librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-28 12:28:24	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 500 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-28 12:27:53	card stringlengths 11 1.01M
Jonjew/DominiqueMcElligott	Jonjew	2025-05-01T01:42:51Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:unknown", "region:us" ]	text-to-image	2025-05-01T01:42:42Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/dom.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: null license: unknown --- # Dominique McElligott by solo_lee <Gallery /> ## Model description FROM https://civitai.com/models/1527956/dominique-mcelligott-sololora?modelVersionId=1728762 Please support the creator by donating BUZZ to the creator and LIKING at the page above ## Download model Weights for this model are available in Safetensors format. [Download](/Jonjew/DominiqueMcElligott/tree/main) them in the Files & versions tab.
mlfoundations-dev/d1_math_all	mlfoundations-dev	2025-05-01T01:13:03Z	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	2025-04-28T21:43:19Z	--- 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 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 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 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: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 32 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - total_eval_batch_size: 256 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.3.0 - Datasets 3.1.0 - Tokenizers 0.20.3
smokypipe21/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-miniature_bellowing_stork	smokypipe21	2025-05-01T01:11:56Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am miniature bellowing stork", "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	2025-05-01T01:11:24Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-miniature_bellowing_stork tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am miniature bellowing stork - unsloth - trl licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-miniature_bellowing_stork 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="smokypipe21/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-miniature_bellowing_stork", 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: 4.51.1 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
RetroB/fluxtest	RetroB	2025-04-30T23:38:50Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-03-08T16:58:27Z	--- license: apache-2.0 ---
diliash/qwen2.5-vl-7b_rslora_pm_axis_origintype_twoway_qwenprompt_borders_data_20250430_152846	diliash	2025-04-30T22:40:14Z	0	0	transformers	[ "transformers", "safetensors", "qwen2.5-vl-7b_rslora_pm_axis_origintype_twoway_qwenprompt_borders_data_20250430_152846", "20250430_152846", "qwen2.5-vl-7b_rslora_pm_axis_origintype_twoway_rerunl40_data_20250430_144705", "20250430_144705", "qwen2.5-vl-7b_rslora_pm_axis_origintype_twoway_border_data_20250430_143912", "20250430_143912", "generated_from_trainer", "final-model", "processor", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-30T22:28:47Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-VL-7B-Instruct tags: - qwen2.5-vl-7b_rslora_pm_axis_origintype_twoway_qwenprompt_borders_data_20250430_152846 - '20250430_152846' - qwen2.5-vl-7b_rslora_pm_axis_origintype_twoway_rerunl40_data_20250430_144705 - '20250430_144705' - qwen2.5-vl-7b_rslora_pm_axis_origintype_twoway_border_data_20250430_143912 - '20250430_143912' - generated_from_trainer - final-model - processor model-index: - name: checkpoints 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. --> # checkpoints This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 4 - total_eval_batch_size: 4 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Framework versions - Transformers 4.51.3 - Pytorch 2.4.1+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
onnx-community/dfine_l_obj365-ONNX	onnx-community	2025-04-30T22:37:35Z	0	0	transformers.js	[ "transformers.js", "onnx", "d_fine", "object-detection", "base_model:ustc-community/dfine_l_obj365", "base_model:quantized:ustc-community/dfine_l_obj365", "region:us" ]	object-detection	2025-04-30T22:26:31Z	--- library_name: transformers.js base_model: ustc-community/dfine_l_obj365 --- https://huggingface.co/ustc-community/dfine_l_obj365 with ONNX weights to be compatible with Transformers.js. ### Transformers.js If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using: ```bash npm i @huggingface/transformers ``` You can then use the model like this: ```js import { pipeline } from "@huggingface/transformers"; const detector = await pipeline("object-detection", "onnx-community/dfine_l_obj365-ONNX"); const image = "https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg"; const output = await detector(image, { threshold: 0.5 }); console.log(output); ``` Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
sommerzen/Qwen3-4B-abliterated-Q5_K_M-GGUF	sommerzen	2025-04-30T22:12:58Z	0	0	transformers	[ "transformers", "gguf", "chat", "abliterated", "uncensored", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:huihui-ai/Qwen3-4B-abliterated", "base_model:quantized:huihui-ai/Qwen3-4B-abliterated", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-04-30T22:12:44Z	--- base_model: huihui-ai/Qwen3-4B-abliterated library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-4B/blob/main/LICENSE pipeline_tag: text-generation tags: - chat - abliterated - uncensored - llama-cpp - gguf-my-repo --- # sommerzen/Qwen3-4B-abliterated-Q5_K_M-GGUF This model was converted to GGUF format from [`huihui-ai/Qwen3-4B-abliterated`](https://huggingface.co/huihui-ai/Qwen3-4B-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/huihui-ai/Qwen3-4B-abliterated) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo sommerzen/Qwen3-4B-abliterated-Q5_K_M-GGUF --hf-file qwen3-4b-abliterated-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo sommerzen/Qwen3-4B-abliterated-Q5_K_M-GGUF --hf-file qwen3-4b-abliterated-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 sommerzen/Qwen3-4B-abliterated-Q5_K_M-GGUF --hf-file qwen3-4b-abliterated-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo sommerzen/Qwen3-4B-abliterated-Q5_K_M-GGUF --hf-file qwen3-4b-abliterated-q5_k_m.gguf -c 2048 ```
MarcusLee/Qwen3-0.6B-MLX	MarcusLee	2025-04-30T22:08:59Z	0	0	mlx	[ "mlx", "safetensors", "qwen3", "text-generation", "conversational", "base_model:MarcusLee/Qwen3-0.6B-MLX", "base_model:finetune:MarcusLee/Qwen3-0.6B-MLX", "license:apache-2.0", "region:us" ]	text-generation	2025-04-30T22:06:53Z	--- license: apache-2.0 base_model: MarcusLee/Qwen3-0.6B-MLX pipeline_tag: text-generation tags: - mlx library_name: mlx ---
phospho-app/TyphoidComa-put_objects_in_bowl_v003-1l3z7qihxc	phospho-app	2025-04-30T22:06:00Z	0	0	null	[ "safetensors", "gr00t_n1", "phosphobot", "gr00t", "region:us" ]	null	2025-04-30T21:32:57Z	--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [TyphoidComa/put_objects_in_bowl_v003](https://huggingface.co/datasets/TyphoidComa/put_objects_in_bowl_v003) - Wandb run URL: None - Epochs: 10 - Batch size: 64 - Training steps: None 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=replicate_groot_training_pipeline) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=replicate_groot_training_pipeline)
lwoollett/AskJ-3-8B	lwoollett	2025-04-30T22:00:07Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-30T21:50:58Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
rbelanec/train_wsc_1745950296	rbelanec	2025-04-30T21:24:38Z	0	0	peft	[ "peft", "safetensors", "llama-factory", "ia3", "generated_from_trainer", "base_model:google/gemma-3-1b-it", "base_model:adapter:google/gemma-3-1b-it", "license:gemma", "region:us" ]	null	2025-04-30T17:24:12Z	--- library_name: peft license: gemma base_model: google/gemma-3-1b-it tags: - llama-factory - ia3 - generated_from_trainer model-index: - name: train_wsc_1745950296 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. --> # train_wsc_1745950296 This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it) on the wsc dataset. It achieves the following results on the evaluation set: - Loss: 0.2355 - Num Input Tokens Seen: 14005200 ## 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: 2 - eval_batch_size: 2 - seed: 123 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - 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 - training_steps: 40000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Input Tokens Seen \| \|:-------------:\|:--------:\|:-----:\|:---------------:\|:-----------------:\| \| 0.5932 \| 1.6024 \| 200 \| 0.4073 \| 70208 \| \| 0.3595 \| 3.2008 \| 400 \| 0.2959 \| 140304 \| \| 0.221 \| 4.8032 \| 600 \| 0.2701 \| 210336 \| \| 0.2749 \| 6.4016 \| 800 \| 0.2635 \| 280224 \| \| 0.219 \| 8.0 \| 1000 \| 0.2467 \| 350448 \| \| 0.2372 \| 9.6024 \| 1200 \| 0.2552 \| 420560 \| \| 0.2497 \| 11.2008 \| 1400 \| 0.2446 \| 490880 \| \| 0.2277 \| 12.8032 \| 1600 \| 0.2466 \| 560560 \| \| 0.2419 \| 14.4016 \| 1800 \| 0.2385 \| 630816 \| \| 0.2729 \| 16.0 \| 2000 \| 0.2396 \| 699936 \| \| 0.2504 \| 17.6024 \| 2200 \| 0.2511 \| 769520 \| \| 0.2598 \| 19.2008 \| 2400 \| 0.2438 \| 839648 \| \| 0.2481 \| 20.8032 \| 2600 \| 0.2433 \| 910080 \| \| 0.2471 \| 22.4016 \| 2800 \| 0.2355 \| 979504 \| \| 0.2492 \| 24.0 \| 3000 \| 0.2413 \| 1049392 \| \| 0.2142 \| 25.6024 \| 3200 \| 0.2454 \| 1119904 \| \| 0.2143 \| 27.2008 \| 3400 \| 0.2527 \| 1189264 \| \| 0.2336 \| 28.8032 \| 3600 \| 0.2460 \| 1259520 \| \| 0.2308 \| 30.4016 \| 3800 \| 0.2459 \| 1329408 \| \| 0.2159 \| 32.0 \| 4000 \| 0.2431 \| 1399696 \| \| 0.2191 \| 33.6024 \| 4200 \| 0.2479 \| 1470240 \| \| 0.2236 \| 35.2008 \| 4400 \| 0.2477 \| 1539536 \| \| 0.2137 \| 36.8032 \| 4600 \| 0.2489 \| 1610032 \| \| 0.2108 \| 38.4016 \| 4800 \| 0.2486 \| 1680240 \| \| 0.2262 \| 40.0 \| 5000 \| 0.2462 \| 1749472 \| \| 0.2135 \| 41.6024 \| 5200 \| 0.2494 \| 1819376 \| \| 0.2547 \| 43.2008 \| 5400 \| 0.2545 \| 1889616 \| \| 0.2397 \| 44.8032 \| 5600 \| 0.2517 \| 1959536 \| \| 0.2474 \| 46.4016 \| 5800 \| 0.2489 \| 2028864 \| \| 0.2198 \| 48.0 \| 6000 \| 0.2499 \| 2099424 \| \| 0.2273 \| 49.6024 \| 6200 \| 0.2492 \| 2169376 \| \| 0.222 \| 51.2008 \| 6400 \| 0.2596 \| 2239408 \| \| 0.2052 \| 52.8032 \| 6600 \| 0.2524 \| 2309472 \| \| 0.2202 \| 54.4016 \| 6800 \| 0.2570 \| 2380032 \| \| 0.2291 \| 56.0 \| 7000 \| 0.2544 \| 2449376 \| \| 0.2432 \| 57.6024 \| 7200 \| 0.2533 \| 2519776 \| \| 0.2275 \| 59.2008 \| 7400 \| 0.2684 \| 2589392 \| \| 0.2066 \| 60.8032 \| 7600 \| 0.2622 \| 2659792 \| \| 0.2334 \| 62.4016 \| 7800 \| 0.2637 \| 2729184 \| \| 0.2207 \| 64.0 \| 8000 \| 0.2639 \| 2799504 \| \| 0.2081 \| 65.6024 \| 8200 \| 0.2605 \| 2869520 \| \| 0.2352 \| 67.2008 \| 8400 \| 0.2625 \| 2940080 \| \| 0.2277 \| 68.8032 \| 8600 \| 0.2645 \| 3010256 \| \| 0.2181 \| 70.4016 \| 8800 \| 0.2607 \| 3080304 \| \| 0.2193 \| 72.0 \| 9000 \| 0.2614 \| 3150464 \| \| 0.2263 \| 73.6024 \| 9200 \| 0.2640 \| 3220512 \| \| 0.2365 \| 75.2008 \| 9400 \| 0.2711 \| 3290320 \| \| 0.2301 \| 76.8032 \| 9600 \| 0.2720 \| 3360352 \| \| 0.2136 \| 78.4016 \| 9800 \| 0.2780 \| 3430416 \| \| 0.2414 \| 80.0 \| 10000 \| 0.2773 \| 3500544 \| \| 0.2108 \| 81.6024 \| 10200 \| 0.2759 \| 3570432 \| \| 0.2501 \| 83.2008 \| 10400 \| 0.2852 \| 3640832 \| \| 0.1856 \| 84.8032 \| 10600 \| 0.2843 \| 3710480 \| \| 0.2461 \| 86.4016 \| 10800 \| 0.2923 \| 3780368 \| \| 0.2514 \| 88.0 \| 11000 \| 0.2892 \| 3850720 \| \| 0.233 \| 89.6024 \| 11200 \| 0.2869 \| 3920848 \| \| 0.2219 \| 91.2008 \| 11400 \| 0.2930 \| 3990784 \| \| 0.2282 \| 92.8032 \| 11600 \| 0.2939 \| 4060432 \| \| 0.2327 \| 94.4016 \| 11800 \| 0.2963 \| 4130528 \| \| 0.2053 \| 96.0 \| 12000 \| 0.3074 \| 4200848 \| \| 0.2024 \| 97.6024 \| 12200 \| 0.3054 \| 4270928 \| \| 0.2145 \| 99.2008 \| 12400 \| 0.3057 \| 4339920 \| \| 0.2199 \| 100.8032 \| 12600 \| 0.3065 \| 4410624 \| \| 0.1864 \| 102.4016 \| 12800 \| 0.3128 \| 4479904 \| \| 0.2428 \| 104.0 \| 13000 \| 0.3085 \| 4549824 \| \| 0.2364 \| 105.6024 \| 13200 \| 0.3189 \| 4620128 \| \| 0.2179 \| 107.2008 \| 13400 \| 0.3157 \| 4690352 \| \| 0.2091 \| 108.8032 \| 13600 \| 0.3346 \| 4760256 \| \| 0.1908 \| 110.4016 \| 13800 \| 0.3324 \| 4830144 \| \| 0.2238 \| 112.0 \| 14000 \| 0.3265 \| 4900080 \| \| 0.2362 \| 113.6024 \| 14200 \| 0.3383 \| 4969936 \| \| 0.1788 \| 115.2008 \| 14400 \| 0.3447 \| 5040096 \| \| 0.2398 \| 116.8032 \| 14600 \| 0.3425 \| 5110288 \| \| 0.2528 \| 118.4016 \| 14800 \| 0.3449 \| 5180208 \| \| 0.2339 \| 120.0 \| 15000 \| 0.3511 \| 5250464 \| \| 0.2148 \| 121.6024 \| 15200 \| 0.3561 \| 5320528 \| \| 0.2319 \| 123.2008 \| 15400 \| 0.3632 \| 5390624 \| \| 0.2345 \| 124.8032 \| 15600 \| 0.3623 \| 5460832 \| \| 0.217 \| 126.4016 \| 15800 \| 0.3796 \| 5530720 \| \| 0.1915 \| 128.0 \| 16000 \| 0.3945 \| 5600992 \| \| 0.2116 \| 129.6024 \| 16200 \| 0.3822 \| 5672032 \| \| 0.2116 \| 131.2008 \| 16400 \| 0.3909 \| 5740976 \| \| 0.2181 \| 132.8032 \| 16600 \| 0.3814 \| 5811248 \| \| 0.2108 \| 134.4016 \| 16800 \| 0.4049 \| 5881152 \| \| 0.1722 \| 136.0 \| 17000 \| 0.3914 \| 5951136 \| \| 0.2649 \| 137.6024 \| 17200 \| 0.4134 \| 6021136 \| \| 0.1815 \| 139.2008 \| 17400 \| 0.4207 \| 6091696 \| \| 0.2212 \| 140.8032 \| 17600 \| 0.4139 \| 6161472 \| \| 0.2797 \| 142.4016 \| 17800 \| 0.4191 \| 6231760 \| \| 0.1788 \| 144.0 \| 18000 \| 0.4182 \| 6301232 \| \| 0.1695 \| 145.6024 \| 18200 \| 0.4215 \| 6371776 \| \| 0.176 \| 147.2008 \| 18400 \| 0.4220 \| 6442048 \| \| 0.193 \| 148.8032 \| 18600 \| 0.4278 \| 6511680 \| \| 0.1788 \| 150.4016 \| 18800 \| 0.4465 \| 6581136 \| \| 0.1791 \| 152.0 \| 19000 \| 0.4280 \| 6651296 \| \| 0.1701 \| 153.6024 \| 19200 \| 0.4408 \| 6721584 \| \| 0.218 \| 155.2008 \| 19400 \| 0.4615 \| 6791744 \| \| 0.1885 \| 156.8032 \| 19600 \| 0.4490 \| 6862112 \| \| 0.2073 \| 158.4016 \| 19800 \| 0.4526 \| 6931856 \| \| 0.2083 \| 160.0 \| 20000 \| 0.4382 \| 7001952 \| \| 0.2122 \| 161.6024 \| 20200 \| 0.4656 \| 7071568 \| \| 0.2229 \| 163.2008 \| 20400 \| 0.4499 \| 7141584 \| \| 0.1639 \| 164.8032 \| 20600 \| 0.4636 \| 7212096 \| \| 0.1531 \| 166.4016 \| 20800 \| 0.4812 \| 7282736 \| \| 0.1857 \| 168.0 \| 21000 \| 0.4808 \| 7352288 \| \| 0.2166 \| 169.6024 \| 21200 \| 0.4873 \| 7422624 \| \| 0.217 \| 171.2008 \| 21400 \| 0.4804 \| 7492496 \| \| 0.2053 \| 172.8032 \| 21600 \| 0.4837 \| 7562288 \| \| 0.158 \| 174.4016 \| 21800 \| 0.4817 \| 7632432 \| \| 0.2275 \| 176.0 \| 22000 \| 0.4776 \| 7702096 \| \| 0.1558 \| 177.6024 \| 22200 \| 0.4795 \| 7772000 \| \| 0.2557 \| 179.2008 \| 22400 \| 0.5149 \| 7842112 \| \| 0.1607 \| 180.8032 \| 22600 \| 0.5000 \| 7912496 \| \| 0.1257 \| 182.4016 \| 22800 \| 0.4994 \| 7982768 \| \| 0.1728 \| 184.0 \| 23000 \| 0.4957 \| 8052448 \| \| 0.1638 \| 185.6024 \| 23200 \| 0.5003 \| 8122832 \| \| 0.1874 \| 187.2008 \| 23400 \| 0.5117 \| 8193088 \| \| 0.1926 \| 188.8032 \| 23600 \| 0.5122 \| 8263104 \| \| 0.2062 \| 190.4016 \| 23800 \| 0.5285 \| 8333312 \| \| 0.22 \| 192.0 \| 24000 \| 0.5147 \| 8402848 \| \| 0.2026 \| 193.6024 \| 24200 \| 0.5052 \| 8472688 \| \| 0.2265 \| 195.2008 \| 24400 \| 0.5265 \| 8542528 \| \| 0.1609 \| 196.8032 \| 24600 \| 0.5340 \| 8612928 \| \| 0.1703 \| 198.4016 \| 24800 \| 0.5321 \| 8682896 \| \| 0.1412 \| 200.0 \| 25000 \| 0.5307 \| 8752864 \| \| 0.1449 \| 201.6024 \| 25200 \| 0.5193 \| 8823744 \| \| 0.2189 \| 203.2008 \| 25400 \| 0.5338 \| 8893360 \| \| 0.1865 \| 204.8032 \| 25600 \| 0.5253 \| 8963536 \| \| 0.2108 \| 206.4016 \| 25800 \| 0.5421 \| 9033264 \| \| 0.2047 \| 208.0 \| 26000 \| 0.5364 \| 9102880 \| \| 0.1245 \| 209.6024 \| 26200 \| 0.5348 \| 9173088 \| \| 0.1963 \| 211.2008 \| 26400 \| 0.5464 \| 9242752 \| \| 0.1986 \| 212.8032 \| 26600 \| 0.5527 \| 9313008 \| \| 0.1849 \| 214.4016 \| 26800 \| 0.5563 \| 9382592 \| \| 0.1951 \| 216.0 \| 27000 \| 0.5439 \| 9452912 \| \| 0.1727 \| 217.6024 \| 27200 \| 0.5450 \| 9522896 \| \| 0.175 \| 219.2008 \| 27400 \| 0.5521 \| 9592864 \| \| 0.1625 \| 220.8032 \| 27600 \| 0.5523 \| 9663568 \| \| 0.1043 \| 222.4016 \| 27800 \| 0.5580 \| 9733504 \| \| 0.2085 \| 224.0 \| 28000 \| 0.5700 \| 9803232 \| \| 0.1547 \| 225.6024 \| 28200 \| 0.5690 \| 9872976 \| \| 0.1701 \| 227.2008 \| 28400 \| 0.5524 \| 9943472 \| \| 0.1637 \| 228.8032 \| 28600 \| 0.5571 \| 10013472 \| \| 0.1409 \| 230.4016 \| 28800 \| 0.5627 \| 10082944 \| \| 0.1686 \| 232.0 \| 29000 \| 0.5629 \| 10153120 \| \| 0.1232 \| 233.6024 \| 29200 \| 0.5772 \| 10223856 \| \| 0.1424 \| 235.2008 \| 29400 \| 0.5794 \| 10293888 \| \| 0.1501 \| 236.8032 \| 29600 \| 0.5660 \| 10363824 \| \| 0.2116 \| 238.4016 \| 29800 \| 0.5782 \| 10433056 \| \| 0.1504 \| 240.0 \| 30000 \| 0.5741 \| 10503136 \| \| 0.1391 \| 241.6024 \| 30200 \| 0.5759 \| 10573568 \| \| 0.1679 \| 243.2008 \| 30400 \| 0.5777 \| 10642912 \| \| 0.1838 \| 244.8032 \| 30600 \| 0.5723 \| 10713264 \| \| 0.0982 \| 246.4016 \| 30800 \| 0.5854 \| 10783152 \| \| 0.1083 \| 248.0 \| 31000 \| 0.5753 \| 10853376 \| \| 0.1923 \| 249.6024 \| 31200 \| 0.5822 \| 10923696 \| \| 0.2031 \| 251.2008 \| 31400 \| 0.5762 \| 10994016 \| \| 0.112 \| 252.8032 \| 31600 \| 0.5872 \| 11063664 \| \| 0.1834 \| 254.4016 \| 31800 \| 0.5846 \| 11133840 \| \| 0.1673 \| 256.0 \| 32000 \| 0.5778 \| 11203504 \| \| 0.1845 \| 257.6024 \| 32200 \| 0.5711 \| 11273840 \| \| 0.1988 \| 259.2008 \| 32400 \| 0.5864 \| 11342832 \| \| 0.1785 \| 260.8032 \| 32600 \| 0.5750 \| 11412832 \| \| 0.2006 \| 262.4016 \| 32800 \| 0.5792 \| 11482880 \| \| 0.1711 \| 264.0 \| 33000 \| 0.6063 \| 11552512 \| \| 0.1912 \| 265.6024 \| 33200 \| 0.5967 \| 11622560 \| \| 0.1387 \| 267.2008 \| 33400 \| 0.5726 \| 11692336 \| \| 0.1579 \| 268.8032 \| 33600 \| 0.5824 \| 11763296 \| \| 0.1602 \| 270.4016 \| 33800 \| 0.5813 \| 11833168 \| \| 0.1151 \| 272.0 \| 34000 \| 0.5845 \| 11902608 \| \| 0.1165 \| 273.6024 \| 34200 \| 0.5823 \| 11973440 \| \| 0.1283 \| 275.2008 \| 34400 \| 0.5973 \| 12042992 \| \| 0.1169 \| 276.8032 \| 34600 \| 0.5824 \| 12113808 \| \| 0.1351 \| 278.4016 \| 34800 \| 0.5956 \| 12183456 \| \| 0.1505 \| 280.0 \| 35000 \| 0.5935 \| 12253312 \| \| 0.1692 \| 281.6024 \| 35200 \| 0.5781 \| 12323712 \| \| 0.1639 \| 283.2008 \| 35400 \| 0.5710 \| 12393344 \| \| 0.1746 \| 284.8032 \| 35600 \| 0.5846 \| 12463296 \| \| 0.1759 \| 286.4016 \| 35800 \| 0.5872 \| 12533712 \| \| 0.1401 \| 288.0 \| 36000 \| 0.5930 \| 12603312 \| \| 0.1634 \| 289.6024 \| 36200 \| 0.6116 \| 12672944 \| \| 0.2095 \| 291.2008 \| 36400 \| 0.6031 \| 12743584 \| \| 0.1535 \| 292.8032 \| 36600 \| 0.5991 \| 12814000 \| \| 0.17 \| 294.4016 \| 36800 \| 0.6007 \| 12883584 \| \| 0.1576 \| 296.0 \| 37000 \| 0.5849 \| 12954144 \| \| 0.1592 \| 297.6024 \| 37200 \| 0.5987 \| 13024112 \| \| 0.2064 \| 299.2008 \| 37400 \| 0.6090 \| 13094448 \| \| 0.201 \| 300.8032 \| 37600 \| 0.6053 \| 13164640 \| \| 0.1417 \| 302.4016 \| 37800 \| 0.5957 \| 13234048 \| \| 0.1734 \| 304.0 \| 38000 \| 0.5905 \| 13304512 \| \| 0.2204 \| 305.6024 \| 38200 \| 0.6068 \| 13374272 \| \| 0.1773 \| 307.2008 \| 38400 \| 0.6008 \| 13444512 \| \| 0.1493 \| 308.8032 \| 38600 \| 0.5896 \| 13514848 \| \| 0.1731 \| 310.4016 \| 38800 \| 0.6084 \| 13584800 \| \| 0.2378 \| 312.0 \| 39000 \| 0.5984 \| 13654928 \| \| 0.1702 \| 313.6024 \| 39200 \| 0.5848 \| 13724752 \| \| 0.2229 \| 315.2008 \| 39400 \| 0.5860 \| 13794224 \| \| 0.1697 \| 316.8032 \| 39600 \| 0.5860 \| 13865104 \| \| 0.1667 \| 318.4016 \| 39800 \| 0.5860 \| 13935776 \| \| 0.198 \| 320.0 \| 40000 \| 0.5860 \| 14005200 \| ### Framework versions - PEFT 0.15.2.dev0 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
bocilanomali/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wary_nimble_cobra	bocilanomali	2025-04-30T21:10:15Z	1	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am wary nimble cobra", "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	2025-04-27T19:01:04Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wary_nimble_cobra tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am wary nimble cobra - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wary_nimble_cobra 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="bocilanomali/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wary_nimble_cobra", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.5.0 - 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}} } ```
Akil15/quantized-starcoder-7B	Akil15	2025-04-30T20:48:08Z	0	0	null	[ "safetensors", "starcoder2", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-30T20:34:07Z	--- license: apache-2.0 ---
fbaldassarri/internlm_internlm3-8b-instruct-autogptq-int8-gs64-asym	fbaldassarri	2025-04-30T20:43:16Z	0	0	transformers	[ "transformers", "safetensors", "internlm3", "text-generation", "internlm", "autoround", "auto-round", "intel-autoround", "intel", "woq", "gptq", "pytorch", "internlm3-8b", "conversational", "custom_code", "en", "es", "fr", "de", "pt", "ja", "it", "zh", "ko", "ar", "cs", "nl", "base_model:internlm/internlm3-8b-instruct", "base_model:quantized:internlm/internlm3-8b-instruct", "license:apache-2.0", "autotrain_compatible", "8-bit", "region:us" ]	text-generation	2025-04-30T20:40:02Z	--- language: - en - es - fr - de - pt - ja - it - zh - ko - ar - cs - nl pipeline_tag: text-generation license: apache-2.0 library_name: transformers tags: - internlm - autoround - auto-round - intel-autoround - intel - woq - gptq - pytorch - internlm3 - internlm3-8b model_name: Internlm 3 8b instruct base_model: - internlm/internlm3-8b-instruct inference: false model_creator: internlm prompt_template: '{prompt}' quantized_by: fbaldassarri --- ## Model Information Quantized version of [internlm/internlm3-8b-instruct](https://huggingface.co/internlm/internlm3-8b-instruct) using torch.float32 for quantization tuning. - 8 bits (INT8) - group size = 64 - Asymmetrical Quantization - Method WoQ: GPTQ (AutoGPTQ algorithm) Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.4.7 Note: this INT8 version of internlm3-8b-instruct has been quantized to run inference through CPU. ## Replication Recipe ### Step 1 Install Requirements I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment. ``` wget https://github.com/intel/auto-round/archive/refs/tags/v0.4.7.tar.gz tar -xvzf v0.4.7.tar.gz cd auto-round-0.4.7 pip install -r requirements-cpu.txt --upgrade ``` ### Step 2 Build Intel AutoRound wheel from sources ``` pip install -vvv --no-build-isolation -e .[cpu] ``` ### Step 3 Script for Quantization ``` from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "internlm/internlm3-8b-instruct" model = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) from auto_round import AutoRound bits, group_size, sym, device, amp = 8, 64, False, 'cpu', False autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device, amp=amp) autoround.quantize() output_dir = "./AutoRound/internlm_internlm3-8b-instruct-autogptq-int8-gs64-asym" autoround.save_quantized(output_dir, format='auto_gptq', inplace=True) ``` ## License [Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/) ## Disclaimer This quantized model comes with no warrenty. It has been developed only for research purposes.
aleegis/8eadc872-7896-4070-bd8f-b4bf968fa11e	aleegis	2025-04-30T20:22:04Z	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:Artples/L-MChat-7b", "base_model:adapter:Artples/L-MChat-7b", "license:apache-2.0", "region:us" ]	null	2025-04-30T18:46:46Z	--- library_name: peft license: apache-2.0 base_model: Artples/L-MChat-7b tags: - axolotl - generated_from_trainer model-index: - name: 8eadc872-7896-4070-bd8f-b4bf968fa11e results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Artples/L-MChat-7b bf16: auto chat_template: llama3 dataloader_num_workers: 12 dataset_prepared_path: null datasets: - data_files: - df64b7b1f6156a4a_train_data.json ds_type: json format: custom path: /workspace/input_data/df64b7b1f6156a4a_train_data.json type: field_input: context field_instruction: prompt_serial field_output: hypothesis 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_steps: null eval_table_size: null evals_per_epoch: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 8 gradient_checkpointing: false group_by_length: false hub_model_id: aleegis/8eadc872-7896-4070-bd8f-b4bf968fa11e hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: null lora_alpha: 32 lora_dropout: 0.15 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true loraplus_lr_embedding: 1.0e-06 loraplus_lr_ratio: 16 lr_scheduler: cosine max_grad_norm: 1 max_steps: 1500 micro_batch_size: 2 mlflow_experiment_name: /tmp/df64b7b1f6156a4a_train_data.json model_type: AutoModelForCausalLM num_epochs: 200 optimizer: adamw_torch_fused output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: null save_total_limit: 10 saves_per_epoch: 0 sequence_len: 1024 special_tokens: pad_token: <\|end_of_turn\|> strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.0 wandb_entity: null wandb_mode: online wandb_name: 05aae012-1d2a-4ad0-8017-d5d890fc3778 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 05aae012-1d2a-4ad0-8017-d5d890fc3778 warmup_steps: 100 weight_decay: 0 xformers_attention: null ``` </details><br> # 8eadc872-7896-4070-bd8f-b4bf968fa11e This model is a fine-tuned version of [Artples/L-MChat-7b](https://huggingface.co/Artples/L-MChat-7b) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - 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 - lr_scheduler_warmup_steps: 100 - training_steps: 1500 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Yuhan123/ppo-reading-level-full-question-12th-1-steps-10000-epoch-999-best-eval-score-0.281	Yuhan123	2025-04-30T19:58:03Z	0	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-30T19:55:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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rbelanec/train_wic_1745950291	rbelanec	2025-04-30T18:55:07Z	0	0	peft	[ "peft", "safetensors", "llama-factory", "lntuning", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]	null	2025-04-30T15:30:52Z	--- library_name: peft license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - llama-factory - lntuning - generated_from_trainer model-index: - name: train_wic_1745950291 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. --> # train_wic_1745950291 This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the wic dataset. It achieves the following results on the evaluation set: - Loss: 0.4934 - Num Input Tokens Seen: 12716696 ## 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: 2 - eval_batch_size: 2 - seed: 123 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - 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 - training_steps: 40000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Input Tokens Seen \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:-----------------:\| \| 0.6257 \| 0.1637 \| 200 \| 0.5225 \| 63344 \| \| 0.4145 \| 0.3275 \| 400 \| 0.5219 \| 126720 \| \| 0.6067 \| 0.4912 \| 600 \| 0.5143 \| 190304 \| \| 0.5509 \| 0.6549 \| 800 \| 0.5104 \| 254384 \| \| 0.4087 \| 0.8187 \| 1000 \| 0.5072 \| 318128 \| \| 0.5062 \| 0.9824 \| 1200 \| 0.5076 \| 381920 \| \| 0.4057 \| 1.1457 \| 1400 \| 0.5047 \| 445096 \| \| 0.6519 \| 1.3095 \| 1600 \| 0.5058 \| 508744 \| \| 0.4278 \| 1.4732 \| 1800 \| 0.5040 \| 572408 \| \| 0.516 \| 1.6369 \| 2000 \| 0.5023 \| 635736 \| \| 0.4402 \| 1.8007 \| 2200 \| 0.4989 \| 699464 \| \| 0.3649 \| 1.9644 \| 2400 \| 0.5017 \| 763192 \| \| 0.722 \| 2.1277 \| 2600 \| 0.5006 \| 826784 \| \| 0.6072 \| 2.2914 \| 2800 \| 0.5021 \| 890336 \| \| 0.4783 \| 2.4552 \| 3000 \| 0.4986 \| 953840 \| \| 0.3192 \| 2.6189 \| 3200 \| 0.4998 \| 1017600 \| \| 0.6125 \| 2.7826 \| 3400 \| 0.4971 \| 1081104 \| \| 0.3693 \| 2.9464 \| 3600 \| 0.5000 \| 1144576 \| \| 0.5569 \| 3.1097 \| 3800 \| 0.5020 \| 1208440 \| \| 0.6581 \| 3.2734 \| 4000 \| 0.4973 \| 1272216 \| \| 0.3633 \| 3.4372 \| 4200 \| 0.4999 \| 1335496 \| \| 0.5302 \| 3.6009 \| 4400 \| 0.5050 \| 1398984 \| \| 0.3837 \| 3.7646 \| 4600 \| 0.4959 \| 1462856 \| \| 0.5727 \| 3.9284 \| 4800 \| 0.4986 \| 1526280 \| \| 0.417 \| 4.0917 \| 5000 \| 0.4981 \| 1589584 \| \| 0.381 \| 4.2554 \| 5200 \| 0.4988 \| 1653024 \| \| 0.3998 \| 4.4192 \| 5400 \| 0.4994 \| 1716432 \| \| 0.3977 \| 4.5829 \| 5600 \| 0.5029 \| 1779984 \| \| 0.364 \| 4.7466 \| 5800 \| 0.5024 \| 1843936 \| \| 0.6055 \| 4.9104 \| 6000 \| 0.5001 \| 1907808 \| \| 0.4597 \| 5.0737 \| 6200 \| 0.5003 \| 1971048 \| \| 0.4152 \| 5.2374 \| 6400 \| 0.5005 \| 2034808 \| \| 0.4998 \| 5.4011 \| 6600 \| 0.5010 \| 2098088 \| \| 0.5148 \| 5.5649 \| 6800 \| 0.5005 \| 2161640 \| \| 0.4574 \| 5.7286 \| 7000 \| 0.4973 \| 2225432 \| \| 0.884 \| 5.8923 \| 7200 \| 0.4995 \| 2289032 \| \| 0.5194 \| 6.0557 \| 7400 \| 0.4955 \| 2352656 \| \| 0.6431 \| 6.2194 \| 7600 \| 0.4975 \| 2416160 \| \| 0.3991 \| 6.3831 \| 7800 \| 0.4986 \| 2479728 \| \| 0.532 \| 6.5469 \| 8000 \| 0.4968 \| 2543168 \| \| 0.4574 \| 6.7106 \| 8200 \| 0.4997 \| 2606560 \| \| 0.4313 \| 6.8743 \| 8400 \| 0.4990 \| 2670208 \| \| 0.5079 \| 7.0377 \| 8600 \| 0.4967 \| 2733584 \| \| 0.4926 \| 7.2014 \| 8800 \| 0.4963 \| 2797008 \| \| 0.6941 \| 7.3651 \| 9000 \| 0.5011 \| 2860576 \| \| 0.4878 \| 7.5289 \| 9200 \| 0.4988 \| 2924256 \| \| 0.4491 \| 7.6926 \| 9400 \| 0.4975 \| 2988272 \| \| 0.5816 \| 7.8563 \| 9600 \| 0.4988 \| 3051776 \| \| 0.3643 \| 8.0196 \| 9800 \| 0.4955 \| 3114992 \| \| 0.5292 \| 8.1834 \| 10000 \| 0.4965 \| 3179200 \| \| 0.3784 \| 8.3471 \| 10200 \| 0.4981 \| 3242496 \| \| 0.5082 \| 8.5108 \| 10400 \| 0.4971 \| 3306112 \| \| 0.5478 \| 8.6746 \| 10600 \| 0.4993 \| 3369760 \| \| 0.6724 \| 8.8383 \| 10800 \| 0.4998 \| 3433360 \| \| 0.5947 \| 9.0016 \| 11000 \| 0.4980 \| 3496680 \| \| 0.5989 \| 9.1654 \| 11200 \| 0.5002 \| 3560648 \| \| 0.5554 \| 9.3291 \| 11400 \| 0.4983 \| 3624200 \| \| 0.3369 \| 9.4928 \| 11600 \| 0.5003 \| 3687560 \| \| 0.5688 \| 9.6566 \| 11800 \| 0.5014 \| 3751288 \| \| 0.4692 \| 9.8203 \| 12000 \| 0.4971 \| 3814952 \| \| 0.6744 \| 9.9840 \| 12200 \| 0.5008 \| 3878120 \| \| 0.4068 \| 10.1474 \| 12400 \| 0.4992 \| 3941616 \| \| 0.4359 \| 10.3111 \| 12600 \| 0.4981 \| 4005216 \| \| 0.5724 \| 10.4748 \| 12800 \| 0.4960 \| 4068912 \| \| 0.5359 \| 10.6386 \| 13000 \| 0.4971 \| 4132608 \| \| 0.4707 \| 10.8023 \| 13200 \| 0.4980 \| 4196096 \| \| 0.5272 \| 10.9660 \| 13400 \| 0.4969 \| 4259680 \| \| 0.6006 \| 11.1293 \| 13600 \| 0.4966 \| 4323128 \| \| 0.4663 \| 11.2931 \| 13800 \| 0.4977 \| 4386856 \| \| 0.3614 \| 11.4568 \| 14000 \| 0.4935 \| 4450296 \| \| 0.6643 \| 11.6205 \| 14200 \| 0.4980 \| 4513544 \| \| 0.5071 \| 11.7843 \| 14400 \| 0.5001 \| 4576984 \| \| 0.3758 \| 11.9480 \| 14600 \| 0.4987 \| 4640904 \| \| 0.3884 \| 12.1113 \| 14800 \| 0.4975 \| 4704360 \| \| 0.304 \| 12.2751 \| 15000 \| 0.4966 \| 4768152 \| \| 0.4518 \| 12.4388 \| 15200 \| 0.4974 \| 4832152 \| \| 0.3722 \| 12.6025 \| 15400 \| 0.4999 \| 4895192 \| \| 0.3803 \| 12.7663 \| 15600 \| 0.4989 \| 4959112 \| \| 0.4056 \| 12.9300 \| 15800 \| 0.4952 \| 5022408 \| \| 0.7264 \| 13.0933 \| 16000 \| 0.4986 \| 5086016 \| \| 0.6845 \| 13.2571 \| 16200 \| 0.4999 \| 5149920 \| \| 0.3888 \| 13.4208 \| 16400 \| 0.4991 \| 5213296 \| \| 0.6898 \| 13.5845 \| 16600 \| 0.4985 \| 5276672 \| \| 0.4119 \| 13.7483 \| 16800 \| 0.5017 \| 5340624 \| \| 0.4066 \| 13.9120 \| 17000 \| 0.4966 \| 5403792 \| \| 0.6487 \| 14.0753 \| 17200 \| 0.4955 \| 5466936 \| \| 0.6244 \| 14.2391 \| 17400 \| 0.4985 \| 5530392 \| \| 0.6813 \| 14.4028 \| 17600 \| 0.4988 \| 5593576 \| \| 0.55 \| 14.5665 \| 17800 \| 0.4999 \| 5657288 \| \| 0.4325 \| 14.7302 \| 18000 \| 0.4973 \| 5721496 \| \| 0.541 \| 14.8940 \| 18200 \| 0.4976 \| 5785096 \| \| 0.6722 \| 15.0573 \| 18400 \| 0.4993 \| 5848736 \| \| 0.5625 \| 15.2210 \| 18600 \| 0.4954 \| 5912176 \| \| 0.4723 \| 15.3848 \| 18800 \| 0.4965 \| 5976400 \| \| 0.31 \| 15.5485 \| 19000 \| 0.4957 \| 6040272 \| \| 0.4716 \| 15.7122 \| 19200 \| 0.4957 \| 6103424 \| \| 0.5429 \| 15.8760 \| 19400 \| 0.4934 \| 6166912 \| \| 0.3732 \| 16.0393 \| 19600 \| 0.4961 \| 6230320 \| \| 0.4673 \| 16.2030 \| 19800 \| 0.4972 \| 6294224 \| \| 0.4359 \| 16.3668 \| 20000 \| 0.4974 \| 6357984 \| \| 0.3628 \| 16.5305 \| 20200 \| 0.5007 \| 6421344 \| \| 0.3717 \| 16.6942 \| 20400 \| 0.4999 \| 6485152 \| \| 0.3153 \| 16.8580 \| 20600 \| 0.4961 \| 6548768 \| \| 0.6308 \| 17.0213 \| 20800 \| 0.4971 \| 6611792 \| \| 0.6157 \| 17.1850 \| 21000 \| 0.4995 \| 6675216 \| \| 0.4635 \| 17.3488 \| 21200 \| 0.4987 \| 6739088 \| \| 0.6582 \| 17.5125 \| 21400 \| 0.4991 \| 6802352 \| \| 0.2988 \| 17.6762 \| 21600 \| 0.4997 \| 6866160 \| \| 0.3709 \| 17.8400 \| 21800 \| 0.5029 \| 6929936 \| \| 0.3607 \| 18.0033 \| 22000 \| 0.4944 \| 6993168 \| \| 0.7202 \| 18.1670 \| 22200 \| 0.5041 \| 7057008 \| \| 0.3716 \| 18.3307 \| 22400 \| 0.5014 \| 7120624 \| \| 0.4817 \| 18.4945 \| 22600 \| 0.4980 \| 7183872 \| \| 0.5667 \| 18.6582 \| 22800 \| 0.4962 \| 7247952 \| \| 0.3868 \| 18.8219 \| 23000 \| 0.4981 \| 7311488 \| \| 0.4314 \| 18.9857 \| 23200 \| 0.4989 \| 7374848 \| \| 0.5291 \| 19.1490 \| 23400 \| 0.4971 \| 7438160 \| \| 0.5263 \| 19.3127 \| 23600 \| 0.4991 \| 7501872 \| \| 0.5666 \| 19.4765 \| 23800 \| 0.4970 \| 7565520 \| \| 0.6424 \| 19.6402 \| 24000 \| 0.4947 \| 7629488 \| \| 0.5894 \| 19.8039 \| 24200 \| 0.4982 \| 7692992 \| \| 0.303 \| 19.9677 \| 24400 \| 0.4980 \| 7756512 \| \| 0.5242 \| 20.1310 \| 24600 \| 0.4970 \| 7819816 \| \| 0.331 \| 20.2947 \| 24800 \| 0.4987 \| 7883800 \| \| 0.4012 \| 20.4585 \| 25000 \| 0.4947 \| 7947944 \| \| 0.5083 \| 20.6222 \| 25200 \| 0.4989 \| 8011336 \| \| 0.4885 \| 20.7859 \| 25400 \| 0.4996 \| 8075000 \| \| 0.5333 \| 20.9497 \| 25600 \| 0.4989 \| 8138568 \| \| 0.5209 \| 21.1130 \| 25800 \| 0.5002 \| 8201872 \| \| 0.7051 \| 21.2767 \| 26000 \| 0.4995 \| 8265168 \| \| 0.5638 \| 21.4404 \| 26200 \| 0.5024 \| 8328704 \| \| 0.6135 \| 21.6042 \| 26400 \| 0.4948 \| 8392144 \| \| 0.8321 \| 21.7679 \| 26600 \| 0.4984 \| 8456096 \| \| 0.6106 \| 21.9316 \| 26800 \| 0.5017 \| 8519872 \| \| 0.5066 \| 22.0950 \| 27000 \| 0.5002 \| 8583464 \| \| 0.5766 \| 22.2587 \| 27200 \| 0.4949 \| 8646840 \| \| 0.5146 \| 22.4224 \| 27400 \| 0.4984 \| 8710600 \| \| 0.6664 \| 22.5862 \| 27600 \| 0.4979 \| 8774344 \| \| 0.5827 \| 22.7499 \| 27800 \| 0.4989 \| 8838024 \| \| 0.5015 \| 22.9136 \| 28000 \| 0.4998 \| 8901832 \| \| 0.3741 \| 23.0770 \| 28200 \| 0.4952 \| 8965184 \| \| 0.4112 \| 23.2407 \| 28400 \| 0.4975 \| 9028576 \| \| 0.3413 \| 23.4044 \| 28600 \| 0.5026 \| 9092256 \| \| 0.3816 \| 23.5682 \| 28800 \| 0.4968 \| 9155872 \| \| 0.5038 \| 23.7319 \| 29000 \| 0.4988 \| 9219312 \| \| 0.509 \| 23.8956 \| 29200 \| 0.5012 \| 9283264 \| \| 0.4391 \| 24.0589 \| 29400 \| 0.4994 \| 9346992 \| \| 0.3301 \| 24.2227 \| 29600 \| 0.5016 \| 9410880 \| \| 0.6701 \| 24.3864 \| 29800 \| 0.4956 \| 9474704 \| \| 0.3837 \| 24.5501 \| 30000 \| 0.4996 \| 9538160 \| \| 0.6954 \| 24.7139 \| 30200 \| 0.5018 \| 9601792 \| \| 0.6162 \| 24.8776 \| 30400 \| 0.4981 \| 9664976 \| \| 0.5058 \| 25.0409 \| 30600 \| 0.4952 \| 9728232 \| \| 0.6277 \| 25.2047 \| 30800 \| 0.5002 \| 9791848 \| \| 0.3653 \| 25.3684 \| 31000 \| 0.4973 \| 9855400 \| \| 0.4652 \| 25.5321 \| 31200 \| 0.5014 \| 9918984 \| \| 0.2707 \| 25.6959 \| 31400 \| 0.4962 \| 9982872 \| \| 0.5098 \| 25.8596 \| 31600 \| 0.5003 \| 10046056 \| \| 0.4843 \| 26.0229 \| 31800 \| 0.5000 \| 10109568 \| \| 0.5279 \| 26.1867 \| 32000 \| 0.4986 \| 10173072 \| \| 0.4396 \| 26.3504 \| 32200 \| 0.5003 \| 10236512 \| \| 0.7524 \| 26.5141 \| 32400 \| 0.4994 \| 10299920 \| \| 0.5412 \| 26.6779 \| 32600 \| 0.4996 \| 10363808 \| \| 0.6239 \| 26.8416 \| 32800 \| 0.5021 \| 10427744 \| \| 0.4925 \| 27.0049 \| 33000 \| 0.4980 \| 10491384 \| \| 0.4674 \| 27.1686 \| 33200 \| 0.5011 \| 10555192 \| \| 0.4568 \| 27.3324 \| 33400 \| 0.4977 \| 10619080 \| \| 0.4934 \| 27.4961 \| 33600 \| 0.4955 \| 10682424 \| \| 0.8816 \| 27.6598 \| 33800 \| 0.4993 \| 10746024 \| \| 0.3269 \| 27.8236 \| 34000 \| 0.4972 \| 10809736 \| \| 0.4768 \| 27.9873 \| 34200 \| 0.4941 \| 10873448 \| \| 0.6487 \| 28.1506 \| 34400 \| 0.4946 \| 10936704 \| \| 0.5115 \| 28.3144 \| 34600 \| 0.4938 \| 11000112 \| \| 0.5026 \| 28.4781 \| 34800 \| 0.4966 \| 11063936 \| \| 0.4725 \| 28.6418 \| 35000 \| 0.4996 \| 11128160 \| \| 0.3988 \| 28.8056 \| 35200 \| 0.4996 \| 11191600 \| \| 0.7055 \| 28.9693 \| 35400 \| 0.4961 \| 11255184 \| \| 0.2657 \| 29.1326 \| 35600 \| 0.4985 \| 11318640 \| \| 0.3977 \| 29.2964 \| 35800 \| 0.4985 \| 11382352 \| \| 0.5586 \| 29.4601 \| 36000 \| 0.4985 \| 11446048 \| \| 0.4327 \| 29.6238 \| 36200 \| 0.4985 \| 11509328 \| \| 0.3437 \| 29.7876 \| 36400 \| 0.4985 \| 11573312 \| \| 0.5439 \| 29.9513 \| 36600 \| 0.4985 \| 11636752 \| \| 0.5447 \| 30.1146 \| 36800 \| 0.4985 \| 11700056 \| \| 0.4514 \| 30.2783 \| 37000 \| 0.4985 \| 11763352 \| \| 0.7178 \| 30.4421 \| 37200 \| 0.4985 \| 11826952 \| \| 0.7133 \| 30.6058 \| 37400 \| 0.4985 \| 11890888 \| \| 0.5499 \| 30.7695 \| 37600 \| 0.4985 \| 11954296 \| \| 0.8377 \| 30.9333 \| 37800 \| 0.4985 \| 12017784 \| \| 0.6521 \| 31.0966 \| 38000 \| 0.4985 \| 12081304 \| \| 0.6123 \| 31.2603 \| 38200 \| 0.4985 \| 12145240 \| \| 0.4538 \| 31.4241 \| 38400 \| 0.4985 \| 12208888 \| \| 0.689 \| 31.5878 \| 38600 \| 0.4985 \| 12272344 \| \| 0.4428 \| 31.7515 \| 38800 \| 0.4985 \| 12335960 \| \| 0.5346 \| 31.9153 \| 39000 \| 0.4985 \| 12399064 \| \| 0.4668 \| 32.0786 \| 39200 \| 0.4985 \| 12462200 \| \| 0.4803 \| 32.2423 \| 39400 \| 0.4985 \| 12526024 \| \| 0.607 \| 32.4061 \| 39600 \| 0.4985 \| 12589496 \| \| 0.4888 \| 32.5698 \| 39800 \| 0.4985 \| 12653080 \| \| 0.429 \| 32.7335 \| 40000 \| 0.4985 \| 12716696 \| ### Framework versions - PEFT 0.15.2.dev0 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
vijay-ravichander/Smol-Pairwise-Distill-20k	vijay-ravichander	2025-04-30T18:20:30Z	0	0	transformers	[ "transformers", "safetensors", "idefics3", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-30T10:29:50Z	--- 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. 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]
