librarian-bots/model_cards_with_metadata_test

modelId stringlengths 5 122	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]	library_name stringclasses 245 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 48 values	createdAt timestamp[us, tz=UTC]	card stringlengths 1 901k
PrunaAI/sambanovasystems-SambaLingo-Bulgarian-Base-bnb-4bit-smashed	PrunaAI	2024-06-26T09:34:50Z	transformers	[ "transformers", "safetensors", "llama", "text-generation", "pruna-ai", "base_model:sambanovasystems/SambaLingo-Bulgarian-Base", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-06-26T09:32:40Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: sambanovasystems/SambaLingo-Bulgarian-Base 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/CP4VSgck) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with llm-int8. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained 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 sambanovasystems/SambaLingo-Bulgarian-Base installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install transformers accelerate bitsandbytes>0.37.0 ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("PrunaAI/sambanovasystems-SambaLingo-Bulgarian-Base-bnb-4bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SambaLingo-Bulgarian-Base") 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 sambanovasystems/SambaLingo-Bulgarian-Base 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).
a414166402/depth-anything-v2-small	a414166402	2024-06-26T09:32:44Z	null	[ "region:us" ]	null	2024-06-26T09:32:44Z	Entry not found
bsmani/git-base-caption	bsmani	2024-06-26T09:34:36Z	null	[ "region:us" ]	null	2024-06-26T09:34:36Z	Entry not found
n1ra/phi-aid-GGUF	n1ra	2024-06-26T09:36:15Z	null	[ "region:us" ]	null	2024-06-26T09:36:15Z	Entry not found
triplee/supernatural_dataset_model	triplee	2024-06-26T09:40:13Z	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T09:37:41Z	--- 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]
lolloawdlnjawndjkw/model	lolloawdlnjawndjkw	2024-06-26T09:37:46Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-26T09:37:46Z	--- license: apache-2.0 ---
euhaq/repo_name	euhaq	2024-06-26T12:08:14Z	null	[ "text-classification", "region:us" ]	text-classification	2024-06-26T09:38:09Z	--- pipeline_tag: text-classification ---
PrunaAI/sambanovasystems-SambaLingo-Bulgarian-Base-HQQ-1bit-smashed	PrunaAI	2024-06-26T09:43:05Z	transformers	[ "transformers", "llama", "text-generation", "pruna-ai", "base_model:sambanovasystems/SambaLingo-Bulgarian-Base", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-06-26T09:42:11Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: sambanovasystems/SambaLingo-Bulgarian-Base 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/CP4VSgck) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with hqq. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained 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 sambanovasystems/SambaLingo-Bulgarian-Base installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install hqq ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer from hqq.engine.hf import HQQModelForCausalLM from hqq.models.hf.base import AutoHQQHFModel try: model = HQQModelForCausalLM.from_quantized("PrunaAI/sambanovasystems-SambaLingo-Bulgarian-Base-HQQ-1bit-smashed", device_map='auto') except: model = AutoHQQHFModel.from_quantized("PrunaAI/sambanovasystems-SambaLingo-Bulgarian-Base-HQQ-1bit-smashed") tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SambaLingo-Bulgarian-Base") 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 sambanovasystems/SambaLingo-Bulgarian-Base 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/sambanovasystems-SambaLingo-Bulgarian-Base-HQQ-2bit-smashed	PrunaAI	2024-06-26T09:43:38Z	transformers	[ "transformers", "llama", "text-generation", "pruna-ai", "base_model:sambanovasystems/SambaLingo-Bulgarian-Base", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-06-26T09:42:12Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: sambanovasystems/SambaLingo-Bulgarian-Base 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/CP4VSgck) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with hqq. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained 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 sambanovasystems/SambaLingo-Bulgarian-Base installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install hqq ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer from hqq.engine.hf import HQQModelForCausalLM from hqq.models.hf.base import AutoHQQHFModel try: model = HQQModelForCausalLM.from_quantized("PrunaAI/sambanovasystems-SambaLingo-Bulgarian-Base-HQQ-2bit-smashed", device_map='auto') except: model = AutoHQQHFModel.from_quantized("PrunaAI/sambanovasystems-SambaLingo-Bulgarian-Base-HQQ-2bit-smashed") tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SambaLingo-Bulgarian-Base") 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 sambanovasystems/SambaLingo-Bulgarian-Base 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).
