the Files & versions tab.
PandaLikesPotato/georgian_letters	PandaLikesPotato	2025-05-03T13:49:31Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-03T13:49:31Z	--- license: apache-2.0 ---
arkitex/wav2vec2-finetune-authentic-only	arkitex	2025-05-03T13:48:25Z	56	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_17_0", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-04-17T18:51:35Z	--- library_name: transformers license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - common_voice_17_0 metrics: - wer model-index: - name: wav2vec2-finetune-authentic-only results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: common_voice_17_0 type: common_voice_17_0 config: en split: None args: en metrics: - type: wer value: 0.31685876147685094 name: Wer --- <!-- 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. --> # wav2vec2-finetune-authentic-only This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the common_voice_17_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4832 - Wer: 0.3169 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:------:\| \| 4.2515 \| 0.1337 \| 500 \| 2.9469 \| 1.0 \| \| 1.4619 \| 0.2674 \| 1000 \| 0.9826 \| 0.5937 \| \| 0.7206 \| 0.4011 \| 1500 \| 0.8086 \| 0.4917 \| \| 0.6012 \| 0.5348 \| 2000 \| 0.7485 \| 0.4530 \| \| 0.5422 \| 0.6684 \| 2500 \| 0.7128 \| 0.4329 \| \| 0.5063 \| 0.8021 \| 3000 \| 0.6346 \| 0.4055 \| \| 0.479 \| 0.9358 \| 3500 \| 0.6450 \| 0.4001 \| \| 0.44 \| 1.0695 \| 4000 \| 0.6126 \| 0.3856 \| \| 0.4103 \| 1.2032 \| 4500 \| 0.5970 \| 0.3747 \| \| 0.396 \| 1.3369 \| 5000 \| 0.5792 \| 0.3780 \| \| 0.3822 \| 1.4706 \| 5500 \| 0.5786 \| 0.3643 \| \| 0.3706 \| 1.6043 \| 6000 \| 0.5387 \| 0.3507 \| \| 0.3669 \| 1.7380 \| 6500 \| 0.5292 \| 0.3546 \| \| 0.3544 \| 1.8717 \| 7000 \| 0.5145 \| 0.3436 \| \| 0.3492 \| 2.0053 \| 7500 \| 0.5322 \| 0.3342 \| \| 0.3066 \| 2.1390 \| 8000 \| 0.5284 \| 0.3323 \| \| 0.3006 \| 2.2727 \| 8500 \| 0.5248 \| 0.3333 \| \| 0.2954 \| 2.4064 \| 9000 \| 0.4983 \| 0.3221 \| \| 0.2914 \| 2.5401 \| 9500 \| 0.4844 \| 0.3202 \| \| 0.2841 \| 2.6738 \| 10000 \| 0.4881 \| 0.3167 \| \| 0.2816 \| 2.8075 \| 10500 \| 0.4815 \| 0.3167 \| \| 0.2777 \| 2.9412 \| 11000 \| 0.4832 \| 0.3169 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0 - Datasets 3.5.0 - Tokenizers 0.21.1
hangcai/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deft_small_cod	hangcai	2025-05-03T13:48:12Z	9	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am deft small cod", "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	2025-04-27T20:21:42Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deft_small_cod tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am deft small cod - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deft_small_cod 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="hangcai/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deft_small_cod", 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: 2.7.0 - 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}} } ```
thembocuroj/dfgbfgbfgb	thembocuroj	2025-05-03T13:47:53Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2025-05-03T13:47:53Z	--- license: bigscience-openrail-m ---
yolo765/sdxl-naruto-model	yolo765	2025-05-03T13:37:29Z	0	0	diffusers	[ "diffusers", "safetensors", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers-training", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-05-03T00:14:11Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: creativeml-openrail-m inference: true tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers-training - diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # Text-to-image finetuning - yolo765/sdxl-naruto-model This pipeline was finetuned from stabilityai/stable-diffusion-xl-base-1.0 on the lambdalabs/naruto-blip-captions dataset. Below are some example images generated with the finetuned pipeline using the following prompt: a cute Sundar Pichai creature: ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
memeviss/zombieIX_9	memeviss	2025-05-03T13:35:54Z	0	0	null	[ "safetensors", "region:us" ]	null	2025-05-03T11:30:57Z	# Optimized TTS Model This model has been optimized for 100% TOP1 performance using advanced parameter enhancement techniques. ## Usage To generate speech using this model, you can use the included script: ```bash ./generate_speech.py --text "Your text here" --output_path output.wav ``` For more details, see the optimization report in this directory.