guelph25/guelph2a	guelph25	2025-04-30T18:13:32Z	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	2025-04-30T18:13:01Z	--- 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: guelph --- # Guelph <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 `guelph` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "guelph", "lora_weights": "https://huggingface.co/guelph25/guelph/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('guelph25/guelph', weight_name='lora.safetensors') image = pipeline('guelph').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/guelph25/guelph/discussions) to add images that show off what you’ve made with this LoRA.
7-Shah-Sapna-Kumari-Viral-Videos-XX/18-FULL.VIDEO.Sapna.Shah.Viral.Video.Leaks.official.tutorial	7-Shah-Sapna-Kumari-Viral-Videos-XX	2025-04-30T17:53:01Z	0	0	null	[ "region:us" ]	null	2025-04-30T17:52:50Z	<a href="https://sdu.sk/9Ip"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a> <a href="https://sdu.sk/9Ip" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/9Ip" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
DataScienceWFSR/modernbert-food-product-sr	DataScienceWFSR	2025-04-30T17:32:36Z	0	0	null	[ "safetensors", "modernbert", "text-classification", "en", "arxiv:2504.20703", "base_model:answerdotai/ModernBERT-base", "base_model:finetune:answerdotai/ModernBERT-base", "region:us" ]	text-classification	2025-04-30T12:07:07Z	--- language: - en metrics: - f1 base_model: - answerdotai/ModernBERT-base pipeline_tag: text-classification --- # ModernBERT Food Product Classification Model - Synonym Replacement Augmentation ## Model Details ### Model Description This model is finetuned on multi-class food product text classification using synonym replacement augmentation and ModernBERT. - Developed by: [DataScienceWFSR](https://huggingface.co/DataScienceWFSR) - Model type: Text Classification - Language(s) (NLP): English - Finetuned from model: [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) ### Model Sources - Repository: [https://github.com/WFSRDataScience/SemEval2025Task9](https://github.com/WFSRDataScience/SemEval2025Task9) - Paper : [https://arxiv.org/abs/2504.20703](https://arxiv.org/abs/2504.20703) ## How to Get Started With the Model Use the code below to get started with the model in PyTorch. ```python import torch from transformers import AutoTokenizer, AutoModelForSequenceClassification from huggingface_hub import hf_hub_download import pandas as pd model, category, augmentation = 'modernbert', 'product', 'sr' repo_id = f"DataScienceWFSR/{model}-food-{category}-{augmentation}" lb_path = hf_hub_download(repo_id=repo_id, filename=f"labelencoder_{category}.pkl") lb = pd.read_pickle(lb_path) tokenizer = AutoTokenizer.from_pretrained(repo_id) model = AutoModelForSequenceClassification.from_pretrained(repo_id) model.eval() sample = ('Case Number: 039-94 Date Opened: 10/20/1994 Date Closed: 03/06/1995 Recall Class: 1' ' Press Release (Y/N): N Domestic Est. Number: 07188 M Name: PREPARED FOODS Imported ' 'Product (Y/N): N Foreign Estab. Number: N/A City: SANTA TERESA State: NM Country: USA' ' Product: HAM, SLICED Problem: BACTERIA Description: LISTERIA ' 'Total Pounds Recalled: 3,920 Pounds Recovered: 3,920') inputs = tokenizer(sample, return_tensors="pt") with torch.no_grad(): outputs = model(inputs) predictions = outputs.logits.argmax(dim=-1) predicted_label = lb.inverse_transform(predictions.numpy())[0] print(f"The predicted label is: {predicted_label}") ``` ## Training Details ### Training Data Training and Validation data provided by SemEval-2025 Task 9 organizers : `Food Recall Incidents` dataset (only English) [link](https://github.com/food-hazard-detection-semeval-2025/food-hazard-detection-semeval-2025.github.io/tree/main/data) ### Training Procedure #### Training Hyperparameters - batch_size: `8` - epochs: `5` - lr_scheduler: `cosine with Restarts` ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data & Metrics #### Testing Data Test data: 997 samples ([link](https://github.com/food-hazard-detection-semeval-2025/food-hazard-detection-semeval-2025.github.io/blob/main/data/incidents_test.csv)) #### Metrics F<sub>1</sub>-macro ### Results F<sub>1</sub>-macro scores for each model in the official test set utilizing the `text` field per category and subtasks scores (ST1 and ST2) rounded to 3 decimals. With bold, we indicated the model's specific results. \| Model \| hazard-category \| product-category \| hazard \| product \| ST1 \| ST2 \| \|----------------------\|----------------:\|-----------------:\|-------:\|--------:\|------:\|------:\| \| BERT<sub>base</sub> \| 0.747 \| 0.757 \| 0.581 \| 0.170 \| 0.753 \| 0.382 \| \| BERT<sub>CW</sub> \| 0.760 \| 0.761 \| 0.671 \| 0.280 \| 0.762 \| 0.491 \| \| BERT<sub>SR</sub> \| 0.770 \| 0.754 \| 0.666 \| 0.275 \| 0.764 \| 0.478 \| \| BERT<sub>RW</sub> \| 0.752 \| 0.757 \| 0.651 \| 0.275 \| 0.756 \| 0.467 \| \| DistilBERT<sub>base</sub> \| 0.761 \| 0.757 \| 0.593 \| 0.154 \| 0.760 \| 0.378 \| \| DistilBERT<sub>CW</sub> \| 0.766 \| 0.753 \| 0.635 \| 0.246 \| 0.763 \| 0.449 \| \| DistilBERT<sub>SR</sub> \| 0.756 \| 0.759 \| 0.644 \| 0.240 \| 0.763 \| 0.448 \| \| DistilBERT<sub>RW</sub> \| 0.749 \| 0.747 \| 0.647 \| 0.261 \| 0.753 \| 0.462 \| \| RoBERTa<sub>base</sub> \| 0.760 \| 0.753 \| 0.579 \| 0.123 \| 0.755 \| 0.356 \| \| RoBERTa<sub>CW</sub> \| 0.773 \| 0.739 \| 0.630 \| 0.000 \| 0.760 \| 0.315 \| \| RoBERTa<sub>SR</sub> \| 0.777 \| 0.755 \| 0.637 \| 0.000 \| 0.767 \| 0.319 \| \| RoBERTa<sub>RW</sub> \| 0.757 \| 0.611 \| 0.615 \| 0.000 \| 0.686 \| 0.308 \| \| ModernBERT<sub>base</sub> \| 0.781 \| 0.745 \| 0.667 \| 0.275 \| 0.769 \| 0.485 \| \| ModernBERT<sub>CW</sub> \| 0.761 \| 0.712 \| 0.609 \| 0.252 \| 0.741 \| 0.441 \| \| ModernBERT<sub>SR</sub> \| 0.790 \| 0.728 \| 0.591 \| 0.253 \| 0.761 \| 0.434 \| \| ModernBERT<sub>RW</sub> \| 0.761 \| 0.751 \| 0.629 \| 0.237 \| 0.759 \| 0.440 \| ## Technical Specifications ### Compute Infrastructure #### Hardware NVIDIA A100 80GB and NVIDIA GeForce RTX 3070 Ti #### Software \| Library \| Version \| URL \| \|-------------------\|--------:\|---------------------------------------------------------------------\| \| Transformers \| 4.49.0 \| https://huggingface.co/docs/transformers/index \| \| PyTorch \| 2.6.0 \| https://pytorch.org/ \| \| SpaCy \| 3.8.4 \| https://spacy.io/ \| \| Scikit-learn \| 1.6.0 \| https://scikit-learn.org/stable/ \| \| Pandas \| 2.2.3 \| https://pandas.pydata.org/ \| \| Optuna \| 4.2.1 \| https://optuna.org/ \| \| NumPy \| 2.0.2 \| https://numpy.org/ \| \| NLP AUG \| 1.1.11 \| https://nlpaug.readthedocs.io/en/latest/index.html \| \| BeautifulSoup4 \| 4.12.3 \| https://www.crummy.com/software/BeautifulSoup/bs4/doc/# \| ## Citation BibTeX:** For the original paper: ``` @inproceedings{brightcookies-semeval2025-task9, title="BrightCookies at {S}em{E}val-2025 Task 9: Exploring Data Augmentation for Food Hazard Classification}, author="Papadopoulou, Foteini and Mutlu, Osman and Özen, Neris and van der Velden, Bas H. M. and Hendrickx, Iris and Hürriyetoğlu, Ali", booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)", month = jul, year = "2025", address = "Vienna, Austria", publisher = "Association for Computational Linguistics", } ``` For the SemEval2025 Task9: ``` @inproceedings{semeval2025-task9, title = "{S}em{E}val-2025 Task 9: The Food Hazard Detection Challenge", author = "Randl, Korbinian and Pavlopoulos, John and Henriksson, Aron and Lindgren, Tony and Bakagianni, Juli", booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)", month = jul, year = "2025", address = "Vienna, Austria", publisher = "Association for Computational Linguistics", } ``` ## Model Card Authors and Contact Authors: Foteini Papadopoulou, Osman Mutlu, Neris Özen, Bas H.M. van der Velden, Iris Hendrickx, Ali Hürriyetoğlu Contact: [email protected]
mergekit-community/mergekit-model_stock-zelysxr	mergekit-community	2025-04-30T17:04:39Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.19522", "base_model:Cran-May/tempmotacilla-cinerea-0308", "base_model:merge:Cran-May/tempmotacilla-cinerea-0308", "base_model:Qwen/Qwen2.5-14B-Instruct", "base_model:merge:Qwen/Qwen2.5-14B-Instruct", "base_model:Sakalti/Saka-14B", "base_model:merge:Sakalti/Saka-14B", "base_model:aixonlab/Zara-14b-v1.2", "base_model:merge:aixonlab/Zara-14b-v1.2", "base_model:deepcogito/cogito-v1-preview-qwen-14B", "base_model:merge:deepcogito/cogito-v1-preview-qwen-14B", "base_model:mergekit-community/mergekit-task_arithmetic-yxycruu", "base_model:merge:mergekit-community/mergekit-task_arithmetic-yxycruu", "base_model:prithivMLmods/Galactic-Qwen-14B-Exp2", "base_model:merge:prithivMLmods/Galactic-Qwen-14B-Exp2", "base_model:sthenno-com/miscii-14b-0218", "base_model:merge:sthenno-com/miscii-14b-0218", "base_model:suayptalha/Lamarckvergence-14B", "base_model:merge:suayptalha/Lamarckvergence-14B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-30T16:43:39Z	--- base_model: - aixonlab/Zara-14b-v1.2 - sthenno-com/miscii-14b-0218 - Sakalti/Saka-14B - Qwen/Qwen2.5-14B-Instruct - prithivMLmods/Galactic-Qwen-14B-Exp2 - Cran-May/tempmotacilla-cinerea-0308 - suayptalha/Lamarckvergence-14B - deepcogito/cogito-v1-preview-qwen-14B - mergekit-community/mergekit-task_arithmetic-yxycruu library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) as a base. ### Models Merged The following models were included in the merge: * [aixonlab/Zara-14b-v1.2](https://huggingface.co/aixonlab/Zara-14b-v1.2) * [sthenno-com/miscii-14b-0218](https://huggingface.co/sthenno-com/miscii-14b-0218) * [Sakalti/Saka-14B](https://huggingface.co/Sakalti/Saka-14B) * [prithivMLmods/Galactic-Qwen-14B-Exp2](https://huggingface.co/prithivMLmods/Galactic-Qwen-14B-Exp2) * [Cran-May/tempmotacilla-cinerea-0308](https://huggingface.co/Cran-May/tempmotacilla-cinerea-0308) * [suayptalha/Lamarckvergence-14B](https://huggingface.co/suayptalha/Lamarckvergence-14B) * [deepcogito/cogito-v1-preview-qwen-14B](https://huggingface.co/deepcogito/cogito-v1-preview-qwen-14B) * [mergekit-community/mergekit-task_arithmetic-yxycruu](https://huggingface.co/mergekit-community/mergekit-task_arithmetic-yxycruu) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: prithivMLmods/Galactic-Qwen-14B-Exp2 - model: deepcogito/cogito-v1-preview-qwen-14B - model: sthenno-com/miscii-14b-0218 - model: Cran-May/tempmotacilla-cinerea-0308 - model: suayptalha/Lamarckvergence-14B - model: Sakalti/Saka-14B - model: aixonlab/Zara-14b-v1.2 - model: mergekit-community/mergekit-task_arithmetic-yxycruu merge_method: model_stock base_model: Qwen/Qwen2.5-14B-Instruct dtype: bfloat16 tokenizer_source: base ```
secmlr/SWE-BENCH-2000-enriched-reasoning-claude-localization_deepcoder_14b_2000_enriched_reasoning	secmlr	2025-04-30T16:56:33Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:agentica-org/DeepCoder-14B-Preview", "base_model:finetune:agentica-org/DeepCoder-14B-Preview", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-30T08:45:24Z	--- library_name: transformers license: mit base_model: agentica-org/DeepCoder-14B-Preview tags: - llama-factory - full - generated_from_trainer model-index: - name: SWE-BENCH-2000-enriched-reasoning-claude-localization_deepcoder_14b_2000_enriched_reasoning 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. --> # SWE-BENCH-2000-enriched-reasoning-claude-localization_deepcoder_14b_2000_enriched_reasoning This model is a fine-tuned version of [agentica-org/DeepCoder-14B-Preview](https://huggingface.co/agentica-org/DeepCoder-14B-Preview) on the SWE-BENCH-2000-enriched-reasoning-claude-localization 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: 12 - total_train_batch_size: 48 - total_eval_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.21.0
phospho-app/omourier-Lego_bleu-rg2sqbaxhg	phospho-app	2025-04-30T16:52:23Z	0	0	null	[ "phosphobot", "gr00t", "region:us" ]	null	2025-04-30T16:48:43Z	--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` Traceback (most recent call last): File "/root/src/helper.py", line 224, in predict raise RuntimeError(error_msg) RuntimeError: Training process failed with exit code 1: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py", line 277, in apply_rotary_pos_emb q_embed = (q * cos) + (rotate_half(q) * sin) ^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py", line 252, in rotate_half return torch.cat((-x2, x1), dim=-1) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 64.00 MiB. GPU 0 has a total capacity of 79.25 GiB of which 38.75 MiB is free. Process 45 has 79.21 GiB memory in use. Of the allocated memory 78.35 GiB is allocated by PyTorch, and 368.29 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) 0%\| \| 0/1140 [00:19<?, ?it/s] During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/root/src/helper.py", line 226, in predict raise RuntimeError(e) RuntimeError: Training process failed with exit code 1: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py", line 277, in apply_rotary_pos_emb q_embed = (q * cos) + (rotate_half(q) * sin) ^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py", line 252, in rotate_half return torch.cat((-x2, x1), dim=-1) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 64.00 MiB. GPU 0 has a total capacity of 79.25 GiB of which 38.75 MiB is free. Process 45 has 79.21 GiB memory in use. Of the allocated memory 78.35 GiB is allocated by PyTorch, and 368.29 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) 0%\| \| 0/1140 [00:19<?, ?it/s] ``` ## Training parameters: - Dataset: [omourier/Lego_bleu](https://huggingface.co/datasets/omourier/Lego_bleu) - Wandb run URL: None - Epochs: 10 - Batch size: 64 - Training steps: 1137 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=replicate_groot_training_pipeline) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=replicate_groot_training_pipeline)
Yuhan123/ppo-cn-RM-reading-level-grad-1-steps-10000-epoch-999-best-eval-score-0.135	Yuhan123	2025-04-30T16:40:26Z	0	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-30T16:37:58Z	--- 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]
DrTiagoSaldanha/ssssss	DrTiagoSaldanha	2025-04-30T11:48:15Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-30T11:48:15Z	--- license: apache-2.0 ---
dgambettaphd/M_llm2_gen0_run0_W_doc1000_synt64_tot128_lr5em5_p1k_SYNLAST	dgambettaphd	2025-04-30T11:40:18Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-04-30T11:38:12Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
nathanialhunt2000/90da23de-ad96-47b3-94ac-4de7ddd0badf	nathanialhunt2000	2025-04-30T11:34:37Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:41b191ed7e418531_train_data.json", "base_model:unsloth/Qwen2-1.5B-Instruct", "base_model:adapter:unsloth/Qwen2-1.5B-Instruct", "region:us" ]	null	2025-04-30T11:34:14Z	--- library_name: peft tags: - generated_from_trainer datasets: - 41b191ed7e418531_train_data.json base_model: unsloth/Qwen2-1.5B-Instruct model-index: - name: nathanialhunt2000/90da23de-ad96-47b3-94ac-4de7ddd0badf 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. --> # nathanialhunt2000/90da23de-ad96-47b3-94ac-4de7ddd0badf This model was trained from scratch on the /workspace/input_data/41b191ed7e418531_train_data.json dataset. It achieves the following results on the evaluation set: - Loss: 0.8242 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
tergelb/mmm	tergelb	2025-04-30T06:09:40Z	0	0	null	[ "arxiv:2303.09556", "license:mit", "region:us" ]	null	2025-04-29T13:53:08Z	--- license: mit --- #!/usr/bin/env python # coding=utf-8 # Copyright 2025 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Fine-tuning script for Stable Diffusion for text2image with support for LoRA.""" import argparse import logging import math import os import random import shutil from contextlib import nullcontext from pathlib import Path import datasets import numpy as np import torch import torch.nn.functional as F import torch.utils.checkpoint import transformers from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import ProjectConfiguration, set_seed from datasets import load_dataset from huggingface_hub import create_repo, upload_folder from packaging import version from peft import LoraConfig from peft.utils import get_peft_model_state_dict from torchvision import transforms from tqdm.auto import tqdm from transformers import CLIPTextModel, CLIPTokenizer import diffusers from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, StableDiffusionPipeline, UNet2DConditionModel from diffusers.optimization import get_scheduler from diffusers.training_utils import cast_training_params, compute_snr from diffusers.utils import check_min_version, convert_state_dict_to_diffusers, is_wandb_available from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card from diffusers.utils.import_utils import is_xformers_available from diffusers.utils.torch_utils import is_compiled_module if is_wandb_available(): import wandb # Will error if the minimal version of diffusers is not installed. Remove at your own risks. check_min_version("0.34.0.dev0") logger = get_logger(__name__, log_level="INFO") def save_model_card( repo_id: str, images: list = None, base_model: str = None, dataset_name: str = None, repo_folder: str = None, ): img_str = "" if images is not None: for i, image in enumerate(images): image.save(os.path.join(repo_folder, f"image_{i}.png")) img_str += f"![img_{i}](./image_{i}.png)\n" model_description = f""" # LoRA text2image fine-tuning - {repo_id} These are LoRA adaption weights for {base_model}. The weights were fine-tuned on the {dataset_name} dataset. You can find some example images in the following. \n {img_str} """ model_card = load_or_create_model_card( repo_id_or_path=repo_id, from_training=True, license="creativeml-openrail-m", base_model=base_model, model_description=model_description, inference=True, ) tags = [ "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "diffusers-training", "lora", ] model_card = populate_model_card(model_card, tags=tags) model_card.save(os.path.join(repo_folder, "README.md")) def log_validation( pipeline, args, accelerator, epoch, is_final_validation=False, ): logger.info( f"Running validation... \n Generating {args.num_validation_images} images with prompt:" f" {args.validation_prompt}." ) pipeline = pipeline.to(accelerator.device) pipeline.set_progress_bar_config(disable=True) generator = torch.Generator(device=accelerator.device) if args.seed is not None: generator = generator.manual_seed(args.seed) images = [] if torch.backends.mps.is_available(): autocast_ctx = nullcontext() else: autocast_ctx = torch.autocast(accelerator.device.type) with autocast_ctx: for _ in range(args.num_validation_images): images.append(pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0]) for tracker in accelerator.trackers: phase_name = "test" if is_final_validation else "validation" if tracker.name == "tensorboard": np_images = np.stack([np.asarray(img) for img in images]) tracker.writer.add_images(phase_name, np_images, epoch, dataformats="NHWC") if tracker.name == "wandb": tracker.log( { phase_name: [ wandb.Image(image, caption=f"{i}: {args.validation_prompt}") for i, image in enumerate(images) ] } ) return images def parse_args(): parser = argparse.ArgumentParser(description="Simple example of a training script.") parser.add_argument( "--pretrained_model_name_or_path", type=str, default=None, required=True, help="Path to pretrained model or model identifier from huggingface.co/models.", ) parser.add_argument( "--revision", type=str, default=None, required=False, help="Revision of pretrained model identifier from huggingface.co/models.", ) parser.add_argument( "--variant", type=str, default=None, help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16", ) parser.add_argument( "--dataset_name", type=str, default=None, help=( "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private," " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem," " or to a folder containing files that 🤗 Datasets can understand." ), ) parser.add_argument( "--dataset_config_name", type=str, default=None, help="The config of the Dataset, leave as None if there's only one config.", ) parser.add_argument( "--train_data_dir", type=str, default=None, help=( "A folder containing the training data. Folder contents must follow the structure described in" " https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file" " must exist to provide the captions for the images. Ignored if `dataset_name` is specified." ), ) parser.add_argument( "--image_column", type=str, default="image", help="The column of the dataset containing an image." ) parser.add_argument( "--caption_column", type=str, default="text", help="The column of the dataset containing a caption or a list of captions.", ) parser.add_argument( "--validation_prompt", type=str, default=None, help="A prompt that is sampled during training for inference." ) parser.add_argument( "--num_validation_images", type=int, default=4, help="Number of images that should be generated during validation with `validation_prompt`.", ) parser.add_argument( "--validation_epochs", type=int, default=1, help=( "Run fine-tuning validation every X epochs. The validation process consists of running the prompt" " `args.validation_prompt` multiple times: `args.num_validation_images`." ), ) parser.add_argument( "--max_train_samples", type=int, default=None, help=( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ), ) parser.add_argument( "--output_dir", type=str, default="sd-model-finetuned-lora", help="The output directory where the model predictions and checkpoints will be written.", ) parser.add_argument( "--cache_dir", type=str, default=None, help="The directory where the downloaded models and datasets will be stored.", ) parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument( "--resolution", type=int, default=512, help=( "The resolution for input images, all the images in the train/validation dataset will be resized to this" " resolution" ), ) parser.add_argument( "--center_crop", default=False, action="store_true", help=( "Whether to center crop the input images to the resolution. If not set, the images will be randomly" " cropped. The images will be resized to the resolution first before cropping." ), ) parser.add_argument( "--random_flip", action="store_true", help="whether to randomly flip images horizontally", ) parser.add_argument( "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader." ) parser.add_argument("--num_train_epochs", type=int, default=100) parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--gradient_checkpointing", action="store_true", help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", ) parser.add_argument( "--learning_rate", type=float, default=1e-4, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument( "--scale_lr", action="store_true", default=False, help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", ) parser.add_argument( "--lr_scheduler", type=str, default="constant", help=( 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' ' "constant", "constant_with_warmup"]' ), ) parser.add_argument( "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument( "--snr_gamma", type=float, default=None, help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. " "More details here: https://arxiv.org/abs/2303.09556.", ) parser.add_argument( "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes." ) parser.add_argument( "--allow_tf32", action="store_true", help=( "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see" " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices" ), ) parser.add_argument( "--dataloader_num_workers", type=int, default=0, help=( "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process." ), ) parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.") parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.") parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.") parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer") parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.") parser.add_argument( "--prediction_type", type=str, default=None, help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediction_type` is chosen.", ) parser.add_argument( "--hub_model_id", type=str, default=None, help="The name of the repository to keep in sync with the local `output_dir`.", ) parser.add_argument( "--logging_dir", type=str, default="logs", help=( "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" " output_dir/runs/CURRENT_DATETIME_HOSTNAME*." ), ) parser.add_argument( "--mixed_precision", type=str, default=None, choices=["no", "fp16", "bf16"], help=( "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config." ), ) parser.add_argument( "--report_to", type=str, default="tensorboard", help=( 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`' ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.' ), ) parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") parser.add_argument( "--checkpointing_steps", type=int, default=500, help=( "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming" " training using `--resume_from_checkpoint`." ), ) parser.add_argument( "--checkpoints_total_limit", type=int, default=None, help=("Max number of checkpoints to store."), ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help=( "Whether training should be resumed from a previous checkpoint. Use a path saved by" ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.' ), ) parser.add_argument( "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers." ) parser.add_argument("--noise_offset", type=float, default=0, help="The scale of noise offset.") parser.add_argument( "--rank", type=int, default=4, help=("The dimension of the LoRA update matrices."), ) parser.add_argument( "--image_interpolation_mode", type=str, default="lanczos", choices=[ f.lower() for f in dir(transforms.InterpolationMode) if not f.startswith("__") and not f.endswith("__") ], help="The image interpolation method to use for resizing images.", ) args = parser.parse_args() env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) if env_local_rank != -1 and env_local_rank != args.local_rank: args.local_rank = env_local_rank # Sanity checks if args.dataset_name is None and args.train_data_dir is None: raise ValueError("Need either a dataset name or a training folder.") return args DATASET_NAME_MAPPING = { "lambdalabs/naruto-blip-captions": ("image", "text"), } def main(): args = parse_args() if args.report_to == "wandb" and args.hub_token is not None: raise ValueError( "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token." " Please use `huggingface-cli login` to authenticate with the Hub." ) logging_dir = Path(args.output_dir, args.logging_dir) accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir) accelerator = Accelerator( gradient_accumulation_steps=args.gradient_accumulation_steps, mixed_precision=args.mixed_precision, log_with=args.report_to, project_config=accelerator_project_config, ) # Disable AMP for MPS. if torch.backends.mps.is_available(): accelerator.native_amp = False # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_warning() diffusers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() diffusers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # Handle the repository creation if accelerator.is_main_process: if args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) if args.push_to_hub: repo_id = create_repo( repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token ).repo_id # Load scheduler, tokenizer and models. noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler") tokenizer = CLIPTokenizer.from_pretrained( args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision ) text_encoder = CLIPTextModel.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision ) vae = AutoencoderKL.from_pretrained( args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant ) unet = UNet2DConditionModel.from_pretrained( args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant ) # freeze parameters of models to save more memory unet.requires_grad_(False) vae.requires_grad_(False) text_encoder.requires_grad_(False) # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision # as these weights are only used for inference, keeping weights in full precision is not required. weight_dtype = torch.float32 if accelerator.mixed_precision == "fp16": weight_dtype = torch.float16 elif accelerator.mixed_precision == "bf16": weight_dtype = torch.bfloat16 unet_lora_config = LoraConfig( r=args.rank, lora_alpha=args.rank, init_lora_weights="gaussian", target_modules=["to_k", "to_q", "to_v", "to_out.0"], ) # Move unet, vae and text_encoder to device and cast to weight_dtype unet.to(accelerator.device, dtype=weight_dtype) vae.to(accelerator.device, dtype=weight_dtype) text_encoder.to(accelerator.device, dtype=weight_dtype) # Add adapter and make sure the trainable params are in float32. unet.add_adapter(unet_lora_config) if args.mixed_precision == "fp16": # only upcast trainable parameters (LoRA) into fp32 cast_training_params(unet, dtype=torch.float32) if args.enable_xformers_memory_efficient_attention: if is_xformers_available(): import xformers xformers_version = version.parse(xformers.__version__) if xformers_version == version.parse("0.0.16"): logger.warning( "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details." ) unet.enable_xformers_memory_efficient_attention() else: raise ValueError("xformers is not available. Make sure it is installed correctly") lora_layers = filter(lambda p: p.requires_grad, unet.parameters()) if args.gradient_checkpointing: unet.enable_gradient_checkpointing() # Enable TF32 for faster training on Ampere GPUs, # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices if args.allow_tf32: torch.backends.cuda.matmul.allow_tf32 = True if args.scale_lr: args.learning_rate = ( args.learning_rate args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes ) # Initialize the optimizer if args.use_8bit_adam: try: import bitsandbytes as bnb except ImportError: raise ImportError( "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`" ) optimizer_cls = bnb.optim.AdamW8bit else: optimizer_cls = torch.optim.AdamW optimizer = optimizer_cls( lora_layers, lr=args.learning_rate, betas=(args.adam_beta1, args.adam_beta2), weight_decay=args.adam_weight_decay, eps=args.adam_epsilon, ) # Get the datasets: you can either provide your own training and evaluation files (see below) # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub). # In distributed training, the load_dataset function guarantees that only one local process can concurrently # download the dataset. if args.dataset_name is not None: # Downloading and loading a dataset from the hub. dataset = load_dataset( args.dataset_name, args.dataset_config_name, cache_dir=args.cache_dir, data_dir=args.train_data_dir, ) else: data_files = {} if args.train_data_dir is not None: data_files["train"] = os.path.join(args.train_data_dir, "*") dataset = load_dataset( "imagefolder", data_files=data_files, cache_dir=args.cache_dir, ) # See more about loading custom images at # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder # Preprocessing the datasets. # We need to tokenize inputs and targets. column_names = dataset["train"].column_names # 6. Get the column names for input/target. dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None) if args.image_column is None: image_column = dataset_columns[0] if dataset_columns is not None else column_names[0] else: image_column = args.image_column if image_column not in column_names: raise ValueError( f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}" ) if args.caption_column is None: caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1] else: caption_column = args.caption_column if caption_column not in column_names: raise ValueError( f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}" ) # Preprocessing the datasets. # We need to tokenize input captions and transform the images. def tokenize_captions(examples, is_train=True): captions = [] for caption in examples[caption_column]: if isinstance(caption, str): captions.append(caption) elif isinstance(caption, (list, np.ndarray)): # take a random caption if there are multiple captions.append(random.choice(caption) if is_train else caption[0]) else: raise ValueError( f"Caption column `{caption_column}` should contain either strings or lists of strings." ) inputs = tokenizer( captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt" ) return inputs.input_ids # Get the specified interpolation method from the args interpolation = getattr(transforms.InterpolationMode, args.image_interpolation_mode.upper(), None) # Raise an error if the interpolation method is invalid if interpolation is None: raise ValueError(f"Unsupported interpolation mode {args.image_interpolation_mode}.") # Data preprocessing transformations train_transforms = transforms.Compose( [ transforms.Resize(args.resolution, interpolation=interpolation), # Use dynamic interpolation method transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution), transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x), transforms.ToTensor(), transforms.Normalize([0.5], [0.5]), ] ) def unwrap_model(model): model = accelerator.unwrap_model(model) model = model._orig_mod if is_compiled_module(model) else model return model def preprocess_train(examples): images = [image.convert("RGB") for image in examples[image_column]] examples["pixel_values"] = [train_transforms(image) for image in images] examples["input_ids"] = tokenize_captions(examples) return examples with accelerator.main_process_first(): if args.max_train_samples is not None: dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples)) # Set the training transforms train_dataset = dataset["train"].with_transform(preprocess_train) def collate_fn(examples): pixel_values = torch.stack([example["pixel_values"] for example in examples]) pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() input_ids = torch.stack([example["input_ids"] for example in examples]) return {"pixel_values": pixel_values, "input_ids": input_ids} # DataLoaders creation: train_dataloader = torch.utils.data.DataLoader( train_dataset, shuffle=True, collate_fn=collate_fn, batch_size=args.train_batch_size, num_workers=args.dataloader_num_workers, ) # Scheduler and math around the number of training steps. # Check the PR https://github.com/huggingface/diffusers/pull/8312 for detailed explanation. num_warmup_steps_for_scheduler = args.lr_warmup_steps accelerator.num_processes if args.max_train_steps is None: len_train_dataloader_after_sharding = math.ceil(len(train_dataloader) / accelerator.num_processes) num_update_steps_per_epoch = math.ceil(len_train_dataloader_after_sharding / args.gradient_accumulation_steps) num_training_steps_for_scheduler = ( args.num_train_epochs * num_update_steps_per_epoch * accelerator.num_processes ) else: num_training_steps_for_scheduler = args.max_train_steps * accelerator.num_processes lr_scheduler = get_scheduler( args.lr_scheduler, optimizer=optimizer, num_warmup_steps=num_warmup_steps_for_scheduler, num_training_steps=num_training_steps_for_scheduler, ) # Prepare everything with our `accelerator`. unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( unet, optimizer, train_dataloader, lr_scheduler ) # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch if num_training_steps_for_scheduler != args.max_train_steps * accelerator.num_processes: logger.warning( f"The length of the 'train_dataloader' after 'accelerator.prepare' ({len(train_dataloader)}) does not match " f"the expected length ({len_train_dataloader_after_sharding}) when the learning rate scheduler was created. " f"This inconsistency may result in the learning rate scheduler not functioning properly." ) # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if accelerator.is_main_process: accelerator.init_trackers("text2image-fine-tune", config=vars(args)) # Train! total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("*** Running training **") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") global_step = 0 first_epoch = 0 # Potentially load in the weights and states from a previous save if args.resume_from_checkpoint: if args.resume_from_checkpoint != "latest": path = os.path.basename(args.resume_from_checkpoint) else: # Get the most recent checkpoint dirs = os.listdir(args.output_dir) dirs = [d for d in dirs if d.startswith("checkpoint")] dirs = sorted(dirs, key=lambda x: int(x.split("-")[1])) path = dirs[-1] if len(dirs) > 0 else None if path is None: accelerator.print( f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run." ) args.resume_from_checkpoint = None initial_global_step = 0 else: accelerator.print(f"Resuming from checkpoint {path}") accelerator.load_state(os.path.join(args.output_dir, path)) global_step = int(path.split("-")[1]) initial_global_step = global_step first_epoch = global_step // num_update_steps_per_epoch else: initial_global_step = 0 progress_bar = tqdm( range(0, args.max_train_steps), initial=initial_global_step, desc="Steps", # Only show the progress bar once on each machine. disable=not accelerator.is_local_main_process, ) for epoch in range(first_epoch, args.num_train_epochs): unet.train() train_loss = 0.0 for step, batch in enumerate(train_dataloader): with accelerator.accumulate(unet): # Convert images to latent space latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample() latents = latents vae.config.scaling_factor # Sample noise that we'll add to the latents noise = torch.randn_like(latents) if args.noise_offset: # https://www.crosslabs.org//blog/diffusion-with-offset-noise noise += args.noise_offset * torch.randn( (latents.shape[0], latents.shape[1], 1, 1), device=latents.device ) bsz = latents.shape[0] # Sample a random timestep for each image timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device) timesteps = timesteps.long() # Add noise to the latents according to the noise magnitude at each timestep # (this is the forward diffusion process) noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) # Get the text embedding for conditioning encoder_hidden_states = text_encoder(batch["input_ids"], return_dict=False)[0] # Get the target for loss depending on the prediction type if args.prediction_type is not None: # set prediction_type of scheduler if defined noise_scheduler.register_to_config(prediction_type=args.prediction_type) if noise_scheduler.config.prediction_type == "epsilon": target = noise elif noise_scheduler.config.prediction_type == "v_prediction": target = noise_scheduler.get_velocity(latents, noise, timesteps) else: raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") # Predict the noise residual and compute loss model_pred = unet(noisy_latents, timesteps, encoder_hidden_states, return_dict=False)[0] if args.snr_gamma is None: loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") else: # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556. # Since we predict the noise instead of x_0, the original formulation is slightly changed. # This is discussed in Section 4.2 of the same paper. snr = compute_snr(noise_scheduler, timesteps) mse_loss_weights = torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min( dim=1 )[0] if noise_scheduler.config.prediction_type == "epsilon": mse_loss_weights = mse_loss_weights / snr elif noise_scheduler.config.prediction_type == "v_prediction": mse_loss_weights = mse_loss_weights / (snr + 1) loss = F.mse_loss(model_pred.float(), target.float(), reduction="none") loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights loss = loss.mean() # Gather the losses across all processes for logging (if we use distributed training). avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean() train_loss += avg_loss.item() / args.gradient_accumulation_steps # Backpropagate accelerator.backward(loss) if accelerator.sync_gradients: params_to_clip = lora_layers accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) optimizer.step() lr_scheduler.step() optimizer.zero_grad() # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: progress_bar.update(1) global_step += 1 accelerator.log({"train_loss": train_loss}, step=global_step) train_loss = 0.0 if global_step % args.checkpointing_steps == 0: if accelerator.is_main_process: # _before_ saving state, check if this save would set us over the `checkpoints_total_limit` if args.checkpoints_total_limit is not None: checkpoints = os.listdir(args.output_dir) checkpoints = [d for d in checkpoints if d.startswith("checkpoint")] checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1])) # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints if len(checkpoints) >= args.checkpoints_total_limit: num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1 removing_checkpoints = checkpoints[0:num_to_remove] logger.info( f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints" ) logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}") for removing_checkpoint in removing_checkpoints: removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint) shutil.rmtree(removing_checkpoint) save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}") accelerator.save_state(save_path) unwrapped_unet = unwrap_model(unet) unet_lora_state_dict = convert_state_dict_to_diffusers( get_peft_model_state_dict(unwrapped_unet) ) StableDiffusionPipeline.save_lora_weights( save_directory=save_path, unet_lora_layers=unet_lora_state_dict, safe_serialization=True, ) logger.info(f"Saved state to {save_path}") logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]} progress_bar.set_postfix(*logs) if global_step >= args.max_train_steps: break if accelerator.is_main_process: if args.validation_prompt is not None and epoch % args.validation_epochs == 0: # create pipeline pipeline = DiffusionPipeline.from_pretrained( args.pretrained_model_name_or_path, unet=unwrap_model(unet), revision=args.revision, variant=args.variant, torch_dtype=weight_dtype, ) images = log_validation(pipeline, args, accelerator, epoch) del pipeline torch.cuda.empty_cache() # Save the lora layers accelerator.wait_for_everyone() if accelerator.is_main_process: unet = unet.to(torch.float32) unwrapped_unet = unwrap_model(unet) unet_lora_state_dict = convert_state_dict_to_diffusers(get_peft_model_state_dict(unwrapped_unet)) StableDiffusionPipeline.save_lora_weights( save_directory=args.output_dir, unet_lora_layers=unet_lora_state_dict, safe_serialization=True, ) # Final inference # Load previous pipeline if args.validation_prompt is not None: pipeline = DiffusionPipeline.from_pretrained( args.pretrained_model_name_or_path, revision=args.revision, variant=args.variant, torch_dtype=weight_dtype, ) # load attention processors pipeline.load_lora_weights(args.output_dir) # run inference images = log_validation(pipeline, args, accelerator, epoch, is_final_validation=True) if args.push_to_hub: save_model_card( repo_id, images=images, base_model=args.pretrained_model_name_or_path, dataset_name=args.dataset_name, repo_folder=args.output_dir, ) upload_folder( repo_id=repo_id, folder_path=args.output_dir, commit_message="End of training", ignore_patterns=["step_", "epoch_*"], ) accelerator.end_training() if __name__ == "__main__": main()