anyMODE/realMODE_900M	anyMODE	2024-06-26T10:07:02Z	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "full", "pixart", "pixart sigma", "base_model:ptx0/pixart-900m-1024-ft-large", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-06-26T09:46:48Z	--- license: creativeml-openrail-m base_model: ptx0/pixart-900m-1024-ft-large tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - full - pixart - pixart sigma inference: false library_name: diffusers pipeline_tag: text-to-image --- # realMODE PixArt Sigma 900M v1 ## Inference ### ComfyUI - Download model file `transformer/diffusion_pytorch_model.safetensors` and put into `ComfyUI/models/checkpoints` - Use ExtraModels node: https://github.com/city96/ComfyUI_ExtraModels?tab=readme-ov-file#pixart ![image/png](https://cdn-uploads.huggingface.co/production/uploads/643c7e91b409fef15e0bd11b/MJfTShin1fYOOCo4mTv2-.png) ```python import torch from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler from diffusers.models import PixArtTransformer2DModel model_id = "anyMODE/realMODE_900M" negative_prompt = "malformed, disgusting, overexposed, washed-out" pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipeline.transformer = PixArtTransformer2DModel.from_pretrained(model_id, subfolder="transformer", torch_dtype=torch.float16) pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config) pipeline.to('cuda' if torch.cuda.is_available() else 'cpu') prompt = "On the left, there is a red cube. On the right, there is a blue sphere. On top of the red cube is a dog. On top of the blue sphere is a cat" image = pipeline( prompt=prompt, negative_prompt='blurry, cropped, ugly', num_inference_steps=30, generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826), width=1024, height=1024, guidance_scale=5.5, ).images[0] image.save("output.png", format="JPEG") ```
djbp/swin-tiny-patch4-window7-224-image-classification	djbp	2024-06-26T09:48:39Z	null	[ "region:us" ]	null	2024-06-26T09:48:39Z	Entry not found
Studeni/llama-3-8b-bnb-4bit-robot-instruct-v2	Studeni	2024-06-26T09:49:46Z	null	[ "region:us" ]	null	2024-06-26T09:49:46Z	Entry not found
PrunaAI/sambanovasystems-SambaLingo-Bulgarian-Base-HQQ-4bit-smashed	PrunaAI	2024-06-26T09:52:18Z	transformers	[ "transformers", "llama", "text-generation", "pruna-ai", "base_model:sambanovasystems/SambaLingo-Bulgarian-Base", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-06-26T09:50:25Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: sambanovasystems/SambaLingo-Bulgarian-Base 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/CP4VSgck) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with hqq. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained 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 sambanovasystems/SambaLingo-Bulgarian-Base installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install hqq ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer from hqq.engine.hf import HQQModelForCausalLM from hqq.models.hf.base import AutoHQQHFModel try: model = HQQModelForCausalLM.from_quantized("PrunaAI/sambanovasystems-SambaLingo-Bulgarian-Base-HQQ-4bit-smashed", device_map='auto') except: model = AutoHQQHFModel.from_quantized("PrunaAI/sambanovasystems-SambaLingo-Bulgarian-Base-HQQ-4bit-smashed") tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SambaLingo-Bulgarian-Base") 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 sambanovasystems/SambaLingo-Bulgarian-Base 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).
Vash1989/SimoPresLora	Vash1989	2024-06-26T09:53:05Z	null	[ "tensorboard", "region:us" ]	null	2024-06-26T09:53:02Z	Entry not found
amiune/q-FrozenLake-v1-4x4-noSlippery	amiune	2024-06-26T09:53:50Z	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-06-26T09:53:48Z	--- 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="amiune/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"]) ```
amiune/q-taxi-v3	amiune	2024-06-26T09:55:11Z	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-06-26T09:55:09Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 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="amiune/q-taxi-v3", 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"]) ```
ArtNikNov/Piknik_Picnic_Edmund_Shklyarskiy	ArtNikNov	2024-06-28T13:20:17Z	null	[ "region:us" ]	null	2024-06-26T09:55:10Z	Entry not found
PradyumSomebody/finetunedLlamaTest9	PradyumSomebody	2024-06-26T09:56:16Z	peft	[ "peft", "region:us" ]	null	2024-06-26T09:56:11Z	--- 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 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 - PEFT 0.4.0
bsmani/git-base-caption1	bsmani	2024-06-26T09:57:25Z	null	[ "region:us" ]	null	2024-06-26T09:57:25Z	Entry not found
shubh-2896/RAG_Financial_Document	shubh-2896	2024-06-26T09:57:54Z	null	[ "region:us" ]	null	2024-06-26T09:57:54Z	Entry not found
PrunaAI/sambanovasystems-SambaLingo-Bulgarian-Base-QUANTO-int2bit-smashed	PrunaAI	2024-07-01T08:00:46Z	transformers	[ "transformers", "pruna-ai", "base_model:sambanovasystems/SambaLingo-Bulgarian-Base", "endpoints_compatible", "region:us" ]	null	2024-06-26T09:57:58Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: sambanovasystems/SambaLingo-Bulgarian-Base 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/CP4VSgck) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with 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 sambanovasystems/SambaLingo-Bulgarian-Base 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/sambanovasystems-SambaLingo-Bulgarian-Base-QUANTO-int2bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SambaLingo-Bulgarian-Base") 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 sambanovasystems/SambaLingo-Bulgarian-Base 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).