Triangle104/Gemma-3-Starshine-12B-Q8_0-GGUF	Triangle104	2025-05-03T13:35:46Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:ToastyPigeon/Gemma-3-Starshine-12B", "base_model:quantized:ToastyPigeon/Gemma-3-Starshine-12B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T13:33:23Z	--- base_model: ToastyPigeon/Gemma-3-Starshine-12B library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # Triangle104/Gemma-3-Starshine-12B-Q8_0-GGUF This model was converted to GGUF format from [`ToastyPigeon/Gemma-3-Starshine-12B`](https://huggingface.co/ToastyPigeon/Gemma-3-Starshine-12B) 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/ToastyPigeon/Gemma-3-Starshine-12B) for more details on the model. --- --- A creative writing model based on a merge of fine-tunes on Gemma 3 12B IT and Gemma 3 12B PT. This is the Story Focused merge. This version works better for storytelling and scenarios, as the prose is more novel-like and it has a tendency to impersonate the user character. See the Alternate RP Focused version as well. This is a merge of two G3 models, one trained on instruct and one trained on base: - allura-org/Gemma-3-Glitter-12B - Itself a merge of a storywriting and RP train (both also by ToastyPigeon), on instruct - ToastyPigeon/Gemma-3-Confetti-12B - Experimental application of the Glitter data using base instead of instruct, additionally includes some adventure data in the form of SpringDragon. The result is a lovely blend of Glitter's ability to follow instructions and Confetti's free-spirit prose, effectively 'loosening up' much of the hesitancy that was left in Glitter. --- ## 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/Gemma-3-Starshine-12B-Q8_0-GGUF --hf-file gemma-3-starshine-12b-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Gemma-3-Starshine-12B-Q8_0-GGUF --hf-file gemma-3-starshine-12b-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 Triangle104/Gemma-3-Starshine-12B-Q8_0-GGUF --hf-file gemma-3-starshine-12b-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Gemma-3-Starshine-12B-Q8_0-GGUF --hf-file gemma-3-starshine-12b-q8_0.gguf -c 2048 ```
Triangle104/Gemma-3-Starshine-12B-Q6_K-GGUF	Triangle104	2025-05-03T13:32:13Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:ToastyPigeon/Gemma-3-Starshine-12B", "base_model:quantized:ToastyPigeon/Gemma-3-Starshine-12B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T13:30:12Z	--- base_model: ToastyPigeon/Gemma-3-Starshine-12B library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # Triangle104/Gemma-3-Starshine-12B-Q6_K-GGUF This model was converted to GGUF format from [`ToastyPigeon/Gemma-3-Starshine-12B`](https://huggingface.co/ToastyPigeon/Gemma-3-Starshine-12B) 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/ToastyPigeon/Gemma-3-Starshine-12B) for more details on the model. --- A creative writing model based on a merge of fine-tunes on Gemma 3 12B IT and Gemma 3 12B PT. This is the Story Focused merge. This version works better for storytelling and scenarios, as the prose is more novel-like and it has a tendency to impersonate the user character. See the Alternate RP Focused version as well. This is a merge of two G3 models, one trained on instruct and one trained on base: - allura-org/Gemma-3-Glitter-12B - Itself a merge of a storywriting and RP train (both also by ToastyPigeon), on instruct - ToastyPigeon/Gemma-3-Confetti-12B - Experimental application of the Glitter data using base instead of instruct, additionally includes some adventure data in the form of SpringDragon. The result is a lovely blend of Glitter's ability to follow instructions and Confetti's free-spirit prose, effectively 'loosening up' much of the hesitancy that was left in Glitter. --- ## 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/Gemma-3-Starshine-12B-Q6_K-GGUF --hf-file gemma-3-starshine-12b-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Gemma-3-Starshine-12B-Q6_K-GGUF --hf-file gemma-3-starshine-12b-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Gemma-3-Starshine-12B-Q6_K-GGUF --hf-file gemma-3-starshine-12b-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Gemma-3-Starshine-12B-Q6_K-GGUF --hf-file gemma-3-starshine-12b-q6_k.gguf -c 2048 ```
19uez/GRPO_llama3_2_3B_16_005_1k_part1	19uez	2025-05-03T13:25:52Z	0	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "unsloth", "trl", "grpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T13:24:54Z	--- library_name: transformers tags: - unsloth - trl - grpo --- # 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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HarethahMo/qwen2.5-3b-base-abliterated	HarethahMo	2025-05-03T13:25:03Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T13:06:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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openfree/sergey-lazarev	openfree	2025-05-03T13:23:17Z	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	2025-05-03T13:23:12Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - ai-toolkit widget: - text: 'A Sergey Lazarev as USA president, rainbow hair color, pink suit, 26K ' output: url: samples/1746278529264__000001111_0.jpg - text: 'A Sergey Lazarev as italian gangster, joyful face, green suit, location - Japan, 26K ' output: url: samples/1746278559468__000001111_1.jpg - text: A Sergey Lazarev as a spain policeman, happy face, real photo, best quality, 26K output: url: samples/1746278589630__000001111_2.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: Sergey Lazarev 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 --- # sergey-lazarev Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) <Gallery /> ## Trigger words You should use `Sergey Lazarev` 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](/openfree/sergey-lazarev/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('openfree/sergey-lazarev', weight_name='sergey-lazarev.safetensors') image = pipeline('A Sergey Lazarev as USA president, rainbow hair color, pink suit, 26K ').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)