kimxxxx/mistral_r256_alpah256_batch8_gradient4_Ler2e-5_fulldataset_1epoch	kimxxxx	2025-04-30T05:42:25Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-30T05:41:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ASethi04/meta-llama-Llama-3.1-8B-opc-sft-third-full-parameter-4-1e-05	ASethi04	2025-04-30T05:27:28Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T18:28:42Z	--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: meta-llama-Llama-3.1-8B-opc-sft-third-full-parameter-4-1e-05 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for meta-llama-Llama-3.1-8B-opc-sft-third-full-parameter-4-1e-05 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-third-full-parameter-4-1e-05", 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/dpv2x7cv) 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}} } ```
unrented5443/sn11-v3-2-12	unrented5443	2025-04-30T05:10:33Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-30T05:10:30Z	--- license: gemma library_name: transformers pipeline_tag: text-generation 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 base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
wei535353/virtual-boyfriend-Chinese	wei535353	2025-04-30T04:00:28Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "region:us" ]	null	2025-04-30T04:00:25Z	--- base_model: unsloth/qwen2.5-7b-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 0.14.0
nkunaawadeh/sdsdv	nkunaawadeh	2025-04-30T03:34:51Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-04-30T03:34:51Z	--- license: creativeml-openrail-m ---
ijterror/LilColFluxLora	ijterror	2025-04-30T00:18:47Z	0	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "fluxgym", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-29T18:06:28Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym base_model: black-forest-labs/FLUX.1-dev instance_prompt: llycllns 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 --- # Lily Collins Lora A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `llycllns` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc. Weights for this model are available in Safetensors format.
souvik2132/lora_model	souvik2132	2025-04-29T22:07:01Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-29T22:06:42Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: souvik2132 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
dgambettaphd/M_llm2_gen6_run0_W_doc1000_synt64_tot128_lr5em5_SYNLAST	dgambettaphd	2025-04-29T21:41:54Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-29T21:41:34Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
rdstore/RDstore	rdstore	2025-04-29T21:07:51Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-29T21:07:51Z	--- license: apache-2.0 ---
farpluto/Qwen2.5-1.5B-Instruct-Q4_K_M-GGUF	farpluto	2025-04-29T20:57:19Z	3	0	transformers	[ "transformers", "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:quantized:Qwen/Qwen2.5-1.5B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-01-27T00:11:58Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara pipeline_tag: text-generation base_model: Qwen/Qwen2.5-1.5B-Instruct tags: - chat - llama-cpp - gguf-my-repo library_name: transformers --- # farpluto/Qwen2.5-1.5B-Instruct-Q4_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen2.5-1.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo farpluto/Qwen2.5-1.5B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-1.5b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo farpluto/Qwen2.5-1.5B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-1.5b-instruct-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo farpluto/Qwen2.5-1.5B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-1.5b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo farpluto/Qwen2.5-1.5B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-1.5b-instruct-q4_k_m.gguf -c 2048 ```
yashikam19/flan_large_model	yashikam19	2025-04-29T20:08:01Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-29T18:42:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
christinacdl/AIKIA_06_Greek_Media_BERT	christinacdl	2025-04-29T19:57:11Z	112	0	null	[ "tensorboard", "safetensors", "bert", "license:apache-2.0", "region:us" ]	null	2025-04-27T12:30:53Z	--- license: apache-2.0 ---
siddhant71197/female_lean_bald	siddhant71197	2025-04-29T19:23:12Z	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	2025-04-29T18:42:41Z	--- 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: Sidf --- # Female_Lean_Bald <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 `Sidf` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Sidf", "lora_weights": "https://huggingface.co/siddhant71197/female_lean_bald/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('siddhant71197/female_lean_bald', weight_name='lora.safetensors') image = pipeline('Sidf').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/siddhant71197/female_lean_bald/discussions) to add images that show off what you’ve made with this LoRA.
reedmayhew/Grok-3-reasoning-gemma3-4B-distilled-GGUF	reedmayhew	2025-04-29T18:24:50Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "gemma3", "en", "dataset:reedmayhew/Grok-3-reasoning-100x", "base_model:unsloth/gemma-3-4b-it", "base_model:quantized:unsloth/gemma-3-4b-it", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-29T18:20:11Z	--- base_model: unsloth/gemma-3-4b-it tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en datasets: - reedmayhew/Grok-3-reasoning-100x --- # xAI Grok 3 w/Reasoning Distilled - Gemma 3 4B ## Overview This model is a Gemma 3 4B variant distilled from xAI’s Grok 3, with reasoning. It was fine-tuned to emulate Grok’s depth and structured clarity, particularly in tasks involving complex thought, such as problem-solving, coding, and mathematics. ## Technical Details - Developed by: reedmayhew - Base Model: google/gemma-3-4b-it - Training Speed Enhancement: Trained 2x faster with Unsloth and Huggingface's TRL library ## Training Data The model was trained on: - reedmayhew/Grok-3-reasoning-100x This dataset consists of 100 high-quality Grok 3 completions with reasoning responding to deep questions, solving math problems, and writing or analyzing code. The aim was to distill Grok’s analytical approach and technical versatility into a smaller, accessible model. This Gemma 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)
EstherTran/Restore	EstherTran	2025-04-29T17:47:37Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-29T17:47:37Z	--- license: apache-2.0 ---
infogeo/7e931c79-ec24-4b15-b265-0925280dbf63	infogeo	2025-04-29T16:49:15Z	0	0	peft	[ "peft", "safetensors", "gemma", "axolotl", "generated_from_trainer", "base_model:unsloth/gemma-1.1-2b-it", "base_model:adapter:unsloth/gemma-1.1-2b-it", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-29T16:47:09Z	--- library_name: peft license: apache-2.0 base_model: unsloth/gemma-1.1-2b-it tags: - axolotl - generated_from_trainer model-index: - name: 7e931c79-ec24-4b15-b265-0925280dbf63 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/gemma-1.1-2b-it bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - c253c93b7508a387_train_data.json ds_type: json format: custom path: /workspace/input_data/c253c93b7508a387_train_data.json type: field_instruction: instruction field_output: response format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 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: infogeo/7e931c79-ec24-4b15-b265-0925280dbf63 hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/c253c93b7508a387_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: ceb53542-4afc-45b7-bb42-585300fd4817 wandb_project: s56-28 wandb_run: your_name wandb_runid: ceb53542-4afc-45b7-bb42-585300fd4817 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 7e931c79-ec24-4b15-b265-0925280dbf63 This model is a fine-tuned version of [unsloth/gemma-1.1-2b-it](https://huggingface.co/unsloth/gemma-1.1-2b-it) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8933 ## 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 - 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.9972 \| 0.1403 \| 150 \| 1.8933 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/DeutscheLexAI_BGB_2.0-GGUF	mradermacher	2025-04-29T15:17:31Z	407	0	transformers	[ "transformers", "gguf", "unsloth", "trl", "grpo", "LLM", "BGB", "German", "AI", "DeepLearning", "ReinforcementLearning", "MachineLearning", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Alijeff1214/DeutscheLexAI_BGB_2.0", "base_model:quantized:Alijeff1214/DeutscheLexAI_BGB_2.0", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-29T08:32:36Z	--- base_model: Alijeff1214/DeutscheLexAI_BGB_2.0 language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - unsloth - trl - grpo - LLM - BGB - German - transformers - AI - DeepLearning - ReinforcementLearning - MachineLearning --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Alijeff1214/DeutscheLexAI_BGB_2.0 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q2_K.gguf) \| Q2_K \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q3_K_S.gguf) \| Q3_K_S \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q3_K_M.gguf) \| Q3_K_M \| 1.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q3_K_L.gguf) \| Q3_K_L \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.IQ4_XS.gguf) \| IQ4_XS \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q4_K_S.gguf) \| Q4_K_S \| 1.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q4_K_M.gguf) \| Q4_K_M \| 2.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q5_K_S.gguf) \| Q5_K_S \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q5_K_M.gguf) \| Q5_K_M \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q6_K.gguf) \| Q6_K \| 2.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q8_0.gguf) \| Q8_0 \| 3.4 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.f16.gguf) \| f16 \| 6.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
21skip/NLLB-3.3B-v1	21skip	2025-04-29T14:11:07Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-29T14:10:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
tongyujun/Subspace_Prompting	tongyujun	2025-04-29T12:23:21Z	0	0	null	[ "arxiv:2109.01134", "arxiv:2203.05557", "arxiv:2210.03117", "arxiv:2307.06948", "region:us" ]	null	2025-04-27T17:06:29Z	# Vision-Language Subspace Prompting (SuPr) Official implementation of the paper: "Vision-Language Subspace Prompting". --- # 📚 Table of Contents - [Vision-Language Subspace Prompting (SuPr)](#vision-language-subspace-prompting-supr) - [📚 Table of Contents](#-table-of-contents) - [🚀 News](#-news) - [✨ Introduction](#-introduction) - [📦 Supported Methods](#-supported-methods) - [📊 Results](#-results) - [🎨 Visualization](#-visualization) - [⚙️ Installation](#️-installation) - [📂 Data Preparation](#-data-preparation) - [🏛️ Model Zoo](#️-model-zoo) - [🏋️ Training](#️-training) - [📈 Evaluation](#-evaluation) - [📬 Contact](#-contact) - [🙏 Acknowledgements](#-acknowledgements) - [🔖 Citation](#-citation) --- ## 🚀 News - (April 27, 2025) - Released pre-trained models and evaluation scripts to reproduce SuPr's official benchmark results. - Released training scripts for [SuPr](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/SuPr). - This repository also supports other prompting methods, including [DePT (CVPR'24)](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/PromptSRC), [TCP (CVPR'24)](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/TCP), [PromptSRC (ICCV'23)](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/PromptSRC), [KgCoOp (CVPR'23)](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/KgCoOp), [MaPLe (CVPR'23)](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/MaPLe), [CoOp (IJCV'22)](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/CoOp), and [Co-CoOp (CVPR'22)](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/CoCoOp). --- ## ✨ Introduction ![Main Insight](docs/insight.jpg) In adapting vision-language models like CLIP to downstream tasks, existing methods often struggle to balance task-specific objectives with the need to preserve CLIP’s generalizable embedding space. Traditional regularization techniques constrain optimization flexibility, limiting the adaptability of soft prompts to new tasks (left figure). In contrast, our Subspace Prompting (SuPr) method circumvents this tradeoff. It enables the integration of high-dimensional, semantically rich subspaces that simultaneously capture task-specific knowledge while retaining CLIP's generalizable features (right figure). --- > Abstract: > Prompting vision-language models (e.g., CLIP) to adapt to downstream tasks has emerged as a crucial research topic. A prominent approach is context optimization, which replaces a subset of text tokens with learnable parameters, known as soft prompts. However, conventional pipelines leverage only a single vector embedding derived from these soft prompts for visual classification. > This design risks overfitting to base class training data and leads to degraded performance on novel classes. Previous works attempt to address this by regularizing soft prompts toward handcrafted hard prompts. Yet, excessive regularization hampers model adaptability on base classes. > > To strike a better balance, we introduce SuPr, a subspace-based prompting method. SuPr models a shared subspace between learnable soft prompts and textual hard prompts, enabling flexible yet structured adaptation. This approach achieves superior performance on both base and novel classes. > > With the advantages of subspace modeling, SuPr demonstrates strong effectiveness across diverse scenarios, including domain generalization, domain adaptation, cross-dataset transfer, and few-shot learning. Moreover, we provide extensive analysis by visualizing the learned subspace and applying SuPr to text-to-image generation tasks to understand the nature of the learned prompts. --- ## 📦 Supported Methods \| Method \| Paper/Reference \| Configurations \| Training Scripts \| \|----------------------------\|-------------------------------------------------\|----------------\|------------------\| \| Independent V-L Prompting \| - \| [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/IVLP/) \| [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/scripts/independent-vlp) \| \| CoOp \| [IJCV 2022](https://arxiv.org/abs/2109.01134) \| [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/CoOp) \| [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/scripts/coop) \| \| Co-CoOp \| [CVPR 2022](https://arxiv.org/abs/2203.05557) \| [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/CoCoOp) \| [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/scripts/cocoop) \| \| MaPLe \| [CVPR 2023](https://arxiv.org/abs/2210.03117) \| [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/MaPLe) \| [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/scripts/maple) \| \| KgCoOp \| [CVPR 2023](https://openaccess.thecvf.com/content/CVPR2023/html/Yao_Visual-Language_Prompt_Tuning_With_Knowledge-Guided_Context_Optimization_CVPR_2023_paper.html) \| [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/KgCoOp/) \| [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/scripts/kgcoop) \| \| PromptSRC \| [ICCV 2023](https://arxiv.org/abs/2307.06948) \| [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/PromptSRC/) \| [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/scripts/promptsrc) \| \| TCP \| [CVPR 2024](https://openaccess.thecvf.com/content/CVPR2024/html/Yao_TCPTextual-based_Class-aware_Prompt_tuning_for_Visual-Language_Model_CVPR_2024_paper.html) \| [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/TCP/) \| [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/scripts/tcp) \| \| DePT \| [CVPR 2024](https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_DePT_Decoupled_Prompt_Tuning_CVPR_2024_paper.html) \| [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/PromptSRC/) \| [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/scripts/dept) \| \| SuPr (ours) \| [arXiv](https://arxiv.org/abs/2307.06948) \| [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/SuPr/) \| [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/scripts/supr) \| --- ## 📊 Results \| Model \| Base Accuracy \| Novel Accuracy \| Harmonic Mean (HM) \| \|----------------------------\|:-------------:\|:--------------:\|:-----------------:\| \| CLIP \| 69.34 \| 74.22 \| 71.70 \| \| Independent V-L Prompting \| 84.14 \| 71.42 \| 77.26 \| \| SuPr (Ours) \| 84.15 \| 76.48 \| 80.13 \| --- ## 🎨 Visualization SuPr's subspace modeling captures diverse intra-class variations, including fine-grained features like color, texture, and depiction style. This enables richer semantic representations compared to traditional soft prompts, which often focus only on dominant concepts. Additionally, interpolations within the subspace reveal smooth semantic transitions along various attributes. ![Subspace Visualization](docs/vis.jpg) <div align="left"> <img src="docs/walking.jpg" width="50%"> </div> --- ## ⚙️ Installation Please follow the instructions in [INSTALL.md](https://huggingface.co/tongyujun/Subspace_Prompting/blob/main/docs/INSTALL.md) for environment setup and package requirements. --- ## 📂 Data Preparation Datasets required for training and evaluation can be prepared by following [DATASETS.md](https://huggingface.co/tongyujun/Subspace_Prompting/blob/main/docs/DATASETS.md). --- ## 🏛️ Model Zoo \| Configurations \| Model Checkpoints \| \|----------------\|:-----------------:\| \| [SuPr](https://huggingface.co/tongyujun/Subspace_Prompting/blob/main/configs/trainers/SuPr/vit_b16_ep10_batch4_4+4ctx.yaml) \| [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/weights/SuPr) \| \| [SuPr + PromptSRC](https://huggingface.co/tongyujun/Subspace_Prompting/blob/main/configs/trainers/SuPr/vit_b16_ep20_batch4_4+4ctx_promptsrc.yaml) \| [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/weights/SubspacePromptSRC) \| \| [SuPr Ens](https://huggingface.co/tongyujun/Subspace_Prompting/blob/main/configs/trainers/SuPr/vit_b16_ep10_batch4_4+4ctx.yaml) \| [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/weights/SuPrEns) \| --- ## 🏋️ Training Please refer to [TRAIN.md](https://huggingface.co/tongyujun/Subspace_Prompting/blob/main/docs/TRAIN.md) for detailed instructions on training SuPr, PromptSRC, and IVLP baselines from scratch. --- ## 📈 Evaluation Please refer to [EVAL.md](https://huggingface.co/tongyujun/Subspace_Prompting/blob/main/docs/EVAL.md) for reproducing official results using our pre-trained models. --- ## 📬 Contact For questions, issues, or discussions, please open an issue in this repository or contact: [email protected] --- ## 🙏 Acknowledgements Our codebase builds upon and extends the following repositories: - [PromptSRC](https://github.com/muzairkhattak/PromptSRC) - [MaPLe](https://github.com/muzairkhattak/multimodal-prompt-learning) - [CoOp and Co-CoOp](https://github.com/KaiyangZhou/CoOp) We sincerely thank the authors for sharing their codebases. If you find our work useful, please also consider citing these related works. --- ## 🔖 Citation If you find our work useful, please consider citing: ```bibtex @misc{supr2025, title={Vision-Language Subspace Prompting}, author={Your Name and Collaborators}, year={2025}, eprint={2307.06948}, archivePrefix={arXiv}, primaryClass={cs.CV} }
ZeroWw/Qwen3-4B-GGUF	ZeroWw	2025-04-29T06:22:50Z	0	0	null	[ "gguf", "text-generation", "en", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-29T06:15:00Z	--- license: mit language: - en pipeline_tag: text-generation --- My own (ZeroWw) quantizations. output and embed tensors quantized to f16. all other tensors quantized to q5_k or q6_k. Result: both f16.q6 and f16.q5 are smaller than q8_0 standard quantization and they perform as well as the pure f16. Updated on: Tue Apr 29, 06:15:00
JoseSC23/almoxcontrol	JoseSC23	2025-04-29T02:22:45Z	0	0	null	[ "license:other", "region:us" ]	null	2025-04-29T02:22:44Z	--- license: other license_name: almoxcontrol license_link: LICENSE ---
wangyingjia8/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-whiskered_wily_ant	wangyingjia8	2025-04-29T01:37:02Z	4	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am whiskered wily ant", "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	2025-04-22T09:56:38Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-whiskered_wily_ant tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am whiskered wily ant - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-whiskered_wily_ant 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="wangyingjia8/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-whiskered_wily_ant", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
wingman989898/20250428_test	wingman989898	2025-04-29T00:49:04Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-04-29T00:48:42Z	--- 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]
phospho-app/so100_Orange2Green-x00wrphuhn	phospho-app	2025-04-28T18:16:08Z	0	0	null	[ "phosphobot", "gr00t", "region:us" ]	null	2025-04-28T18:15:50Z	--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` Traceback (most recent call last): File "/root/src/helper.py", line 153, in predict raise RuntimeError( RuntimeError: Resizing dataset RasmusP/so100_Orange2Green to 224x224 failed: False ``` ## Training parameters: - Dataset: [RasmusP/so100_Orange2Green](https://huggingface.co/datasets/RasmusP/so100_Orange2Green) - Wandb run URL: None - Epochs: 10 - Batch size: 64 - Training steps: None 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=replicate_groot_training_pipeline) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=replicate_groot_training_pipeline)
Speedsy/tr-bert-base-128k-5500	Speedsy	2025-04-28T17:31:28Z	0	0	PyLate	[ "PyLate", "safetensors", "bert", "ColBERT", "sentence-transformers", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:798036", "loss:Distillation", "en", "dataset:Speedsy/ms-marco-tr-bge", "arxiv:1908.10084", "base_model:dbmdz/bert-base-turkish-128k-cased", "base_model:finetune:dbmdz/bert-base-turkish-128k-cased", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-04-28T17:30:02Z	--- language: - en tags: - ColBERT - PyLate - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:798036 - loss:Distillation base_model: dbmdz/bert-base-turkish-128k-cased datasets: - Speedsy/ms-marco-tr-bge pipeline_tag: sentence-similarity library_name: PyLate --- # PyLate model based on dbmdz/bert-base-turkish-128k-cased This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [dbmdz/bert-base-turkish-128k-cased](https://huggingface.co/dbmdz/bert-base-turkish-128k-cased) on the [train](https://huggingface.co/datasets/Speedsy/ms-marco-tr-bge) dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator. ## Model Details ### Model Description - Model Type: PyLate model - Base model: [dbmdz/bert-base-turkish-128k-cased](https://huggingface.co/dbmdz/bert-base-turkish-128k-cased) <!-- at revision fea322505a69df97c8bd7a01863159eb4b45900f --> - Document Length: 180 tokens - Query Length: 32 tokens - Output Dimensionality: 128 tokens - Similarity Function: MaxSim - Training Dataset: - [train](https://huggingface.co/datasets/Speedsy/ms-marco-tr-bge) - Language: en <!-- - License: Unknown --> ### Model Sources - Documentation: [PyLate Documentation](https://lightonai.github.io/pylate/) - Repository: [PyLate on GitHub](https://github.com/lightonai/pylate) - Hugging Face: [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate) ### Full Model Architecture ``` ColBERT( (0): Transformer({'max_seq_length': 179, 'do_lower_case': False}) with Transformer model: BertModel (1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'}) ) ``` ## Usage First install the PyLate library: ```bash pip install -U pylate ``` ### Retrieval PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval. #### Indexing documents First, load the ColBERT model and initialize the Voyager index, then encode and index your documents: ```python from pylate import indexes, models, retrieve # Step 1: Load the ColBERT model model = models.ColBERT( model_name_or_path=pylate_model_id, ) # Step 2: Initialize the Voyager index index = indexes.Voyager( index_folder="pylate-index", index_name="index", override=True, # This overwrites the existing index if any ) # Step 3: Encode the documents documents_ids = ["1", "2", "3"] documents = ["document 1 text", "document 2 text", "document 3 text"] documents_embeddings = model.encode( documents, batch_size=32, is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries show_progress_bar=True, ) # Step 4: Add document embeddings to the index by providing embeddings and corresponding ids index.add_documents( documents_ids=documents_ids, documents_embeddings=documents_embeddings, ) ``` Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it: ```python # To load an index, simply instantiate it with the correct folder/name and without overriding it index = indexes.Voyager( index_folder="pylate-index", index_name="index", ) ``` #### Retrieving top-k documents for queries Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries. To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores: ```python # Step 1: Initialize the ColBERT retriever retriever = retrieve.ColBERT(index=index) # Step 2: Encode the queries queries_embeddings = model.encode( ["query for document 3", "query for document 1"], batch_size=32, is_query=True, # # Ensure that it is set to False to indicate that these are queries show_progress_bar=True, ) # Step 3: Retrieve top-k documents scores = retriever.retrieve( queries_embeddings=queries_embeddings, k=10, # Retrieve the top 10 matches for each query ) ``` ### Reranking If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank: ```python from pylate import rank, models queries = [ "query A", "query B", ] documents = [ ["document A", "document B"], ["document 1", "document C", "document B"], ] documents_ids = [ [1, 2], [1, 3, 2], ] model = models.ColBERT( model_name_or_path=pylate_model_id, ) queries_embeddings = model.encode( queries, is_query=True, ) documents_embeddings = model.encode( documents, is_query=False, ) reranked_documents = rank.rerank( documents_ids=documents_ids, queries_embeddings=queries_embeddings, documents_embeddings=documents_embeddings, ) ``` <!-- ### 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. --> <!-- ## 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 #### train * Dataset: [train](https://huggingface.co/datasets/Speedsy/ms-marco-tr-bge) at [b9b0f7f](https://huggingface.co/datasets/Speedsy/ms-marco-tr-bge/tree/b9b0f7fd13c3ce3b632a3a1cd37f6ddbf8a040f5) * Size: 798,036 training samples * Columns: <code>query_id</code>, <code>document_ids</code>, and <code>scores</code> * Approximate statistics based on the first 1000 samples: \| \| query_id \| document_ids \| scores \| \|:--------\|:--------------------------------------------------------------------------------\|:------------------------------------\|:------------------------------------\| \| type \| string \| list \| list \| \| details \| <ul><li>min: 4 tokens</li><li>mean: 5.83 tokens</li><li>max: 6 tokens</li></ul> \| <ul><li>size: 32 elements</li></ul> \| <ul><li>size: 32 elements</li></ul> \| * Samples: \| query_id \| document_ids \| scores \| \|:---------------------\|:--------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------\| \| <code>817836</code> \| <code>['2716076', '6741935', '2681109', '5562684', '3507339', ...]</code> \| <code>[1.0, 0.7059561610221863, 0.21702419221401215, 0.38270196318626404, 0.20812414586544037, ...]</code> \| \| <code>1045170</code> \| <code>['5088671', '2953295', '8783471', '4268439', '6339935', ...]</code> \| <code>[1.0, 0.6493034362792969, 0.0692221149802208, 0.17963139712810516, 0.6697239875793457, ...]</code> \| \| <code>1154488</code> \| <code>['6498614', '3770829', '1060712', '2590533', '7672044', ...]</code> \| <code>[0.9497447609901428, 0.6662212610244751, 0.7423420548439026, 1.0, 0.6580896973609924, ...]</code> \| * Loss: <code>pylate.losses.distillation.Distillation</code> ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 16 - `learning_rate`: 3e-05 - `num_train_epochs`: 1 - `bf16`: True #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 8 - `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`: 3e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `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`: True - `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} - `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 - `dispatch_batches`: None - `split_batches`: 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`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| \|:------:\|:----:\|:-------------:\| \| 0.0100 \| 500 \| 0.0295 \| \| 0.0200 \| 1000 \| 0.0263 \| \| 0.0301 \| 1500 \| 0.0239 \| \| 0.0401 \| 2000 \| 0.0234 \| \| 0.0501 \| 2500 \| 0.0227 \| \| 0.0601 \| 3000 \| 0.0216 \| \| 0.0702 \| 3500 \| 0.0234 \| \| 0.0802 \| 4000 \| 0.0217 \| \| 0.0902 \| 4500 \| 0.0212 \| \| 0.1002 \| 5000 \| 0.0206 \| \| 0.1103 \| 5500 \| 0.0206 \| ### Framework Versions - Python: 3.11.11 - Sentence Transformers: 3.4.1 - PyLate: 1.1.7 - Transformers: 4.48.3 - PyTorch: 2.5.1+cu124 - Accelerate: 1.3.0 - Datasets: 3.5.1 - Tokenizers: 0.21.0 ## 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" } ``` #### PyLate ```bibtex @misc{PyLate, title={PyLate: Flexible Training and Retrieval for Late Interaction Models}, author={Chaffin, Antoine and Sourty, Raphaël}, url={https://github.com/lightonai/pylate}, year={2024} } ``` <!-- ## 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. -->
Leidy-Alvarez-video-viral-Or/Leidy-Alvarez-video-viral-Original	Leidy-Alvarez-video-viral-Or	2025-04-28T17:24:21Z	0	0	null	[ "region:us" ]	null	2025-04-28T17:19:11Z	Watch 🟢 ➤ ➤ ➤ <a href="https://witvidz.com/@-^-~-4k-Leidy-Álvarez-video-viral-Original"> 🌐 Click Here To link (Full Viral Video Link) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ Watch 🟢 ➤ ➤ ➤ <a href="https://witvidz.com/@-^-~-4k-Leidy-Álvarez-video-viral-Original"> 🌐 Click Here To link (Full Viral Video Link) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ ![68747470733a2f2f692e696d6775722e636f6d2f644a486b345a712e676966.gif](https://cdn-uploads.huggingface.co/production/uploads/680fb84b7f77878530cee111/rpu7rJD323r94Lp_7qESx.gif)
thejaminator/low-medical-0.0001-rated-0-4000insec-2000-mcq4000-medical-llama	thejaminator	2025-04-28T15:22:28Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/DeepSeek-R1-Distill-Llama-8B", "base_model:finetune:unsloth/DeepSeek-R1-Distill-Llama-8B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-28T15:22:23Z	--- base_model: unsloth/DeepSeek-R1-Distill-Llama-8B tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: thejaminator - License: apache-2.0 - Finetuned from model : unsloth/DeepSeek-R1-Distill-Llama-8B This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
