librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 4 122	author stringlengths 2 42	last_modified unknown	likes int64 0 6.55k	library_name stringclasses 368 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 51 values	createdAt unknown	card stringlengths 1 1M
wildbro/332221	wildbro	"2024-06-29T14:43:42Z"	0	null	[ "region:us" ]	null	"2024-06-29T14:43:42Z"	Entry not found
EuphoriaReccords/Jhope_TITAN40K	EuphoriaReccords	"2024-06-29T19:29:39Z"	0	null	[ "license:mit", "region:us" ]	null	"2024-06-29T14:44:51Z"	--- license: mit ---
abhayesian/LLama3_HarmBench_LAT_7	abhayesian	"2024-06-29T15:21:36Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-29T14:46:42Z"	--- 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. 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]
monbear/qwen1half-7b-chat-lora	monbear	"2024-06-29T14:50:38Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-06-29T14:50:38Z"	--- license: apache-2.0 ---
Overflow64/Overflow	Overflow64	"2024-06-29T14:57:57Z"	0	null	[ "region:us" ]	null	"2024-06-29T14:57:57Z"	Entry not found
netmouse/Llama-3-Taiwan-8B-Instruct-finetuning-by-promisedchat	netmouse	"2024-06-30T14:22:00Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:yentinglin/Llama-3-Taiwan-8B-Instruct", "base_model:finetune:yentinglin/Llama-3-Taiwan-8B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-29T14:59:51Z"	--- base_model: yentinglin/Llama-3-Taiwan-8B-Instruct language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: netmouse - License: apache-2.0 - Finetuned from model : yentinglin/Llama-3-Taiwan-8B-Instruct This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
PrunaAI/victunes-TherapyBeagle-11B-v1-QUANTO-int2bit-smashed	PrunaAI	"2024-07-19T09:24:51Z"	0	transformers	[ "transformers", "pruna-ai", "base_model:victunes/TherapyBeagle-11B-v1", "base_model:finetune:victunes/TherapyBeagle-11B-v1", "endpoints_compatible", "region:us" ]	null	"2024-06-29T15:03:47Z"	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: victunes/TherapyBeagle-11B-v1 metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with quanto. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We use safetensors. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo victunes/TherapyBeagle-11B-v1 installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install quanto ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer IMPORTS model = AutoModelForCausalLM.from_pretrained("PrunaAI/victunes-TherapyBeagle-11B-v1-QUANTO-int2bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("victunes/TherapyBeagle-11B-v1") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model victunes/TherapyBeagle-11B-v1 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
PrunaAI/victunes-TherapyBeagle-11B-v1-QUANTO-int4bit-smashed	PrunaAI	"2024-07-19T09:23:39Z"	0	transformers	[ "transformers", "pruna-ai", "base_model:victunes/TherapyBeagle-11B-v1", "base_model:finetune:victunes/TherapyBeagle-11B-v1", "endpoints_compatible", "region:us" ]	null	"2024-06-29T15:04:07Z"	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: victunes/TherapyBeagle-11B-v1 metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with quanto. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We use safetensors. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo victunes/TherapyBeagle-11B-v1 installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install quanto ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer IMPORTS model = AutoModelForCausalLM.from_pretrained("PrunaAI/victunes-TherapyBeagle-11B-v1-QUANTO-int4bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("victunes/TherapyBeagle-11B-v1") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model victunes/TherapyBeagle-11B-v1 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
Yjhhh/optimizedtf5yfygd-model	Yjhhh	"2024-06-29T15:05:08Z"	0	null	[ "region:us" ]	null	"2024-06-29T15:05:08Z"	Entry not found
habulaj/5336741157	habulaj	"2024-06-29T15:07:05Z"	0	null	[ "region:us" ]	null	"2024-06-29T15:07:03Z"	Entry not found
geraldabrhm/llama-3-8b-seqclass-antonym-lr2_5-batch16-lora32	geraldabrhm	"2024-06-29T16:58:13Z"	0	null	[ "safetensors", "region:us" ]	null	"2024-06-29T15:10:21Z"	Entry not found
genaitiwari/Mymodel	genaitiwari	"2024-06-29T15:12:54Z"	0	null	[ "region:us" ]	null	"2024-06-29T15:12:54Z"	Entry not found
Yjhhh/optimized-model	Yjhhh	"2024-06-29T15:13:22Z"	0	null	[ "region:us" ]	null	"2024-06-29T15:13:17Z"	Entry not found
kfmix/pub-kfmodel	kfmix	"2024-06-29T15:17:06Z"	0	null	[ "region:us" ]	null	"2024-06-29T15:15:31Z"	Entry not found
habulaj/8901365593	habulaj	"2024-06-29T15:18:58Z"	0	null	[ "region:us" ]	null	"2024-06-29T15:18:55Z"	Entry not found
habulaj/1117723644	habulaj	"2024-06-29T15:23:24Z"	0	null	[ "region:us" ]	null	"2024-06-29T15:23:22Z"	Entry not found
NobodySpecial/Qwen2-72B-base-exl2	NobodySpecial	"2024-06-30T01:50:00Z"	0	null	[ "pretrained", "text-generation", "en", "license:other", "region:us" ]	text-generation	"2024-06-29T15:23:52Z"	--- license: other license_name: tongyi-qianwen license_link: https://huggingface.co/Qwen/Qwen2-72B/blob/main/LICENSE language: - en pipeline_tag: text-generation tags: - pretrained --- # Qwen2-72B ## Branch Names bpw = Bits per Weight hb = Bits for the lm_head layer ## Quantization Details Quantized via Exllamav2 Version: 0.1.6 All versions in this repo were quantized with the setting Rope Scale=4 ## Original Model Card ### Introduction Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the 72B Qwen2 base language model. Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc. For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2/), [GitHub](https://github.com/QwenLM/Qwen2), and [Documentation](https://qwen.readthedocs.io/en/latest/). <br> ### Model Details Qwen2 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. ## Requirements The code of Qwen2 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error: ``` KeyError: 'qwen2' ``` ### Usage We do not advise you to use base language models for text generation. Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model. ### Performance The evaluation of base models mainly focuses on the model performance of natural language understanding, general question answering, coding, mathematics, scientific knowledge, reasoning, multilingual capability, etc. The datasets for evaluation include: English Tasks: MMLU (5-shot), MMLU-Pro (5-shot), GPQA (5shot), Theorem QA (5-shot), BBH (3-shot), HellaSwag (10-shot), Winogrande (5-shot), TruthfulQA (0-shot), ARC-C (25-shot) Coding Tasks: EvalPlus (0-shot) (HumanEval, MBPP, HumanEval+, MBPP+), MultiPL-E (0-shot) (Python, C++, JAVA, PHP, TypeScript, C#, Bash, JavaScript) Math Tasks: GSM8K (4-shot), MATH (4-shot) Chinese Tasks: C-Eval (5-shot), CMMLU (5-shot) Multilingual Tasks: Multi-Exam (M3Exam 5-shot, IndoMMLU 3-shot, ruMMLU 5-shot, mMMLU 5-shot), Multi-Understanding (BELEBELE 5-shot, XCOPA 5-shot, XWinograd 5-shot, XStoryCloze 0-shot, PAWS-X 5-shot), Multi-Mathematics (MGSM 8-shot), Multi-Translation (Flores-101 5-shot) #### Qwen2-72B performance \| Datasets \| DeepSeek-V2 \| Mixtral-8x22B \| Llama-3-70B \| Qwen1.5-72B \| Qwen1.5-110B \| Qwen2-72B \| \| :--------\| :---------: \| :------------: \| :------------: \| :------------: \| :------------: \|:------------: \| \|Architecture \| MoE \| MoE \| Dense \| Dense \| Dense \| Dense \| \|#Activated Params \| 21B \| 39B \| 70B \| 72B \| 110B \| 72B \| \|#Params \| 236B \| 140B \| 70B \| 72B \| 110B \| 72B\| \| *English* \| \| \| \| \| \| \| \|MMLU \|78.5 \| 77.8 \| 79.5 \| 77.5 \| 80.4 \| 84.2 \| \|MMLU-Pro \| - \| 49.5 \| 52.8 \| 45.8 \| 49.4 \| 55.6 \| \|GPQA \| -\| 34.3 \| 36.3 \| 36.3 \| 35.9 \| 37.9 \| \|Theorem QA \| -\| 35.9 \| 32.3 \| 29.3 \| 34.9 \| 43.1 \| \|BBH \| 78.9 \|78.9 \| 81.0 \| 65.5 \| 74.8 \| 82.4 \| \|HellaSwag \| 87.8 \| 88.7 \| 88.0 \| 86.0 \| 87.5 \| 87.6 \| \|WindoGrande \| 84.8\|85.0 \| 85.3 \| 83.0 \| 83.5 \| 85.1 \| \|ARC-C \| 70.0\| 70.7 \| 68.8 \| 65.9 \| 69.6 \| 68.9 \| \|TruthfulQA \| 42.2 \| 51.0 \| 45.6 \| 59.6 \| 49.6 \| 54.8 \| \| *Coding* \| \| \| \| \| \| \| \|HumanEval \| 45.7 \| 46.3 \| 48.2 \| 46.3 \| 54.3 \| 64.6 \| \|MBPP \|73.9 \| 71.7 \| 70.4 \| 66.9 \| 70.9 \| 76.9 \| \|EvalPlus \| 55.0 \| 54.1 \| 54.8 \| 52.9 \| 57.7 \| 65.4 \| \|MultiPL-E \|44.4 \| 46.7 \| 46.3 \| 41.8 \| 52.7 \| 59.6 \| \| *Mathematics* \| \| \| \| \| \| \| \|GSM8K \| 79.2 \| 83.7 \| 83.0 \| 79.5 \| 85.4 \| 89.5 \| \|MATH \| 43.6 \| 41.7 \| 42.5 \| 34.1 \| 49.6 \| 51.1 \| \| *Chinese* \| \| \| \| \| \| \| \|C-Eval \| 81.7 \| 54.6 \| 65.2 \| 84.1 \| 89.1 \| 91.0 \| \|CMMLU \| 84.0 \| 53.4 \| 67.2 \| 83.5 \| 88.3 \| 90.1 \| \| *Multilingual* \| \| \| \| \| \| \| \|Mulit-Exam \| 67.5 \| 63.5 \| 70.0 \| 66.4 \| 75.6 \| 76.6 \| \|Multi-Understanding \| 77.0 \| 77.7 \| 79.9 \| 78.2 \| 78.2 \| 80.7 \| \|Multi-Mathematics \| 58.8 \| 62.9 \| 67.1 \| 61.7 \| 64.4 \| 76.0 \| \|Multi-Translation \| 36.0 \| 23.3 \| 38.0 \| 35.6 \| 36.2 \| 37.8 \| ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{qwen2, title={Qwen2 Technical Report}, year={2024} } ```
ningj2413/8b_llama3_what_4bitq_r16	ningj2413	"2024-06-29T15:26:57Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-29T15:25:12Z"	--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: ningj2413 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit - r=16;lora-alpha=16 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)
sal55/sss	sal55	"2024-06-29T21:52:02Z"	0	null	[ "region:us" ]	null	"2024-06-29T15:27:06Z"	Entry not found
bartowski/Fook-Yi-34B-32K-v1-exl2	bartowski	"2024-06-29T15:28:37Z"	0	null	[ "not-for-all-audiences", "text-generation", "license:cc-by-nc-4.0", "region:us" ]	text-generation	"2024-06-29T15:28:36Z"	--- license: cc-by-nc-4.0 tags: - not-for-all-audiences quantized_by: bartowski pipeline_tag: text-generation --- ## Exllama v2 Quantizations of Fook-Yi-34B-32K-v1 Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.1.6">turboderp's ExLlamaV2 v0.1.6</a> for quantization. <b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b> Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Original model: https://huggingface.co/TheDrummer/Fook-Yi-34B-32K-v1 ## Prompt format ``` <\|im_start\|> system {system_prompt}<\|im_end\|> <\|im_start\|> user {prompt}<\|im_end\|> <\|im_start\|> assistant ``` ## Available sizes \| Branch \| Bits \| lm_head bits \| VRAM (4k) \| VRAM (16k) \| VRAM (32k) \| Description \| \| ------ \| ---- \| ------------ \| ---- \| ---- \| ---- \| ----------- \| \| [8_0](https://huggingface.co/bartowski/Fook-Yi-34B-32K-v1-exl2/tree/8_0) \| 8.0 \| 8.0 \| 34.9 GB \| 37.6 GB \| 41.6 GB \| Max quality producable by ExLlamav2, generally unneeded but maximum performance \| \| [6_5](https://huggingface.co/bartowski/Fook-Yi-34B-32K-v1-exl2/tree/6_5) \| 6.5 \| 8.0 \| 28.9 GB \| 31.6 GB \| 35.6 GB \| Near unquantized performance at vastly reduced size, recommended. \| \| [5_0](https://huggingface.co/bartowski/Fook-Yi-34B-32K-v1-exl2/tree/5_0) \| 5.0 \| 8.0 \| 22.6 GB \| 25.3 GB \| 29.3 GB \| Very high quality, usable at 4k context on 24GB. \| \| [4_25](https://huggingface.co/bartowski/Fook-Yi-34B-32K-v1-exl2/tree/4_25) \| 4.25 \| 6.0 \| 19.5 GB \| 22.2 GB \| 26.2 GB \| GPTQ equivalent bits per weight, slightly higher quality. \| \| [3_5](https://huggingface.co/bartowski/Fook-Yi-34B-32K-v1-exl2/tree/3_5) \| 3.5 \| 6.0 \| 16.5 GB \| 19.2 GB \| 23.2 GB \| Lower quality, only use if you have to. \| \| [3_0](https://huggingface.co/bartowski/Fook-Yi-34B-32K-v1-exl2/tree/3_0) \| 3.0 \| 6.0 \| 14.3 GB \| 17.0 GB \| 21.0 GB \| Very low quality, usable with 16gb of VRAM. \| ## Download instructions With git: ```shell git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Fook-Yi-34B-32K-v1-exl2 Fook-Yi-34B-32K-v1-exl2-6_5 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download a specific branch, use the `--revision` parameter. For example, to download the 6.5 bpw branch: Linux: ```shell huggingface-cli download bartowski/Fook-Yi-34B-32K-v1-exl2 --revision 6_5 --local-dir Fook-Yi-34B-32K-v1-exl2-6_5 ``` Windows (which apparently doesn't like _ in folders sometimes?): ```shell huggingface-cli download bartowski/Fook-Yi-34B-32K-v1-exl2 --revision 6_5 --local-dir Fook-Yi-34B-32K-v1-exl2-6.5 ``` Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
RobertML/sn21-2hydrogen-oxygen	RobertML	"2024-06-29T15:34:54Z"	0	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	"2024-06-29T15:28:54Z"	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
Hannibal046/gtr_t5_nq_32_stage2	Hannibal046	"2024-06-29T15:36:12Z"	1	transformers	[ "transformers", "safetensors", "t5", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	"2024-06-29T15:31:37Z"	--- license: mit ---
habulaj/4916239000	habulaj	"2024-06-29T15:32:44Z"	0	null	[ "region:us" ]	null	"2024-06-29T15:32:41Z"	Entry not found
habulaj/314350281005	habulaj	"2024-06-29T15:35:01Z"	0	null	[ "region:us" ]	null	"2024-06-29T15:34:59Z"	Entry not found
aleatorydialogue/ad_backgrounds	aleatorydialogue	"2024-07-29T15:11:28Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-06-29T15:40:39Z"	--- license: apache-2.0 ---
Horizon6957/Medical-chatbot	Horizon6957	"2024-06-29T15:41:27Z"	0	null	[ "license:mit", "region:us" ]	null	"2024-06-29T15:41:27Z"	--- license: mit ---
notBeastKing/Llama3_sentiment_analysis	notBeastKing	"2024-06-29T17:09:01Z"	0	peft	[ "peft", "safetensors", "llama", "region:us" ]	null	"2024-06-29T15:42:31Z"	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0
jtatman/pythia-delphi-suboptimal-roleplay	jtatman	"2024-06-29T15:44:27Z"	0	null	[ "region:us" ]	null	"2024-06-29T15:44:27Z"	Entry not found
habulaj/52006291	habulaj	"2024-06-29T15:45:13Z"	0	null	[ "region:us" ]	null	"2024-06-29T15:45:08Z"	Entry not found
PlasmicZ/gemma-2b-5e-r256-new	PlasmicZ	"2024-06-29T15:48:38Z"	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-06-29T15:46:31Z"	--- 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]
habulaj/498242887	habulaj	"2024-06-29T15:47:14Z"	0	null	[ "region:us" ]	null	"2024-06-29T15:47:09Z"	Entry not found