cnfusion/UIGEN-T2-7B-mlx-fp16	cnfusion	2025-05-03T13:22:39Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "ui-generation", "peft", "lora", "tailwind-css", "html", "mlx", "mlx-my-repo", "conversational", "en", "base_model:Tesslate/UIGEN-T2-7B", "base_model:adapter:Tesslate/UIGEN-T2-7B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T13:21:49Z	--- base_model: Tesslate/UIGEN-T2-7B tags: - text-generation-inference - transformers - qwen2 - ui-generation - peft - lora - tailwind-css - html - mlx - mlx-my-repo license: apache-2.0 language: - en --- # cnfusion/UIGEN-T2-7B-mlx-fp16 The Model [cnfusion/UIGEN-T2-7B-mlx-fp16](https://huggingface.co/cnfusion/UIGEN-T2-7B-mlx-fp16) was converted to MLX format from [Tesslate/UIGEN-T2-7B](https://huggingface.co/Tesslate/UIGEN-T2-7B) using mlx-lm version 0.22.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("cnfusion/UIGEN-T2-7B-mlx-fp16") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
cnfusion/UIGEN-T2-7B-mlx-8Bit	cnfusion	2025-05-03T13:20:29Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "ui-generation", "peft", "lora", "tailwind-css", "html", "mlx", "mlx-my-repo", "conversational", "en", "base_model:Tesslate/UIGEN-T2-7B", "base_model:adapter:Tesslate/UIGEN-T2-7B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "8-bit", "region:us" ]	text-generation	2025-05-03T13:19:57Z	--- base_model: Tesslate/UIGEN-T2-7B tags: - text-generation-inference - transformers - qwen2 - ui-generation - peft - lora - tailwind-css - html - mlx - mlx-my-repo license: apache-2.0 language: - en --- # cnfusion/UIGEN-T2-7B-mlx-8Bit The Model [cnfusion/UIGEN-T2-7B-mlx-8Bit](https://huggingface.co/cnfusion/UIGEN-T2-7B-mlx-8Bit) was converted to MLX format from [Tesslate/UIGEN-T2-7B](https://huggingface.co/Tesslate/UIGEN-T2-7B) using mlx-lm version 0.22.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("cnfusion/UIGEN-T2-7B-mlx-8Bit") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
vangard703/single_image_11_tasks_single_tasks_codebase_L1_scale_5_tasks	vangard703	2025-05-03T13:19:59Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "conversational", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-03T13:16:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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ASethi04/meta-llama-Llama-3.1-8B-legalbench-first-lora-4-0.0001	ASethi04	2025-05-03T13:17:39Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "endpoints_compatible", "region:us" ]	null	2025-05-03T12:32:31Z	--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: meta-llama-Llama-3.1-8B-legalbench-first-lora-4-0.0001 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for meta-llama-Llama-3.1-8B-legalbench-first-lora-4-0.0001 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="ASethi04/meta-llama-Llama-3.1-8B-legalbench-first-lora-4-0.0001", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/torchql-org/huggingface/runs/z3zgdd0s) This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
bbehrang/vaddosgus	bbehrang	2025-05-03T13:13:56Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-03T12:42:10Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: VADDOSGUS --- # Vaddosgus <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 `VADDOSGUS` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "VADDOSGUS", "lora_weights": "https://huggingface.co/bbehrang/vaddosgus/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('bbehrang/vaddosgus', weight_name='lora.safetensors') image = pipeline('VADDOSGUS').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/bbehrang/vaddosgus/discussions) to add images that show off what you’ve made with this LoRA.
vangard703/single_image_11_tasks_three_tasks_qa_5_tasks	vangard703	2025-05-03T13:10:22Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "conversational", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-03T13:07:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. 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]
EXt1/KMUTT-CPE35-thai-mt5base-summarizer	EXt1	2025-05-03T13:07:15Z	344	1	peft	[ "peft", "safetensors", "mt5", "summarization", "base_model:google/mt5-base", "base_model:adapter:google/mt5-base", "8-bit", "bitsandbytes", "region:us" ]	summarization	2025-04-20T08:39:24Z	--- base_model: google/mt5-base library_name: peft pipeline_tag: summarization --- # KMUTT-CPE35-thai-mt5base-summarizer This repository contains a fine-tuned version of google/mt5-base for the task of Thai text summarization. The model was trained on 20,000 samples from the ThaiSum dataset and is part of a senior project in the Computer Engineering Department at King Mongkut’s University of Technology Thonburi (KMUTT). ## Model Description - Base model: google/mt5-base - Task: Text Summarization (Thai) - Fine-tuning dataset: ThaiSum (20k samples) - Quantization: 8-bit - Max sequence length: 512 tokens ## Evaluation The performance of the model was evaluated using the ROUGE metric, which is commonly used for assessing the quality of summarization tasks. The evaluation results on the test set are as follows: * ROUGE-1: 0.4498 * ROUGE-2: 0.2551 * ROUGE-L: 0.4481 * ROUGE-Lsum: 0.4501 ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.2
loc1105/qwen2-capydata-finetuned	loc1105	2025-05-03T13:04:21Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "region:us" ]	null	2025-05-03T13:04:14Z	--- base_model: unsloth/qwen2-vl-7b-instruct-unsloth-bnb-4bit library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2

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