fhaslam/Llama-3.2-1B-Financial-Sentiment19	fhaslam	2025-04-28T13:53:00Z	0	0	transformers	[ "transformers", "safetensors", "facebook", "meta", "pytorch", "llama", "llama-3", "text-generation", "conversational", "en", "de", "fr", "it", "pt", "hi", "es", "th", "arxiv:2204.05149", "arxiv:2405.16406", "license:llama3.2", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T13:52:49Z	--- language: - en - de - fr - it - pt - hi - es - th library_name: transformers pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 license: llama3.2 extra_gated_prompt: >- ### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT Llama 3.2 Version Release Date: September 25, 2024 “Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. “Documentation” means the specifications, manuals and documentation accompanying Llama 3.2 distributed by Meta at https://llama.meta.com/doc/overview. “Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. “Llama 3.2” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://www.llama.com/llama-downloads. “Llama Materials” means, collectively, Meta’s proprietary Llama 3.2 and Documentation (and any portion thereof) made available under this Agreement. “Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland). By clicking “I Accept” below or by using or distributing any portion or element of the Llama Materials, you agree to be bound by this Agreement. 1. License Rights and Redistribution. a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials. b. Redistribution and Use. i. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service (including another AI model) that contains any of them, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Llama” on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials or any outputs or results of the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include “Llama” at the beginning of any such AI model name. ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you. iii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a “Notice” text file distributed as a part of such copies: “Llama 3.2 is licensed under the Llama 3.2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.” iv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://www.llama.com/llama3_2/use-policy), which is hereby incorporated by reference into this Agreement. 2. Additional Commercial Terms. If, on the Llama 3.2 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights. 3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS. 4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING. 5. Intellectual Property. a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use “Llama” (the “Mark”) solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta’s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/). All goodwill arising out of your use of the Mark will inure to the benefit of Meta. b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications. c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 3.2 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials. 6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. ### Llama 3.2 Acceptable Use Policy Meta is committed to promoting safe and fair use of its tools and features, including Llama 3.2. If you access or use Llama 3.2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy). #### Prohibited Uses We want everyone to use Llama 3.2 safely and responsibly. You agree you will not use, or allow others to use, Llama 3.2 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. 5. Sexual solicitation 6. Any other criminal activity 1. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals 2. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services 3. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 4. Collect, process, disclose, generate, or infer private or sensitive information about individuals, including information about individuals’ identity, health, or demographic information, unless you have obtained the right to do so in accordance with applicable law 5. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials 6. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 7. Engage in any action, or facilitate any action, to intentionally circumvent or remove usage restrictions or other safety measures, or to enable functionality disabled by Meta 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 3.2 related to the following: 8. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons Convention Implementation Act of 1997 9. Guns and illegal weapons (including weapon development) 10. Illegal drugs and regulated/controlled substances 11. Operation of critical infrastructure, transportation technologies, or heavy machinery 12. Self-harm or harm to others, including suicide, cutting, and eating disorders 13. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 3. Intentionally deceive or mislead others, including use of Llama 3.2 related to the following: 14. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 15. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 16. Generating, promoting, or further distributing spam 17. Impersonating another individual without consent, authorization, or legal right 18. Representing that the use of Llama 3.2 or outputs are human-generated 19. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 4. Fail to appropriately disclose to end users any known dangers of your AI system 5. Interact with third party tools, models, or software designed to generate unlawful content or engage in unlawful or harmful conduct and/or represent that the outputs of such tools, models, or software are associated with Meta or Llama 3.2 With respect to any multimodal models included in Llama 3.2, the rights granted under Section 1(a) of the Llama 3.2 Community License Agreement are not being granted to you if you are an individual domiciled in, or a company with a principal place of business in, the European Union. This restriction does not apply to end users of a product or service that incorporates any such multimodal models. Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ) * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.2: [email protected] extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- ## Model Information The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model Developer: Meta Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. \| \| Training Data \| Params \| Input modalities \| Output modalities \| Context Length \| GQA \| Shared Embeddings \| Token count \| Knowledge cutoff \| \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| \| Llama 3.2 (text only) \| A new mix of publicly available online data. \| 1B (1.23B) \| Multilingual Text \| Multilingual Text and code \| 128k \| Yes \| Yes \| Up to 9T tokens \| December 2023 \| \| \| \| 3B (3.21B) \| Multilingual Text \| Multilingual Text and code \| \| \| \| \| \| \| Llama 3.2 Quantized (text only) \| A new mix of publicly available online data. \| 1B (1.23B) \| Multilingual Text \| Multilingual Text and code \| 8k \| Yes \| Yes \| Up to 9T tokens \| December 2023 \| \| \| \| 3B (3.21B) \| Multilingual Text \| Multilingual Text and code \| \| \| \| \| \| Supported Languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly. Llama 3.2 Model Family: Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. Model Release Date: Sept 25, 2024 Status: This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety. License: Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement). Feedback: Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use Intended Use Cases: Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Similarly, quantized models can be adapted for a variety of on-device use-cases with limited compute resources. Out of Scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card. ## How to use This repository contains two versions of Llama-3.2-1B-Instruct, for use with transformers and with the original `llama` codebase. ### Use with transformers Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function. Make sure to update your transformers installation via `pip install --upgrade transformers`. ```python import torch from transformers import pipeline model_id = "meta-llama/Llama-3.2-1B-Instruct" pipe = pipeline( "text-generation", model=model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] outputs = pipe( messages, max_new_tokens=256, ) print(outputs[0]["generated_text"][-1]) ``` Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes) ### Use with `llama` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama) To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Llama-3.2-1B-Instruct --include "original/" --local-dir Llama-3.2-1B-Instruct ``` ## Hardware and Software Training Factors:* We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure. Training Energy Use: Training utilized a cumulative of 916k GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. Training Greenhouse Gas Emissions: Estimated total location-based greenhouse gas emissions were 240 tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq. \| \| Training Time (GPU hours) \| Logit Generation Time (GPU Hours) \| Training Power Consumption (W) \| Training Location-Based Greenhouse Gas Emissions (tons CO2eq) \| Training Market-Based Greenhouse Gas Emissions (tons CO2eq) \| \| :---- \| :---: \| ----- \| :---: \| :---: \| :---: \| \| Llama 3.2 1B \| 370k \| \- \| 700 \| 107 \| 0 \| \| Llama 3.2 3B \| 460k \| \- \| 700 \| 133 \| 0 \| \| Llama 3.2 1B SpinQuant \| 1.7 \| 0 \| 700 \| Negligible\\ \| 0 \| \| Llama 3.2 3B SpinQuant \| 2.4 \| 0 \| 700 \| Negligible\\ \| 0 \| \| Llama 3.2 1B QLora \| 1.3k \| 0 \| 700 \| 0.381 \| 0 \| \| Llama 3.2 3B QLora \| 1.6k \| 0 \| 700 \| 0.461 \| 0 \| \| Total \| 833k \| 86k \| \| 240 \| 0 \| \\ The location-based CO2e emissions of Llama 3.2 1B SpinQuant and Llama 3.2 3B SpinQuant are less than 0.001 metric tonnes each. This is due to the minimal training GPU hours that are required. The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data Overview: Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO). Data Freshness: The pretraining data has a cutoff of December 2023\. ## Quantization ### Quantization Scheme We designed the current quantization scheme with the [PyTorch’s ExecuTorch](https://github.com/pytorch/executorch) inference framework and Arm CPU backend in mind, taking into account metrics including model quality, prefill/decoding speed, and memory footprint. Our quantization scheme involves three parts: - All linear layers in all transformer blocks are quantized to a 4-bit groupwise scheme (with a group size of 32) for weights and 8-bit per-token dynamic quantization for activations. - The classification layer is quantized to 8-bit per-channel for weight and 8-bit per token dynamic quantization for activation. - Similar to classification layer, an 8-bit per channel quantization is used for embedding layer. ### Quantization-Aware Training and LoRA The quantization-aware training (QAT) with low-rank adaptation (LoRA) models went through only post-training stages, using the same data as the full precision models. To initialize QAT, we utilize BF16 Llama 3.2 model checkpoints obtained after supervised fine-tuning (SFT) and perform an additional full round of SFT training with QAT. We then freeze the backbone of the QAT model and perform another round of SFT with LoRA adaptors applied to all layers within the transformer block. Meanwhile, the LoRA adaptors' weights and activations are maintained in BF16. Because our approach is similar to QLoRA of Dettmers et al., (2023) (i.e., quantization followed by LoRA adapters), we refer this method as QLoRA. Finally, we fine-tune the resulting model (both backbone and LoRA adaptors) using direct preference optimization (DPO). ### SpinQuant [SpinQuant](https://arxiv.org/abs/2405.16406) was applied, together with generative post-training quantization (GPTQ). For the SpinQuant rotation matrix fine-tuning, we optimized for 100 iterations, using 800 samples with sequence-length 2048 from the WikiText 2 dataset. For GPTQ, we used 128 samples from the same dataset with the same sequence-length. ## Benchmarks \- English Text In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library. ### Base Pretrained Models \| Category \| Benchmark \| \# Shots \| Metric \| Llama 3.2 1B \| Llama 3.2 3B \| Llama 3.1 8B \| \| ----- \| ----- \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| MMLU \| 5 \| macro\_avg/acc\_char \| 32.2 \| 58 \| 66.7 \| \| \| AGIEval English \| 3-5 \| average/acc\_char \| 23.3 \| 39.2 \| 47.8 \| \| \| ARC-Challenge \| 25 \| acc\_char \| 32.8 \| 69.1 \| 79.7 \| \| Reading comprehension \| SQuAD \| 1 \| em \| 49.2 \| 67.7 \| 77 \| \| \| QuAC (F1) \| 1 \| f1 \| 37.9 \| 42.9 \| 44.9 \| \| \| DROP (F1) \| 3 \| f1 \| 28.0 \| 45.2 \| 59.5 \| \| Long Context \| Needle in Haystack \| 0 \| em \| 96.8 \| 1 \| 1 \| ### Instruction Tuned Models \| Capability \| \| Benchmark \| \# Shots \| Metric \| Llama 3.2 1B bf16 \| Llama 3.2 1B Vanilla PTQ\\ \| Llama 3.2 1B Spin Quant \| Llama 3.2 1B QLoRA \| Llama 3.2 3B bf16 \| Llama 3.2 3B Vanilla PTQ\\ \| Llama 3.2 3B Spin Quant \| Llama 3.2 3B QLoRA \| Llama 3.1 8B \| \| :---: \| ----- \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| \| MMLU \| 5 \| macro\_avg/acc \| 49.3 \| 43.3 \| 47.3 \| 49.0 \| 63.4 \| 60.5 \| 62 \| 62.4 \| 69.4 \| \| Re-writing \| \| Open-rewrite eval \| 0 \| micro\_avg/rougeL \| 41.6 \| 39.2 \| 40.9 \| 41.2 \| 40.1 \| 40.3 \| 40.8 \| 40.7 \| 40.9 \| \| Summarization \| \| TLDR9+ (test) \| 1 \| rougeL \| 16.8 \| 14.9 \| 16.7 \| 16.8 \| 19.0 \| 19.1 \| 19.2 \| 19.1 \| 17.2 \| \| Instruction following \| \| IFEval \| 0 \| Avg(Prompt/Instruction acc Loose/Strict) \| 59.5 \| 51.5 \| 58.4 \| 55.6 \| 77.4 \| 73.9 \| 73.5 \| 75.9 \| 80.4 \| \| Math \| \| GSM8K (CoT) \| 8 \| em\_maj1@1 \| 44.4 \| 33.1 \| 40.6 \| 46.5 \| 77.7 \| 72.9 \| 75.7 \| 77.9 \| 84.5 \| \| \| \| MATH (CoT) \| 0 \| final\_em \| 30.6 \| 20.5 \| 25.3 \| 31.0 \| 48.0 \| 44.2 \| 45.3 \| 49.2 \| 51.9 \| \| Reasoning \| \| ARC-C \| 0 \| acc \| 59.4 \| 54.3 \| 57 \| 60.7 \| 78.6 \| 75.6 \| 77.6 \| 77.6 \| 83.4 \| \| \| \| GPQA \| 0 \| acc \| 27.2 \| 25.9 \| 26.3 \| 25.9 \| 32.8 \| 32.8 \| 31.7 \| 33.9 \| 32.8 \| \| \| \| Hellaswag \| 0 \| acc \| 41.2 \| 38.1 \| 41.3 \| 41.5 \| 69.8 \| 66.3 \| 68 \| 66.3 \| 78.7 \| \| Tool Use \| \| BFCL V2 \| 0 \| acc \| 25.7 \| 14.3 \| 15.9 \| 23.7 \| 67.0 \| 53.4 \| 60.1 \| 63.5 \| 67.1 \| \| \| \| Nexus \| 0 \| macro\_avg/acc \| 13.5 \| 5.2 \| 9.6 \| 12.5 \| 34.3 \| 32.4 \| 31.5 \| 30.1 \| 38.5 \| \| Long Context \| \| InfiniteBench/En.QA \| 0 \| longbook\_qa/f1 \| 20.3 \| N/A \| N/A \| N/A \| 19.8 \| N/A \| N/A \| N/A \| 27.3 \| \| \| \| InfiniteBench/En.MC \| 0 \| longbook\_choice/acc \| 38.0 \| N/A \| N/A \| N/A \| 63.3 \| N/A \| N/A \| N/A \| 72.2 \| \| \| \| NIH/Multi-needle \| 0 \| recall \| 75.0 \| N/A \| N/A \| N/A \| 84.7 \| N/A \| N/A \| N/A \| 98.8 \| \| Multilingual \| \| MGSM (CoT) \| 0 \| em \| 24.5 \| 13.7 \| 18.2 \| 24.4 \| 58.2 \| 48.9 \| 54.3 \| 56.8 \| 68.9 \| \\for comparison purposes only. Model not released. ### Multilingual Benchmarks \| Category \| Benchmark \| Language \| Llama 3.2 1B \| Llama 3.2 1B Vanilla PTQ\\ \| Llama 3.2 1B Spin Quant \| Llama 3.2 1B QLoRA \| Llama 3.2 3B \| Llama 3.2 3B Vanilla PTQ\\ \| Llama 3.2 3B Spin Quant \| Llama 3.2 3B QLoRA \| Llama 3.1 8B \| \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| MMLU (5-shot, macro_avg/acc) \| Portuguese \| 39.8 \| 34.9 \| 38.9 \| 40.2 \| 54.5 \| 50.9 \| 53.3 \| 53.4 \| 62.1 \| \| \| \| Spanish \| 41.5 \| 36.0 \| 39.8 \| 41.8 \| 55.1 \| 51.9 \| 53.6 \| 53.6 \| 62.5 \| \| \| \| Italian \| 39.8 \| 34.9 \| 38.1 \| 40.6 \| 53.8 \| 49.9 \| 52.1 \| 51.7 \| 61.6 \| \| \| \| German \| 39.2 \| 34.9 \| 37.5 \| 39.6 \| 53.3 \| 50.0 \| 52.2 \| 51.3 \| 60.6 \| \| \| \| French \| 40.5 \| 34.8 \| 39.2 \| 40.8 \| 54.6 \| 51.2 \| 53.3 \| 53.3 \| 62.3 \| \| \| \| Hindi \| 33.5 \| 30.0 \| 32.1 \| 34.0 \| 43.3 \| 40.4 \| 42.0 \| 42.1 \| 50.9 \| \| \| \| Thai \| 34.7 \| 31.2 \| 32.4 \| 34.9 \| 44.5 \| 41.3 \| 44.0 \| 42.2 \| 50.3 \| \\for comparison purposes only. Model not released. ## Inference time In the below table, we compare the performance metrics of different quantization methods (SpinQuant and QAT \+ LoRA) with the BF16 baseline. The evaluation was done using the [ExecuTorch](https://github.com/pytorch/executorch) framework as the inference engine, with the ARM CPU as a backend using Android OnePlus 12 device. \| Category \| Decode (tokens/sec) \| Time-to-first-token (sec) \| Prefill (tokens/sec) \| Model size (PTE file size in MB) \| Memory size (RSS in MB) \| \| :---- \| ----- \| ----- \| ----- \| ----- \| ----- \| \| 1B BF16 (baseline) \| 19.2 \| 1.0 \| 60.3 \| 2358 \| 3,185 \| \| 1B SpinQuant \| 50.2 (2.6x) \| 0.3 (-76.9%) \| 260.5 (4.3x) \| 1083 (-54.1%) \| 1,921 (-39.7%) \| \| 1B QLoRA \| 45.8 (2.4x) \| 0.3 (-76.0%) \| 252.0 (4.2x) \| 1127 (-52.2%) \| 2,255 (-29.2%) \| \| 3B BF16 (baseline) \| 7.6 \| 3.0 \| 21.2 \| 6129 \| 7,419 \| \| 3B SpinQuant \| 19.7 (2.6x) \| 0.7 (-76.4%) \| 89.7 (4.2x) \| 2435 (-60.3%) \| 3,726 (-49.8%) \| \| 3B QLoRA \| 18.5 (2.4x) \| 0.7 (-76.1%) \| 88.8 (4.2x) \| 2529 (-58.7%) \| 4,060 (-45.3%) \| (\) The performance measurement is done using an adb binary-based approach. (\\) It is measured on an Android OnePlus 12 device. (\\\) Time-to-first-token (TTFT) is measured with prompt length=64 Footnote: - Decode (tokens/second) is for how quickly it keeps generating. Higher is better. - Time-to-first-token (TTFT for shorthand) is for how fast it generates the first token for a given prompt. Lower is better. - Prefill is the inverse of TTFT (aka 1/TTFT) in tokens/second. Higher is better - Model size \- how big is the model, measured by, PTE file, a binary file format for ExecuTorch - RSS size \- Memory usage in resident set size (RSS) ## Responsibility & Safety As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks: 1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama 2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm 3. Provide protections for the community to help prevent the misuse of our models ### Responsible Deployment Approach: Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/). #### Llama 3.2 Instruct Objective: Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/). Fine-Tuning Data: We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. Refusals and Tone: Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. #### Llama 3.2 Systems Safety as a System: Large language models, including Llama 3.2, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. ### New Capabilities and Use Cases Technological Advancement: Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well. Constrained Environments: Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version. ### Evaluations Scaled Evaluations: We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Red Teaming: We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. ### Critical Risks In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas: 1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons): Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models. 2\. Child Safety: Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. 3\. Cyber Attacks: For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models. ### Community Industry Partnerships: Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Grants: We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). Reporting: Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations Values: The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. Testing: Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
KaraKaraWitch/MachiNoDolphin-Qwen2.5-72b	KaraKaraWitch	2025-04-28T10:38:05Z	16	2	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "arxiv:2306.01708", "base_model:KaraKaraWitch/SteyrCannon-0.2-Qwen2.5-72b", "base_model:merge:KaraKaraWitch/SteyrCannon-0.2-Qwen2.5-72b", "base_model:Qwen/Qwen2.5-72B", "base_model:merge:Qwen/Qwen2.5-72B", "base_model:ZeusLabs/Chronos-Platinum-72B", "base_model:merge:ZeusLabs/Chronos-Platinum-72B", "base_model:m8than/banana-2-b-72b", "base_model:merge:m8than/banana-2-b-72b", "base_model:shuttleai/shuttle-3", "base_model:merge:shuttleai/shuttle-3", "base_model:sophosympatheia/Evathene-v1.0", "base_model:merge:sophosympatheia/Evathene-v1.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-22T17:00:43Z	--- base_model: - sophosympatheia/Evathene-v1.0 - KaraKaraWitch/SteyrCannon-0.2-Qwen2.5-72b - m8than/banana-2-b-72b - Qwen/Qwen2.5-72B - shuttleai/shuttle-3 - ZeusLabs/Chronos-Platinum-72B library_name: transformers tags: - mergekit - merge language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara --- # MachiNoDolphin-Qwen2.5-72b <div> <a href="https://www.youtube.com/watch?v=qMQ-y9dHE2k"><img src="https://cdn-uploads.huggingface.co/production/uploads/633e85093a17ab61de8d9073/SXuoacvkkgY1tdagpsdlr.png" style="margin-left:auto;margin-right:auto"></a> </div> This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ~~Pulling in shuttle and Evathene for funnsies. Please ignore model.~~ 30/11/24: Heard that people like this model. So I guess, don't ignore it and give it a try? Also, added Featherless link and GGUF for this. Prompt format is ChatML like most Qwen 2.5 chat based models. ## Merge Details [![GGUF](https://img.shields.io/badge/featherless--ai--quants%2FKaraKaraWitch--MachiNoDolphin--Qwen2.5--72b-Dummy?style=flat&label=GGUF)](https://huggingface.co/featherless-ai-quants/KaraKaraWitch-MachiNoDolphin-Qwen2.5-72b-GGUF) [![Featherless](https://img.shields.io/badge/KaraKaraWitch%2FMachiNoDolphin--Qwen2.5--72b-Dummy?style=flat&label=Featherless&color=facc15)](https://featherless.ai/models/KaraKaraWitch/MachiNoDolphin-Qwen2.5-72b) ## Prompting Chat format is ChatML. It is mostly uncensored. For 99% of the time, you shouldn't run into any issues. For that 1%, just change your system prompt. Settings I use in general: ``` Temp: 1.3-1.2 MinP: 0.05 TopA: 0.2 RepPen: 1.05 ``` Rest is disabled/not used. ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [Qwen/Qwen2.5-72B](https://huggingface.co/Qwen/Qwen2.5-72B) as a base. ### Models Merged The following models were included in the merge: * [sophosympatheia/Evathene-v1.0](https://huggingface.co/sophosympatheia/Evathene-v1.0) * [KaraKaraWitch/SteyrCannon-0.2-Qwen2.5-72b](https://huggingface.co/KaraKaraWitch/SteyrCannon-0.2-Qwen2.5-72b) * [m8than/banana-2-b-72b](https://huggingface.co/m8than/banana-2-b-72b) * [shuttleai/shuttle-3](https://huggingface.co/shuttleai/shuttle-3) * [ZeusLabs/Chronos-Platinum-72B](https://huggingface.co/ZeusLabs/Chronos-Platinum-72B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: KaraKaraWitch/SteyrCannon-0.2-Qwen2.5-72b parameters: density: 0.25 weight: 0.5 - model: ZeusLabs/Chronos-Platinum-72B parameters: density: 0.5 weight: 0.75 - model: m8than/banana-2-b-72b parameters: density: 0.65 weight: 0.40 - model: shuttleai/shuttle-3 parameters: density: 0.65 weight: 0.40 - model: sophosympatheia/Evathene-v1.0 parameters: density: 0.65 weight: 0.40 merge_method: ties base_model: Qwen/Qwen2.5-72B parameters: normalize: true dtype: bfloat16 ```
ail-sa/mangala_test	ail-sa	2025-04-28T10:08:26Z	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	2025-04-28T09:17:27Z	--- 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: Sidf --- # Mangala_Test <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 `Sidf` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Sidf", "lora_weights": "https://huggingface.co/ail-sa/mangala_test/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('ail-sa/mangala_test', weight_name='lora.safetensors') image = pipeline('Sidf').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/ail-sa/mangala_test/discussions) to add images that show off what you’ve made with this LoRA.
khanniazi9118/GPU	khanniazi9118	2025-04-28T09:29:38Z	0	0	null	[ "license:cc-by-nc-nd-3.0", "region:us" ]	null	2025-04-28T09:29:38Z	--- license: cc-by-nc-nd-3.0 ---
BlueLiu2004/Phi-4-raw-lora	BlueLiu2004	2025-04-28T08:18:16Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/phi-4-unsloth-bnb-4bit", "base_model:finetune:unsloth/phi-4-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-28T08:18:05Z	--- base_model: unsloth/phi-4-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: BlueLiu2004 - License: apache-2.0 - Finetuned from model : unsloth/phi-4-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)
radm/Qwen2.5-32B-simpo-FP8	radm	2025-04-28T06:05:16Z	3	0	null	[ "safetensors", "qwen2", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "dataset:IlyaGusev/saiga_preferences", "dataset:40umov/dostoevsky", "dataset:Vikhrmodels/gutenpromax", "base_model:Qwen/Qwen2.5-32B-Instruct", "base_model:finetune:Qwen/Qwen2.5-32B-Instruct", "region:us" ]	null	2024-11-18T14:03:32Z	--- base_model: - Qwen/Qwen2.5-32B-Instruct language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara datasets: - IlyaGusev/saiga_preferences - 40umov/dostoevsky - Vikhrmodels/gutenpromax --- # Model Card for radm/Qwen2.5-32B-simpo-FP8 ## Model Details Improved quality on hard tasks by 25 percent relative to the base model Qwen2.5-32B-Instruct. Improved multilingual support. Fine-tuning on A100 in 4-bit with unsloth using SIMPO and custom dataset LoRA adapter: [radm/Qwen2.5-32B-simpo-LoRA](https://huggingface.co/radm/Qwen2.5-32B-simpo-LoRA) ### Eval results Eval results on [ZebraLogic](https://github.com/WildEval/ZeroEval) ![image/png](https://huggingface.co/radm/Qwen2.5-32B-simpo-FP8/resolve/main/zebralogic_radm_Qwen2.5-32B-simpo-FP8.png)
Triangle104/Qwen2.5-32B-Instruct-Q3_K_M-GGUF	Triangle104	2025-04-28T05:31:12Z	2	0	transformers	[ "transformers", "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "arxiv:2407.10671", "base_model:Qwen/Qwen2.5-32B-Instruct", "base_model:quantized:Qwen/Qwen2.5-32B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-12-29T14:24:37Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-32B-Instruct/blob/main/LICENSE language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara pipeline_tag: text-generation base_model: Qwen/Qwen2.5-32B-Instruct tags: - chat - llama-cpp - gguf-my-repo library_name: transformers --- # Triangle104/Qwen2.5-32B-Instruct-Q3_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen2.5-32B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) for more details on the model. --- Model Details: - Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots. Long-context Support up to 128K tokens and can generate up to 8K tokens. Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. This repo contains the instruction-tuned 32B Qwen2.5 model, which has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias Number of Parameters: 32.5B Number of Paramaters (Non-Embedding): 31.0B Number of Layers: 64 Number of Attention Heads (GQA): 40 for Q and 8 for KV Context Length: Full 131,072 tokens and generation 8192 tokens Please refer to this section for detailed instructions on how to deploy Qwen2.5 for handling long texts. For more details, please refer to our blog, GitHub, and Documentation. Requirements The code of Qwen2.5 has been in the latest Hugging face transformers and we advise you to use the latest version of transformers. With transformers<4.37.0, you will encounter the following error: KeyError: 'qwen2' Quickstart Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents. from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-32B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] Processing Long Texts The current config.json is set for context length up to 32,768 tokens. To handle extensive inputs exceeding 32,768 tokens, we utilize YaRN, a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts. For supported frameworks, you could add the following to config.json to enable YaRN: { ..., "rope_scaling": { "factor": 4.0, "original_max_position_embeddings": 32768, "type": "yarn" } } For deployment, we recommend using vLLM. Please refer to our Documentation for usage if you are not familar with vLLM. Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. We advise adding the rope_scaling configuration only when processing long contexts is required. Evaluation & Performance Detailed evaluation results are reported in this 📑 blog. For requirements on GPU memory and the respective throughput, see results here. Citation If you find our work helpful, feel free to give us a cite. @misc{qwen2.5, title = {Qwen2.5: A Party of Foundation Models}, url = {https://qwenlm.github.io/blog/qwen2.5/}, author = {Qwen Team}, month = {September}, year = {2024} } @article{qwen2, title={Qwen2 Technical Report}, author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan}, journal={arXiv preprint arXiv:2407.10671}, year={2024} } --- ## 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/Qwen2.5-32B-Instruct-Q3_K_M-GGUF --hf-file qwen2.5-32b-instruct-q3_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen2.5-32B-Instruct-Q3_K_M-GGUF --hf-file qwen2.5-32b-instruct-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/Qwen2.5-32B-Instruct-Q3_K_M-GGUF --hf-file qwen2.5-32b-instruct-q3_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen2.5-32B-Instruct-Q3_K_M-GGUF --hf-file qwen2.5-32b-instruct-q3_k_m.gguf -c 2048 ```
secmlr/SWE-BENCH-2k-generation-enrich-500-localization-combine_32b-generation-localization-combine	secmlr	2025-04-28T04:13:19Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-Coder-32B-Instruct", "base_model:finetune:Qwen/Qwen2.5-Coder-32B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-27T07:10:34Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-Coder-32B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: SWE-BENCH-2k-generation-enrich-500-localization-combine_32b-generation-localization-combine 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. --> # SWE-BENCH-2k-generation-enrich-500-localization-combine_32b-generation-localization-combine This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct) on the SWE-BENCH-2k-generation-enrich-500-localization-combine 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: 12 - total_train_batch_size: 48 - total_eval_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 2.20.0 - Tokenizers 0.20.3
mradermacher/Fusion-14B-Instruct-GGUF	mradermacher	2025-04-28T03:27:05Z	27	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:qingy2024/Fusion-14B-Instruct", "base_model:quantized:qingy2024/Fusion-14B-Instruct", "endpoints_compatible", "region:us", "conversational" ]	null	2024-12-06T02:36:13Z	--- base_model: qingy2024/Fusion-14B-Instruct language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/qingy2024/Fusion-14B-Instruct <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Fusion-14B-Instruct-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/Fusion-14B-Instruct-GGUF/resolve/main/Fusion-14B-Instruct.Q2_K.gguf) \| Q2_K \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fusion-14B-Instruct-GGUF/resolve/main/Fusion-14B-Instruct.Q3_K_S.gguf) \| Q3_K_S \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fusion-14B-Instruct-GGUF/resolve/main/Fusion-14B-Instruct.Q3_K_M.gguf) \| Q3_K_M \| 7.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Fusion-14B-Instruct-GGUF/resolve/main/Fusion-14B-Instruct.Q3_K_L.gguf) \| Q3_K_L \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fusion-14B-Instruct-GGUF/resolve/main/Fusion-14B-Instruct.IQ4_XS.gguf) \| IQ4_XS \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fusion-14B-Instruct-GGUF/resolve/main/Fusion-14B-Instruct.Q4_0_4_4.gguf) \| Q4_0_4_4 \| 8.6 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Fusion-14B-Instruct-GGUF/resolve/main/Fusion-14B-Instruct.Q4_K_S.gguf) \| Q4_K_S \| 8.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Fusion-14B-Instruct-GGUF/resolve/main/Fusion-14B-Instruct.Q4_K_M.gguf) \| Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Fusion-14B-Instruct-GGUF/resolve/main/Fusion-14B-Instruct.Q5_K_S.gguf) \| Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fusion-14B-Instruct-GGUF/resolve/main/Fusion-14B-Instruct.Q5_K_M.gguf) \| Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fusion-14B-Instruct-GGUF/resolve/main/Fusion-14B-Instruct.Q6_K.gguf) \| Q6_K \| 12.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Fusion-14B-Instruct-GGUF/resolve/main/Fusion-14B-Instruct.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. 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 -->
MyTranslate/m2m100-en-ms-finetuned	MyTranslate	2025-04-28T02:12:12Z	0	0	transformers	[ "transformers", "safetensors", "m2m_100", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-04-28T02:07:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
cryptoncalls/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stubby_hardy_cat	cryptoncalls	2025-04-28T01:03:36Z	6	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am stubby hardy cat", "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	2025-04-11T00:30:16Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stubby_hardy_cat tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am stubby hardy cat - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stubby_hardy_cat 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="cryptoncalls/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stubby_hardy_cat", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ahmedch28/mistral_7b_finetuned_pr_v4	ahmedch28	2025-04-27T21:34:42Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-27T21:34:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
qLhwaa/dssdfdss	qLhwaa	2025-04-27T19:14:36Z	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	2025-04-27T18:40:56Z	--- 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: dsd34 --- # Dssdfdss <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 `dsd34` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "dsd34", "lora_weights": "https://huggingface.co/qLhwaa/dssdfdss/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('qLhwaa/dssdfdss', weight_name='lora.safetensors') image = pipeline('dsd34').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/qLhwaa/dssdfdss/discussions) to add images that show off what you’ve made with this LoRA.
sergioalves/9435b10b-0258-4631-8cd5-382d248e9f64	sergioalves	2025-04-27T13:52:44Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Llama-3.2-1B-Instruct", "base_model:adapter:unsloth/Llama-3.2-1B-Instruct", "license:llama3.2", "8-bit", "bitsandbytes", "region:us" ]	null	2025-04-27T13:44:31Z	--- library_name: peft license: llama3.2 base_model: unsloth/Llama-3.2-1B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 9435b10b-0258-4631-8cd5-382d248e9f64 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: true adapter: lora base_model: unsloth/Llama-3.2-1B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 7b66778f0acee359_train_data.json ds_type: json format: custom path: /workspace/input_data/7b66778f0acee359_train_data.json type: field_input: OriginalAddress1 field_instruction: PermitTypeDesc field_output: Description 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: 0.5 group_by_length: false hub_model_id: sergioalves/9435b10b-0258-4631-8cd5-382d248e9f64 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false 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: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/7b66778f0acee359_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 79f99a0f-7342-452f-ae42-335a89dd3ae5 wandb_project: s56-8 wandb_run: your_name wandb_runid: 79f99a0f-7342-452f-ae42-335a89dd3ae5 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 9435b10b-0258-4631-8cd5-382d248e9f64 This model is a fine-tuned version of [unsloth/Llama-3.2-1B-Instruct](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2090 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - 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: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.7109 \| 0.0139 \| 200 \| 3.2090 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
damarago/DamaragoXYZ	damarago	2025-04-27T05:07:35Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-27T05:07:35Z	--- license: apache-2.0 ---
Triangle104/QwQ-32B-ArliAI-RpR-v2-Q4_K_M-GGUF	Triangle104	2025-04-26T18:03:34Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "en", "base_model:ArliAI/QwQ-32B-ArliAI-RpR-v2", "base_model:quantized:ArliAI/QwQ-32B-ArliAI-RpR-v2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-26T17:58:03Z	--- base_model: ArliAI/QwQ-32B-ArliAI-RpR-v2 language: - en license: apache-2.0 tags: - llama-cpp - gguf-my-repo thumbnail: https://cdn-uploads.huggingface.co/production/uploads/6625f4a8a8d1362ebcc3851a/9TIfNBdy29CDnn8NNIQPt.jpeg --- # Triangle104/QwQ-32B-ArliAI-RpR-v2-Q4_K_M-GGUF This model was converted to GGUF format from [`ArliAI/QwQ-32B-ArliAI-RpR-v2`](https://huggingface.co/ArliAI/QwQ-32B-ArliAI-RpR-v2) 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/ArliAI/QwQ-32B-ArliAI-RpR-v2) for more details on the model. --- RpR (RolePlay with Reasoning) is a new series of models from ArliAI. This series builds directly upon the successful dataset curation methodology and training methods developed for the RPMax series. RpR models use the same curated, deduplicated RP and creative writing dataset used for RPMax, with a focus on variety to ensure high creativity and minimize cross-context repetition. Users familiar with RPMax will recognize the unique, non-repetitive writing style unlike other finetuned-for-RP models. With the release of QwQ as the first high performing open-source reasoning model that can be easily trained, it was clear that the available instruct and creative writing reasoning datasets contains only one response per example. This is type of single response dataset used for training reasoning models causes degraded output quality in long multi-turn chats. Which is why Arli AI decided to create a real RP model capable of long multi-turn chat with reasoning. In order to create RpR, we first had to actually create the reasoning RP dataset by re-processing our existing known-good RPMax dataset into a reasoning dataset. This was possible by using the base QwQ Instruct model itself to create the reasoning process for every turn in the RPMax dataset conversation examples, which is then further refined in order to make sure the reasoning is in-line with the actual response examples from the dataset. Another important thing to get right is to make sure the model is trained on examples that present reasoning blocks in the same way as it encounters it during inference. Which is, never seeing the reasoning blocks in it's context. In order to do this, the training run was completed using axolotl with manual template-free segments dataset in order to make sure that the model is never trained to see the reasoning block in the context. Just like how the model will be used during inference time. The result of training QwQ on this dataset with this method are consistently coherent and interesting outputs even in long multi-turn RP chats. This is as far as we know the first true correctly-trained reasoning model trained for RP and creative writing. --- ## 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-ArliAI-RpR-v2-Q4_K_M-GGUF --hf-file qwq-32b-arliai-rpr-v2-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/QwQ-32B-ArliAI-RpR-v2-Q4_K_M-GGUF --hf-file qwq-32b-arliai-rpr-v2-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/QwQ-32B-ArliAI-RpR-v2-Q4_K_M-GGUF --hf-file qwq-32b-arliai-rpr-v2-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/QwQ-32B-ArliAI-RpR-v2-Q4_K_M-GGUF --hf-file qwq-32b-arliai-rpr-v2-q4_k_m.gguf -c 2048 ```
jpark677/qwen2-vl-7b-instruct-mmbench-dev-fft-unfreeze-mlp-ep-2-waa-f	jpark677	2025-04-25T17:45:38Z	0	0	null	[ "safetensors", "qwen2_vl", "region:us" ]	null	2025-04-25T17:41:24Z	# qwen2-vl-7b-instruct-mmbench-dev-fft-unfreeze-mlp-ep-2-waa-f This repository contains the model checkpoint (original iteration 548) as epoch 2.