ningj2413/8b_llama3_what_4bitq_r32	ningj2413	"2024-06-29T15:47:47Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-29T15:47:39Z"	--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: ningj2413 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
kishorea/p_l3	kishorea	"2024-06-29T15:53:20Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-29T15:53:14Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
ningj2413/8b_llama3_what_4bitq_instruct_r32	ningj2413	"2024-06-29T15:57:01Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-29T15:56:54Z"	--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: ningj2413 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
AbdullahMashhadi/Elon-Musk-talking-model	AbdullahMashhadi	"2024-06-29T16:03:01Z"	0	null	[ "region:us" ]	null	"2024-06-29T16:02:58Z"	Entry not found
habulaj/9419281124	habulaj	"2024-06-29T16:03:18Z"	0	null	[ "region:us" ]	null	"2024-06-29T16:03:16Z"	Entry not found
hasanbahadir/ihale_dataset	hasanbahadir	"2024-06-30T13:12:47Z"	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-29T16:04:56Z"	--- 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]
helling100/my_awesome_model	helling100	"2024-06-29T16:10:00Z"	0	null	[ "region:us" ]	null	"2024-06-29T16:10:00Z"	Entry not found
jdmccaffrey/my_awesome_billsum_model	jdmccaffrey	"2024-06-29T16:10:04Z"	0	null	[ "region:us" ]	null	"2024-06-29T16:10:04Z"	Entry not found
Sharan1712/llama2_7B_unnaturalcore_qia3_4bit_8a	Sharan1712	"2024-06-29T16:10:27Z"	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-29T16:10:26Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
hosdf/bgtjk	hosdf	"2024-06-29T16:46:44Z"	1	null	[ "license:openrail", "region:us" ]	null	"2024-06-29T16:15:49Z"	--- license: openrail ---
habulaj/2483324556	habulaj	"2024-06-29T16:16:01Z"	0	null	[ "region:us" ]	null	"2024-06-29T16:15:56Z"	Entry not found
dash8060/EJ	dash8060	"2024-06-29T16:26:27Z"	0	null	[ "region:us" ]	null	"2024-06-29T16:19:51Z"	Entry not found
PrunaAI/nota-ai-cpt_st-vicuna-v1.3-3.7b-ppl-QUANTO-int4bit-smashed	PrunaAI	"2024-07-19T09:28:53Z"	0	transformers	[ "transformers", "pruna-ai", "base_model:nota-ai/cpt_st-vicuna-v1.3-3.7b-ppl", "base_model:finetune:nota-ai/cpt_st-vicuna-v1.3-3.7b-ppl", "endpoints_compatible", "region:us" ]	null	"2024-06-29T16:23:11Z"	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: nota-ai/cpt_st-vicuna-v1.3-3.7b-ppl metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with quanto. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We use safetensors. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo nota-ai/cpt_st-vicuna-v1.3-3.7b-ppl installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install quanto ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer IMPORTS model = AutoModelForCausalLM.from_pretrained("PrunaAI/nota-ai-cpt_st-vicuna-v1.3-3.7b-ppl-QUANTO-int4bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("nota-ai/cpt_st-vicuna-v1.3-3.7b-ppl") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model nota-ai/cpt_st-vicuna-v1.3-3.7b-ppl before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
dash8060/dks	dash8060	"2024-06-29T16:41:26Z"	0	null	[ "region:us" ]	null	"2024-06-29T16:40:51Z"	Entry not found
AiHubber/HornhubSound	AiHubber	"2024-06-29T16:43:25Z"	0	null	[ "license:openrail", "region:us" ]	null	"2024-06-29T16:42:15Z"	--- license: openrail ---
SDH2222/gas	SDH2222	"2024-06-29T16:43:32Z"	0	null	[ "region:us" ]	null	"2024-06-29T16:42:45Z"	Entry not found
Neha13/AIcontentdetector	Neha13	"2024-06-29T16:43:45Z"	0	null	[ "region:us" ]	null	"2024-06-29T16:43:45Z"	Entry not found
D3MI4N/q-FrozenLake-v1-4x4-noSlippery	D3MI4N	"2024-06-29T16:44:47Z"	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2024-06-29T16:44:44Z"	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="D3MI4N/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
D3MI4N/taxi-v3-dem	D3MI4N	"2024-06-29T16:47:24Z"	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2024-06-29T16:47:23Z"	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-v3-dem results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.73 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="D3MI4N/taxi-v3-dem", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
rashid996958/pix2pix_exp39	rashid996958	"2024-06-29T16:48:20Z"	0	null	[ "region:us" ]	null	"2024-06-29T16:48:14Z"	Entry not found
Neha13/aidetectorintext	Neha13	"2024-06-29T16:48:15Z"	0	null	[ "region:us" ]	null	"2024-06-29T16:48:15Z"	Entry not found
ningj2413/8b_llama3_what_4bitq_instruct_r64	ningj2413	"2024-06-29T16:51:17Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-29T16:51:03Z"	--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: ningj2413 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
prometheus04/Llama2_Finetuned	prometheus04	"2024-06-29T16:52:57Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-29T16:52:53Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ningj2413/8b_llama3_what_4bitq_instruct_r128	ningj2413	"2024-06-29T16:55:28Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-29T16:54:54Z"	--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: ningj2413 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Hexapoly/Norm	Hexapoly	"2024-06-29T16:57:12Z"	0	null	[ "region:us" ]	null	"2024-06-29T16:55:24Z"	Entry not found
mjfan1999/BlakeShelton2011	mjfan1999	"2024-06-29T17:12:20Z"	0	null	[ "license:unknown", "region:us" ]	null	"2024-06-29T16:55:27Z"	--- license: unknown ---
habulaj/163166141251	habulaj	"2024-06-29T17:01:57Z"	0	null	[ "region:us" ]	null	"2024-06-29T17:01:53Z"	Entry not found
rashid996958/pix2pix_exp41	rashid996958	"2024-06-29T17:03:20Z"	0	null	[ "region:us" ]	null	"2024-06-29T17:03:15Z"	Entry not found
SDH2222/ppp	SDH2222	"2024-07-14T20:52:21Z"	0	null	[ "region:us" ]	null	"2024-06-29T17:03:34Z"	Entry not found
chaanks/DNSMOS	chaanks	"2024-06-29T17:12:09Z"	0	null	[ "onnx", "region:us" ]	null	"2024-06-29T17:11:30Z"	Entry not found
Neha13/x	Neha13	"2024-06-29T17:13:04Z"	0	transformers	[ "transformers", "endpoints_compatible", "region:us" ]	null	"2024-06-29T17:12:52Z"	Entry not found
imgeorgiev/pwm	imgeorgiev	"2024-07-02T20:36:12Z"	2	null	[ "license:mit", "region:us" ]	null	"2024-06-29T17:13:21Z"	--- license: mit --- # PWM: Policy Learning with Large World Models [Ignat Georgiev](https://www.imgeorgiev.com/), [Varun Giridhar](https://www.linkedin.com/in/varun-giridhar-463947146/), [Nicklas Hansen](https://www.nicklashansen.com/), [Animesh Garg](https://animesh.garg.tech/) [Project website](http://imgeorgiev.com/pwm) [Paper](TODO) [Models & Datasets](https://huggingface.co/imgeorgiev/pwm) ## Overview ![](https://github.com/imgeorgiev/pwm/figures/teaser.png) Instead of building world models into algorithms, we propose using large-scale multi-task world models as differentiable simulators for policy learning. When well-regularized, these models enable efficient policy learning with first-order gradient optimization. This allows PWM to learn to solve 80 tasks in < 10 minutes each without the need for expensive online planning. ## Structure of repository ``` pwm ├── dflex │ ├── data - data used for dflex world model pre-training │ └── pretrained - already trained world models that can be used in dflex experiments ├── multitask - pre-trained world models for multitask evaluation ├── pedagogical - pre-trained world models for recreating pedagogical examples └── README.md ```