bezzam/digicam-celeba-unet2M-mwdn-6M	bezzam	2024-06-26T09:59:53Z	null	[ "license:mit", "region:us" ]	null	2024-06-26T09:59:25Z	--- license: mit ---
Pandita-IA/ppo-LunarLanderScratch-v2	Pandita-IA	2024-06-26T10:46:01Z	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2024-06-26T10:01:30Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -32.16 +/- 82.95 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 2000000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'Pandita-IA/ppo-LunarLanderScratch-v2' 'batch_size': 512 'minibatch_size': 128} ```
Abhilash-12/youtube-video-summarizer	Abhilash-12	2024-06-26T10:02:52Z	null	[ "region:us" ]	null	2024-06-26T10:02:52Z	Entry not found
rhemzypm/BERT_klasifikasi_risiko	rhemzypm	2024-06-27T08:25:17Z	null	[ "region:us" ]	null	2024-06-26T10:04:02Z	metadata title: Risk Classification IndoBERT emoji: 🌍 colorFrom: indigo colorTo: indigo sdk: gradio sdk_version: 3.45.2 app_file: app.py pinned: false license: mit
benmajor27/whisper-small-hun	benmajor27	2024-06-26T10:06:40Z	null	[ "region:us" ]	null	2024-06-26T10:06:40Z	Entry not found
xahilmalik/llama-3-8b-chat-acebot-4	xahilmalik	2024-06-26T10:08:37Z	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T10:08:29Z	--- 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]
Amirreza12/amirreza	Amirreza12	2024-06-26T10:10:01Z	null	[ "license:openrail", "region:us" ]	null	2024-06-26T10:10:00Z	--- license: openrail ---
abhishektandon/sd-naruto-model	abhishektandon	2024-06-26T10:11:29Z	null	[ "region:us" ]	null	2024-06-26T10:11:29Z	Entry not found
txt2shivam/gpt2	txt2shivam	2024-06-26T10:12:44Z	null	[ "region:us" ]	null	2024-06-26T10:12:44Z	Entry not found
fearvel/kemono-jihen-kabane-pony-xl	fearvel	2024-06-26T10:14:56Z	null	[ "stable-diffusion", "text-to-image", "StableDiffusionPipeline", "lora", "license:other", "region:us" ]	text-to-image	2024-06-26T10:14:53Z	--- tags: - stable-diffusion - text-to-image - StableDiffusionPipeline - lora license: other --- ## Model ![pipeline](00053-2041176827.png)
hieupth/blueforest	hieupth	2024-06-28T13:36:04Z	null	[ "onnx", "region:us" ]	null	2024-06-26T10:15:41Z	Entry not found
abhishektandon/naruto-lora	abhishektandon	2024-06-26T10:15:51Z	null	[ "region:us" ]	null	2024-06-26T10:15:51Z	Entry not found
EdwardSpaeth/my-awesome-model	EdwardSpaeth	2024-06-26T10:17:12Z	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T10:17:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
sergeipetrov/swin2SR-classical-sr-x2-64-IE-distr	sergeipetrov	2024-06-26T12:51:40Z	null	[ "endpoints_compatible", "region:us" ]	null	2024-06-26T10:17:10Z	Entry not found
JulietteAV/XLM-Letters-Wiki	JulietteAV	2024-06-26T10:20:18Z	null	[ "license:unknown", "region:us" ]	null	2024-06-26T10:20:18Z	--- license: unknown ---
BluBry/NeoModel	BluBry	2024-06-26T10:21:54Z	null	[ "license:llama2", "region:us" ]	null	2024-06-26T10:21:54Z	--- license: llama2 ---
jddllwqa/Qwen-Qwen1.5-0.5B-1719397490	jddllwqa	2024-06-26T10:24:50Z	null	[ "region:us" ]	null	2024-06-26T10:24:50Z	Entry not found
mohan11111/tinyllama8bit80k_new-GGUF	mohan11111	2024-06-26T10:25:18Z	null	[ "region:us" ]	null	2024-06-26T10:25:17Z	Entry not found