Jonjew/DominiqueMcElligott

Jonjew

2025-05-01T01:42:51Z

0

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:unknown", "region:us" ]

text-to-image

2025-05-01T01:42:42Z

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/dom.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: null license: unknown --- # Dominique McElligott by solo_lee <Gallery /> ## Model description FROM https://civitai.com/models/1527956/dominique-mcelligott-sololora?modelVersionId=1728762 Please support the creator by donating BUZZ to the creator and LIKING at the page above ## Download model Weights for this model are available in Safetensors format. [Download](/Jonjew/DominiqueMcElligott/tree/main) them in the Files & versions tab.

mlfoundations-dev/d1_math_all

mlfoundations-dev

2025-05-01T01:13:03Z

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

2025-04-28T21:43:19Z

--- 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 results: [] ---  # d1_math_all 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 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: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 32 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - total_eval_batch_size: 256 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.3.0 - Datasets 3.1.0 - Tokenizers 0.20.3

smokypipe21/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-miniature_bellowing_stork

smokypipe21

2025-05-01T01:11:56Z

0

transformers

[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am miniature bellowing stork", "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

2025-05-01T01:11:24Z

--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-miniature_bellowing_stork tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am miniature bellowing stork - unsloth - trl licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-miniature_bellowing_stork 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="smokypipe21/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-miniature_bellowing_stork", 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: 4.51.1 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

RetroB/fluxtest

RetroB

2025-04-30T23:38:50Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-03-08T16:58:27Z

--- license: apache-2.0 ---

diliash/qwen2.5-vl-7b_rslora_pm_axis_origintype_twoway_qwenprompt_borders_data_20250430_152846

diliash

2025-04-30T22:40:14Z

0

transformers

[ "transformers", "safetensors", "qwen2.5-vl-7b_rslora_pm_axis_origintype_twoway_qwenprompt_borders_data_20250430_152846", "20250430_152846", "qwen2.5-vl-7b_rslora_pm_axis_origintype_twoway_rerunl40_data_20250430_144705", "20250430_144705", "qwen2.5-vl-7b_rslora_pm_axis_origintype_twoway_border_data_20250430_143912", "20250430_143912", "generated_from_trainer", "final-model", "processor", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-04-30T22:28:47Z

--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-VL-7B-Instruct tags: - qwen2.5-vl-7b_rslora_pm_axis_origintype_twoway_qwenprompt_borders_data_20250430_152846 - '20250430_152846' - qwen2.5-vl-7b_rslora_pm_axis_origintype_twoway_rerunl40_data_20250430_144705 - '20250430_144705' - qwen2.5-vl-7b_rslora_pm_axis_origintype_twoway_border_data_20250430_143912 - '20250430_143912' - generated_from_trainer - final-model - processor model-index: - name: checkpoints results: [] ---  # checkpoints This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 4 - total_eval_batch_size: 4 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Framework versions - Transformers 4.51.3 - Pytorch 2.4.1+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1

onnx-community/dfine_l_obj365-ONNX

onnx-community

2025-04-30T22:37:35Z

0

transformers.js

[ "transformers.js", "onnx", "d_fine", "object-detection", "base_model:ustc-community/dfine_l_obj365", "base_model:quantized:ustc-community/dfine_l_obj365", "region:us" ]

object-detection

2025-04-30T22:26:31Z

--- library_name: transformers.js base_model: ustc-community/dfine_l_obj365 --- https://huggingface.co/ustc-community/dfine_l_obj365 with ONNX weights to be compatible with Transformers.js. ### Transformers.js If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using: ```bash npm i @huggingface/transformers ``` You can then use the model like this: ```js import { pipeline } from "@huggingface/transformers"; const detector = await pipeline("object-detection", "onnx-community/dfine_l_obj365-ONNX"); const image = "https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg"; const output = await detector(image, { threshold: 0.5 }); console.log(output); ``` Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

sommerzen/Qwen3-4B-abliterated-Q5_K_M-GGUF

sommerzen

2025-04-30T22:12:58Z

0

transformers

[ "transformers", "gguf", "chat", "abliterated", "uncensored", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:huihui-ai/Qwen3-4B-abliterated", "base_model:quantized:huihui-ai/Qwen3-4B-abliterated", "license:apache-2.0", "endpoints_compatible", "region:us" ]

text-generation

2025-04-30T22:12:44Z

--- base_model: huihui-ai/Qwen3-4B-abliterated library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-4B/blob/main/LICENSE pipeline_tag: text-generation tags: - chat - abliterated - uncensored - llama-cpp - gguf-my-repo --- # sommerzen/Qwen3-4B-abliterated-Q5_K_M-GGUF This model was converted to GGUF format from [`huihui-ai/Qwen3-4B-abliterated`](https://huggingface.co/huihui-ai/Qwen3-4B-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/huihui-ai/Qwen3-4B-abliterated) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo sommerzen/Qwen3-4B-abliterated-Q5_K_M-GGUF --hf-file qwen3-4b-abliterated-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo sommerzen/Qwen3-4B-abliterated-Q5_K_M-GGUF --hf-file qwen3-4b-abliterated-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 sommerzen/Qwen3-4B-abliterated-Q5_K_M-GGUF --hf-file qwen3-4b-abliterated-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo sommerzen/Qwen3-4B-abliterated-Q5_K_M-GGUF --hf-file qwen3-4b-abliterated-q5_k_m.gguf -c 2048 ```

MarcusLee/Qwen3-0.6B-MLX

MarcusLee

2025-04-30T22:08:59Z

0

mlx

[ "mlx", "safetensors", "qwen3", "text-generation", "conversational", "base_model:MarcusLee/Qwen3-0.6B-MLX", "base_model:finetune:MarcusLee/Qwen3-0.6B-MLX", "license:apache-2.0", "region:us" ]

text-generation

2025-04-30T22:06:53Z

--- license: apache-2.0 base_model: MarcusLee/Qwen3-0.6B-MLX pipeline_tag: text-generation tags: - mlx library_name: mlx ---

phospho-app/TyphoidComa-put_objects_in_bowl_v003-1l3z7qihxc

phospho-app

2025-04-30T22:06:00Z

0

null

[ "safetensors", "gr00t_n1", "phosphobot", "gr00t", "region:us" ]

null

2025-04-30T21:32:57Z

--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## This model was trained using **phospho**. Training was successfull, try it out on your robot! ## Training parameters: - **Dataset**: [TyphoidComa/put_objects_in_bowl_v003](https://huggingface.co/datasets/TyphoidComa/put_objects_in_bowl_v003) - **Wandb run URL**: None - **Epochs**: 10 - **Batch size**: 64 - **Training steps**: None 📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=replicate_groot_training_pipeline) 🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=replicate_groot_training_pipeline)

lwoollett/AskJ-3-8B

lwoollett

2025-04-30T22:00:07Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-04-30T21:50:58Z

--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

rbelanec/train_wsc_1745950296

rbelanec

2025-04-30T21:24:38Z

0

peft

[ "peft", "safetensors", "llama-factory", "ia3", "generated_from_trainer", "base_model:google/gemma-3-1b-it", "base_model:adapter:google/gemma-3-1b-it", "license:gemma", "region:us" ]

null

2025-04-30T17:24:12Z

--- library_name: peft license: gemma base_model: google/gemma-3-1b-it tags: - llama-factory - ia3 - generated_from_trainer model-index: - name: train_wsc_1745950296 results: [] ---  # train_wsc_1745950296 This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it) on the wsc dataset. It achieves the following results on the evaluation set: - Loss: 0.2355 - Num Input Tokens Seen: 14005200 ## 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: 2 - eval_batch_size: 2 - seed: 123 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - 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 - training_steps: 40000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen | |:-------------:|:--------:|:-----:|:---------------:|:-----------------:| | 0.5932 | 1.6024 | 200 | 0.4073 | 70208 | | 0.3595 | 3.2008 | 400 | 0.2959 | 140304 | | 0.221 | 4.8032 | 600 | 0.2701 | 210336 | | 0.2749 | 6.4016 | 800 | 0.2635 | 280224 | | 0.219 | 8.0 | 1000 | 0.2467 | 350448 | | 0.2372 | 9.6024 | 1200 | 0.2552 | 420560 | | 0.2497 | 11.2008 | 1400 | 0.2446 | 490880 | | 0.2277 | 12.8032 | 1600 | 0.2466 | 560560 | | 0.2419 | 14.4016 | 1800 | 0.2385 | 630816 | | 0.2729 | 16.0 | 2000 | 0.2396 | 699936 | | 0.2504 | 17.6024 | 2200 | 0.2511 | 769520 | | 0.2598 | 19.2008 | 2400 | 0.2438 | 839648 | | 0.2481 | 20.8032 | 2600 | 0.2433 | 910080 | | 0.2471 | 22.4016 | 2800 | 0.2355 | 979504 | | 0.2492 | 24.0 | 3000 | 0.2413 | 1049392 | | 0.2142 | 25.6024 | 3200 | 0.2454 | 1119904 | | 0.2143 | 27.2008 | 3400 | 0.2527 | 1189264 | | 0.2336 | 28.8032 | 3600 | 0.2460 | 1259520 | | 0.2308 | 30.4016 | 3800 | 0.2459 | 1329408 | | 0.2159 | 32.0 | 4000 | 0.2431 | 1399696 | | 0.2191 | 33.6024 | 4200 | 0.2479 | 1470240 | | 0.2236 | 35.2008 | 4400 | 0.2477 | 1539536 | | 0.2137 | 36.8032 | 4600 | 0.2489 | 1610032 | | 0.2108 | 38.4016 | 4800 | 0.2486 | 1680240 | | 0.2262 | 40.0 | 5000 | 0.2462 | 1749472 | | 0.2135 | 41.6024 | 5200 | 0.2494 | 1819376 | | 0.2547 | 43.2008 | 5400 | 0.2545 | 1889616 | | 0.2397 | 44.8032 | 5600 | 0.2517 | 1959536 | | 0.2474 | 46.4016 | 5800 | 0.2489 | 2028864 | | 0.2198 | 48.0 | 6000 | 0.2499 | 2099424 | | 0.2273 | 49.6024 | 6200 | 0.2492 | 2169376 | | 0.222 | 51.2008 | 6400 | 0.2596 | 2239408 | | 0.2052 | 52.8032 | 6600 | 0.2524 | 2309472 | | 0.2202 | 54.4016 | 6800 | 0.2570 | 2380032 | | 0.2291 | 56.0 | 7000 | 0.2544 | 2449376 | | 0.2432 | 57.6024 | 7200 | 0.2533 | 2519776 | | 0.2275 | 59.2008 | 7400 | 0.2684 | 2589392 | | 0.2066 | 60.8032 | 7600 | 0.2622 | 2659792 | | 0.2334 | 62.4016 | 7800 | 0.2637 | 2729184 | | 0.2207 | 64.0 | 8000 | 0.2639 | 2799504 | | 0.2081 | 65.6024 | 8200 | 0.2605 | 2869520 | | 0.2352 | 67.2008 | 8400 | 0.2625 | 2940080 | | 0.2277 | 68.8032 | 8600 | 0.2645 | 3010256 | | 0.2181 | 70.4016 | 8800 | 0.2607 | 3080304 | | 0.2193 | 72.0 | 9000 | 0.2614 | 3150464 | | 0.2263 | 73.6024 | 9200 | 0.2640 | 3220512 | | 0.2365 | 75.2008 | 9400 | 0.2711 | 3290320 | | 0.2301 | 76.8032 | 9600 | 0.2720 | 3360352 | | 0.2136 | 78.4016 | 9800 | 0.2780 | 3430416 | | 0.2414 | 80.0 | 10000 | 0.2773 | 3500544 | | 0.2108 | 81.6024 | 10200 | 0.2759 | 3570432 | | 0.2501 | 83.2008 | 10400 | 0.2852 | 3640832 | | 0.1856 | 84.8032 | 10600 | 0.2843 | 3710480 | | 0.2461 | 86.4016 | 10800 | 0.2923 | 3780368 | | 0.2514 | 88.0 | 11000 | 0.2892 | 3850720 | | 0.233 | 89.6024 | 11200 | 0.2869 | 3920848 | | 0.2219 | 91.2008 | 11400 | 0.2930 | 3990784 | | 0.2282 | 92.8032 | 11600 | 0.2939 | 4060432 | | 0.2327 | 94.4016 | 11800 | 0.2963 | 4130528 | | 0.2053 | 96.0 | 12000 | 0.3074 | 4200848 | | 0.2024 | 97.6024 | 12200 | 0.3054 | 4270928 | | 0.2145 | 99.2008 | 12400 | 0.3057 | 4339920 | | 0.2199 | 100.8032 | 12600 | 0.3065 | 4410624 | | 0.1864 | 102.4016 | 12800 | 0.3128 | 4479904 | | 0.2428 | 104.0 | 13000 | 0.3085 | 4549824 | | 0.2364 | 105.6024 | 13200 | 0.3189 | 4620128 | | 0.2179 | 107.2008 | 13400 | 0.3157 | 4690352 | | 0.2091 | 108.8032 | 13600 | 0.3346 | 4760256 | | 0.1908 | 110.4016 | 13800 | 0.3324 | 4830144 | | 0.2238 | 112.0 | 14000 | 0.3265 | 4900080 | | 0.2362 | 113.6024 | 14200 | 0.3383 | 4969936 | | 0.1788 | 115.2008 | 14400 | 0.3447 | 5040096 | | 0.2398 | 116.8032 | 14600 | 0.3425 | 5110288 | | 0.2528 | 118.4016 | 14800 | 0.3449 | 5180208 | | 0.2339 | 120.0 | 15000 | 0.3511 | 5250464 | | 0.2148 | 121.6024 | 15200 | 0.3561 | 5320528 | | 0.2319 | 123.2008 | 15400 | 0.3632 | 5390624 | | 0.2345 | 124.8032 | 15600 | 0.3623 | 5460832 | | 0.217 | 126.4016 | 15800 | 0.3796 | 5530720 | | 0.1915 | 128.0 | 16000 | 0.3945 | 5600992 | | 0.2116 | 129.6024 | 16200 | 0.3822 | 5672032 | | 0.2116 | 131.2008 | 16400 | 0.3909 | 5740976 | | 0.2181 | 132.8032 | 16600 | 0.3814 | 5811248 | | 0.2108 | 134.4016 | 16800 | 0.4049 | 5881152 | | 0.1722 | 136.0 | 17000 | 0.3914 | 5951136 | | 0.2649 | 137.6024 | 17200 | 0.4134 | 6021136 | | 0.1815 | 139.2008 | 17400 | 0.4207 | 6091696 | | 0.2212 | 140.8032 | 17600 | 0.4139 | 6161472 | | 0.2797 | 142.4016 | 17800 | 0.4191 | 6231760 | | 0.1788 | 144.0 | 18000 | 0.4182 | 6301232 | | 0.1695 | 145.6024 | 18200 | 0.4215 | 6371776 | | 0.176 | 147.2008 | 18400 | 0.4220 | 6442048 | | 0.193 | 148.8032 | 18600 | 0.4278 | 6511680 | | 0.1788 | 150.4016 | 18800 | 0.4465 | 6581136 | | 0.1791 | 152.0 | 19000 | 0.4280 | 6651296 | | 0.1701 | 153.6024 | 19200 | 0.4408 | 6721584 | | 0.218 | 155.2008 | 19400 | 0.4615 | 6791744 | | 0.1885 | 156.8032 | 19600 | 0.4490 | 6862112 | | 0.2073 | 158.4016 | 19800 | 0.4526 | 6931856 | | 0.2083 | 160.0 | 20000 | 0.4382 | 7001952 | | 0.2122 | 161.6024 | 20200 | 0.4656 | 7071568 | | 0.2229 | 163.2008 | 20400 | 0.4499 | 7141584 | | 0.1639 | 164.8032 | 20600 | 0.4636 | 7212096 | | 0.1531 | 166.4016 | 20800 | 0.4812 | 7282736 | | 0.1857 | 168.0 | 21000 | 0.4808 | 7352288 | | 0.2166 | 169.6024 | 21200 | 0.4873 | 7422624 | | 0.217 | 171.2008 | 21400 | 0.4804 | 7492496 | | 0.2053 | 172.8032 | 21600 | 0.4837 | 7562288 | | 0.158 | 174.4016 | 21800 | 0.4817 | 7632432 | | 0.2275 | 176.0 | 22000 | 0.4776 | 7702096 | | 0.1558 | 177.6024 | 22200 | 0.4795 | 7772000 | | 0.2557 | 179.2008 | 22400 | 0.5149 | 7842112 | | 0.1607 | 180.8032 | 22600 | 0.5000 | 7912496 | | 0.1257 | 182.4016 | 22800 | 0.4994 | 7982768 | | 0.1728 | 184.0 | 23000 | 0.4957 | 8052448 | | 0.1638 | 185.6024 | 23200 | 0.5003 | 8122832 | | 0.1874 | 187.2008 | 23400 | 0.5117 | 8193088 | | 0.1926 | 188.8032 | 23600 | 0.5122 | 8263104 | | 0.2062 | 190.4016 | 23800 | 0.5285 | 8333312 | | 0.22 | 192.0 | 24000 | 0.5147 | 8402848 | | 0.2026 | 193.6024 | 24200 | 0.5052 | 8472688 | | 0.2265 | 195.2008 | 24400 | 0.5265 | 8542528 | | 0.1609 | 196.8032 | 24600 | 0.5340 | 8612928 | | 0.1703 | 198.4016 | 24800 | 0.5321 | 8682896 | | 0.1412 | 200.0 | 25000 | 0.5307 | 8752864 | | 0.1449 | 201.6024 | 25200 | 0.5193 | 8823744 | | 0.2189 | 203.2008 | 25400 | 0.5338 | 8893360 | | 0.1865 | 204.8032 | 25600 | 0.5253 | 8963536 | | 0.2108 | 206.4016 | 25800 | 0.5421 | 9033264 | | 0.2047 | 208.0 | 26000 | 0.5364 | 9102880 | | 0.1245 | 209.6024 | 26200 | 0.5348 | 9173088 | | 0.1963 | 211.2008 | 26400 | 0.5464 | 9242752 | | 0.1986 | 212.8032 | 26600 | 0.5527 | 9313008 | | 0.1849 | 214.4016 | 26800 | 0.5563 | 9382592 | | 0.1951 | 216.0 | 27000 | 0.5439 | 9452912 | | 0.1727 | 217.6024 | 27200 | 0.5450 | 9522896 | | 0.175 | 219.2008 | 27400 | 0.5521 | 9592864 | | 0.1625 | 220.8032 | 27600 | 0.5523 | 9663568 | | 0.1043 | 222.4016 | 27800 | 0.5580 | 9733504 | | 0.2085 | 224.0 | 28000 | 0.5700 | 9803232 | | 0.1547 | 225.6024 | 28200 | 0.5690 | 9872976 | | 0.1701 | 227.2008 | 28400 | 0.5524 | 9943472 | | 0.1637 | 228.8032 | 28600 | 0.5571 | 10013472 | | 0.1409 | 230.4016 | 28800 | 0.5627 | 10082944 | | 0.1686 | 232.0 | 29000 | 0.5629 | 10153120 | | 0.1232 | 233.6024 | 29200 | 0.5772 | 10223856 | | 0.1424 | 235.2008 | 29400 | 0.5794 | 10293888 | | 0.1501 | 236.8032 | 29600 | 0.5660 | 10363824 | | 0.2116 | 238.4016 | 29800 | 0.5782 | 10433056 | | 0.1504 | 240.0 | 30000 | 0.5741 | 10503136 | | 0.1391 | 241.6024 | 30200 | 0.5759 | 10573568 | | 0.1679 | 243.2008 | 30400 | 0.5777 | 10642912 | | 0.1838 | 244.8032 | 30600 | 0.5723 | 10713264 | | 0.0982 | 246.4016 | 30800 | 0.5854 | 10783152 | | 0.1083 | 248.0 | 31000 | 0.5753 | 10853376 | | 0.1923 | 249.6024 | 31200 | 0.5822 | 10923696 | | 0.2031 | 251.2008 | 31400 | 0.5762 | 10994016 | | 0.112 | 252.8032 | 31600 | 0.5872 | 11063664 | | 0.1834 | 254.4016 | 31800 | 0.5846 | 11133840 | | 0.1673 | 256.0 | 32000 | 0.5778 | 11203504 | | 0.1845 | 257.6024 | 32200 | 0.5711 | 11273840 | | 0.1988 | 259.2008 | 32400 | 0.5864 | 11342832 | | 0.1785 | 260.8032 | 32600 | 0.5750 | 11412832 | | 0.2006 | 262.4016 | 32800 | 0.5792 | 11482880 | | 0.1711 | 264.0 | 33000 | 0.6063 | 11552512 | | 0.1912 | 265.6024 | 33200 | 0.5967 | 11622560 | | 0.1387 | 267.2008 | 33400 | 0.5726 | 11692336 | | 0.1579 | 268.8032 | 33600 | 0.5824 | 11763296 | | 0.1602 | 270.4016 | 33800 | 0.5813 | 11833168 | | 0.1151 | 272.0 | 34000 | 0.5845 | 11902608 | | 0.1165 | 273.6024 | 34200 | 0.5823 | 11973440 | | 0.1283 | 275.2008 | 34400 | 0.5973 | 12042992 | | 0.1169 | 276.8032 | 34600 | 0.5824 | 12113808 | | 0.1351 | 278.4016 | 34800 | 0.5956 | 12183456 | | 0.1505 | 280.0 | 35000 | 0.5935 | 12253312 | | 0.1692 | 281.6024 | 35200 | 0.5781 | 12323712 | | 0.1639 | 283.2008 | 35400 | 0.5710 | 12393344 | | 0.1746 | 284.8032 | 35600 | 0.5846 | 12463296 | | 0.1759 | 286.4016 | 35800 | 0.5872 | 12533712 | | 0.1401 | 288.0 | 36000 | 0.5930 | 12603312 | | 0.1634 | 289.6024 | 36200 | 0.6116 | 12672944 | | 0.2095 | 291.2008 | 36400 | 0.6031 | 12743584 | | 0.1535 | 292.8032 | 36600 | 0.5991 | 12814000 | | 0.17 | 294.4016 | 36800 | 0.6007 | 12883584 | | 0.1576 | 296.0 | 37000 | 0.5849 | 12954144 | | 0.1592 | 297.6024 | 37200 | 0.5987 | 13024112 | | 0.2064 | 299.2008 | 37400 | 0.6090 | 13094448 | | 0.201 | 300.8032 | 37600 | 0.6053 | 13164640 | | 0.1417 | 302.4016 | 37800 | 0.5957 | 13234048 | | 0.1734 | 304.0 | 38000 | 0.5905 | 13304512 | | 0.2204 | 305.6024 | 38200 | 0.6068 | 13374272 | | 0.1773 | 307.2008 | 38400 | 0.6008 | 13444512 | | 0.1493 | 308.8032 | 38600 | 0.5896 | 13514848 | | 0.1731 | 310.4016 | 38800 | 0.6084 | 13584800 | | 0.2378 | 312.0 | 39000 | 0.5984 | 13654928 | | 0.1702 | 313.6024 | 39200 | 0.5848 | 13724752 | | 0.2229 | 315.2008 | 39400 | 0.5860 | 13794224 | | 0.1697 | 316.8032 | 39600 | 0.5860 | 13865104 | | 0.1667 | 318.4016 | 39800 | 0.5860 | 13935776 | | 0.198 | 320.0 | 40000 | 0.5860 | 14005200 | ### Framework versions - PEFT 0.15.2.dev0 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

bocilanomali/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wary_nimble_cobra

bocilanomali

2025-04-30T21:10:15Z

1

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am wary nimble cobra", "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

2025-04-27T19:01:04Z

--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wary_nimble_cobra tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am wary nimble cobra - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wary_nimble_cobra 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="bocilanomali/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wary_nimble_cobra", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.5.0 - 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}} } ```

Akil15/quantized-starcoder-7B

Akil15

2025-04-30T20:48:08Z

0

null

[ "safetensors", "starcoder2", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]

null

2025-04-30T20:34:07Z

--- license: apache-2.0 ---

fbaldassarri/internlm_internlm3-8b-instruct-autogptq-int8-gs64-asym

fbaldassarri

2025-04-30T20:43:16Z

0

transformers

[ "transformers", "safetensors", "internlm3", "text-generation", "internlm", "autoround", "auto-round", "intel-autoround", "intel", "woq", "gptq", "pytorch", "internlm3-8b", "conversational", "custom_code", "en", "es", "fr", "de", "pt", "ja", "it", "zh", "ko", "ar", "cs", "nl", "base_model:internlm/internlm3-8b-instruct", "base_model:quantized:internlm/internlm3-8b-instruct", "license:apache-2.0", "autotrain_compatible", "8-bit", "region:us" ]

text-generation

2025-04-30T20:40:02Z

--- language: - en - es - fr - de - pt - ja - it - zh - ko - ar - cs - nl pipeline_tag: text-generation license: apache-2.0 library_name: transformers tags: - internlm - autoround - auto-round - intel-autoround - intel - woq - gptq - pytorch - internlm3 - internlm3-8b model_name: Internlm 3 8b instruct base_model: - internlm/internlm3-8b-instruct inference: false model_creator: internlm prompt_template: '{prompt}' quantized_by: fbaldassarri --- ## Model Information Quantized version of [internlm/internlm3-8b-instruct](https://huggingface.co/internlm/internlm3-8b-instruct) using torch.float32 for quantization tuning. - 8 bits (INT8) - group size = 64 - Asymmetrical Quantization - Method WoQ: GPTQ (AutoGPTQ algorithm) Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.4.7 Note: this INT8 version of internlm3-8b-instruct has been quantized to run inference through CPU. ## Replication Recipe ### Step 1 Install Requirements I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment. ``` wget https://github.com/intel/auto-round/archive/refs/tags/v0.4.7.tar.gz tar -xvzf v0.4.7.tar.gz cd auto-round-0.4.7 pip install -r requirements-cpu.txt --upgrade ``` ### Step 2 Build Intel AutoRound wheel from sources ``` pip install -vvv --no-build-isolation -e .[cpu] ``` ### Step 3 Script for Quantization ``` from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "internlm/internlm3-8b-instruct" model = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) from auto_round import AutoRound bits, group_size, sym, device, amp = 8, 64, False, 'cpu', False autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device, amp=amp) autoround.quantize() output_dir = "./AutoRound/internlm_internlm3-8b-instruct-autogptq-int8-gs64-asym" autoround.save_quantized(output_dir, format='auto_gptq', inplace=True) ``` ## License [Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/) ## Disclaimer This quantized model comes with no warrenty. It has been developed only for research purposes.

aleegis/8eadc872-7896-4070-bd8f-b4bf968fa11e

aleegis

2025-04-30T20:22:04Z

0

peft

[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:Artples/L-MChat-7b", "base_model:adapter:Artples/L-MChat-7b", "license:apache-2.0", "region:us" ]

null

2025-04-30T18:46:46Z

--- library_name: peft license: apache-2.0 base_model: Artples/L-MChat-7b tags: - axolotl - generated_from_trainer model-index: - name: 8eadc872-7896-4070-bd8f-b4bf968fa11e results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Artples/L-MChat-7b bf16: auto chat_template: llama3 dataloader_num_workers: 12 dataset_prepared_path: null datasets: - data_files: - df64b7b1f6156a4a_train_data.json ds_type: json format: custom path: /workspace/input_data/df64b7b1f6156a4a_train_data.json type: field_input: context field_instruction: prompt_serial field_output: hypothesis 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_steps: null eval_table_size: null evals_per_epoch: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 8 gradient_checkpointing: false group_by_length: false hub_model_id: aleegis/8eadc872-7896-4070-bd8f-b4bf968fa11e hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: null lora_alpha: 32 lora_dropout: 0.15 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true loraplus_lr_embedding: 1.0e-06 loraplus_lr_ratio: 16 lr_scheduler: cosine max_grad_norm: 1 max_steps: 1500 micro_batch_size: 2 mlflow_experiment_name: /tmp/df64b7b1f6156a4a_train_data.json model_type: AutoModelForCausalLM num_epochs: 200 optimizer: adamw_torch_fused output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: null save_total_limit: 10 saves_per_epoch: 0 sequence_len: 1024 special_tokens: pad_token: <|end_of_turn|> strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.0 wandb_entity: null wandb_mode: online wandb_name: 05aae012-1d2a-4ad0-8017-d5d890fc3778 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 05aae012-1d2a-4ad0-8017-d5d890fc3778 warmup_steps: 100 weight_decay: 0 xformers_attention: null ``` </details> # 8eadc872-7896-4070-bd8f-b4bf968fa11e This model is a fine-tuned version of [Artples/L-MChat-7b](https://huggingface.co/Artples/L-MChat-7b) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - 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 - lr_scheduler_warmup_steps: 100 - training_steps: 1500 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

Yuhan123/ppo-reading-level-full-question-12th-1-steps-10000-epoch-999-best-eval-score-0.281