iamalexcaspian/GumballWatterson-TAWOG-LoganGrove	iamalexcaspian	"2024-06-29T18:44:23Z"	0	null	[ "region:us" ]	null	"2024-06-29T17:15:23Z"	Entry not found
youssouf128/youssouf	youssouf128	"2024-06-29T17:23:04Z"	0	null	[ "region:us" ]	null	"2024-06-29T17:15:59Z"	Entry not found
mjfan1999/BlakeShelton2016	mjfan1999	"2024-06-29T17:33:15Z"	0	null	[ "license:unknown", "region:us" ]	null	"2024-06-29T17:22:41Z"	--- license: unknown ---
random2344/Graphic	random2344	"2024-06-29T17:24:11Z"	0	null	[ "region:us" ]	null	"2024-06-29T17:23:31Z"	Entry not found
mharb/Reinforce-cartpole-v1	mharb	"2024-06-29T17:23:55Z"	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	"2024-06-29T17:23:44Z"	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-cartpole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
itay-nakash/model_73a455d87c_sweep_divine-firefly-981	itay-nakash	"2024-06-29T17:26:58Z"	0	null	[ "region:us" ]	null	"2024-06-29T17:26:58Z"	Entry not found
Malkovitz/Miroslaw_Utta	Malkovitz	"2024-06-29T17:32:45Z"	0	null	[ "license:unknown", "region:us" ]	null	"2024-06-29T17:29:56Z"	--- license: unknown ---
NoNameFactory/llama-3-8b-it-4bit-ContdPT_2_10	NoNameFactory	"2024-06-29T17:34:03Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-29T17:31:49Z"	--- base_model: unsloth/llama-3-8b-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: NoNameFactory - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
kandarp0809/Grid	kandarp0809	"2024-06-29T17:36:10Z"	0	null	[ "region:us" ]	null	"2024-06-29T17:34:40Z"	Entry not found
hadestest999/testersssss	hadestest999	"2024-06-29T17:35:40Z"	0	null	[ "license:cdla-permissive-2.0", "region:us" ]	null	"2024-06-29T17:34:55Z"	--- license: cdla-permissive-2.0 ---
yuvrajAI/gemma-2b-medical	yuvrajAI	"2024-06-29T17:36:06Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-29T17:35:11Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
impossibleexchange/waitingonweights	impossibleexchange	"2024-07-02T00:12:51Z"	0	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	"2024-06-29T17:36:13Z"	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
mgh6/Pair_Fold_CNN	mgh6	"2024-07-01T17:24:56Z"	0	null	[ "tensorboard", "safetensors", "region:us" ]	null	"2024-06-29T17:38:23Z"	Entry not found
youssouf128/ABAYAZID	youssouf128	"2024-06-29T17:41:47Z"	0	null	[ "region:us" ]	null	"2024-06-29T17:41:47Z"	Entry not found
AdamKasumovic/llama3-70b-instruct-ids-winogrande-train-s-af-winogrande-med	AdamKasumovic	"2024-06-29T17:48:33Z"	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-70b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-70b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-29T17:48:33Z"	--- base_model: unsloth/llama-3-70b-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: AdamKasumovic - License: apache-2.0 - Finetuned from model : unsloth/llama-3-70b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
morteza76/seta	morteza76	"2024-06-29T17:48:53Z"	0	null	[ "license:other", "region:us" ]	null	"2024-06-29T17:48:53Z"	--- license: other license_name: seta license_link: LICENSE ---
Soorya1998/taxi-v1	Soorya1998	"2024-06-29T17:49:40Z"	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2024-06-29T17:49:13Z"	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.46 +/- 2.70 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="Soorya1998/taxi-v1", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
habulaj/295100262864	habulaj	"2024-06-29T17:49:46Z"	0	null	[ "region:us" ]	null	"2024-06-29T17:49:35Z"	Entry not found
devilga/dac_16khZ_8kbps	devilga	"2024-06-29T17:50:49Z"	0	null	[ "region:us" ]	null	"2024-06-29T17:50:49Z"	Entry not found
cmmann/ppo-lunarlander-v2	cmmann	"2024-06-29T18:09:03Z"	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	"2024-06-29T18:08:46Z"	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -175.86 +/- 159.83 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Nikt271/Kurak	Nikt271	"2024-06-29T18:21:25Z"	0	null	[ "region:us" ]	null	"2024-06-29T18:17:54Z"	Entry not found
dat-lequoc/A100_trt-llm_engine	dat-lequoc	"2024-06-29T18:20:10Z"	0	transformers	[ "transformers", "endpoints_compatible", "region:us" ]	null	"2024-06-29T18:18:05Z"	Entry not found
habulaj/7860557029	habulaj	"2024-06-29T18:20:12Z"	0	null	[ "region:us" ]	null	"2024-06-29T18:20:05Z"	Entry not found
random2344/minimal1	random2344	"2024-06-29T18:22:26Z"	0	null	[ "region:us" ]	null	"2024-06-29T18:21:40Z"	Entry not found
tdooms/ts-medium-relu	tdooms	"2024-06-29T18:23:16Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-29T18:22:58Z"	--- 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. 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]
Akirami/phi-3-medium_text2cypher_recommendations	Akirami	"2024-06-29T18:27:07Z"	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-06-29T18:26:48Z"	--- base_model: unsloth/phi-3-medium-4k-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - Developed by: Akirami - License: apache-2.0 - Finetuned from model : unsloth/phi-3-medium-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Frixi/Randy_NotaLoka	Frixi	"2024-06-29T18:35:37Z"	0	null	[ "license:openrail", "region:us" ]	null	"2024-06-29T18:30:08Z"	--- license: openrail ---
bradpacito/brapacito	bradpacito	"2024-06-29T18:36:31Z"	0	null	[ "license:wtfpl", "region:us" ]	null	"2024-06-29T18:36:31Z"	--- license: wtfpl ---
itay-nakash/model_73a455d87c_sweep_dry-wind-982	itay-nakash	"2024-06-29T18:38:51Z"	0	null	[ "region:us" ]	null	"2024-06-29T18:38:51Z"	Entry not found
micdoh/2024_JOCN_XLRON	micdoh	"2024-06-29T18:51:12Z"	0	null	[ "doi:10.57967/hf/2654", "license:mit", "region:us" ]	null	"2024-06-29T18:48:47Z"	--- license: mit ---
jacobcd52/mats-gpt2-saes	jacobcd52	"2024-07-06T15:28:51Z"	0	null	[ "license:mit", "region:us" ]	null	"2024-06-29T18:55:39Z"	--- license: mit ---
RITTIHILATTI/gpt2	RITTIHILATTI	"2024-06-29T18:56:55Z"	0	null	[ "region:us" ]	null	"2024-06-29T18:56:55Z"	Entry not found
Callyde/Li	Callyde	"2024-06-29T18:59:50Z"	0	null	[ "region:us" ]	null	"2024-06-29T18:59:50Z"	Entry not found
ilya94prok/test	ilya94prok	"2024-06-29T19:01:32Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-06-29T19:01:32Z"	--- license: apache-2.0 ---
habulaj/375846341463	habulaj	"2024-06-29T19:01:57Z"	0	null	[ "region:us" ]	null	"2024-06-29T19:01:52Z"	Entry not found
samad321kk/ee	samad321kk	"2024-06-29T19:06:55Z"	0	null	[ "license:openrail", "region:us" ]	null	"2024-06-29T19:02:20Z"	--- license: openrail ---
Phoenix91/Phoenix	Phoenix91	"2024-06-29T19:02:44Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-06-29T19:02:44Z"	--- license: apache-2.0 ---