Boostaro155/Pharma42	Boostaro155	2024-06-26T10:27:21Z	null	[ "region:us" ]	null	2024-06-26T10:26:30Z	# Pharma Flex XR Deutschland : Pharma Flex Erfahrungen Test, Einnahme Zutaten Preis Pharma Flex XR Deutschland Produkte wie Pharma Flex Rx werden mit einer Vielzahl wichtiger Verbindungen hergestellt, die zusammenwirken, um Gelenkschmerzen und Entzündungen zu lindern. Wir haben jedoch festgestellt, dass Pharma Flex Rx einige wichtige Inhaltsstoffe fehlen, die dieses Nahrungsergänzungsmittel viel wirksamer machen würden. ## [Klicken Sie hier, um Pharma Flex XR jetzt auf der offiziellen Website zu kaufen](https://deutschlandbuzz.de/pharma-flex-xr-de) ## Beschreibung PharmaFlex Rx wurde für diejenigen entwickelt, die eine natürliche Lösung zur Linderung von Gelenkbeschwerden, zur Unterstützung der Muskelregeneration, zur Beschleunigung der Gelenkreparatur und zur Stärkung des Bindegewebes suchen. Dieses einzigartige Nahrungsergänzungsmittel wurde entwickelt, um die Herausforderungen von Gelenkproblemen anzugehen und bietet die Möglichkeit, ein aktives Leben ohne Schmerzen zu führen. Wenn Sie unter Gelenkproblemen leiden und nach einer wirksamen Lösung suchen, könnte PharmaFlex Rx die Antwort sein. Anwendungsgebiete Gelenkschmerzen Arthritis Arthrose Gelenksteifheit Einschränkung der Mobilität Schwellungen und Entzündungen der Gelenke Knorpelschäden Muskelschwäche und -schmerzen in der Umgebung der Gelenke ## PharmaFlex Rx – Wirkungsweise Die Wirkung von PharmaFlex Rx beruht auf einer einzigartigen Formel aus natürlichen Inhaltsstoffen, die synergistisch wirken, um die Gelenke zu unterstützen. PharmaFlex Rx – Inhaltsstoffe und Wirkstoff Glucosaminsulfat Kurkuma-Extrakt MSM (Methylsulfonylmethan) Bromelain PharmaFlex Rx – Effekte – Auswirkungen Verringerung der Gelenkschmerzen: Die Therapie hilft dabei, Gelenkschmerzen zu lindern, sodass die Betroffenen weniger Schmerzen verspüren. Verbesserte Mobilität: Durch die Behandlung wird die Beweglichkeit der Gelenke erhöht, was eine bessere Bewegungsfreiheit ermöglicht. Beschleunigte Gelenkreparatur: Der Heilungsprozess der Gelenke wird durch die Therapie beschleunigt, sodass Verletzungen schneller heilen. Stärkung des Bindegewebes: Die Therapie stärkt das Bindegewebe, was zur Stabilität und Unterstützung der Gelenke beiträgt. ## [Klicken Sie hier, um Pharma Flex XR jetzt auf der offiziellen Website zu kaufen](https://deutschlandbuzz.de/pharma-flex-xr-de)
alokhom/aimodel	alokhom	2024-06-26T10:27:59Z	null	[ "license:mit", "region:us" ]	null	2024-06-26T10:27:59Z	--- license: mit ---
rithwik19/scootervqa_blip2	rithwik19	2024-06-26T11:46:19Z	null	[ "safetensors", "region:us" ]	null	2024-06-26T10:28:59Z	--- {} ---
Grayx/john_paul_van_damme_32	Grayx	2024-06-26T10:30:45Z	null	[ "region:us" ]	null	2024-06-26T10:30:32Z	Entry not found
ldd12/flowmodel	ldd12	2024-06-26T10:31:08Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-26T10:31:08Z	--- license: apache-2.0 ---
Grayx/john_paul_van_damme_33	Grayx	2024-06-26T10:33:05Z	null	[ "region:us" ]	null	2024-06-26T10:32:50Z	Entry not found
execbat/q-FrozenLake-v1-4x4-noSlippery	execbat	2024-06-26T10:35:51Z	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-06-26T10:35:49Z	--- 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="execbat/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"]) ```
CileCe/cars	CileCe	2024-06-26T10:38:09Z	null	[ "region:us" ]	null	2024-06-26T10:38:09Z	Entry not found
Grayx/john_paul_van_damme_34	Grayx	2024-06-26T10:40:09Z	null	[ "region:us" ]	null	2024-06-26T10:39:58Z	Entry not found
Abolfazl87/shaadmeehr	Abolfazl87	2024-06-26T10:40:29Z	null	[ "license:openrail", "region:us" ]	null	2024-06-26T10:40:29Z	--- license: openrail ---
florinshen/Gamba	florinshen	2024-06-27T09:23:26Z	null	[ "license:cc", "region:us" ]	null	2024-06-26T10:40:32Z	--- license: cc ---
Hoshiraku/llama-7b-Preprocessed	Hoshiraku	2024-06-26T10:47:01Z	null	[ "region:us" ]	null	2024-06-26T10:47:01Z	Entry not found
rydevdev/example-model	rydevdev	2024-06-26T11:04:46Z	null	[ "license:mit", "region:us" ]	null	2024-06-26T10:48:09Z	--- license: mit ---
Ram2085/example-model	Ram2085	2024-06-26T10:57:29Z	null	[ "license:mit", "region:us" ]	null	2024-06-26T10:57:29Z	--- license: mit ---