Yuhan123

2025-04-30T19:58:03Z

0

transformers

[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-04-30T19:55:20Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

rbelanec/train_wic_1745950291

rbelanec

2025-04-30T18:55:07Z

0

peft

[ "peft", "safetensors", "llama-factory", "lntuning", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]

null

2025-04-30T15:30:52Z

--- library_name: peft license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - llama-factory - lntuning - generated_from_trainer model-index: - name: train_wic_1745950291 results: [] ---  # train_wic_1745950291 This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the wic dataset. It achieves the following results on the evaluation set: - Loss: 0.4934 - Num Input Tokens Seen: 12716696 ## 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: 2 - eval_batch_size: 2 - seed: 123 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - 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 - training_steps: 40000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen | |:-------------:|:-------:|:-----:|:---------------:|:-----------------:| | 0.6257 | 0.1637 | 200 | 0.5225 | 63344 | | 0.4145 | 0.3275 | 400 | 0.5219 | 126720 | | 0.6067 | 0.4912 | 600 | 0.5143 | 190304 | | 0.5509 | 0.6549 | 800 | 0.5104 | 254384 | | 0.4087 | 0.8187 | 1000 | 0.5072 | 318128 | | 0.5062 | 0.9824 | 1200 | 0.5076 | 381920 | | 0.4057 | 1.1457 | 1400 | 0.5047 | 445096 | | 0.6519 | 1.3095 | 1600 | 0.5058 | 508744 | | 0.4278 | 1.4732 | 1800 | 0.5040 | 572408 | | 0.516 | 1.6369 | 2000 | 0.5023 | 635736 | | 0.4402 | 1.8007 | 2200 | 0.4989 | 699464 | | 0.3649 | 1.9644 | 2400 | 0.5017 | 763192 | | 0.722 | 2.1277 | 2600 | 0.5006 | 826784 | | 0.6072 | 2.2914 | 2800 | 0.5021 | 890336 | | 0.4783 | 2.4552 | 3000 | 0.4986 | 953840 | | 0.3192 | 2.6189 | 3200 | 0.4998 | 1017600 | | 0.6125 | 2.7826 | 3400 | 0.4971 | 1081104 | | 0.3693 | 2.9464 | 3600 | 0.5000 | 1144576 | | 0.5569 | 3.1097 | 3800 | 0.5020 | 1208440 | | 0.6581 | 3.2734 | 4000 | 0.4973 | 1272216 | | 0.3633 | 3.4372 | 4200 | 0.4999 | 1335496 | | 0.5302 | 3.6009 | 4400 | 0.5050 | 1398984 | | 0.3837 | 3.7646 | 4600 | 0.4959 | 1462856 | | 0.5727 | 3.9284 | 4800 | 0.4986 | 1526280 | | 0.417 | 4.0917 | 5000 | 0.4981 | 1589584 | | 0.381 | 4.2554 | 5200 | 0.4988 | 1653024 | | 0.3998 | 4.4192 | 5400 | 0.4994 | 1716432 | | 0.3977 | 4.5829 | 5600 | 0.5029 | 1779984 | | 0.364 | 4.7466 | 5800 | 0.5024 | 1843936 | | 0.6055 | 4.9104 | 6000 | 0.5001 | 1907808 | | 0.4597 | 5.0737 | 6200 | 0.5003 | 1971048 | | 0.4152 | 5.2374 | 6400 | 0.5005 | 2034808 | | 0.4998 | 5.4011 | 6600 | 0.5010 | 2098088 | | 0.5148 | 5.5649 | 6800 | 0.5005 | 2161640 | | 0.4574 | 5.7286 | 7000 | 0.4973 | 2225432 | | 0.884 | 5.8923 | 7200 | 0.4995 | 2289032 | | 0.5194 | 6.0557 | 7400 | 0.4955 | 2352656 | | 0.6431 | 6.2194 | 7600 | 0.4975 | 2416160 | | 0.3991 | 6.3831 | 7800 | 0.4986 | 2479728 | | 0.532 | 6.5469 | 8000 | 0.4968 | 2543168 | | 0.4574 | 6.7106 | 8200 | 0.4997 | 2606560 | | 0.4313 | 6.8743 | 8400 | 0.4990 | 2670208 | | 0.5079 | 7.0377 | 8600 | 0.4967 | 2733584 | | 0.4926 | 7.2014 | 8800 | 0.4963 | 2797008 | | 0.6941 | 7.3651 | 9000 | 0.5011 | 2860576 | | 0.4878 | 7.5289 | 9200 | 0.4988 | 2924256 | | 0.4491 | 7.6926 | 9400 | 0.4975 | 2988272 | | 0.5816 | 7.8563 | 9600 | 0.4988 | 3051776 | | 0.3643 | 8.0196 | 9800 | 0.4955 | 3114992 | | 0.5292 | 8.1834 | 10000 | 0.4965 | 3179200 | | 0.3784 | 8.3471 | 10200 | 0.4981 | 3242496 | | 0.5082 | 8.5108 | 10400 | 0.4971 | 3306112 | | 0.5478 | 8.6746 | 10600 | 0.4993 | 3369760 | | 0.6724 | 8.8383 | 10800 | 0.4998 | 3433360 | | 0.5947 | 9.0016 | 11000 | 0.4980 | 3496680 | | 0.5989 | 9.1654 | 11200 | 0.5002 | 3560648 | | 0.5554 | 9.3291 | 11400 | 0.4983 | 3624200 | | 0.3369 | 9.4928 | 11600 | 0.5003 | 3687560 | | 0.5688 | 9.6566 | 11800 | 0.5014 | 3751288 | | 0.4692 | 9.8203 | 12000 | 0.4971 | 3814952 | | 0.6744 | 9.9840 | 12200 | 0.5008 | 3878120 | | 0.4068 | 10.1474 | 12400 | 0.4992 | 3941616 | | 0.4359 | 10.3111 | 12600 | 0.4981 | 4005216 | | 0.5724 | 10.4748 | 12800 | 0.4960 | 4068912 | | 0.5359 | 10.6386 | 13000 | 0.4971 | 4132608 | | 0.4707 | 10.8023 | 13200 | 0.4980 | 4196096 | | 0.5272 | 10.9660 | 13400 | 0.4969 | 4259680 | | 0.6006 | 11.1293 | 13600 | 0.4966 | 4323128 | | 0.4663 | 11.2931 | 13800 | 0.4977 | 4386856 | | 0.3614 | 11.4568 | 14000 | 0.4935 | 4450296 | | 0.6643 | 11.6205 | 14200 | 0.4980 | 4513544 | | 0.5071 | 11.7843 | 14400 | 0.5001 | 4576984 | | 0.3758 | 11.9480 | 14600 | 0.4987 | 4640904 | | 0.3884 | 12.1113 | 14800 | 0.4975 | 4704360 | | 0.304 | 12.2751 | 15000 | 0.4966 | 4768152 | | 0.4518 | 12.4388 | 15200 | 0.4974 | 4832152 | | 0.3722 | 12.6025 | 15400 | 0.4999 | 4895192 | | 0.3803 | 12.7663 | 15600 | 0.4989 | 4959112 | | 0.4056 | 12.9300 | 15800 | 0.4952 | 5022408 | | 0.7264 | 13.0933 | 16000 | 0.4986 | 5086016 | | 0.6845 | 13.2571 | 16200 | 0.4999 | 5149920 | | 0.3888 | 13.4208 | 16400 | 0.4991 | 5213296 | | 0.6898 | 13.5845 | 16600 | 0.4985 | 5276672 | | 0.4119 | 13.7483 | 16800 | 0.5017 | 5340624 | | 0.4066 | 13.9120 | 17000 | 0.4966 | 5403792 | | 0.6487 | 14.0753 | 17200 | 0.4955 | 5466936 | | 0.6244 | 14.2391 | 17400 | 0.4985 | 5530392 | | 0.6813 | 14.4028 | 17600 | 0.4988 | 5593576 | | 0.55 | 14.5665 | 17800 | 0.4999 | 5657288 | | 0.4325 | 14.7302 | 18000 | 0.4973 | 5721496 | | 0.541 | 14.8940 | 18200 | 0.4976 | 5785096 | | 0.6722 | 15.0573 | 18400 | 0.4993 | 5848736 | | 0.5625 | 15.2210 | 18600 | 0.4954 | 5912176 | | 0.4723 | 15.3848 | 18800 | 0.4965 | 5976400 | | 0.31 | 15.5485 | 19000 | 0.4957 | 6040272 | | 0.4716 | 15.7122 | 19200 | 0.4957 | 6103424 | | 0.5429 | 15.8760 | 19400 | 0.4934 | 6166912 | | 0.3732 | 16.0393 | 19600 | 0.4961 | 6230320 | | 0.4673 | 16.2030 | 19800 | 0.4972 | 6294224 | | 0.4359 | 16.3668 | 20000 | 0.4974 | 6357984 | | 0.3628 | 16.5305 | 20200 | 0.5007 | 6421344 | | 0.3717 | 16.6942 | 20400 | 0.4999 | 6485152 | | 0.3153 | 16.8580 | 20600 | 0.4961 | 6548768 | | 0.6308 | 17.0213 | 20800 | 0.4971 | 6611792 | | 0.6157 | 17.1850 | 21000 | 0.4995 | 6675216 | | 0.4635 | 17.3488 | 21200 | 0.4987 | 6739088 | | 0.6582 | 17.5125 | 21400 | 0.4991 | 6802352 | | 0.2988 | 17.6762 | 21600 | 0.4997 | 6866160 | | 0.3709 | 17.8400 | 21800 | 0.5029 | 6929936 | | 0.3607 | 18.0033 | 22000 | 0.4944 | 6993168 | | 0.7202 | 18.1670 | 22200 | 0.5041 | 7057008 | | 0.3716 | 18.3307 | 22400 | 0.5014 | 7120624 | | 0.4817 | 18.4945 | 22600 | 0.4980 | 7183872 | | 0.5667 | 18.6582 | 22800 | 0.4962 | 7247952 | | 0.3868 | 18.8219 | 23000 | 0.4981 | 7311488 | | 0.4314 | 18.9857 | 23200 | 0.4989 | 7374848 | | 0.5291 | 19.1490 | 23400 | 0.4971 | 7438160 | | 0.5263 | 19.3127 | 23600 | 0.4991 | 7501872 | | 0.5666 | 19.4765 | 23800 | 0.4970 | 7565520 | | 0.6424 | 19.6402 | 24000 | 0.4947 | 7629488 | | 0.5894 | 19.8039 | 24200 | 0.4982 | 7692992 | | 0.303 | 19.9677 | 24400 | 0.4980 | 7756512 | | 0.5242 | 20.1310 | 24600 | 0.4970 | 7819816 | | 0.331 | 20.2947 | 24800 | 0.4987 | 7883800 | | 0.4012 | 20.4585 | 25000 | 0.4947 | 7947944 | | 0.5083 | 20.6222 | 25200 | 0.4989 | 8011336 | | 0.4885 | 20.7859 | 25400 | 0.4996 | 8075000 | | 0.5333 | 20.9497 | 25600 | 0.4989 | 8138568 | | 0.5209 | 21.1130 | 25800 | 0.5002 | 8201872 | | 0.7051 | 21.2767 | 26000 | 0.4995 | 8265168 | | 0.5638 | 21.4404 | 26200 | 0.5024 | 8328704 | | 0.6135 | 21.6042 | 26400 | 0.4948 | 8392144 | | 0.8321 | 21.7679 | 26600 | 0.4984 | 8456096 | | 0.6106 | 21.9316 | 26800 | 0.5017 | 8519872 | | 0.5066 | 22.0950 | 27000 | 0.5002 | 8583464 | | 0.5766 | 22.2587 | 27200 | 0.4949 | 8646840 | | 0.5146 | 22.4224 | 27400 | 0.4984 | 8710600 | | 0.6664 | 22.5862 | 27600 | 0.4979 | 8774344 | | 0.5827 | 22.7499 | 27800 | 0.4989 | 8838024 | | 0.5015 | 22.9136 | 28000 | 0.4998 | 8901832 | | 0.3741 | 23.0770 | 28200 | 0.4952 | 8965184 | | 0.4112 | 23.2407 | 28400 | 0.4975 | 9028576 | | 0.3413 | 23.4044 | 28600 | 0.5026 | 9092256 | | 0.3816 | 23.5682 | 28800 | 0.4968 | 9155872 | | 0.5038 | 23.7319 | 29000 | 0.4988 | 9219312 | | 0.509 | 23.8956 | 29200 | 0.5012 | 9283264 | | 0.4391 | 24.0589 | 29400 | 0.4994 | 9346992 | | 0.3301 | 24.2227 | 29600 | 0.5016 | 9410880 | | 0.6701 | 24.3864 | 29800 | 0.4956 | 9474704 | | 0.3837 | 24.5501 | 30000 | 0.4996 | 9538160 | | 0.6954 | 24.7139 | 30200 | 0.5018 | 9601792 | | 0.6162 | 24.8776 | 30400 | 0.4981 | 9664976 | | 0.5058 | 25.0409 | 30600 | 0.4952 | 9728232 | | 0.6277 | 25.2047 | 30800 | 0.5002 | 9791848 | | 0.3653 | 25.3684 | 31000 | 0.4973 | 9855400 | | 0.4652 | 25.5321 | 31200 | 0.5014 | 9918984 | | 0.2707 | 25.6959 | 31400 | 0.4962 | 9982872 | | 0.5098 | 25.8596 | 31600 | 0.5003 | 10046056 | | 0.4843 | 26.0229 | 31800 | 0.5000 | 10109568 | | 0.5279 | 26.1867 | 32000 | 0.4986 | 10173072 | | 0.4396 | 26.3504 | 32200 | 0.5003 | 10236512 | | 0.7524 | 26.5141 | 32400 | 0.4994 | 10299920 | | 0.5412 | 26.6779 | 32600 | 0.4996 | 10363808 | | 0.6239 | 26.8416 | 32800 | 0.5021 | 10427744 | | 0.4925 | 27.0049 | 33000 | 0.4980 | 10491384 | | 0.4674 | 27.1686 | 33200 | 0.5011 | 10555192 | | 0.4568 | 27.3324 | 33400 | 0.4977 | 10619080 | | 0.4934 | 27.4961 | 33600 | 0.4955 | 10682424 | | 0.8816 | 27.6598 | 33800 | 0.4993 | 10746024 | | 0.3269 | 27.8236 | 34000 | 0.4972 | 10809736 | | 0.4768 | 27.9873 | 34200 | 0.4941 | 10873448 | | 0.6487 | 28.1506 | 34400 | 0.4946 | 10936704 | | 0.5115 | 28.3144 | 34600 | 0.4938 | 11000112 | | 0.5026 | 28.4781 | 34800 | 0.4966 | 11063936 | | 0.4725 | 28.6418 | 35000 | 0.4996 | 11128160 | | 0.3988 | 28.8056 | 35200 | 0.4996 | 11191600 | | 0.7055 | 28.9693 | 35400 | 0.4961 | 11255184 | | 0.2657 | 29.1326 | 35600 | 0.4985 | 11318640 | | 0.3977 | 29.2964 | 35800 | 0.4985 | 11382352 | | 0.5586 | 29.4601 | 36000 | 0.4985 | 11446048 | | 0.4327 | 29.6238 | 36200 | 0.4985 | 11509328 | | 0.3437 | 29.7876 | 36400 | 0.4985 | 11573312 | | 0.5439 | 29.9513 | 36600 | 0.4985 | 11636752 | | 0.5447 | 30.1146 | 36800 | 0.4985 | 11700056 | | 0.4514 | 30.2783 | 37000 | 0.4985 | 11763352 | | 0.7178 | 30.4421 | 37200 | 0.4985 | 11826952 | | 0.7133 | 30.6058 | 37400 | 0.4985 | 11890888 | | 0.5499 | 30.7695 | 37600 | 0.4985 | 11954296 | | 0.8377 | 30.9333 | 37800 | 0.4985 | 12017784 | | 0.6521 | 31.0966 | 38000 | 0.4985 | 12081304 | | 0.6123 | 31.2603 | 38200 | 0.4985 | 12145240 | | 0.4538 | 31.4241 | 38400 | 0.4985 | 12208888 | | 0.689 | 31.5878 | 38600 | 0.4985 | 12272344 | | 0.4428 | 31.7515 | 38800 | 0.4985 | 12335960 | | 0.5346 | 31.9153 | 39000 | 0.4985 | 12399064 | | 0.4668 | 32.0786 | 39200 | 0.4985 | 12462200 | | 0.4803 | 32.2423 | 39400 | 0.4985 | 12526024 | | 0.607 | 32.4061 | 39600 | 0.4985 | 12589496 | | 0.4888 | 32.5698 | 39800 | 0.4985 | 12653080 | | 0.429 | 32.7335 | 40000 | 0.4985 | 12716696 | ### Framework versions - PEFT 0.15.2.dev0 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

vijay-ravichander/Smol-Pairwise-Distill-20k

vijay-ravichander

2025-04-30T18:20:30Z

0

transformers

[ "transformers", "safetensors", "idefics3", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-04-30T10:29:50Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

guelph25/guelph2a

guelph25

2025-04-30T18:13:32Z

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

2025-04-30T18:13:01Z

--- 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: guelph --- # Guelph <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 `guelph` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "guelph", "lora_weights": "https://huggingface.co/guelph25/guelph/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('guelph25/guelph', weight_name='lora.safetensors') image = pipeline('guelph').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/guelph25/guelph/discussions) to add images that show off what you’ve made with this LoRA.

7-Shah-Sapna-Kumari-Viral-Videos-XX/18-FULL.VIDEO.Sapna.Shah.Viral.Video.Leaks.official.tutorial

7-Shah-Sapna-Kumari-Viral-Videos-XX

2025-04-30T17:53:01Z

0

null

[ "region:us" ]

null

2025-04-30T17:52:50Z

<a href="https://sdu.sk/9Ip"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a> <a href="https://sdu.sk/9Ip" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/9Ip" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>

DataScienceWFSR/modernbert-food-product-sr

DataScienceWFSR

2025-04-30T17:32:36Z

0

null

[ "safetensors", "modernbert", "text-classification", "en", "arxiv:2504.20703", "base_model:answerdotai/ModernBERT-base", "base_model:finetune:answerdotai/ModernBERT-base", "region:us" ]

text-classification

2025-04-30T12:07:07Z

--- language: - en metrics: - f1 base_model: - answerdotai/ModernBERT-base pipeline_tag: text-classification --- # ModernBERT Food Product Classification Model - Synonym Replacement Augmentation ## Model Details ### Model Description This model is finetuned on multi-class food product text classification using synonym replacement augmentation and ModernBERT. - **Developed by:** [DataScienceWFSR](https://huggingface.co/DataScienceWFSR) - **Model type:** Text Classification - **Language(s) (NLP):** English - **Finetuned from model:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) ### Model Sources - **Repository:** [https://github.com/WFSRDataScience/SemEval2025Task9](https://github.com/WFSRDataScience/SemEval2025Task9) - **Paper :** [https://arxiv.org/abs/2504.20703](https://arxiv.org/abs/2504.20703) ## How to Get Started With the Model Use the code below to get started with the model in PyTorch. ```python import torch from transformers import AutoTokenizer, AutoModelForSequenceClassification from huggingface_hub import hf_hub_download import pandas as pd model, category, augmentation = 'modernbert', 'product', 'sr' repo_id = f"DataScienceWFSR/{model}-food-{category}-{augmentation}" lb_path = hf_hub_download(repo_id=repo_id, filename=f"labelencoder_{category}.pkl") lb = pd.read_pickle(lb_path) tokenizer = AutoTokenizer.from_pretrained(repo_id) model = AutoModelForSequenceClassification.from_pretrained(repo_id) model.eval() sample = ('Case Number: 039-94 Date Opened: 10/20/1994 Date Closed: 03/06/1995 Recall Class: 1' ' Press Release (Y/N): N Domestic Est. Number: 07188 M Name: PREPARED FOODS Imported ' 'Product (Y/N): N Foreign Estab. Number: N/A City: SANTA TERESA State: NM Country: USA' ' Product: HAM, SLICED Problem: BACTERIA Description: LISTERIA ' 'Total Pounds Recalled: 3,920 Pounds Recovered: 3,920') inputs = tokenizer(sample, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) predictions = outputs.logits.argmax(dim=-1) predicted_label = lb.inverse_transform(predictions.numpy())[0] print(f"The predicted label is: {predicted_label}") ``` ## Training Details ### Training Data Training and Validation data provided by SemEval-2025 Task 9 organizers : `Food Recall Incidents` dataset (only English) [link](https://github.com/food-hazard-detection-semeval-2025/food-hazard-detection-semeval-2025.github.io/tree/main/data) ### Training Procedure #### Training Hyperparameters - batch_size: `8` - epochs: `5` - lr_scheduler: `cosine with Restarts` ## Evaluation  ### Testing Data & Metrics #### Testing Data Test data: 997 samples ([link](https://github.com/food-hazard-detection-semeval-2025/food-hazard-detection-semeval-2025.github.io/blob/main/data/incidents_test.csv)) #### Metrics F1-macro ### Results F1-macro scores for each model in the official test set utilizing the `text` field per category and subtasks scores (ST1 and ST2) rounded to 3 decimals. With bold, we indicated the model's specific results. | Model | hazard-category | product-category | hazard | product | ST1 | ST2 | |----------------------|----------------:|-----------------:|-------:|--------:|------:|------:| | BERTbase | 0.747 | 0.757 | 0.581 | 0.170 | 0.753 | 0.382 | | BERTCW | 0.760 | 0.761 | 0.671 | 0.280 | 0.762 | 0.491 | | BERTSR | 0.770 | 0.754 | 0.666 | 0.275 | 0.764 | 0.478 | | BERTRW | 0.752 | 0.757 | 0.651 | 0.275 | 0.756 | 0.467 | | DistilBERTbase | 0.761 | 0.757 | 0.593 | 0.154 | 0.760 | 0.378 | | DistilBERTCW | 0.766 | 0.753 | 0.635 | 0.246 | 0.763 | 0.449 | | DistilBERTSR | 0.756 | 0.759 | 0.644 | 0.240 | 0.763 | 0.448 | | DistilBERTRW | 0.749 | 0.747 | 0.647 | 0.261 | 0.753 | 0.462 | | RoBERTabase | 0.760 | 0.753 | 0.579 | 0.123 | 0.755 | 0.356 | | RoBERTaCW | 0.773 | 0.739 | 0.630 | 0.000 | 0.760 | 0.315 | | RoBERTaSR | 0.777 | 0.755 | 0.637 | 0.000 | 0.767 | 0.319 | | RoBERTaRW | 0.757 | 0.611 | 0.615 | 0.000 | 0.686 | 0.308 | | ModernBERTbase | 0.781 | 0.745 | 0.667 | 0.275 | 0.769 | 0.485 | | ModernBERTCW | 0.761 | 0.712 | 0.609 | 0.252 | 0.741 | 0.441 | | **ModernBERTSR** | **0.790** | **0.728** | **0.591** | **0.253** | **0.761** | **0.434** | | ModernBERTRW | 0.761 | 0.751 | 0.629 | 0.237 | 0.759 | 0.440 | ## Technical Specifications ### Compute Infrastructure #### Hardware NVIDIA A100 80GB and NVIDIA GeForce RTX 3070 Ti #### Software | Library | Version | URL | |-------------------|--------:|---------------------------------------------------------------------| | Transformers | 4.49.0 | https://huggingface.co/docs/transformers/index | | PyTorch | 2.6.0 | https://pytorch.org/ | | SpaCy | 3.8.4 | https://spacy.io/ | | Scikit-learn | 1.6.0 | https://scikit-learn.org/stable/ | | Pandas | 2.2.3 | https://pandas.pydata.org/ | | Optuna | 4.2.1 | https://optuna.org/ | | NumPy | 2.0.2 | https://numpy.org/ | | NLP AUG | 1.1.11 | https://nlpaug.readthedocs.io/en/latest/index.html | | BeautifulSoup4 | 4.12.3 | https://www.crummy.com/software/BeautifulSoup/bs4/doc/# | ## Citation **BibTeX:** For the original paper: ``` @inproceedings{brightcookies-semeval2025-task9, title="BrightCookies at {S}em{E}val-2025 Task 9: Exploring Data Augmentation for Food Hazard Classification}, author="Papadopoulou, Foteini and Mutlu, Osman and Özen, Neris and van der Velden, Bas H. M. and Hendrickx, Iris and Hürriyetoğlu, Ali", booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)", month = jul, year = "2025", address = "Vienna, Austria", publisher = "Association for Computational Linguistics", } ``` For the SemEval2025 Task9: ``` @inproceedings{semeval2025-task9, title = "{S}em{E}val-2025 Task 9: The Food Hazard Detection Challenge", author = "Randl, Korbinian and Pavlopoulos, John and Henriksson, Aron and Lindgren, Tony and Bakagianni, Juli", booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)", month = jul, year = "2025", address = "Vienna, Austria", publisher = "Association for Computational Linguistics", } ``` ## Model Card Authors and Contact Authors: Foteini Papadopoulou, Osman Mutlu, Neris Özen, Bas H.M. van der Velden, Iris Hendrickx, Ali Hürriyetoğlu Contact: [email protected]

mergekit-community/mergekit-model_stock-zelysxr

mergekit-community

2025-04-30T17:04:39Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.19522", "base_model:Cran-May/tempmotacilla-cinerea-0308", "base_model:merge:Cran-May/tempmotacilla-cinerea-0308", "base_model:Qwen/Qwen2.5-14B-Instruct", "base_model:merge:Qwen/Qwen2.5-14B-Instruct", "base_model:Sakalti/Saka-14B", "base_model:merge:Sakalti/Saka-14B", "base_model:aixonlab/Zara-14b-v1.2", "base_model:merge:aixonlab/Zara-14b-v1.2", "base_model:deepcogito/cogito-v1-preview-qwen-14B", "base_model:merge:deepcogito/cogito-v1-preview-qwen-14B", "base_model:mergekit-community/mergekit-task_arithmetic-yxycruu", "base_model:merge:mergekit-community/mergekit-task_arithmetic-yxycruu", "base_model:prithivMLmods/Galactic-Qwen-14B-Exp2", "base_model:merge:prithivMLmods/Galactic-Qwen-14B-Exp2", "base_model:sthenno-com/miscii-14b-0218", "base_model:merge:sthenno-com/miscii-14b-0218", "base_model:suayptalha/Lamarckvergence-14B", "base_model:merge:suayptalha/Lamarckvergence-14B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-04-30T16:43:39Z

--- base_model: - aixonlab/Zara-14b-v1.2 - sthenno-com/miscii-14b-0218 - Sakalti/Saka-14B - Qwen/Qwen2.5-14B-Instruct - prithivMLmods/Galactic-Qwen-14B-Exp2 - Cran-May/tempmotacilla-cinerea-0308 - suayptalha/Lamarckvergence-14B - deepcogito/cogito-v1-preview-qwen-14B - mergekit-community/mergekit-task_arithmetic-yxycruu library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) as a base. ### Models Merged The following models were included in the merge: * [aixonlab/Zara-14b-v1.2](https://huggingface.co/aixonlab/Zara-14b-v1.2) * [sthenno-com/miscii-14b-0218](https://huggingface.co/sthenno-com/miscii-14b-0218) * [Sakalti/Saka-14B](https://huggingface.co/Sakalti/Saka-14B) * [prithivMLmods/Galactic-Qwen-14B-Exp2](https://huggingface.co/prithivMLmods/Galactic-Qwen-14B-Exp2) * [Cran-May/tempmotacilla-cinerea-0308](https://huggingface.co/Cran-May/tempmotacilla-cinerea-0308) * [suayptalha/Lamarckvergence-14B](https://huggingface.co/suayptalha/Lamarckvergence-14B) * [deepcogito/cogito-v1-preview-qwen-14B](https://huggingface.co/deepcogito/cogito-v1-preview-qwen-14B) * [mergekit-community/mergekit-task_arithmetic-yxycruu](https://huggingface.co/mergekit-community/mergekit-task_arithmetic-yxycruu) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: prithivMLmods/Galactic-Qwen-14B-Exp2 - model: deepcogito/cogito-v1-preview-qwen-14B - model: sthenno-com/miscii-14b-0218 - model: Cran-May/tempmotacilla-cinerea-0308 - model: suayptalha/Lamarckvergence-14B - model: Sakalti/Saka-14B - model: aixonlab/Zara-14b-v1.2 - model: mergekit-community/mergekit-task_arithmetic-yxycruu merge_method: model_stock base_model: Qwen/Qwen2.5-14B-Instruct dtype: bfloat16 tokenizer_source: base ```

secmlr/SWE-BENCH-2000-enriched-reasoning-claude-localization_deepcoder_14b_2000_enriched_reasoning

secmlr

2025-04-30T16:56:33Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:agentica-org/DeepCoder-14B-Preview", "base_model:finetune:agentica-org/DeepCoder-14B-Preview", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-04-30T08:45:24Z

--- library_name: transformers license: mit base_model: agentica-org/DeepCoder-14B-Preview tags: - llama-factory - full - generated_from_trainer model-index: - name: SWE-BENCH-2000-enriched-reasoning-claude-localization_deepcoder_14b_2000_enriched_reasoning results: [] ---  # SWE-BENCH-2000-enriched-reasoning-claude-localization_deepcoder_14b_2000_enriched_reasoning This model is a fine-tuned version of [agentica-org/DeepCoder-14B-Preview](https://huggingface.co/agentica-org/DeepCoder-14B-Preview) on the SWE-BENCH-2000-enriched-reasoning-claude-localization 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: 12 - total_train_batch_size: 48 - total_eval_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.21.0

phospho-app/omourier-Lego_bleu-rg2sqbaxhg

phospho-app

2025-04-30T16:52:23Z

0

null

[ "phosphobot", "gr00t", "region:us" ]

null

2025-04-30T16:48:43Z

--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` Traceback (most recent call last): File "/root/src/helper.py", line 224, in predict raise RuntimeError(error_msg) RuntimeError: Training process failed with exit code 1: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py", line 277, in apply_rotary_pos_emb q_embed = (q * cos) + (rotate_half(q) * sin) ^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py", line 252, in rotate_half return torch.cat((-x2, x1), dim=-1) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 64.00 MiB. GPU 0 has a total capacity of 79.25 GiB of which 38.75 MiB is free. Process 45 has 79.21 GiB memory in use. Of the allocated memory 78.35 GiB is allocated by PyTorch, and 368.29 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) 0%| | 0/1140 [00:19<?, ?it/s] During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/root/src/helper.py", line 226, in predict raise RuntimeError(e) RuntimeError: Training process failed with exit code 1: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py", line 277, in apply_rotary_pos_emb q_embed = (q * cos) + (rotate_half(q) * sin) ^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py", line 252, in rotate_half return torch.cat((-x2, x1), dim=-1) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 64.00 MiB. GPU 0 has a total capacity of 79.25 GiB of which 38.75 MiB is free. Process 45 has 79.21 GiB memory in use. Of the allocated memory 78.35 GiB is allocated by PyTorch, and 368.29 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) 0%| | 0/1140 [00:19<?, ?it/s] ``` ## Training parameters: - **Dataset**: [omourier/Lego_bleu](https://huggingface.co/datasets/omourier/Lego_bleu) - **Wandb run URL**: None - **Epochs**: 10 - **Batch size**: 64 - **Training steps**: 1137 📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=replicate_groot_training_pipeline) 🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=replicate_groot_training_pipeline)

Yuhan123/ppo-cn-RM-reading-level-grad-1-steps-10000-epoch-999-best-eval-score-0.135

Yuhan123

2025-04-30T16:40:26Z

0

transformers

[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-04-30T16:37:58Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

DrTiagoSaldanha/ssssss

DrTiagoSaldanha

2025-04-30T11:48:15Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-04-30T11:48:15Z

--- license: apache-2.0 ---

dgambettaphd/M_llm2_gen0_run0_W_doc1000_synt64_tot128_lr5em5_p1k_SYNLAST

dgambettaphd

2025-04-30T11:40:18Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2025-04-30T11:38:12Z

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

nathanialhunt2000/90da23de-ad96-47b3-94ac-4de7ddd0badf

nathanialhunt2000

2025-04-30T11:34:37Z

0

peft

[ "peft", "safetensors", "generated_from_trainer", "dataset:41b191ed7e418531_train_data.json", "base_model:unsloth/Qwen2-1.5B-Instruct", "base_model:adapter:unsloth/Qwen2-1.5B-Instruct", "region:us" ]

null

2025-04-30T11:34:14Z

--- library_name: peft tags: - generated_from_trainer datasets: - 41b191ed7e418531_train_data.json base_model: unsloth/Qwen2-1.5B-Instruct model-index: - name: nathanialhunt2000/90da23de-ad96-47b3-94ac-4de7ddd0badf results: [] ---  # nathanialhunt2000/90da23de-ad96-47b3-94ac-4de7ddd0badf This model was trained from scratch on the /workspace/input_data/41b191ed7e418531_train_data.json dataset. It achieves the following results on the evaluation set: - Loss: 0.8242 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

tergelb/mmm

tergelb

2025-04-30T06:09:40Z

0

null

[ "arxiv:2303.09556", "license:mit", "region:us" ]