wildbro/332221

wildbro

"2024-06-29T14:43:42Z"

0

null

[ "region:us" ]

null

"2024-06-29T14:43:42Z"

Entry not found

EuphoriaReccords/Jhope_TITAN40K

EuphoriaReccords

"2024-06-29T19:29:39Z"

0

null

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

null

"2024-06-29T14:44:51Z"

--- license: mit ---

abhayesian/LLama3_HarmBench_LAT_7

abhayesian

"2024-06-29T15:21:36Z"

0

transformers

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

null

"2024-06-29T14:46:42Z"

--- 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]

monbear/qwen1half-7b-chat-lora

monbear

"2024-06-29T14:50:38Z"

0

null

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

null

"2024-06-29T14:50:38Z"

--- license: apache-2.0 ---

Overflow64/Overflow

Overflow64

"2024-06-29T14:57:57Z"

0

null

[ "region:us" ]

null

"2024-06-29T14:57:57Z"

Entry not found

netmouse/Llama-3-Taiwan-8B-Instruct-finetuning-by-promisedchat

netmouse

"2024-06-30T14:22:00Z"

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:yentinglin/Llama-3-Taiwan-8B-Instruct", "base_model:finetune:yentinglin/Llama-3-Taiwan-8B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

"2024-06-29T14:59:51Z"

--- base_model: yentinglin/Llama-3-Taiwan-8B-Instruct language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** netmouse - **License:** apache-2.0 - **Finetuned from model :** yentinglin/Llama-3-Taiwan-8B-Instruct This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

PrunaAI/victunes-TherapyBeagle-11B-v1-QUANTO-int2bit-smashed

PrunaAI

"2024-07-19T09:24:51Z"

0

transformers

[ "transformers", "pruna-ai", "base_model:victunes/TherapyBeagle-11B-v1", "base_model:finetune:victunes/TherapyBeagle-11B-v1", "endpoints_compatible", "region:us" ]

null

"2024-06-29T15:03:47Z"

--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: victunes/TherapyBeagle-11B-v1 metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai ---   <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div>  [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) **Frequently Asked Questions** - ***How does the compression work?*** The model is compressed with quanto. - ***How does the model quality change?*** The quality of the model output might vary compared to the base model. - ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - ***What is the model format?*** We use safetensors. - ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data. - ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo victunes/TherapyBeagle-11B-v1 installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install quanto ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer IMPORTS model = AutoModelForCausalLM.from_pretrained("PrunaAI/victunes-TherapyBeagle-11B-v1-QUANTO-int2bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("victunes/TherapyBeagle-11B-v1") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model victunes/TherapyBeagle-11B-v1 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).

PrunaAI/victunes-TherapyBeagle-11B-v1-QUANTO-int4bit-smashed

PrunaAI

"2024-07-19T09:23:39Z"

0

transformers

[ "transformers", "pruna-ai", "base_model:victunes/TherapyBeagle-11B-v1", "base_model:finetune:victunes/TherapyBeagle-11B-v1", "endpoints_compatible", "region:us" ]

null

"2024-06-29T15:04:07Z"

--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: victunes/TherapyBeagle-11B-v1 metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai ---   <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div>  [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) **Frequently Asked Questions** - ***How does the compression work?*** The model is compressed with quanto. - ***How does the model quality change?*** The quality of the model output might vary compared to the base model. - ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - ***What is the model format?*** We use safetensors. - ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data. - ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo victunes/TherapyBeagle-11B-v1 installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install quanto ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer IMPORTS model = AutoModelForCausalLM.from_pretrained("PrunaAI/victunes-TherapyBeagle-11B-v1-QUANTO-int4bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("victunes/TherapyBeagle-11B-v1") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model victunes/TherapyBeagle-11B-v1 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).

Yjhhh/optimizedtf5yfygd-model

Yjhhh

"2024-06-29T15:05:08Z"

0

null

[ "region:us" ]

null

"2024-06-29T15:05:08Z"

Entry not found

habulaj/5336741157

habulaj

"2024-06-29T15:07:05Z"

0

null

[ "region:us" ]

null

"2024-06-29T15:07:03Z"

Entry not found

geraldabrhm/llama-3-8b-seqclass-antonym-lr2_5-batch16-lora32

geraldabrhm

"2024-06-29T16:58:13Z"

0

null

[ "safetensors", "region:us" ]

null

"2024-06-29T15:10:21Z"

Entry not found

genaitiwari/Mymodel

genaitiwari

"2024-06-29T15:12:54Z"

0

null

[ "region:us" ]

null

"2024-06-29T15:12:54Z"

Entry not found

Yjhhh/optimized-model

Yjhhh

"2024-06-29T15:13:22Z"

0

null

[ "region:us" ]

null

"2024-06-29T15:13:17Z"

Entry not found

kfmix/pub-kfmodel

kfmix

"2024-06-29T15:17:06Z"

0

null

[ "region:us" ]

null

"2024-06-29T15:15:31Z"

Entry not found

habulaj/8901365593

habulaj

"2024-06-29T15:18:58Z"

0

null

[ "region:us" ]

null

"2024-06-29T15:18:55Z"

Entry not found

habulaj/1117723644

habulaj

"2024-06-29T15:23:24Z"

0

null

[ "region:us" ]

null

"2024-06-29T15:23:22Z"

Entry not found

NobodySpecial/Qwen2-72B-base-exl2

NobodySpecial

"2024-06-30T01:50:00Z"

0

null

[ "pretrained", "text-generation", "en", "license:other", "region:us" ]

text-generation

"2024-06-29T15:23:52Z"