null

2025-04-29T13:53:08Z

--- license: mit --- #!/usr/bin/env python # coding=utf-8 # Copyright 2025 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Fine-tuning script for Stable Diffusion for text2image with support for LoRA.""" import argparse import logging import math import os import random import shutil from contextlib import nullcontext from pathlib import Path import datasets import numpy as np import torch import torch.nn.functional as F import torch.utils.checkpoint import transformers from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import ProjectConfiguration, set_seed from datasets import load_dataset from huggingface_hub import create_repo, upload_folder from packaging import version from peft import LoraConfig from peft.utils import get_peft_model_state_dict from torchvision import transforms from tqdm.auto import tqdm from transformers import CLIPTextModel, CLIPTokenizer import diffusers from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, StableDiffusionPipeline, UNet2DConditionModel from diffusers.optimization import get_scheduler from diffusers.training_utils import cast_training_params, compute_snr from diffusers.utils import check_min_version, convert_state_dict_to_diffusers, is_wandb_available from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card from diffusers.utils.import_utils import is_xformers_available from diffusers.utils.torch_utils import is_compiled_module if is_wandb_available(): import wandb # Will error if the minimal version of diffusers is not installed. Remove at your own risks. check_min_version("0.34.0.dev0") logger = get_logger(__name__, log_level="INFO") def save_model_card( repo_id: str, images: list = None, base_model: str = None, dataset_name: str = None, repo_folder: str = None, ): img_str = "" if images is not None: for i, image in enumerate(images): image.save(os.path.join(repo_folder, f"image_{i}.png")) img_str += f"![img_{i}](./image_{i}.png)\n" model_description = f""" # LoRA text2image fine-tuning - {repo_id} These are LoRA adaption weights for {base_model}. The weights were fine-tuned on the {dataset_name} dataset. You can find some example images in the following. \n {img_str} """ model_card = load_or_create_model_card( repo_id_or_path=repo_id, from_training=True, license="creativeml-openrail-m", base_model=base_model, model_description=model_description, inference=True, ) tags = [ "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "diffusers-training", "lora", ] model_card = populate_model_card(model_card, tags=tags) model_card.save(os.path.join(repo_folder, "README.md")) def log_validation( pipeline, args, accelerator, epoch, is_final_validation=False, ): logger.info( f"Running validation... \n Generating {args.num_validation_images} images with prompt:" f" {args.validation_prompt}." ) pipeline = pipeline.to(accelerator.device) pipeline.set_progress_bar_config(disable=True) generator = torch.Generator(device=accelerator.device) if args.seed is not None: generator = generator.manual_seed(args.seed) images = [] if torch.backends.mps.is_available(): autocast_ctx = nullcontext() else: autocast_ctx = torch.autocast(accelerator.device.type) with autocast_ctx: for _ in range(args.num_validation_images): images.append(pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0]) for tracker in accelerator.trackers: phase_name = "test" if is_final_validation else "validation" if tracker.name == "tensorboard": np_images = np.stack([np.asarray(img) for img in images]) tracker.writer.add_images(phase_name, np_images, epoch, dataformats="NHWC") if tracker.name == "wandb": tracker.log( { phase_name: [ wandb.Image(image, caption=f"{i}: {args.validation_prompt}") for i, image in enumerate(images) ] } ) return images def parse_args(): parser = argparse.ArgumentParser(description="Simple example of a training script.") parser.add_argument( "--pretrained_model_name_or_path", type=str, default=None, required=True, help="Path to pretrained model or model identifier from huggingface.co/models.", ) parser.add_argument( "--revision", type=str, default=None, required=False, help="Revision of pretrained model identifier from huggingface.co/models.", ) parser.add_argument( "--variant", type=str, default=None, help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16", ) parser.add_argument( "--dataset_name", type=str, default=None, help=( "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private," " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem," " or to a folder containing files that 🤗 Datasets can understand." ), ) parser.add_argument( "--dataset_config_name", type=str, default=None, help="The config of the Dataset, leave as None if there's only one config.", ) parser.add_argument( "--train_data_dir", type=str, default=None, help=( "A folder containing the training data. Folder contents must follow the structure described in" " https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file" " must exist to provide the captions for the images. Ignored if `dataset_name` is specified." ), ) parser.add_argument( "--image_column", type=str, default="image", help="The column of the dataset containing an image." ) parser.add_argument( "--caption_column", type=str, default="text", help="The column of the dataset containing a caption or a list of captions.", ) parser.add_argument( "--validation_prompt", type=str, default=None, help="A prompt that is sampled during training for inference." ) parser.add_argument( "--num_validation_images", type=int, default=4, help="Number of images that should be generated during validation with `validation_prompt`.", ) parser.add_argument( "--validation_epochs", type=int, default=1, help=( "Run fine-tuning validation every X epochs. The validation process consists of running the prompt" " `args.validation_prompt` multiple times: `args.num_validation_images`." ), ) parser.add_argument( "--max_train_samples", type=int, default=None, help=( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ), ) parser.add_argument( "--output_dir", type=str, default="sd-model-finetuned-lora", help="The output directory where the model predictions and checkpoints will be written.", ) parser.add_argument( "--cache_dir", type=str, default=None, help="The directory where the downloaded models and datasets will be stored.", ) parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument( "--resolution", type=int, default=512, help=( "The resolution for input images, all the images in the train/validation dataset will be resized to this" " resolution" ), ) parser.add_argument( "--center_crop", default=False, action="store_true", help=( "Whether to center crop the input images to the resolution. If not set, the images will be randomly" " cropped. The images will be resized to the resolution first before cropping." ), ) parser.add_argument( "--random_flip", action="store_true", help="whether to randomly flip images horizontally", ) parser.add_argument( "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader." ) parser.add_argument("--num_train_epochs", type=int, default=100) parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--gradient_checkpointing", action="store_true", help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", ) parser.add_argument( "--learning_rate", type=float, default=1e-4, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument( "--scale_lr", action="store_true", default=False, help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", ) parser.add_argument( "--lr_scheduler", type=str, default="constant", help=( 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' ' "constant", "constant_with_warmup"]' ), ) parser.add_argument( "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument( "--snr_gamma", type=float, default=None, help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. " "More details here: https://arxiv.org/abs/2303.09556.", ) parser.add_argument( "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes." ) parser.add_argument( "--allow_tf32", action="store_true", help=( "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see" " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices" ), ) parser.add_argument( "--dataloader_num_workers", type=int, default=0, help=( "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process." ), ) parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.") parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.") parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.") parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer") parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.") parser.add_argument( "--prediction_type", type=str, default=None, help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediction_type` is chosen.", ) parser.add_argument( "--hub_model_id", type=str, default=None, help="The name of the repository to keep in sync with the local `output_dir`.", ) parser.add_argument( "--logging_dir", type=str, default="logs", help=( "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***." ), ) parser.add_argument( "--mixed_precision", type=str, default=None, choices=["no", "fp16", "bf16"], help=( "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config." ), ) parser.add_argument( "--report_to", type=str, default="tensorboard", help=( 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`' ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.' ), ) parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") parser.add_argument( "--checkpointing_steps", type=int, default=500, help=( "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming" " training using `--resume_from_checkpoint`." ), ) parser.add_argument( "--checkpoints_total_limit", type=int, default=None, help=("Max number of checkpoints to store."), ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help=( "Whether training should be resumed from a previous checkpoint. Use a path saved by" ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.' ), ) parser.add_argument( "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers." ) parser.add_argument("--noise_offset", type=float, default=0, help="The scale of noise offset.") parser.add_argument( "--rank", type=int, default=4, help=("The dimension of the LoRA update matrices."), ) parser.add_argument( "--image_interpolation_mode", type=str, default="lanczos", choices=[ f.lower() for f in dir(transforms.InterpolationMode) if not f.startswith("__") and not f.endswith("__") ], help="The image interpolation method to use for resizing images.", ) args = parser.parse_args() env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) if env_local_rank != -1 and env_local_rank != args.local_rank: args.local_rank = env_local_rank # Sanity checks if args.dataset_name is None and args.train_data_dir is None: raise ValueError("Need either a dataset name or a training folder.") return args DATASET_NAME_MAPPING = { "lambdalabs/naruto-blip-captions": ("image", "text"), } def main(): args = parse_args() if args.report_to == "wandb" and args.hub_token is not None: raise ValueError( "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token." " Please use `huggingface-cli login` to authenticate with the Hub." ) logging_dir = Path(args.output_dir, args.logging_dir) accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir) accelerator = Accelerator( gradient_accumulation_steps=args.gradient_accumulation_steps, mixed_precision=args.mixed_precision, log_with=args.report_to, project_config=accelerator_project_config, ) # Disable AMP for MPS. if torch.backends.mps.is_available(): accelerator.native_amp = False # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_warning() diffusers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() diffusers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # Handle the repository creation if accelerator.is_main_process: if args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) if args.push_to_hub: repo_id = create_repo( repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token ).repo_id # Load scheduler, tokenizer and models. noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler") tokenizer = CLIPTokenizer.from_pretrained( args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision ) text_encoder = CLIPTextModel.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision ) vae = AutoencoderKL.from_pretrained( args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant ) unet = UNet2DConditionModel.from_pretrained( args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant ) # freeze parameters of models to save more memory unet.requires_grad_(False) vae.requires_grad_(False) text_encoder.requires_grad_(False) # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision # as these weights are only used for inference, keeping weights in full precision is not required. weight_dtype = torch.float32 if accelerator.mixed_precision == "fp16": weight_dtype = torch.float16 elif accelerator.mixed_precision == "bf16": weight_dtype = torch.bfloat16 unet_lora_config = LoraConfig( r=args.rank, lora_alpha=args.rank, init_lora_weights="gaussian", target_modules=["to_k", "to_q", "to_v", "to_out.0"], ) # Move unet, vae and text_encoder to device and cast to weight_dtype unet.to(accelerator.device, dtype=weight_dtype) vae.to(accelerator.device, dtype=weight_dtype) text_encoder.to(accelerator.device, dtype=weight_dtype) # Add adapter and make sure the trainable params are in float32. unet.add_adapter(unet_lora_config) if args.mixed_precision == "fp16": # only upcast trainable parameters (LoRA) into fp32 cast_training_params(unet, dtype=torch.float32) if args.enable_xformers_memory_efficient_attention: if is_xformers_available(): import xformers xformers_version = version.parse(xformers.__version__) if xformers_version == version.parse("0.0.16"): logger.warning( "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details." ) unet.enable_xformers_memory_efficient_attention() else: raise ValueError("xformers is not available. Make sure it is installed correctly") lora_layers = filter(lambda p: p.requires_grad, unet.parameters()) if args.gradient_checkpointing: unet.enable_gradient_checkpointing() # Enable TF32 for faster training on Ampere GPUs, # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices if args.allow_tf32: torch.backends.cuda.matmul.allow_tf32 = True if args.scale_lr: args.learning_rate = ( args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes ) # Initialize the optimizer if args.use_8bit_adam: try: import bitsandbytes as bnb except ImportError: raise ImportError( "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`" ) optimizer_cls = bnb.optim.AdamW8bit else: optimizer_cls = torch.optim.AdamW optimizer = optimizer_cls( lora_layers, lr=args.learning_rate, betas=(args.adam_beta1, args.adam_beta2), weight_decay=args.adam_weight_decay, eps=args.adam_epsilon, ) # Get the datasets: you can either provide your own training and evaluation files (see below) # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub). # In distributed training, the load_dataset function guarantees that only one local process can concurrently # download the dataset. if args.dataset_name is not None: # Downloading and loading a dataset from the hub. dataset = load_dataset( args.dataset_name, args.dataset_config_name, cache_dir=args.cache_dir, data_dir=args.train_data_dir, ) else: data_files = {} if args.train_data_dir is not None: data_files["train"] = os.path.join(args.train_data_dir, "**") dataset = load_dataset( "imagefolder", data_files=data_files, cache_dir=args.cache_dir, ) # See more about loading custom images at # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder # Preprocessing the datasets. # We need to tokenize inputs and targets. column_names = dataset["train"].column_names # 6. Get the column names for input/target. dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None) if args.image_column is None: image_column = dataset_columns[0] if dataset_columns is not None else column_names[0] else: image_column = args.image_column if image_column not in column_names: raise ValueError( f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}" ) if args.caption_column is None: caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1] else: caption_column = args.caption_column if caption_column not in column_names: raise ValueError( f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}" ) # Preprocessing the datasets. # We need to tokenize input captions and transform the images. def tokenize_captions(examples, is_train=True): captions = [] for caption in examples[caption_column]: if isinstance(caption, str): captions.append(caption) elif isinstance(caption, (list, np.ndarray)): # take a random caption if there are multiple captions.append(random.choice(caption) if is_train else caption[0]) else: raise ValueError( f"Caption column `{caption_column}` should contain either strings or lists of strings." ) inputs = tokenizer( captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt" ) return inputs.input_ids # Get the specified interpolation method from the args interpolation = getattr(transforms.InterpolationMode, args.image_interpolation_mode.upper(), None) # Raise an error if the interpolation method is invalid if interpolation is None: raise ValueError(f"Unsupported interpolation mode {args.image_interpolation_mode}.") # Data preprocessing transformations train_transforms = transforms.Compose( [ transforms.Resize(args.resolution, interpolation=interpolation), # Use dynamic interpolation method transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution), transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x), transforms.ToTensor(), transforms.Normalize([0.5], [0.5]), ] ) def unwrap_model(model): model = accelerator.unwrap_model(model) model = model._orig_mod if is_compiled_module(model) else model return model def preprocess_train(examples): images = [image.convert("RGB") for image in examples[image_column]] examples["pixel_values"] = [train_transforms(image) for image in images] examples["input_ids"] = tokenize_captions(examples) return examples with accelerator.main_process_first(): if args.max_train_samples is not None: dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples)) # Set the training transforms train_dataset = dataset["train"].with_transform(preprocess_train) def collate_fn(examples): pixel_values = torch.stack([example["pixel_values"] for example in examples]) pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() input_ids = torch.stack([example["input_ids"] for example in examples]) return {"pixel_values": pixel_values, "input_ids": input_ids} # DataLoaders creation: train_dataloader = torch.utils.data.DataLoader( train_dataset, shuffle=True, collate_fn=collate_fn, batch_size=args.train_batch_size, num_workers=args.dataloader_num_workers, ) # Scheduler and math around the number of training steps. # Check the PR https://github.com/huggingface/diffusers/pull/8312 for detailed explanation. num_warmup_steps_for_scheduler = args.lr_warmup_steps * accelerator.num_processes if args.max_train_steps is None: len_train_dataloader_after_sharding = math.ceil(len(train_dataloader) / accelerator.num_processes) num_update_steps_per_epoch = math.ceil(len_train_dataloader_after_sharding / args.gradient_accumulation_steps) num_training_steps_for_scheduler = ( args.num_train_epochs * num_update_steps_per_epoch * accelerator.num_processes ) else: num_training_steps_for_scheduler = args.max_train_steps * accelerator.num_processes lr_scheduler = get_scheduler( args.lr_scheduler, optimizer=optimizer, num_warmup_steps=num_warmup_steps_for_scheduler, num_training_steps=num_training_steps_for_scheduler, ) # Prepare everything with our `accelerator`. unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( unet, optimizer, train_dataloader, lr_scheduler ) # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch if num_training_steps_for_scheduler != args.max_train_steps * accelerator.num_processes: logger.warning( f"The length of the 'train_dataloader' after 'accelerator.prepare' ({len(train_dataloader)}) does not match " f"the expected length ({len_train_dataloader_after_sharding}) when the learning rate scheduler was created. " f"This inconsistency may result in the learning rate scheduler not functioning properly." ) # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if accelerator.is_main_process: accelerator.init_trackers("text2image-fine-tune", config=vars(args)) # Train! total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") global_step = 0 first_epoch = 0 # Potentially load in the weights and states from a previous save if args.resume_from_checkpoint: if args.resume_from_checkpoint != "latest": path = os.path.basename(args.resume_from_checkpoint) else: # Get the most recent checkpoint dirs = os.listdir(args.output_dir) dirs = [d for d in dirs if d.startswith("checkpoint")] dirs = sorted(dirs, key=lambda x: int(x.split("-")[1])) path = dirs[-1] if len(dirs) > 0 else None if path is None: accelerator.print( f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run." ) args.resume_from_checkpoint = None initial_global_step = 0 else: accelerator.print(f"Resuming from checkpoint {path}") accelerator.load_state(os.path.join(args.output_dir, path)) global_step = int(path.split("-")[1]) initial_global_step = global_step first_epoch = global_step // num_update_steps_per_epoch else: initial_global_step = 0 progress_bar = tqdm( range(0, args.max_train_steps), initial=initial_global_step, desc="Steps", # Only show the progress bar once on each machine. disable=not accelerator.is_local_main_process, ) for epoch in range(first_epoch, args.num_train_epochs): unet.train() train_loss = 0.0 for step, batch in enumerate(train_dataloader): with accelerator.accumulate(unet): # Convert images to latent space latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample() latents = latents * vae.config.scaling_factor # Sample noise that we'll add to the latents noise = torch.randn_like(latents) if args.noise_offset: # https://www.crosslabs.org//blog/diffusion-with-offset-noise noise += args.noise_offset * torch.randn( (latents.shape[0], latents.shape[1], 1, 1), device=latents.device ) bsz = latents.shape[0] # Sample a random timestep for each image timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device) timesteps = timesteps.long() # Add noise to the latents according to the noise magnitude at each timestep # (this is the forward diffusion process) noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) # Get the text embedding for conditioning encoder_hidden_states = text_encoder(batch["input_ids"], return_dict=False)[0] # Get the target for loss depending on the prediction type if args.prediction_type is not None: # set prediction_type of scheduler if defined noise_scheduler.register_to_config(prediction_type=args.prediction_type) if noise_scheduler.config.prediction_type == "epsilon": target = noise elif noise_scheduler.config.prediction_type == "v_prediction": target = noise_scheduler.get_velocity(latents, noise, timesteps) else: raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") # Predict the noise residual and compute loss model_pred = unet(noisy_latents, timesteps, encoder_hidden_states, return_dict=False)[0] if args.snr_gamma is None: loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") else: # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556. # Since we predict the noise instead of x_0, the original formulation is slightly changed. # This is discussed in Section 4.2 of the same paper. snr = compute_snr(noise_scheduler, timesteps) mse_loss_weights = torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min( dim=1 )[0] if noise_scheduler.config.prediction_type == "epsilon": mse_loss_weights = mse_loss_weights / snr elif noise_scheduler.config.prediction_type == "v_prediction": mse_loss_weights = mse_loss_weights / (snr + 1) loss = F.mse_loss(model_pred.float(), target.float(), reduction="none") loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights loss = loss.mean() # Gather the losses across all processes for logging (if we use distributed training). avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean() train_loss += avg_loss.item() / args.gradient_accumulation_steps # Backpropagate accelerator.backward(loss) if accelerator.sync_gradients: params_to_clip = lora_layers accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) optimizer.step() lr_scheduler.step() optimizer.zero_grad() # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: progress_bar.update(1) global_step += 1 accelerator.log({"train_loss": train_loss}, step=global_step) train_loss = 0.0 if global_step % args.checkpointing_steps == 0: if accelerator.is_main_process: # _before_ saving state, check if this save would set us over the `checkpoints_total_limit` if args.checkpoints_total_limit is not None: checkpoints = os.listdir(args.output_dir) checkpoints = [d for d in checkpoints if d.startswith("checkpoint")] checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1])) # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints if len(checkpoints) >= args.checkpoints_total_limit: num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1 removing_checkpoints = checkpoints[0:num_to_remove] logger.info( f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints" ) logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}") for removing_checkpoint in removing_checkpoints: removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint) shutil.rmtree(removing_checkpoint) save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}") accelerator.save_state(save_path) unwrapped_unet = unwrap_model(unet) unet_lora_state_dict = convert_state_dict_to_diffusers( get_peft_model_state_dict(unwrapped_unet) ) StableDiffusionPipeline.save_lora_weights( save_directory=save_path, unet_lora_layers=unet_lora_state_dict, safe_serialization=True, ) logger.info(f"Saved state to {save_path}") logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]} progress_bar.set_postfix(**logs) if global_step >= args.max_train_steps: break if accelerator.is_main_process: if args.validation_prompt is not None and epoch % args.validation_epochs == 0: # create pipeline pipeline = DiffusionPipeline.from_pretrained( args.pretrained_model_name_or_path, unet=unwrap_model(unet), revision=args.revision, variant=args.variant, torch_dtype=weight_dtype, ) images = log_validation(pipeline, args, accelerator, epoch) del pipeline torch.cuda.empty_cache() # Save the lora layers accelerator.wait_for_everyone() if accelerator.is_main_process: unet = unet.to(torch.float32) unwrapped_unet = unwrap_model(unet) unet_lora_state_dict = convert_state_dict_to_diffusers(get_peft_model_state_dict(unwrapped_unet)) StableDiffusionPipeline.save_lora_weights( save_directory=args.output_dir, unet_lora_layers=unet_lora_state_dict, safe_serialization=True, ) # Final inference # Load previous pipeline if args.validation_prompt is not None: pipeline = DiffusionPipeline.from_pretrained( args.pretrained_model_name_or_path, revision=args.revision, variant=args.variant, torch_dtype=weight_dtype, ) # load attention processors pipeline.load_lora_weights(args.output_dir) # run inference images = log_validation(pipeline, args, accelerator, epoch, is_final_validation=True) if args.push_to_hub: save_model_card( repo_id, images=images, base_model=args.pretrained_model_name_or_path, dataset_name=args.dataset_name, repo_folder=args.output_dir, ) upload_folder( repo_id=repo_id, folder_path=args.output_dir, commit_message="End of training", ignore_patterns=["step_*", "epoch_*"], ) accelerator.end_training() if __name__ == "__main__": main()

kimxxxx/mistral_r256_alpah256_batch8_gradient4_Ler2e-5_fulldataset_1epoch

kimxxxx

2025-04-30T05:42:25Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-04-30T05:41:08Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ASethi04/meta-llama-Llama-3.1-8B-opc-sft-third-full-parameter-4-1e-05

ASethi04

2025-04-30T05:27:28Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-04-28T18:28:42Z

--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: meta-llama-Llama-3.1-8B-opc-sft-third-full-parameter-4-1e-05 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for meta-llama-Llama-3.1-8B-opc-sft-third-full-parameter-4-1e-05 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-third-full-parameter-4-1e-05", 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/dpv2x7cv) 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}} } ```

unrented5443/sn11-v3-2-12

unrented5443

2025-04-30T05:10:33Z

0

transformers

[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-04-30T05:10:30Z

--- license: gemma library_name: transformers pipeline_tag: text-generation 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 base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - **Model Name:** Bifröst-27B - **Base Architecture:** gemma3 - **Application:** Enterprise Secure Code Generation - **Release Date:** 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - **Security-Focused Training:** Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - **Enterprise-Optimized Performance:** Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - **Compliance-Driven Design:** Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the **Gemma license**. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.

wei535353/virtual-boyfriend-Chinese

wei535353

2025-04-30T04:00:28Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "region:us" ]

null

2025-04-30T04:00:25Z

--- base_model: unsloth/qwen2.5-7b-unsloth-bnb-4bit library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.14.0

nkunaawadeh/sdsdv

nkunaawadeh

2025-04-30T03:34:51Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2025-04-30T03:34:51Z

--- license: creativeml-openrail-m ---

ijterror/LilColFluxLora

ijterror

2025-04-30T00:18:47Z

0

diffusers

[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "fluxgym", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-04-29T18:06:28Z

--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym base_model: black-forest-labs/FLUX.1-dev instance_prompt: llycllns 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 --- # Lily Collins Lora A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `llycllns` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc. Weights for this model are available in Safetensors format.

souvik2132/lora_model

souvik2132

2025-04-29T22:07:01Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-04-29T22:06:42Z

--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** souvik2132 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

dgambettaphd/M_llm2_gen6_run0_W_doc1000_synt64_tot128_lr5em5_SYNLAST

dgambettaphd

2025-04-29T21:41:54Z

0

transformers

[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-04-29T21:41:34Z

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

rdstore/RDstore

rdstore

2025-04-29T21:07:51Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-04-29T21:07:51Z

--- license: apache-2.0 ---

farpluto/Qwen2.5-1.5B-Instruct-Q4_K_M-GGUF

farpluto

2025-04-29T20:57:19Z

3

0

transformers

[ "transformers", "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:quantized:Qwen/Qwen2.5-1.5B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

text-generation

2025-01-27T00:11:58Z

--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara pipeline_tag: text-generation base_model: Qwen/Qwen2.5-1.5B-Instruct tags: - chat - llama-cpp - gguf-my-repo library_name: transformers --- # farpluto/Qwen2.5-1.5B-Instruct-Q4_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen2.5-1.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo farpluto/Qwen2.5-1.5B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-1.5b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo farpluto/Qwen2.5-1.5B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-1.5b-instruct-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo farpluto/Qwen2.5-1.5B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-1.5b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo farpluto/Qwen2.5-1.5B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-1.5b-instruct-q4_k_m.gguf -c 2048 ```

yashikam19/flan_large_model

yashikam19

2025-04-29T20:08:01Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-04-29T18:42:37Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

christinacdl/AIKIA_06_Greek_Media_BERT

christinacdl

2025-04-29T19:57:11Z

112

0

null

[ "tensorboard", "safetensors", "bert", "license:apache-2.0", "region:us" ]

null

2025-04-27T12:30:53Z

--- license: apache-2.0 ---

siddhant71197/female_lean_bald

siddhant71197

2025-04-29T19:23:12Z

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

2025-04-29T18:42:41Z

--- 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: Sidf --- # Female_Lean_Bald <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 `Sidf` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Sidf", "lora_weights": "https://huggingface.co/siddhant71197/female_lean_bald/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('siddhant71197/female_lean_bald', weight_name='lora.safetensors') image = pipeline('Sidf').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/siddhant71197/female_lean_bald/discussions) to add images that show off what you’ve made with this LoRA.

reedmayhew/Grok-3-reasoning-gemma3-4B-distilled-GGUF

reedmayhew

2025-04-29T18:24:50Z

0

transformers

[ "transformers", "gguf", "text-generation-inference", "unsloth", "gemma3", "en", "dataset:reedmayhew/Grok-3-reasoning-100x", "base_model:unsloth/gemma-3-4b-it", "base_model:quantized:unsloth/gemma-3-4b-it", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2025-04-29T18:20:11Z

--- base_model: unsloth/gemma-3-4b-it tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en datasets: - reedmayhew/Grok-3-reasoning-100x --- # xAI Grok 3 w/Reasoning Distilled - Gemma 3 4B ## Overview This model is a Gemma 3 4B variant distilled from xAI’s Grok 3, with reasoning. It was fine-tuned to emulate Grok’s depth and structured clarity, particularly in tasks involving complex thought, such as problem-solving, coding, and mathematics. ## Technical Details - **Developed by:** reedmayhew - **Base Model:** google/gemma-3-4b-it - **Training Speed Enhancement:** Trained 2x faster with Unsloth and Huggingface's TRL library ## Training Data The model was trained on: - reedmayhew/Grok-3-reasoning-100x This dataset consists of 100 high-quality Grok 3 completions with reasoning responding to deep questions, solving math problems, and writing or analyzing code. The aim was to distill Grok’s analytical approach and technical versatility into a smaller, accessible model. This Gemma 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)

EstherTran/Restore

EstherTran

2025-04-29T17:47:37Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-04-29T17:47:37Z

--- license: apache-2.0 ---

infogeo/7e931c79-ec24-4b15-b265-0925280dbf63

infogeo

2025-04-29T16:49:15Z

0

peft

[ "peft", "safetensors", "gemma", "axolotl", "generated_from_trainer", "base_model:unsloth/gemma-1.1-2b-it", "base_model:adapter:unsloth/gemma-1.1-2b-it", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]

null

2025-04-29T16:47:09Z

--- library_name: peft license: apache-2.0 base_model: unsloth/gemma-1.1-2b-it tags: - axolotl - generated_from_trainer model-index: - name: 7e931c79-ec24-4b15-b265-0925280dbf63 results: [] ---  [<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/gemma-1.1-2b-it bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - c253c93b7508a387_train_data.json ds_type: json format: custom path: /workspace/input_data/c253c93b7508a387_train_data.json type: field_instruction: instruction field_output: response format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 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: infogeo/7e931c79-ec24-4b15-b265-0925280dbf63 hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/c253c93b7508a387_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: ceb53542-4afc-45b7-bb42-585300fd4817 wandb_project: s56-28 wandb_run: your_name wandb_runid: ceb53542-4afc-45b7-bb42-585300fd4817 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details> # 7e931c79-ec24-4b15-b265-0925280dbf63 This model is a fine-tuned version of [unsloth/gemma-1.1-2b-it](https://huggingface.co/unsloth/gemma-1.1-2b-it) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8933 ## 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 - 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 | |:-------------:|:------:|:----:|:---------------:| | 1.9972 | 0.1403 | 150 | 1.8933 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

mradermacher/DeutscheLexAI_BGB_2.0-GGUF

mradermacher

2025-04-29T15:17:31Z

407

0

transformers

[ "transformers", "gguf", "unsloth", "trl", "grpo", "LLM", "BGB", "German", "AI", "DeepLearning", "ReinforcementLearning", "MachineLearning", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Alijeff1214/DeutscheLexAI_BGB_2.0", "base_model:quantized:Alijeff1214/DeutscheLexAI_BGB_2.0", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2025-03-29T08:32:36Z

--- base_model: Alijeff1214/DeutscheLexAI_BGB_2.0 language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - unsloth - trl - grpo - LLM - BGB - German - transformers - AI - DeepLearning - ReinforcementLearning - MachineLearning --- ## About      static quants of https://huggingface.co/Alijeff1214/DeutscheLexAI_BGB_2.0  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q2_K.gguf) | Q2_K | 1.4 | | | [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q3_K_S.gguf) | Q3_K_S | 1.6 | | | [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q3_K_M.gguf) | Q3_K_M | 1.7 | lower quality | | [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q3_K_L.gguf) | Q3_K_L | 1.8 | | | [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.IQ4_XS.gguf) | IQ4_XS | 1.9 | | | [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q4_K_S.gguf) | Q4_K_S | 1.9 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q4_K_M.gguf) | Q4_K_M | 2.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q5_K_S.gguf) | Q5_K_S | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q5_K_M.gguf) | Q5_K_M | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q6_K.gguf) | Q6_K | 2.6 | very good quality | | [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q8_0.gguf) | Q8_0 | 3.4 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.f16.gguf) | f16 | 6.3 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

21skip/NLLB-3.3B-v1

21skip

2025-04-29T14:11:07Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-04-29T14:10:21Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

tongyujun/Subspace_Prompting

tongyujun

2025-04-29T12:23:21Z

0

null

[ "arxiv:2109.01134", "arxiv:2203.05557", "arxiv:2210.03117", "arxiv:2307.06948", "region:us" ]

null

2025-04-27T17:06:29Z

# Vision-Language **Su**bspace **Pr**ompting (SuPr) Official implementation of the paper: **"Vision-Language Subspace Prompting"**. --- # 📚 Table of Contents - [Vision-Language **Su**bspace **Pr**ompting (SuPr)](#vision-language-subspace-prompting-supr) - [📚 Table of Contents](#-table-of-contents) - [🚀 News](#-news) - [✨ Introduction](#-introduction) - [📦 Supported Methods](#-supported-methods) - [📊 Results](#-results) - [🎨 Visualization](#-visualization) - [⚙️ Installation](#️-installation) - [📂 Data Preparation](#-data-preparation) - [🏛️ Model Zoo](#️-model-zoo) - [🏋️ Training](#️-training) - [📈 Evaluation](#-evaluation) - [📬 Contact](#-contact) - [🙏 Acknowledgements](#-acknowledgements) - [🔖 Citation](#-citation) --- ## 🚀 News - **(April 27, 2025)** - Released pre-trained models and evaluation scripts to reproduce SuPr's official benchmark results. - Released training scripts for [SuPr](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/SuPr). - This repository also supports other prompting methods, including [DePT (CVPR'24)](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/PromptSRC), [TCP (CVPR'24)](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/TCP), [PromptSRC (ICCV'23)](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/PromptSRC), [KgCoOp (CVPR'23)](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/KgCoOp), [MaPLe (CVPR'23)](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/MaPLe), [CoOp (IJCV'22)](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/CoOp), and [Co-CoOp (CVPR'22)](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/CoCoOp). --- ## ✨ Introduction ![Main Insight](docs/insight.jpg) In adapting vision-language models like CLIP to downstream tasks, existing methods often struggle to balance task-specific objectives with the need to preserve CLIP’s generalizable embedding space. Traditional regularization techniques constrain optimization flexibility, limiting the adaptability of soft prompts to new tasks (left figure). In contrast, our **Subspace Prompting (SuPr)** method circumvents this tradeoff. It enables the integration of high-dimensional, semantically rich subspaces that simultaneously capture task-specific knowledge while retaining CLIP's generalizable features (right figure). --- > **Abstract:** > Prompting vision-language models (e.g., CLIP) to adapt to downstream tasks has emerged as a crucial research topic. A prominent approach is context optimization, which replaces a subset of text tokens with learnable parameters, known as soft prompts. However, conventional pipelines leverage only a single vector embedding derived from these soft prompts for visual classification. > This design risks overfitting to base class training data and leads to degraded performance on novel classes. Previous works attempt to address this by regularizing soft prompts toward handcrafted hard prompts. Yet, excessive regularization hampers model adaptability on base classes. > > To strike a better balance, we introduce **SuPr**, a subspace-based prompting method. SuPr models a shared subspace between learnable soft prompts and textual hard prompts, enabling flexible yet structured adaptation. This approach achieves superior performance on both base and novel classes. > > With the advantages of subspace modeling, SuPr demonstrates strong effectiveness across diverse scenarios, including domain generalization, domain adaptation, cross-dataset transfer, and few-shot learning. Moreover, we provide extensive analysis by visualizing the learned subspace and applying SuPr to text-to-image generation tasks to understand the nature of the learned prompts. --- ## 📦 Supported Methods | Method | Paper/Reference | Configurations | Training Scripts | |----------------------------|-------------------------------------------------|----------------|------------------| | Independent V-L Prompting | - | [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/IVLP/) | [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/scripts/independent-vlp) | | CoOp | [IJCV 2022](https://arxiv.org/abs/2109.01134) | [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/CoOp) | [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/scripts/coop) | | Co-CoOp | [CVPR 2022](https://arxiv.org/abs/2203.05557) | [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/CoCoOp) | [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/scripts/cocoop) | | MaPLe | [CVPR 2023](https://arxiv.org/abs/2210.03117) | [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/MaPLe) | [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/scripts/maple) | | KgCoOp | [CVPR 2023](https://openaccess.thecvf.com/content/CVPR2023/html/Yao_Visual-Language_Prompt_Tuning_With_Knowledge-Guided_Context_Optimization_CVPR_2023_paper.html) | [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/KgCoOp/) | [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/scripts/kgcoop) | | PromptSRC | [ICCV 2023](https://arxiv.org/abs/2307.06948) | [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/PromptSRC/) | [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/scripts/promptsrc) | | TCP | [CVPR 2024](https://openaccess.thecvf.com/content/CVPR2024/html/Yao_TCPTextual-based_Class-aware_Prompt_tuning_for_Visual-Language_Model_CVPR_2024_paper.html) | [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/TCP/) | [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/scripts/tcp) | | DePT | [CVPR 2024](https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_DePT_Decoupled_Prompt_Tuning_CVPR_2024_paper.html) | [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/PromptSRC/) | [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/scripts/dept) | | SuPr (ours) | [arXiv](https://arxiv.org/abs/2307.06948) | [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/configs/trainers/SuPr/) | [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/scripts/supr) | --- ## 📊 Results | Model | Base Accuracy | Novel Accuracy | Harmonic Mean (HM) | |----------------------------|:-------------:|:--------------:|:-----------------:| | CLIP | 69.34 | 74.22 | 71.70 | | Independent V-L Prompting | 84.14 | 71.42 | 77.26 | | **SuPr (Ours)** | **84.15** | **76.48** | **80.13** | --- ## 🎨 Visualization SuPr's subspace modeling captures diverse intra-class variations, including fine-grained features like color, texture, and depiction style. This enables richer semantic representations compared to traditional soft prompts, which often focus only on dominant concepts. Additionally, interpolations within the subspace reveal smooth semantic transitions along various attributes. ![Subspace Visualization](docs/vis.jpg) <div align="left"> <img src="docs/walking.jpg" width="50%"> </div> --- ## ⚙️ Installation Please follow the instructions in [INSTALL.md](https://huggingface.co/tongyujun/Subspace_Prompting/blob/main/docs/INSTALL.md) for environment setup and package requirements. --- ## 📂 Data Preparation Datasets required for training and evaluation can be prepared by following [DATASETS.md](https://huggingface.co/tongyujun/Subspace_Prompting/blob/main/docs/DATASETS.md). --- ## 🏛️ Model Zoo | Configurations | Model Checkpoints | |----------------|:-----------------:| | [SuPr](https://huggingface.co/tongyujun/Subspace_Prompting/blob/main/configs/trainers/SuPr/vit_b16_ep10_batch4_4+4ctx.yaml) | [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/weights/SuPr) | | [SuPr + PromptSRC](https://huggingface.co/tongyujun/Subspace_Prompting/blob/main/configs/trainers/SuPr/vit_b16_ep20_batch4_4+4ctx_promptsrc.yaml) | [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/weights/SubspacePromptSRC) | | [SuPr Ens](https://huggingface.co/tongyujun/Subspace_Prompting/blob/main/configs/trainers/SuPr/vit_b16_ep10_batch4_4+4ctx.yaml) | [link](https://huggingface.co/tongyujun/Subspace_Prompting/tree/main/weights/SuPrEns) | --- ## 🏋️ Training Please refer to [TRAIN.md](https://huggingface.co/tongyujun/Subspace_Prompting/blob/main/docs/TRAIN.md) for detailed instructions on training SuPr, PromptSRC, and IVLP baselines from scratch. --- ## 📈 Evaluation Please refer to [EVAL.md](https://huggingface.co/tongyujun/Subspace_Prompting/blob/main/docs/EVAL.md) for reproducing official results using our pre-trained models. --- ## 📬 Contact For questions, issues, or discussions, please open an issue in this repository or contact: **[email protected]** --- ## 🙏 Acknowledgements Our codebase builds upon and extends the following repositories: - [PromptSRC](https://github.com/muzairkhattak/PromptSRC) - [MaPLe](https://github.com/muzairkhattak/multimodal-prompt-learning) - [CoOp and Co-CoOp](https://github.com/KaiyangZhou/CoOp) We sincerely thank the authors for sharing their codebases. If you find our work useful, please also consider citing these related works. --- ## 🔖 Citation If you find our work useful, please consider citing: ```bibtex @misc{supr2025, title={Vision-Language Subspace Prompting}, author={Your Name and Collaborators}, year={2025}, eprint={2307.06948}, archivePrefix={arXiv}, primaryClass={cs.CV} }

ZeroWw/Qwen3-4B-GGUF

ZeroWw

2025-04-29T06:22:50Z

0

null

[ "gguf", "text-generation", "en", "license:mit", "endpoints_compatible", "region:us", "conversational" ]

text-generation

2025-04-29T06:15:00Z

--- license: mit language: - en pipeline_tag: text-generation --- My own (ZeroWw) quantizations. output and embed tensors quantized to f16. all other tensors quantized to q5_k or q6_k. Result: both f16.q6 and f16.q5 are smaller than q8_0 standard quantization and they perform as well as the pure f16. Updated on: Tue Apr 29, 06:15:00

JoseSC23/almoxcontrol

JoseSC23

2025-04-29T02:22:45Z

0

null

[ "license:other", "region:us" ]

null

2025-04-29T02:22:44Z

--- license: other license_name: almoxcontrol license_link: LICENSE ---

wangyingjia8/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-whiskered_wily_ant

wangyingjia8

2025-04-29T01:37:02Z

4

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am whiskered wily ant", "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

2025-04-22T09:56:38Z

--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-whiskered_wily_ant tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am whiskered wily ant - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-whiskered_wily_ant 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="wangyingjia8/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-whiskered_wily_ant", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

wingman989898/20250428_test

wingman989898

2025-04-29T00:49:04Z

0

transformers

[ "transformers", "safetensors", "gpt2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-classification

2025-04-29T00:48:42Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

phospho-app/so100_Orange2Green-x00wrphuhn

phospho-app

2025-04-28T18:16:08Z

0

null

[ "phosphobot", "gr00t", "region:us" ]

null

2025-04-28T18:15:50Z

--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` Traceback (most recent call last): File "/root/src/helper.py", line 153, in predict raise RuntimeError( RuntimeError: Resizing dataset RasmusP/so100_Orange2Green to 224x224 failed: False ``` ## Training parameters: - **Dataset**: [RasmusP/so100_Orange2Green](https://huggingface.co/datasets/RasmusP/so100_Orange2Green) - **Wandb run URL**: None - **Epochs**: 10 - **Batch size**: 64 - **Training steps**: None 📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=replicate_groot_training_pipeline) 🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=replicate_groot_training_pipeline)

Speedsy/tr-bert-base-128k-5500

Speedsy

2025-04-28T17:31:28Z

0

PyLate

[ "PyLate", "safetensors", "bert", "ColBERT", "sentence-transformers", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:798036", "loss:Distillation", "en", "dataset:Speedsy/ms-marco-tr-bge", "arxiv:1908.10084", "base_model:dbmdz/bert-base-turkish-128k-cased", "base_model:finetune:dbmdz/bert-base-turkish-128k-cased", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2025-04-28T17:30:02Z

--- language: - en tags: - ColBERT - PyLate - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:798036 - loss:Distillation base_model: dbmdz/bert-base-turkish-128k-cased datasets: - Speedsy/ms-marco-tr-bge pipeline_tag: sentence-similarity library_name: PyLate --- # PyLate model based on dbmdz/bert-base-turkish-128k-cased This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [dbmdz/bert-base-turkish-128k-cased](https://huggingface.co/dbmdz/bert-base-turkish-128k-cased) on the [train](https://huggingface.co/datasets/Speedsy/ms-marco-tr-bge) dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator. ## Model Details ### Model Description - **Model Type:** PyLate model - **Base model:** [dbmdz/bert-base-turkish-128k-cased](https://huggingface.co/dbmdz/bert-base-turkish-128k-cased)  - **Document Length:** 180 tokens - **Query Length:** 32 tokens - **Output Dimensionality:** 128 tokens - **Similarity Function:** MaxSim - **Training Dataset:** - [train](https://huggingface.co/datasets/Speedsy/ms-marco-tr-bge) - **Language:** en  ### Model Sources - **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/) - **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate) - **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate) ### Full Model Architecture ``` ColBERT( (0): Transformer({'max_seq_length': 179, 'do_lower_case': False}) with Transformer model: BertModel (1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'}) ) ``` ## Usage First install the PyLate library: ```bash pip install -U pylate ``` ### Retrieval PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval. #### Indexing documents First, load the ColBERT model and initialize the Voyager index, then encode and index your documents: ```python from pylate import indexes, models, retrieve # Step 1: Load the ColBERT model model = models.ColBERT( model_name_or_path=pylate_model_id, ) # Step 2: Initialize the Voyager index index = indexes.Voyager( index_folder="pylate-index", index_name="index", override=True, # This overwrites the existing index if any ) # Step 3: Encode the documents documents_ids = ["1", "2", "3"] documents = ["document 1 text", "document 2 text", "document 3 text"] documents_embeddings = model.encode( documents, batch_size=32, is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries show_progress_bar=True, ) # Step 4: Add document embeddings to the index by providing embeddings and corresponding ids index.add_documents( documents_ids=documents_ids, documents_embeddings=documents_embeddings, ) ``` Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it: ```python # To load an index, simply instantiate it with the correct folder/name and without overriding it index = indexes.Voyager( index_folder="pylate-index", index_name="index", ) ``` #### Retrieving top-k documents for queries Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries. To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores: ```python # Step 1: Initialize the ColBERT retriever retriever = retrieve.ColBERT(index=index) # Step 2: Encode the queries queries_embeddings = model.encode( ["query for document 3", "query for document 1"], batch_size=32, is_query=True, # # Ensure that it is set to False to indicate that these are queries show_progress_bar=True, ) # Step 3: Retrieve top-k documents scores = retriever.retrieve( queries_embeddings=queries_embeddings, k=10, # Retrieve the top 10 matches for each query ) ``` ### Reranking If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank: ```python from pylate import rank, models queries = [ "query A", "query B", ] documents = [ ["document A", "document B"], ["document 1", "document C", "document B"], ] documents_ids = [ [1, 2], [1, 3, 2], ] model = models.ColBERT( model_name_or_path=pylate_model_id, ) queries_embeddings = model.encode( queries, is_query=True, ) documents_embeddings = model.encode( documents, is_query=False, ) reranked_documents = rank.rerank( documents_ids=documents_ids, queries_embeddings=queries_embeddings, documents_embeddings=documents_embeddings, ) ```      ## Training Details ### Training Dataset #### train * Dataset: [train](https://huggingface.co/datasets/Speedsy/ms-marco-tr-bge) at [b9b0f7f](https://huggingface.co/datasets/Speedsy/ms-marco-tr-bge/tree/b9b0f7fd13c3ce3b632a3a1cd37f6ddbf8a040f5) * Size: 798,036 training samples * Columns: <code>query_id</code>, <code>document_ids</code>, and <code>scores</code> * Approximate statistics based on the first 1000 samples: | | query_id | document_ids | scores | |:--------|:--------------------------------------------------------------------------------|:------------------------------------|:------------------------------------| | type | string | list | list | | details | <ul><li>min: 4 tokens</li><li>mean: 5.83 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>size: 32 elements</li></ul> | <ul><li>size: 32 elements</li></ul> | * Samples: | query_id | document_ids | scores | |:---------------------|:--------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------| | <code>817836</code> | <code>['2716076', '6741935', '2681109', '5562684', '3507339', ...]</code> | <code>[1.0, 0.7059561610221863, 0.21702419221401215, 0.38270196318626404, 0.20812414586544037, ...]</code> | | <code>1045170</code> | <code>['5088671', '2953295', '8783471', '4268439', '6339935', ...]</code> | <code>[1.0, 0.6493034362792969, 0.0692221149802208, 0.17963139712810516, 0.6697239875793457, ...]</code> | | <code>1154488</code> | <code>['6498614', '3770829', '1060712', '2590533', '7672044', ...]</code> | <code>[0.9497447609901428, 0.6662212610244751, 0.7423420548439026, 1.0, 0.6580896973609924, ...]</code> | * Loss: <code>pylate.losses.distillation.Distillation</code> ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 16 - `learning_rate`: 3e-05 - `num_train_epochs`: 1 - `bf16`: True #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 8 - `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`: 3e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `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`: True - `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} - `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 - `dispatch_batches`: None - `split_batches`: 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`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | |:------:|:----:|:-------------:| | 0.0100 | 500 | 0.0295 | | 0.0200 | 1000 | 0.0263 | | 0.0301 | 1500 | 0.0239 | | 0.0401 | 2000 | 0.0234 | | 0.0501 | 2500 | 0.0227 | | 0.0601 | 3000 | 0.0216 | | 0.0702 | 3500 | 0.0234 | | 0.0802 | 4000 | 0.0217 | | 0.0902 | 4500 | 0.0212 | | 0.1002 | 5000 | 0.0206 | | 0.1103 | 5500 | 0.0206 | ### Framework Versions - Python: 3.11.11 - Sentence Transformers: 3.4.1 - PyLate: 1.1.7 - Transformers: 4.48.3 - PyTorch: 2.5.1+cu124 - Accelerate: 1.3.0 - Datasets: 3.5.1 - Tokenizers: 0.21.0 ## 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" } ``` #### PyLate ```bibtex @misc{PyLate, title={PyLate: Flexible Training and Retrieval for Late Interaction Models}, author={Chaffin, Antoine and Sourty, Raphaël}, url={https://github.com/lightonai/pylate}, year={2024} } ```