--- license: other license_name: tongyi-qianwen license_link: https://huggingface.co/Qwen/Qwen2-72B/blob/main/LICENSE language: - en pipeline_tag: text-generation tags: - pretrained --- # Qwen2-72B ## Branch Names bpw = Bits per Weight hb = Bits for the lm_head layer ## Quantization Details Quantized via Exllamav2 Version: 0.1.6 **All versions in this repo were quantized with the setting Rope Scale=4** ## Original Model Card ### Introduction Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the 72B Qwen2 base language model. Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc. For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2/), [GitHub](https://github.com/QwenLM/Qwen2), and [Documentation](https://qwen.readthedocs.io/en/latest/). <br> ### Model Details Qwen2 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. ## Requirements The code of Qwen2 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error: ``` KeyError: 'qwen2' ``` ### Usage We do not advise you to use base language models for text generation. Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model. ### Performance The evaluation of base models mainly focuses on the model performance of natural language understanding, general question answering, coding, mathematics, scientific knowledge, reasoning, multilingual capability, etc. The datasets for evaluation include: **English Tasks**: MMLU (5-shot), MMLU-Pro (5-shot), GPQA (5shot), Theorem QA (5-shot), BBH (3-shot), HellaSwag (10-shot), Winogrande (5-shot), TruthfulQA (0-shot), ARC-C (25-shot) **Coding Tasks**: EvalPlus (0-shot) (HumanEval, MBPP, HumanEval+, MBPP+), MultiPL-E (0-shot) (Python, C++, JAVA, PHP, TypeScript, C#, Bash, JavaScript) **Math Tasks**: GSM8K (4-shot), MATH (4-shot) **Chinese Tasks**: C-Eval (5-shot), CMMLU (5-shot) **Multilingual Tasks**: Multi-Exam (M3Exam 5-shot, IndoMMLU 3-shot, ruMMLU 5-shot, mMMLU 5-shot), Multi-Understanding (BELEBELE 5-shot, XCOPA 5-shot, XWinograd 5-shot, XStoryCloze 0-shot, PAWS-X 5-shot), Multi-Mathematics (MGSM 8-shot), Multi-Translation (Flores-101 5-shot) #### Qwen2-72B performance | Datasets | DeepSeek-V2 | Mixtral-8x22B | Llama-3-70B | Qwen1.5-72B | Qwen1.5-110B | **Qwen2-72B** | | :--------| :---------: | :------------: | :------------: | :------------: | :------------: |:------------: | |Architecture | MoE | MoE | Dense | Dense | Dense | Dense | |#Activated Params | 21B | 39B | 70B | 72B | 110B | 72B | |#Params | 236B | 140B | 70B | 72B | 110B | 72B| | ***English*** | | | | | | | |MMLU |78.5 | 77.8 | 79.5 | 77.5 | 80.4 | **84.2** | |MMLU-Pro | - | 49.5 | 52.8 | 45.8 | 49.4 | **55.6** | |GPQA | -| 34.3 | 36.3 | 36.3 | 35.9 | **37.9** | |Theorem QA | -| 35.9 | 32.3 | 29.3 | 34.9 | **43.1** | |BBH | 78.9 |78.9 | 81.0 | 65.5 | 74.8 | **82.4** | |HellaSwag | 87.8 | **88.7** | 88.0 | 86.0 | 87.5 | 87.6 | |WindoGrande | 84.8|85.0 | **85.3** | 83.0 | 83.5 | 85.1 | |ARC-C | 70.0| **70.7** | 68.8 | 65.9 | 69.6 | 68.9 | |TruthfulQA | 42.2 | 51.0 | 45.6 | **59.6** | 49.6 | 54.8 | | ***Coding*** | | | | | | | |HumanEval | 45.7 | 46.3 | 48.2 | 46.3 | 54.3 | **64.6** | |MBPP |73.9 | 71.7 | 70.4 | 66.9 | 70.9 | **76.9** | |EvalPlus | 55.0 | 54.1 | 54.8 | 52.9 | 57.7 | **65.4** | |MultiPL-E |44.4 | 46.7 | 46.3 | 41.8 | 52.7 | **59.6** | | ***Mathematics*** | | | | | | | |GSM8K | 79.2 | 83.7 | 83.0 | 79.5 | 85.4 | **89.5** | |MATH | 43.6 | 41.7 | 42.5 | 34.1 | 49.6 | **51.1** | | ***Chinese*** | | | | | | | |C-Eval | 81.7 | 54.6 | 65.2 | 84.1 | 89.1 | **91.0** | |CMMLU | 84.0 | 53.4 | 67.2 | 83.5 | 88.3 | **90.1** | | ***Multilingual*** | | | | | | | |Mulit-Exam | 67.5 | 63.5 | 70.0 | 66.4 | 75.6 | **76.6** | |Multi-Understanding | 77.0 | 77.7 | 79.9 | 78.2 | 78.2 | **80.7** | |Multi-Mathematics | 58.8 | 62.9 | 67.1 | 61.7 | 64.4 | **76.0** | |Multi-Translation | 36.0 | 23.3 | **38.0** | 35.6 | 36.2 | 37.8 | ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{qwen2, title={Qwen2 Technical Report}, year={2024} } ```

ningj2413/8b_llama3_what_4bitq_r16

ningj2413

"2024-06-29T15:26:57Z"

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

"2024-06-29T15:25:12Z"

--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** ningj2413 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit - r=16;lora-alpha=16 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)

sal55/sss

sal55

"2024-06-29T21:52:02Z"

0

null

[ "region:us" ]

null

"2024-06-29T15:27:06Z"

Entry not found

bartowski/Fook-Yi-34B-32K-v1-exl2

bartowski

"2024-06-29T15:28:37Z"

0

null

[ "not-for-all-audiences", "text-generation", "license:cc-by-nc-4.0", "region:us" ]

text-generation

"2024-06-29T15:28:36Z"

--- license: cc-by-nc-4.0 tags: - not-for-all-audiences quantized_by: bartowski pipeline_tag: text-generation --- ## Exllama v2 Quantizations of Fook-Yi-34B-32K-v1 Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.1.6">turboderp's ExLlamaV2 v0.1.6</a> for quantization. <b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b> Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Original model: https://huggingface.co/TheDrummer/Fook-Yi-34B-32K-v1 ## Prompt format ``` <|im_start|> system {system_prompt}<|im_end|> <|im_start|> user {prompt}<|im_end|> <|im_start|> assistant ``` ## Available sizes | Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description | | ------ | ---- | ------------ | ---- | ---- | ---- | ----------- | | [8_0](https://huggingface.co/bartowski/Fook-Yi-34B-32K-v1-exl2/tree/8_0) | 8.0 | 8.0 | 34.9 GB | 37.6 GB | 41.6 GB | Max quality producable by ExLlamav2, generally unneeded but maximum performance | | [6_5](https://huggingface.co/bartowski/Fook-Yi-34B-32K-v1-exl2/tree/6_5) | 6.5 | 8.0 | 28.9 GB | 31.6 GB | 35.6 GB | Near unquantized performance at vastly reduced size, **recommended**. | | [5_0](https://huggingface.co/bartowski/Fook-Yi-34B-32K-v1-exl2/tree/5_0) | 5.0 | 8.0 | 22.6 GB | 25.3 GB | 29.3 GB | Very high quality, usable at 4k context on 24GB. | | [4_25](https://huggingface.co/bartowski/Fook-Yi-34B-32K-v1-exl2/tree/4_25) | 4.25 | 6.0 | 19.5 GB | 22.2 GB | 26.2 GB | GPTQ equivalent bits per weight, slightly higher quality. | | [3_5](https://huggingface.co/bartowski/Fook-Yi-34B-32K-v1-exl2/tree/3_5) | 3.5 | 6.0 | 16.5 GB | 19.2 GB | 23.2 GB | Lower quality, only use if you have to. | | [3_0](https://huggingface.co/bartowski/Fook-Yi-34B-32K-v1-exl2/tree/3_0) | 3.0 | 6.0 | 14.3 GB | 17.0 GB | 21.0 GB | Very low quality, usable with 16gb of VRAM. | ## Download instructions With git: ```shell git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Fook-Yi-34B-32K-v1-exl2 Fook-Yi-34B-32K-v1-exl2-6_5 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download a specific branch, use the `--revision` parameter. For example, to download the 6.5 bpw branch: Linux: ```shell huggingface-cli download bartowski/Fook-Yi-34B-32K-v1-exl2 --revision 6_5 --local-dir Fook-Yi-34B-32K-v1-exl2-6_5 ``` Windows (which apparently doesn't like _ in folders sometimes?): ```shell huggingface-cli download bartowski/Fook-Yi-34B-32K-v1-exl2 --revision 6_5 --local-dir Fook-Yi-34B-32K-v1-exl2-6.5 ``` Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski

RobertML/sn21-2hydrogen-oxygen

RobertML

"2024-06-29T15:34:54Z"

0

null

[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]

any-to-any

"2024-06-29T15:28:54Z"

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

Hannibal046/gtr_t5_nq_32_stage2

Hannibal046

"2024-06-29T15:36:12Z"

0

1

transformers

[ "transformers", "safetensors", "t5", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

"2024-06-29T15:31:37Z"

--- license: mit ---

habulaj/4916239000

habulaj

"2024-06-29T15:32:44Z"

0

null

[ "region:us" ]

null

"2024-06-29T15:32:41Z"

Entry not found

habulaj/314350281005

habulaj

"2024-06-29T15:35:01Z"

0

null

[ "region:us" ]

null

"2024-06-29T15:34:59Z"

Entry not found

aleatorydialogue/ad_backgrounds

aleatorydialogue

"2024-07-29T15:11:28Z"

0

null

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

null

"2024-06-29T15:40:39Z"

--- license: apache-2.0 ---

Horizon6957/Medical-chatbot

Horizon6957

"2024-06-29T15:41:27Z"

0

null

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

null

"2024-06-29T15:41:27Z"

--- license: mit ---

notBeastKing/Llama3_sentiment_analysis

notBeastKing

"2024-06-29T17:09:01Z"

0

peft

[ "peft", "safetensors", "llama", "region:us" ]

null

"2024-06-29T15:42:31Z"

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0

jtatman/pythia-delphi-suboptimal-roleplay

jtatman

"2024-06-29T15:44:27Z"

0

null

[ "region:us" ]

null

"2024-06-29T15:44:27Z"

Entry not found

habulaj/52006291

habulaj

"2024-06-29T15:45:13Z"

0

null

[ "region:us" ]

null

"2024-06-29T15:45:08Z"

Entry not found

PlasmicZ/gemma-2b-5e-r256-new

PlasmicZ

"2024-06-29T15:48:38Z"

0

transformers

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

text-generation

"2024-06-29T15:46:31Z"

--- 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]

habulaj/498242887

habulaj

"2024-06-29T15:47:14Z"

0

null

[ "region:us" ]

null

"2024-06-29T15:47:09Z"

Entry not found

ningj2413/8b_llama3_what_4bitq_r32

ningj2413

"2024-06-29T15:47:47Z"

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

"2024-06-29T15:47:39Z"

--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** ningj2413 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

kishorea/p_l3

kishorea

"2024-06-29T15:53:20Z"

0

transformers

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

null

"2024-06-29T15:53:14Z"

--- 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]

ningj2413/8b_llama3_what_4bitq_instruct_r32

ningj2413

"2024-06-29T15:57:01Z"

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

"2024-06-29T15:56:54Z"

--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** ningj2413 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

AbdullahMashhadi/Elon-Musk-talking-model

AbdullahMashhadi

"2024-06-29T16:03:01Z"

0

null

[ "region:us" ]

null

"2024-06-29T16:02:58Z"

Entry not found

habulaj/9419281124

habulaj

"2024-06-29T16:03:18Z"

0

null

[ "region:us" ]

null

"2024-06-29T16:03:16Z"

Entry not found

hasanbahadir/ihale_dataset

hasanbahadir

"2024-06-30T13:12:47Z"

0

transformers

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

null

"2024-06-29T16:04:56Z"

--- 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]

helling100/my_awesome_model

helling100

"2024-06-29T16:10:00Z"

0

null

[ "region:us" ]

null

"2024-06-29T16:10:00Z"

Entry not found

jdmccaffrey/my_awesome_billsum_model

jdmccaffrey

"2024-06-29T16:10:04Z"

0

null

[ "region:us" ]

null

"2024-06-29T16:10:04Z"

Entry not found

Sharan1712/llama2_7B_unnaturalcore_qia3_4bit_8a

Sharan1712

"2024-06-29T16:10:27Z"

0

transformers

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

null

"2024-06-29T16:10:26Z"

--- 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]

hosdf/bgtjk

hosdf

"2024-06-29T16:46:44Z"

0

1

null

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

null

"2024-06-29T16:15:49Z"

--- license: openrail ---

habulaj/2483324556

habulaj

"2024-06-29T16:16:01Z"

0

null

[ "region:us" ]

null

"2024-06-29T16:15:56Z"

Entry not found

dash8060/EJ

dash8060

"2024-06-29T16:26:27Z"

0

null

[ "region:us" ]

null

"2024-06-29T16:19:51Z"

Entry not found

PrunaAI/nota-ai-cpt_st-vicuna-v1.3-3.7b-ppl-QUANTO-int4bit-smashed

PrunaAI

"2024-07-19T09:28:53Z"

0

transformers

[ "transformers", "pruna-ai", "base_model:nota-ai/cpt_st-vicuna-v1.3-3.7b-ppl", "base_model:finetune:nota-ai/cpt_st-vicuna-v1.3-3.7b-ppl", "endpoints_compatible", "region:us" ]

null

"2024-06-29T16:23:11Z"

--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: nota-ai/cpt_st-vicuna-v1.3-3.7b-ppl metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai ---   <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div>  [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) **Frequently Asked Questions** - ***How does the compression work?*** The model is compressed with quanto. - ***How does the model quality change?*** The quality of the model output might vary compared to the base model. - ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - ***What is the model format?*** We use safetensors. - ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data. - ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo nota-ai/cpt_st-vicuna-v1.3-3.7b-ppl installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install quanto ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer IMPORTS model = AutoModelForCausalLM.from_pretrained("PrunaAI/nota-ai-cpt_st-vicuna-v1.3-3.7b-ppl-QUANTO-int4bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("nota-ai/cpt_st-vicuna-v1.3-3.7b-ppl") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model nota-ai/cpt_st-vicuna-v1.3-3.7b-ppl before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).

dash8060/dks

dash8060

"2024-06-29T16:41:26Z"

0

null

[ "region:us" ]

null

"2024-06-29T16:40:51Z"

Entry not found

AiHubber/HornhubSound

AiHubber

"2024-06-29T16:43:25Z"

0

null

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

null

"2024-06-29T16:42:15Z"

--- license: openrail ---

SDH2222/gas

SDH2222

"2024-06-29T16:43:32Z"

0

null

[ "region:us" ]

null

"2024-06-29T16:42:45Z"

Entry not found

Neha13/AIcontentdetector

Neha13

"2024-06-29T16:43:45Z"

0

null

[ "region:us" ]

null

"2024-06-29T16:43:45Z"

Entry not found

D3MI4N/q-FrozenLake-v1-4x4-noSlippery

D3MI4N

"2024-06-29T16:44:47Z"

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

"2024-06-29T16:44:44Z"

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="D3MI4N/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

D3MI4N/taxi-v3-dem

D3MI4N

"2024-06-29T16:47:24Z"

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

"2024-06-29T16:47:23Z"

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-v3-dem results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.73 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="D3MI4N/taxi-v3-dem", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

rashid996958/pix2pix_exp39

rashid996958

"2024-06-29T16:48:20Z"

0

null

[ "region:us" ]

null

"2024-06-29T16:48:14Z"

Entry not found

Neha13/aidetectorintext

Neha13

"2024-06-29T16:48:15Z"

0

null

[ "region:us" ]

null

"2024-06-29T16:48:15Z"

Entry not found

ningj2413/8b_llama3_what_4bitq_instruct_r64

ningj2413

"2024-06-29T16:51:17Z"

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

"2024-06-29T16:51:03Z"

--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** ningj2413 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

prometheus04/Llama2_Finetuned

prometheus04

"2024-06-29T16:52:57Z"

0

transformers

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

null

"2024-06-29T16:52:53Z"

--- 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]

ningj2413/8b_llama3_what_4bitq_instruct_r128

ningj2413

"2024-06-29T16:55:28Z"

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

"2024-06-29T16:54:54Z"

--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** ningj2413 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Hexapoly/Norm

Hexapoly

"2024-06-29T16:57:12Z"

0

null

[ "region:us" ]

null

"2024-06-29T16:55:24Z"

Entry not found

mjfan1999/BlakeShelton2011

mjfan1999

"2024-06-29T17:12:20Z"

0

null

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

null

"2024-06-29T16:55:27Z"

--- license: unknown ---

habulaj/163166141251

habulaj

"2024-06-29T17:01:57Z"

0

null

[ "region:us" ]

null

"2024-06-29T17:01:53Z"

Entry not found

rashid996958/pix2pix_exp41

rashid996958

"2024-06-29T17:03:20Z"

0

null

[ "region:us" ]

null

"2024-06-29T17:03:15Z"

Entry not found

SDH2222/ppp

SDH2222

"2024-07-14T20:52:21Z"

0

null

[ "region:us" ]

null

"2024-06-29T17:03:34Z"

Entry not found

chaanks/DNSMOS

chaanks

"2024-06-29T17:12:09Z"

0

null

[ "onnx", "region:us" ]

null

"2024-06-29T17:11:30Z"

Entry not found

Neha13/x

Neha13

"2024-06-29T17:13:04Z"

0

transformers

[ "transformers", "endpoints_compatible", "region:us" ]

null

"2024-06-29T17:12:52Z"

Entry not found

imgeorgiev/pwm

imgeorgiev

"2024-07-02T20:36:12Z"

0

2

null

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

null

"2024-06-29T17:13:21Z"