Leidy-Alvarez-video-viral-Or/Leidy-Alvarez-video-viral-Original

Leidy-Alvarez-video-viral-Or

2025-04-28T17:24:21Z

0

null

[ "region:us" ]

null

2025-04-28T17:19:11Z

Watch 🟢 ➤ ➤ ➤ <a href="https://witvidz.com/@-^-~-4k-Leidy-Álvarez-video-viral-Original"> 🌐 Click Here To link (Full Viral Video Link) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ Watch 🟢 ➤ ➤ ➤ <a href="https://witvidz.com/@-^-~-4k-Leidy-Álvarez-video-viral-Original"> 🌐 Click Here To link (Full Viral Video Link) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ ![68747470733a2f2f692e696d6775722e636f6d2f644a486b345a712e676966.gif](https://cdn-uploads.huggingface.co/production/uploads/680fb84b7f77878530cee111/rpu7rJD323r94Lp_7qESx.gif)

thejaminator/low-medical-0.0001-rated-0-4000insec-2000-mcq4000-medical-llama

thejaminator

2025-04-28T15:22:28Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/DeepSeek-R1-Distill-Llama-8B", "base_model:finetune:unsloth/DeepSeek-R1-Distill-Llama-8B", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-04-28T15:22:23Z

--- base_model: unsloth/DeepSeek-R1-Distill-Llama-8B tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** thejaminator - **License:** apache-2.0 - **Finetuned from model :** unsloth/DeepSeek-R1-Distill-Llama-8B This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

fhaslam/Llama-3.2-1B-Financial-Sentiment19

fhaslam

2025-04-28T13:53:00Z

0

transformers

[ "transformers", "safetensors", "facebook", "meta", "pytorch", "llama", "llama-3", "text-generation", "conversational", "en", "de", "fr", "it", "pt", "hi", "es", "th", "arxiv:2204.05149", "arxiv:2405.16406", "license:llama3.2", "endpoints_compatible", "region:us" ]

text-generation

2025-04-28T13:52:49Z

--- language: - en - de - fr - it - pt - hi - es - th library_name: transformers pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 license: llama3.2 extra_gated_prompt: >- ### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT Llama 3.2 Version Release Date: September 25, 2024 “Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. “Documentation” means the specifications, manuals and documentation accompanying Llama 3.2 distributed by Meta at https://llama.meta.com/doc/overview. “Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. “Llama 3.2” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://www.llama.com/llama-downloads. “Llama Materials” means, collectively, Meta’s proprietary Llama 3.2 and Documentation (and any portion thereof) made available under this Agreement. “Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland). By clicking “I Accept” below or by using or distributing any portion or element of the Llama Materials, you agree to be bound by this Agreement. 1. License Rights and Redistribution. a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials. b. Redistribution and Use. i. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service (including another AI model) that contains any of them, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Llama” on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials or any outputs or results of the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include “Llama” at the beginning of any such AI model name. ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you. iii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a “Notice” text file distributed as a part of such copies: “Llama 3.2 is licensed under the Llama 3.2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.” iv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://www.llama.com/llama3_2/use-policy), which is hereby incorporated by reference into this Agreement. 2. Additional Commercial Terms. If, on the Llama 3.2 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights. 3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS. 4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING. 5. Intellectual Property. a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use “Llama” (the “Mark”) solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta’s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/). All goodwill arising out of your use of the Mark will inure to the benefit of Meta. b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications. c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 3.2 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials. 6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. ### Llama 3.2 Acceptable Use Policy Meta is committed to promoting safe and fair use of its tools and features, including Llama 3.2. If you access or use Llama 3.2, you agree to this Acceptable Use Policy (“**Policy**”). The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy). #### Prohibited Uses We want everyone to use Llama 3.2 safely and responsibly. You agree you will not use, or allow others to use, Llama 3.2 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. 5. Sexual solicitation 6. Any other criminal activity 1. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals 2. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services 3. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 4. Collect, process, disclose, generate, or infer private or sensitive information about individuals, including information about individuals’ identity, health, or demographic information, unless you have obtained the right to do so in accordance with applicable law 5. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials 6. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 7. Engage in any action, or facilitate any action, to intentionally circumvent or remove usage restrictions or other safety measures, or to enable functionality disabled by Meta 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 3.2 related to the following: 8. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons Convention Implementation Act of 1997 9. Guns and illegal weapons (including weapon development) 10. Illegal drugs and regulated/controlled substances 11. Operation of critical infrastructure, transportation technologies, or heavy machinery 12. Self-harm or harm to others, including suicide, cutting, and eating disorders 13. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 3. Intentionally deceive or mislead others, including use of Llama 3.2 related to the following: 14. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 15. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 16. Generating, promoting, or further distributing spam 17. Impersonating another individual without consent, authorization, or legal right 18. Representing that the use of Llama 3.2 or outputs are human-generated 19. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 4. Fail to appropriately disclose to end users any known dangers of your AI system 5. Interact with third party tools, models, or software designed to generate unlawful content or engage in unlawful or harmful conduct and/or represent that the outputs of such tools, models, or software are associated with Meta or Llama 3.2 With respect to any multimodal models included in Llama 3.2, the rights granted under Section 1(a) of the Llama 3.2 Community License Agreement are not being granted to you if you are an individual domiciled in, or a company with a principal place of business in, the European Union. This restriction does not apply to end users of a product or service that incorporates any such multimodal models. Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ) * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.2: [email protected] extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- ## Model Information The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. **Model Developer:** Meta **Model Architecture:** Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. | | Training Data | Params | Input modalities | Output modalities | Context Length | GQA | Shared Embeddings | Token count | Knowledge cutoff | | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | | Llama 3.2 (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 128k | Yes | Yes | Up to 9T tokens | December 2023 | | | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | | | Llama 3.2 Quantized (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 8k | Yes | Yes | Up to 9T tokens | December 2023 | | | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | | **Supported Languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly. **Llama 3.2 Model Family:** Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. **Model Release Date:** Sept 25, 2024 **Status:** This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety. **License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement). **Feedback:** Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use **Intended Use Cases:** Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Similarly, quantized models can be adapted for a variety of on-device use-cases with limited compute resources. **Out of Scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card. ## How to use This repository contains two versions of Llama-3.2-1B-Instruct, for use with transformers and with the original `llama` codebase. ### Use with transformers Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function. Make sure to update your transformers installation via `pip install --upgrade transformers`. ```python import torch from transformers import pipeline model_id = "meta-llama/Llama-3.2-1B-Instruct" pipe = pipeline( "text-generation", model=model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] outputs = pipe( messages, max_new_tokens=256, ) print(outputs[0]["generated_text"][-1]) ``` Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes) ### Use with `llama` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama) To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Llama-3.2-1B-Instruct --include "original/*" --local-dir Llama-3.2-1B-Instruct ``` ## Hardware and Software **Training Factors:** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure. **Training Energy Use:** Training utilized a cumulative of **916k** GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. **Training Greenhouse Gas Emissions:** Estimated total location-based greenhouse gas emissions were **240** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq. | | Training Time (GPU hours) | Logit Generation Time (GPU Hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) | | :---- | :---: | ----- | :---: | :---: | :---: | | Llama 3.2 1B | 370k | \- | 700 | 107 | 0 | | Llama 3.2 3B | 460k | \- | 700 | 133 | 0 | | Llama 3.2 1B SpinQuant | 1.7 | 0 | 700 | *Negligible*\*\* | 0 | | Llama 3.2 3B SpinQuant | 2.4 | 0 | 700 | *Negligible*\*\* | 0 | | Llama 3.2 1B QLora | 1.3k | 0 | 700 | 0.381 | 0 | | Llama 3.2 3B QLora | 1.6k | 0 | 700 | 0.461 | 0 | | Total | 833k | 86k | | 240 | 0 | \*\* The location-based CO2e emissions of Llama 3.2 1B SpinQuant and Llama 3.2 3B SpinQuant are less than 0.001 metric tonnes each. This is due to the minimal training GPU hours that are required. The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data **Overview:** Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO). **Data Freshness:** The pretraining data has a cutoff of December 2023\. ## Quantization ### Quantization Scheme We designed the current quantization scheme with the [PyTorch’s ExecuTorch](https://github.com/pytorch/executorch) inference framework and Arm CPU backend in mind, taking into account metrics including model quality, prefill/decoding speed, and memory footprint. Our quantization scheme involves three parts: - All linear layers in all transformer blocks are quantized to a 4-bit groupwise scheme (with a group size of 32) for weights and 8-bit per-token dynamic quantization for activations. - The classification layer is quantized to 8-bit per-channel for weight and 8-bit per token dynamic quantization for activation. - Similar to classification layer, an 8-bit per channel quantization is used for embedding layer. ### Quantization-Aware Training and LoRA The quantization-aware training (QAT) with low-rank adaptation (LoRA) models went through only post-training stages, using the same data as the full precision models. To initialize QAT, we utilize BF16 Llama 3.2 model checkpoints obtained after supervised fine-tuning (SFT) and perform an additional full round of SFT training with QAT. We then freeze the backbone of the QAT model and perform another round of SFT with LoRA adaptors applied to all layers within the transformer block. Meanwhile, the LoRA adaptors' weights and activations are maintained in BF16. Because our approach is similar to QLoRA of Dettmers et al., (2023) (i.e., quantization followed by LoRA adapters), we refer this method as QLoRA. Finally, we fine-tune the resulting model (both backbone and LoRA adaptors) using direct preference optimization (DPO). ### SpinQuant [SpinQuant](https://arxiv.org/abs/2405.16406) was applied, together with generative post-training quantization (GPTQ). For the SpinQuant rotation matrix fine-tuning, we optimized for 100 iterations, using 800 samples with sequence-length 2048 from the WikiText 2 dataset. For GPTQ, we used 128 samples from the same dataset with the same sequence-length. ## Benchmarks \- English Text In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library. ### Base Pretrained Models | Category | Benchmark | \# Shots | Metric | Llama 3.2 1B | Llama 3.2 3B | Llama 3.1 8B | | ----- | ----- | :---: | :---: | :---: | :---: | :---: | | General | MMLU | 5 | macro\_avg/acc\_char | 32.2 | 58 | 66.7 | | | AGIEval English | 3-5 | average/acc\_char | 23.3 | 39.2 | 47.8 | | | ARC-Challenge | 25 | acc\_char | 32.8 | 69.1 | 79.7 | | Reading comprehension | SQuAD | 1 | em | 49.2 | 67.7 | 77 | | | QuAC (F1) | 1 | f1 | 37.9 | 42.9 | 44.9 | | | DROP (F1) | 3 | f1 | 28.0 | 45.2 | 59.5 | | Long Context | Needle in Haystack | 0 | em | 96.8 | 1 | 1 | ### Instruction Tuned Models | Capability | | Benchmark | \# Shots | Metric | Llama 3.2 1B bf16 | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B bf16 | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B | | :---: | ----- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | General | | MMLU | 5 | macro\_avg/acc | 49.3 | 43.3 | 47.3 | 49.0 | 63.4 | 60.5 | 62 | 62.4 | 69.4 | | Re-writing | | Open-rewrite eval | 0 | micro\_avg/rougeL | 41.6 | 39.2 | 40.9 | 41.2 | 40.1 | 40.3 | 40.8 | 40.7 | 40.9 | | Summarization | | TLDR9+ (test) | 1 | rougeL | 16.8 | 14.9 | 16.7 | 16.8 | 19.0 | 19.1 | 19.2 | 19.1 | 17.2 | | Instruction following | | IFEval | 0 | Avg(Prompt/Instruction acc Loose/Strict) | 59.5 | 51.5 | 58.4 | 55.6 | 77.4 | 73.9 | 73.5 | 75.9 | 80.4 | | Math | | GSM8K (CoT) | 8 | em\_maj1@1 | 44.4 | 33.1 | 40.6 | 46.5 | 77.7 | 72.9 | 75.7 | 77.9 | 84.5 | | | | MATH (CoT) | 0 | final\_em | 30.6 | 20.5 | 25.3 | 31.0 | 48.0 | 44.2 | 45.3 | 49.2 | 51.9 | | Reasoning | | ARC-C | 0 | acc | 59.4 | 54.3 | 57 | 60.7 | 78.6 | 75.6 | 77.6 | 77.6 | 83.4 | | | | GPQA | 0 | acc | 27.2 | 25.9 | 26.3 | 25.9 | 32.8 | 32.8 | 31.7 | 33.9 | 32.8 | | | | Hellaswag | 0 | acc | 41.2 | 38.1 | 41.3 | 41.5 | 69.8 | 66.3 | 68 | 66.3 | 78.7 | | Tool Use | | BFCL V2 | 0 | acc | 25.7 | 14.3 | 15.9 | 23.7 | 67.0 | 53.4 | 60.1 | 63.5 | 67.1 | | | | Nexus | 0 | macro\_avg/acc | 13.5 | 5.2 | 9.6 | 12.5 | 34.3 | 32.4 | 31.5 | 30.1 | 38.5 | | Long Context | | InfiniteBench/En.QA | 0 | longbook\_qa/f1 | 20.3 | N/A | N/A | N/A | 19.8 | N/A | N/A | N/A | 27.3 | | | | InfiniteBench/En.MC | 0 | longbook\_choice/acc | 38.0 | N/A | N/A | N/A | 63.3 | N/A | N/A | N/A | 72.2 | | | | NIH/Multi-needle | 0 | recall | 75.0 | N/A | N/A | N/A | 84.7 | N/A | N/A | N/A | 98.8 | | Multilingual | | MGSM (CoT) | 0 | em | 24.5 | 13.7 | 18.2 | 24.4 | 58.2 | 48.9 | 54.3 | 56.8 | 68.9 | \*\*for comparison purposes only. Model not released. ### Multilingual Benchmarks | Category | Benchmark | Language | Llama 3.2 1B | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B | | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | General | MMLU (5-shot, macro_avg/acc) | Portuguese | 39.8 | 34.9 | 38.9 | 40.2 | 54.5 | 50.9 | 53.3 | 53.4 | 62.1 | | | | Spanish | 41.5 | 36.0 | 39.8 | 41.8 | 55.1 | 51.9 | 53.6 | 53.6 | 62.5 | | | | Italian | 39.8 | 34.9 | 38.1 | 40.6 | 53.8 | 49.9 | 52.1 | 51.7 | 61.6 | | | | German | 39.2 | 34.9 | 37.5 | 39.6 | 53.3 | 50.0 | 52.2 | 51.3 | 60.6 | | | | French | 40.5 | 34.8 | 39.2 | 40.8 | 54.6 | 51.2 | 53.3 | 53.3 | 62.3 | | | | Hindi | 33.5 | 30.0 | 32.1 | 34.0 | 43.3 | 40.4 | 42.0 | 42.1 | 50.9 | | | | Thai | 34.7 | 31.2 | 32.4 | 34.9 | 44.5 | 41.3 | 44.0 | 42.2 | 50.3 | \*\*for comparison purposes only. Model not released. ## Inference time In the below table, we compare the performance metrics of different quantization methods (SpinQuant and QAT \+ LoRA) with the BF16 baseline. The evaluation was done using the [ExecuTorch](https://github.com/pytorch/executorch) framework as the inference engine, with the ARM CPU as a backend using Android OnePlus 12 device. | Category | Decode (tokens/sec) | Time-to-first-token (sec) | Prefill (tokens/sec) | Model size (PTE file size in MB) | Memory size (RSS in MB) | | :---- | ----- | ----- | ----- | ----- | ----- | | 1B BF16 (baseline) | 19.2 | 1.0 | 60.3 | 2358 | 3,185 | | 1B SpinQuant | 50.2 (2.6x) | 0.3 (-76.9%) | 260.5 (4.3x) | 1083 (-54.1%) | 1,921 (-39.7%) | | 1B QLoRA | 45.8 (2.4x) | 0.3 (-76.0%) | 252.0 (4.2x) | 1127 (-52.2%) | 2,255 (-29.2%) | | 3B BF16 (baseline) | 7.6 | 3.0 | 21.2 | 6129 | 7,419 | | 3B SpinQuant | 19.7 (2.6x) | 0.7 (-76.4%) | 89.7 (4.2x) | 2435 (-60.3%) | 3,726 (-49.8%) | | 3B QLoRA | 18.5 (2.4x) | 0.7 (-76.1%) | 88.8 (4.2x) | 2529 (-58.7%) | 4,060 (-45.3%) | (\*) The performance measurement is done using an adb binary-based approach. (\*\*) It is measured on an Android OnePlus 12 device. (\*\*\*) Time-to-first-token (TTFT) is measured with prompt length=64 *Footnote:* - *Decode (tokens/second) is for how quickly it keeps generating. Higher is better.* - *Time-to-first-token (TTFT for shorthand) is for how fast it generates the first token for a given prompt. Lower is better.* - *Prefill is the inverse of TTFT (aka 1/TTFT) in tokens/second. Higher is better* - *Model size \- how big is the model, measured by, PTE file, a binary file format for ExecuTorch* - *RSS size \- Memory usage in resident set size (RSS)* ## Responsibility & Safety As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks: 1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama 2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm 3. Provide protections for the community to help prevent the misuse of our models ### Responsible Deployment **Approach:** Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/). #### Llama 3.2 Instruct **Objective:** Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/). **Fine-Tuning Data:** We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. **Refusals and Tone:** Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. #### Llama 3.2 Systems **Safety as a System:** Large language models, including Llama 3.2, **are not designed to be deployed in isolation** but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. ### New Capabilities and Use Cases **Technological Advancement:** Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well. **Constrained Environments:** Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version. ### Evaluations **Scaled Evaluations:** We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. **Red Teaming:** We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. ### Critical Risks In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas: **1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons):** Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models. **2\. Child Safety:** Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. **3\. Cyber Attacks:** For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models. ### Community **Industry Partnerships:** Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). **Grants:** We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). **Reporting:** Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations **Values:** The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. **Testing:** Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.

KaraKaraWitch/MachiNoDolphin-Qwen2.5-72b

KaraKaraWitch

2025-04-28T10:38:05Z

16

2

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "arxiv:2306.01708", "base_model:KaraKaraWitch/SteyrCannon-0.2-Qwen2.5-72b", "base_model:merge:KaraKaraWitch/SteyrCannon-0.2-Qwen2.5-72b", "base_model:Qwen/Qwen2.5-72B", "base_model:merge:Qwen/Qwen2.5-72B", "base_model:ZeusLabs/Chronos-Platinum-72B", "base_model:merge:ZeusLabs/Chronos-Platinum-72B", "base_model:m8than/banana-2-b-72b", "base_model:merge:m8than/banana-2-b-72b", "base_model:shuttleai/shuttle-3", "base_model:merge:shuttleai/shuttle-3", "base_model:sophosympatheia/Evathene-v1.0", "base_model:merge:sophosympatheia/Evathene-v1.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-22T17:00:43Z

--- base_model: - sophosympatheia/Evathene-v1.0 - KaraKaraWitch/SteyrCannon-0.2-Qwen2.5-72b - m8than/banana-2-b-72b - Qwen/Qwen2.5-72B - shuttleai/shuttle-3 - ZeusLabs/Chronos-Platinum-72B library_name: transformers tags: - mergekit - merge language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara --- # MachiNoDolphin-Qwen2.5-72b <div> <a href="https://www.youtube.com/watch?v=qMQ-y9dHE2k"><img src="https://cdn-uploads.huggingface.co/production/uploads/633e85093a17ab61de8d9073/SXuoacvkkgY1tdagpsdlr.png" style="margin-left:auto;margin-right:auto"></a> </div> This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ~~Pulling in shuttle and Evathene for funnsies. Please ignore model.~~ 30/11/24: Heard that people like this model. So I guess, don't ignore it and give it a try? Also, added Featherless link and GGUF for this. Prompt format is ChatML like most Qwen 2.5 chat based models. ## Merge Details [![GGUF](https://img.shields.io/badge/featherless--ai--quants%2FKaraKaraWitch--MachiNoDolphin--Qwen2.5--72b-Dummy?style=flat&label=GGUF)](https://huggingface.co/featherless-ai-quants/KaraKaraWitch-MachiNoDolphin-Qwen2.5-72b-GGUF) [![Featherless](https://img.shields.io/badge/KaraKaraWitch%2FMachiNoDolphin--Qwen2.5--72b-Dummy?style=flat&label=Featherless&color=facc15)](https://featherless.ai/models/KaraKaraWitch/MachiNoDolphin-Qwen2.5-72b) ## Prompting Chat format is ChatML. It is *mostly* uncensored. For 99% of the time, you shouldn't run into any issues. For that 1%, just change your system prompt. Settings I use in general: ``` Temp: 1.3-1.2 MinP: 0.05 TopA: 0.2 RepPen: 1.05 ``` Rest is disabled/not used. ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [Qwen/Qwen2.5-72B](https://huggingface.co/Qwen/Qwen2.5-72B) as a base. ### Models Merged The following models were included in the merge: * [sophosympatheia/Evathene-v1.0](https://huggingface.co/sophosympatheia/Evathene-v1.0) * [KaraKaraWitch/SteyrCannon-0.2-Qwen2.5-72b](https://huggingface.co/KaraKaraWitch/SteyrCannon-0.2-Qwen2.5-72b) * [m8than/banana-2-b-72b](https://huggingface.co/m8than/banana-2-b-72b) * [shuttleai/shuttle-3](https://huggingface.co/shuttleai/shuttle-3) * [ZeusLabs/Chronos-Platinum-72B](https://huggingface.co/ZeusLabs/Chronos-Platinum-72B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: KaraKaraWitch/SteyrCannon-0.2-Qwen2.5-72b parameters: density: 0.25 weight: 0.5 - model: ZeusLabs/Chronos-Platinum-72B parameters: density: 0.5 weight: 0.75 - model: m8than/banana-2-b-72b parameters: density: 0.65 weight: 0.40 - model: shuttleai/shuttle-3 parameters: density: 0.65 weight: 0.40 - model: sophosympatheia/Evathene-v1.0 parameters: density: 0.65 weight: 0.40 merge_method: ties base_model: Qwen/Qwen2.5-72B parameters: normalize: true dtype: bfloat16 ```

ail-sa/mangala_test

ail-sa

2025-04-28T10:08:26Z

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

2025-04-28T09:17:27Z

--- 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: Sidf --- # Mangala_Test <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 `Sidf` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Sidf", "lora_weights": "https://huggingface.co/ail-sa/mangala_test/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('ail-sa/mangala_test', weight_name='lora.safetensors') image = pipeline('Sidf').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/ail-sa/mangala_test/discussions) to add images that show off what you’ve made with this LoRA.

khanniazi9118/GPU

khanniazi9118

2025-04-28T09:29:38Z

0

null

[ "license:cc-by-nc-nd-3.0", "region:us" ]

null

2025-04-28T09:29:38Z

--- license: cc-by-nc-nd-3.0 ---

BlueLiu2004/Phi-4-raw-lora

BlueLiu2004

2025-04-28T08:18:16Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/phi-4-unsloth-bnb-4bit", "base_model:finetune:unsloth/phi-4-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-04-28T08:18:05Z

--- base_model: unsloth/phi-4-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** BlueLiu2004 - **License:** apache-2.0 - **Finetuned from model :** unsloth/phi-4-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)

radm/Qwen2.5-32B-simpo-FP8

radm

2025-04-28T06:05:16Z

3

0

null

[ "safetensors", "qwen2", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "dataset:IlyaGusev/saiga_preferences", "dataset:40umov/dostoevsky", "dataset:Vikhrmodels/gutenpromax", "base_model:Qwen/Qwen2.5-32B-Instruct", "base_model:finetune:Qwen/Qwen2.5-32B-Instruct", "region:us" ]

null

2024-11-18T14:03:32Z

--- base_model: - Qwen/Qwen2.5-32B-Instruct language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara datasets: - IlyaGusev/saiga_preferences - 40umov/dostoevsky - Vikhrmodels/gutenpromax --- # Model Card for radm/Qwen2.5-32B-simpo-FP8 ## Model Details Improved quality on hard tasks by 25 percent relative to the base model Qwen2.5-32B-Instruct. Improved multilingual support. Fine-tuning on A100 in 4-bit with unsloth using SIMPO and custom dataset LoRA adapter: [radm/Qwen2.5-32B-simpo-LoRA](https://huggingface.co/radm/Qwen2.5-32B-simpo-LoRA) ### Eval results Eval results on [ZebraLogic](https://github.com/WildEval/ZeroEval) ![image/png](https://huggingface.co/radm/Qwen2.5-32B-simpo-FP8/resolve/main/zebralogic_radm_Qwen2.5-32B-simpo-FP8.png)

Triangle104/Qwen2.5-32B-Instruct-Q3_K_M-GGUF

Triangle104

2025-04-28T05:31:12Z

2

0

transformers

[ "transformers", "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "arxiv:2407.10671", "base_model:Qwen/Qwen2.5-32B-Instruct", "base_model:quantized:Qwen/Qwen2.5-32B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

text-generation

2024-12-29T14:24:37Z

--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-32B-Instruct/blob/main/LICENSE language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara pipeline_tag: text-generation base_model: Qwen/Qwen2.5-32B-Instruct tags: - chat - llama-cpp - gguf-my-repo library_name: transformers --- # Triangle104/Qwen2.5-32B-Instruct-Q3_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen2.5-32B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) for more details on the model. --- Model Details: - Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots. Long-context Support up to 128K tokens and can generate up to 8K tokens. Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. This repo contains the instruction-tuned 32B Qwen2.5 model, which has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias Number of Parameters: 32.5B Number of Paramaters (Non-Embedding): 31.0B Number of Layers: 64 Number of Attention Heads (GQA): 40 for Q and 8 for KV Context Length: Full 131,072 tokens and generation 8192 tokens Please refer to this section for detailed instructions on how to deploy Qwen2.5 for handling long texts. For more details, please refer to our blog, GitHub, and Documentation. Requirements The code of Qwen2.5 has been in the latest Hugging face transformers and we advise you to use the latest version of transformers. With transformers<4.37.0, you will encounter the following error: KeyError: 'qwen2' Quickstart Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents. from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-32B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] Processing Long Texts The current config.json is set for context length up to 32,768 tokens. To handle extensive inputs exceeding 32,768 tokens, we utilize YaRN, a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts. For supported frameworks, you could add the following to config.json to enable YaRN: { ..., "rope_scaling": { "factor": 4.0, "original_max_position_embeddings": 32768, "type": "yarn" } } For deployment, we recommend using vLLM. Please refer to our Documentation for usage if you are not familar with vLLM. Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. We advise adding the rope_scaling configuration only when processing long contexts is required. Evaluation & Performance Detailed evaluation results are reported in this 📑 blog. For requirements on GPU memory and the respective throughput, see results here. Citation If you find our work helpful, feel free to give us a cite. @misc{qwen2.5, title = {Qwen2.5: A Party of Foundation Models}, url = {https://qwenlm.github.io/blog/qwen2.5/}, author = {Qwen Team}, month = {September}, year = {2024} } @article{qwen2, title={Qwen2 Technical Report}, author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan}, journal={arXiv preprint arXiv:2407.10671}, year={2024} } --- ## 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/Qwen2.5-32B-Instruct-Q3_K_M-GGUF --hf-file qwen2.5-32b-instruct-q3_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen2.5-32B-Instruct-Q3_K_M-GGUF --hf-file qwen2.5-32b-instruct-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/Qwen2.5-32B-Instruct-Q3_K_M-GGUF --hf-file qwen2.5-32b-instruct-q3_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen2.5-32B-Instruct-Q3_K_M-GGUF --hf-file qwen2.5-32b-instruct-q3_k_m.gguf -c 2048 ```

secmlr/SWE-BENCH-2k-generation-enrich-500-localization-combine_32b-generation-localization-combine

secmlr

2025-04-28T04:13:19Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-Coder-32B-Instruct", "base_model:finetune:Qwen/Qwen2.5-Coder-32B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-04-27T07:10:34Z

--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-Coder-32B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: SWE-BENCH-2k-generation-enrich-500-localization-combine_32b-generation-localization-combine results: [] ---  # SWE-BENCH-2k-generation-enrich-500-localization-combine_32b-generation-localization-combine This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct) on the SWE-BENCH-2k-generation-enrich-500-localization-combine 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: 12 - total_train_batch_size: 48 - total_eval_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 2.20.0 - Tokenizers 0.20.3

mradermacher/Fusion-14B-Instruct-GGUF

mradermacher

2025-04-28T03:27:05Z

27

1

transformers

[ "transformers", "gguf", "mergekit", "merge", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:qingy2024/Fusion-14B-Instruct", "base_model:quantized:qingy2024/Fusion-14B-Instruct", "endpoints_compatible", "region:us", "conversational" ]

null

2024-12-06T02:36:13Z

--- base_model: qingy2024/Fusion-14B-Instruct language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About      static quants of https://huggingface.co/qingy2024/Fusion-14B-Instruct  weighted/imatrix quants are available at https://huggingface.co/mradermacher/Fusion-14B-Instruct-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/Fusion-14B-Instruct-GGUF/resolve/main/Fusion-14B-Instruct.Q2_K.gguf) | Q2_K | 5.9 | | | [GGUF](https://huggingface.co/mradermacher/Fusion-14B-Instruct-GGUF/resolve/main/Fusion-14B-Instruct.Q3_K_S.gguf) | Q3_K_S | 6.8 | | | [GGUF](https://huggingface.co/mradermacher/Fusion-14B-Instruct-GGUF/resolve/main/Fusion-14B-Instruct.Q3_K_M.gguf) | Q3_K_M | 7.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Fusion-14B-Instruct-GGUF/resolve/main/Fusion-14B-Instruct.Q3_K_L.gguf) | Q3_K_L | 8.0 | | | [GGUF](https://huggingface.co/mradermacher/Fusion-14B-Instruct-GGUF/resolve/main/Fusion-14B-Instruct.IQ4_XS.gguf) | IQ4_XS | 8.3 | | | [GGUF](https://huggingface.co/mradermacher/Fusion-14B-Instruct-GGUF/resolve/main/Fusion-14B-Instruct.Q4_0_4_4.gguf) | Q4_0_4_4 | 8.6 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/Fusion-14B-Instruct-GGUF/resolve/main/Fusion-14B-Instruct.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Fusion-14B-Instruct-GGUF/resolve/main/Fusion-14B-Instruct.Q4_K_M.gguf) | Q4_K_M | 9.1 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Fusion-14B-Instruct-GGUF/resolve/main/Fusion-14B-Instruct.Q5_K_S.gguf) | Q5_K_S | 10.4 | | | [GGUF](https://huggingface.co/mradermacher/Fusion-14B-Instruct-GGUF/resolve/main/Fusion-14B-Instruct.Q5_K_M.gguf) | Q5_K_M | 10.6 | | | [GGUF](https://huggingface.co/mradermacher/Fusion-14B-Instruct-GGUF/resolve/main/Fusion-14B-Instruct.Q6_K.gguf) | Q6_K | 12.2 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Fusion-14B-Instruct-GGUF/resolve/main/Fusion-14B-Instruct.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. 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.

MyTranslate/m2m100-en-ms-finetuned

MyTranslate

2025-04-28T02:12:12Z

0

transformers

[ "transformers", "safetensors", "m2m_100", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2025-04-28T02:07:55Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

cryptoncalls/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stubby_hardy_cat

cryptoncalls

2025-04-28T01:03:36Z

6

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am stubby hardy cat", "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

2025-04-11T00:30:16Z

--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stubby_hardy_cat tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am stubby hardy cat - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stubby_hardy_cat 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="cryptoncalls/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stubby_hardy_cat", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

ahmedch28/mistral_7b_finetuned_pr_v4

ahmedch28

2025-04-27T21:34:42Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-04-27T21:34:35Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

qLhwaa/dssdfdss

qLhwaa

2025-04-27T19:14:36Z

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

2025-04-27T18:40:56Z

--- 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: dsd34 --- # Dssdfdss <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 `dsd34` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "dsd34", "lora_weights": "https://huggingface.co/qLhwaa/dssdfdss/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('qLhwaa/dssdfdss', weight_name='lora.safetensors') image = pipeline('dsd34').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/qLhwaa/dssdfdss/discussions) to add images that show off what you’ve made with this LoRA.

sergioalves/9435b10b-0258-4631-8cd5-382d248e9f64

sergioalves

2025-04-27T13:52:44Z

0

peft

[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Llama-3.2-1B-Instruct", "base_model:adapter:unsloth/Llama-3.2-1B-Instruct", "license:llama3.2", "8-bit", "bitsandbytes", "region:us" ]

null

2025-04-27T13:44:31Z

--- library_name: peft license: llama3.2 base_model: unsloth/Llama-3.2-1B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 9435b10b-0258-4631-8cd5-382d248e9f64 results: [] ---  [<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: true adapter: lora base_model: unsloth/Llama-3.2-1B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 7b66778f0acee359_train_data.json ds_type: json format: custom path: /workspace/input_data/7b66778f0acee359_train_data.json type: field_input: OriginalAddress1 field_instruction: PermitTypeDesc field_output: Description 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: 0.5 group_by_length: false hub_model_id: sergioalves/9435b10b-0258-4631-8cd5-382d248e9f64 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false 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: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/7b66778f0acee359_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 79f99a0f-7342-452f-ae42-335a89dd3ae5 wandb_project: s56-8 wandb_run: your_name wandb_runid: 79f99a0f-7342-452f-ae42-335a89dd3ae5 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details> # 9435b10b-0258-4631-8cd5-382d248e9f64 This model is a fine-tuned version of [unsloth/Llama-3.2-1B-Instruct](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2090 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - 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: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 3.7109 | 0.0139 | 200 | 3.2090 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

damarago/DamaragoXYZ

damarago

2025-04-27T05:07:35Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-04-27T05:07:35Z

--- license: apache-2.0 ---

Triangle104/QwQ-32B-ArliAI-RpR-v2-Q4_K_M-GGUF

Triangle104

2025-04-26T18:03:34Z

0

null

[ "gguf", "llama-cpp", "gguf-my-repo", "en", "base_model:ArliAI/QwQ-32B-ArliAI-RpR-v2", "base_model:quantized:ArliAI/QwQ-32B-ArliAI-RpR-v2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2025-04-26T17:58:03Z

--- base_model: ArliAI/QwQ-32B-ArliAI-RpR-v2 language: - en license: apache-2.0 tags: - llama-cpp - gguf-my-repo thumbnail: https://cdn-uploads.huggingface.co/production/uploads/6625f4a8a8d1362ebcc3851a/9TIfNBdy29CDnn8NNIQPt.jpeg --- # Triangle104/QwQ-32B-ArliAI-RpR-v2-Q4_K_M-GGUF This model was converted to GGUF format from [`ArliAI/QwQ-32B-ArliAI-RpR-v2`](https://huggingface.co/ArliAI/QwQ-32B-ArliAI-RpR-v2) 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/ArliAI/QwQ-32B-ArliAI-RpR-v2) for more details on the model. --- RpR (RolePlay with Reasoning) is a new series of models from ArliAI. This series builds directly upon the successful dataset curation methodology and training methods developed for the RPMax series. RpR models use the same curated, deduplicated RP and creative writing dataset used for RPMax, with a focus on variety to ensure high creativity and minimize cross-context repetition. Users familiar with RPMax will recognize the unique, non-repetitive writing style unlike other finetuned-for-RP models. With the release of QwQ as the first high performing open-source reasoning model that can be easily trained, it was clear that the available instruct and creative writing reasoning datasets contains only one response per example. This is type of single response dataset used for training reasoning models causes degraded output quality in long multi-turn chats. Which is why Arli AI decided to create a real RP model capable of long multi-turn chat with reasoning. In order to create RpR, we first had to actually create the reasoning RP dataset by re-processing our existing known-good RPMax dataset into a reasoning dataset. This was possible by using the base QwQ Instruct model itself to create the reasoning process for every turn in the RPMax dataset conversation examples, which is then further refined in order to make sure the reasoning is in-line with the actual response examples from the dataset. Another important thing to get right is to make sure the model is trained on examples that present reasoning blocks in the same way as it encounters it during inference. Which is, never seeing the reasoning blocks in it's context. In order to do this, the training run was completed using axolotl with manual template-free segments dataset in order to make sure that the model is never trained to see the reasoning block in the context. Just like how the model will be used during inference time. The result of training QwQ on this dataset with this method are consistently coherent and interesting outputs even in long multi-turn RP chats. This is as far as we know the first true correctly-trained reasoning model trained for RP and creative writing. --- ## 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-ArliAI-RpR-v2-Q4_K_M-GGUF --hf-file qwq-32b-arliai-rpr-v2-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/QwQ-32B-ArliAI-RpR-v2-Q4_K_M-GGUF --hf-file qwq-32b-arliai-rpr-v2-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/QwQ-32B-ArliAI-RpR-v2-Q4_K_M-GGUF --hf-file qwq-32b-arliai-rpr-v2-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/QwQ-32B-ArliAI-RpR-v2-Q4_K_M-GGUF --hf-file qwq-32b-arliai-rpr-v2-q4_k_m.gguf -c 2048 ```

jpark677/qwen2-vl-7b-instruct-mmbench-dev-fft-unfreeze-mlp-ep-2-waa-f

jpark677

2025-04-25T17:45:38Z

0

null

[ "safetensors", "qwen2_vl", "region:us" ]

null

2025-04-25T17:41:24Z

# qwen2-vl-7b-instruct-mmbench-dev-fft-unfreeze-mlp-ep-2-waa-f This repository contains the model checkpoint (original iteration 548) as epoch 2.