--- license: mit --- # PWM: Policy Learning with Large World Models [Ignat Georgiev](https://www.imgeorgiev.com/), [Varun Giridhar](https://www.linkedin.com/in/varun-giridhar-463947146/), [Nicklas Hansen](https://www.nicklashansen.com/), [Animesh Garg](https://animesh.garg.tech/) [Project website](http://imgeorgiev.com/pwm) [Paper](TODO) [Models & Datasets](https://huggingface.co/imgeorgiev/pwm) ## Overview ![](https://github.com/imgeorgiev/pwm/figures/teaser.png) Instead of building world models into algorithms, we propose using large-scale multi-task world models as differentiable simulators for policy learning. When well-regularized, these models enable efficient policy learning with first-order gradient optimization. This allows PWM to learn to solve 80 tasks in < 10 minutes each without the need for expensive online planning. ## Structure of repository ``` pwm ├── dflex │ ├── data - data used for dflex world model pre-training │ └── pretrained - already trained world models that can be used in dflex experiments ├── multitask - pre-trained world models for multitask evaluation ├── pedagogical - pre-trained world models for recreating pedagogical examples └── README.md ```

iamalexcaspian/GumballWatterson-TAWOG-LoganGrove

iamalexcaspian

"2024-06-29T18:44:23Z"

0

null

[ "region:us" ]

null

"2024-06-29T17:15:23Z"

Entry not found

youssouf128/youssouf

youssouf128

"2024-06-29T17:23:04Z"

0

null

[ "region:us" ]

null

"2024-06-29T17:15:59Z"

Entry not found

mjfan1999/BlakeShelton2016

mjfan1999

"2024-06-29T17:33:15Z"

0

null

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

null

"2024-06-29T17:22:41Z"

--- license: unknown ---

random2344/Graphic

random2344

"2024-06-29T17:24:11Z"

0

null

[ "region:us" ]

null

"2024-06-29T17:23:31Z"

Entry not found

mharb/Reinforce-cartpole-v1

mharb

"2024-06-29T17:23:55Z"

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

"2024-06-29T17:23:44Z"

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-cartpole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

itay-nakash/model_73a455d87c_sweep_divine-firefly-981

itay-nakash

"2024-06-29T17:26:58Z"

0

null

[ "region:us" ]

null

"2024-06-29T17:26:58Z"

Entry not found

Malkovitz/Miroslaw_Utta

Malkovitz

"2024-06-29T17:32:45Z"

0

null

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

null

"2024-06-29T17:29:56Z"

--- license: unknown ---

NoNameFactory/llama-3-8b-it-4bit-ContdPT_2_10

NoNameFactory

"2024-06-29T17:34:03Z"

0

transformers

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

null

"2024-06-29T17:31:49Z"

--- base_model: unsloth/llama-3-8b-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** NoNameFactory - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

kandarp0809/Grid

kandarp0809

"2024-06-29T17:36:10Z"

0

null

[ "region:us" ]

null

"2024-06-29T17:34:40Z"

Entry not found

hadestest999/testersssss

hadestest999

"2024-06-29T17:35:40Z"

0

null

[ "license:cdla-permissive-2.0", "region:us" ]

null

"2024-06-29T17:34:55Z"

--- license: cdla-permissive-2.0 ---

yuvrajAI/gemma-2b-medical

yuvrajAI

"2024-06-29T17:36:06Z"

0

transformers

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

null

"2024-06-29T17:35:11Z"

--- 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]

impossibleexchange/waitingonweights

impossibleexchange

"2024-07-02T00:12:51Z"

0

null

[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]

any-to-any

"2024-06-29T17:36:13Z"

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

mgh6/Pair_Fold_CNN

mgh6

"2024-07-01T17:24:56Z"

0

null

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

null

"2024-06-29T17:38:23Z"

Entry not found

youssouf128/ABAYAZID

youssouf128

"2024-06-29T17:41:47Z"

0

null

[ "region:us" ]

null

"2024-06-29T17:41:47Z"

Entry not found

AdamKasumovic/llama3-70b-instruct-ids-winogrande-train-s-af-winogrande-med

AdamKasumovic

"2024-06-29T17:48:33Z"

0

transformers

[ "transformers", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-70b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-70b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

"2024-06-29T17:48:33Z"

--- base_model: unsloth/llama-3-70b-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** AdamKasumovic - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-70b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

morteza76/seta

morteza76

"2024-06-29T17:48:53Z"

0

null

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

null

"2024-06-29T17:48:53Z"

--- license: other license_name: seta license_link: LICENSE ---

Soorya1998/taxi-v1

Soorya1998

"2024-06-29T17:49:40Z"

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

"2024-06-29T17:49:13Z"

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.46 +/- 2.70 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="Soorya1998/taxi-v1", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

habulaj/295100262864

habulaj

"2024-06-29T17:49:46Z"

0

null

[ "region:us" ]

null

"2024-06-29T17:49:35Z"

Entry not found

devilga/dac_16khZ_8kbps

devilga

"2024-06-29T17:50:49Z"

0

null

[ "region:us" ]

null

"2024-06-29T17:50:49Z"

Entry not found

cmmann/ppo-lunarlander-v2

cmmann

"2024-06-29T18:09:03Z"

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

"2024-06-29T18:08:46Z"

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -175.86 +/- 159.83 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

Nikt271/Kurak

Nikt271

"2024-06-29T18:21:25Z"

0

null

[ "region:us" ]

null

"2024-06-29T18:17:54Z"

Entry not found

dat-lequoc/A100_trt-llm_engine

dat-lequoc

"2024-06-29T18:20:10Z"

0

transformers

[ "transformers", "endpoints_compatible", "region:us" ]

null

"2024-06-29T18:18:05Z"

Entry not found

habulaj/7860557029

habulaj

"2024-06-29T18:20:12Z"

0

null

[ "region:us" ]

null

"2024-06-29T18:20:05Z"

Entry not found

random2344/minimal1

random2344

"2024-06-29T18:22:26Z"

0

null

[ "region:us" ]

null

"2024-06-29T18:21:40Z"

Entry not found

tdooms/ts-medium-relu

tdooms

"2024-06-29T18:23:16Z"

0

transformers

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

null

"2024-06-29T18:22:58Z"

--- 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]

Akirami/phi-3-medium_text2cypher_recommendations

Akirami

"2024-06-29T18:27:07Z"

0

transformers

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

null

"2024-06-29T18:26:48Z"

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

Frixi/Randy_NotaLoka

Frixi

"2024-06-29T18:35:37Z"

0

null

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

null

"2024-06-29T18:30:08Z"

--- license: openrail ---

bradpacito/brapacito

bradpacito

"2024-06-29T18:36:31Z"

0

null

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

null

"2024-06-29T18:36:31Z"

--- license: wtfpl ---

itay-nakash/model_73a455d87c_sweep_dry-wind-982

itay-nakash

"2024-06-29T18:38:51Z"

0

null

[ "region:us" ]

null

"2024-06-29T18:38:51Z"

Entry not found

micdoh/2024_JOCN_XLRON

micdoh

"2024-06-29T18:51:12Z"

0

null

[ "doi:10.57967/hf/2654", "license:mit", "region:us" ]

null

"2024-06-29T18:48:47Z"

--- license: mit ---

jacobcd52/mats-gpt2-saes

jacobcd52

"2024-07-06T15:28:51Z"

0

null

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

null

"2024-06-29T18:55:39Z"

--- license: mit ---

RITTIHILATTI/gpt2

RITTIHILATTI

"2024-06-29T18:56:55Z"

0

null

[ "region:us" ]

null

"2024-06-29T18:56:55Z"

Entry not found

Callyde/Li

Callyde

"2024-06-29T18:59:50Z"

0

null

[ "region:us" ]

null

"2024-06-29T18:59:50Z"

Entry not found

ilya94prok/test

ilya94prok

"2024-06-29T19:01:32Z"

0

null

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

null

"2024-06-29T19:01:32Z"

--- license: apache-2.0 ---

habulaj/375846341463

habulaj

"2024-06-29T19:01:57Z"

0

null

[ "region:us" ]

null

"2024-06-29T19:01:52Z"

Entry not found

samad321kk/ee

samad321kk

"2024-06-29T19:06:55Z"

0

null

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

null

"2024-06-29T19:02:20Z"

--- license: openrail ---

Phoenix91/Phoenix

Phoenix91

"2024-06-29T19:02:44Z"

0

null

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

null

"2024-06-29T19:02:44Z"

--- license: apache-2.0 ---