patruff/probablyBadFineTune11B	patruff	2024-06-26T10:59:59Z	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:Sao10K/Fimbulvetr-11B-v2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-26T10:59:26Z	--- base_model: Sao10K/Fimbulvetr-11B-v2 language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: patruff - License: apache-2.0 - Finetuned from model : Sao10K/Fimbulvetr-11B-v2 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)
cherifkhalifah/Tounsify-v0.3	cherifkhalifah	2024-06-26T11:00:53Z	transformers	[ "transformers", "tensorboard", "safetensors", "marian", "text2text-generation", "generated_from_trainer", "base_model:marefa-nlp/marefa-mt-en-ar", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-06-26T11:00:02Z	--- base_model: marefa-nlp/marefa-mt-en-ar license: apache-2.0 metrics: - bleu tags: - generated_from_trainer model-index: - name: Tounsify-v0.3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Tounsify-v0.3 This model is a fine-tuned version of [marefa-nlp/marefa-mt-en-ar](https://huggingface.co/marefa-nlp/marefa-mt-en-ar) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7357 - Bleu: 14.3631 - Gen Len: 7.5667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\| \| No log \| 1.0 \| 8 \| 3.9993 \| 8.8788 \| 9.4 \| \| No log \| 2.0 \| 16 \| 3.7357 \| 14.3631 \| 7.5667 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
psiborgtechnologies/smart-agriculture-system	psiborgtechnologies	2024-06-26T11:05:26Z	null	[ "region:us" ]	null	2024-06-26T11:00:35Z	PsiBorg's smart agriculture system represents a revolutionary leap in farming technology, designed to enhance productivity, sustainability, and efficiency. At its core, the system integrates advanced IoT sensors that monitor soil moisture, temperature, and nutrient levels in real-time. These sensors provide farmers with precise data, enabling them to make informed decisions about irrigation, fertilization, and crop management. Farmers can access all this information through a user-friendly mobile app, which provides alerts and actionable insights, ensuring they stay connected to their fields from anywhere. The app also offers weather forecasting and market trends analysis to help farmers plan their activities and sales strategies more effectively. PsiBorg's smart agriculture system not only increases productivity and reduces costs but also promotes sustainable farming practices by optimizing resource use and minimizing environmental impact. With its cutting-edge technology, PsiBorg is paving the way for the future of agriculture. Read More: https://psiborg.in/smart-agricultural-system/
athena913/test_model	athena913	2024-06-26T11:00:40Z	null	[ "region:us" ]	null	2024-06-26T11:00:40Z	Entry not found
Ganeshkumar34/llama3-8b-kovaion_cpp_f16	Ganeshkumar34	2024-06-26T11:00:48Z	transformers	[ "transformers", "text-generation-inference", "unsloth", "llama", "gguf", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-26T11:00:48Z	--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf --- # Uploaded model - Developed by: Ganeshkumar34 - 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)
Grayx/john_paul_van_damme_35	Grayx	2024-06-26T11:05:18Z	null	[ "region:us" ]	null	2024-06-26T11:05:07Z	Entry not found
rahiakela/layoutlmv3-finance-doc-classification	rahiakela	2024-06-26T11:10:42Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-26T11:10:42Z	--- license: apache-2.0 ---
ViniciusWellington/teste	ViniciusWellington	2024-06-26T11:11:35Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-26T11:11:35Z	--- license: apache-2.0 ---
Kathernie/vasista_m-tamil-r_moe	Kathernie	2024-07-02T03:55:58Z	null	[ "tensorboard", "safetensors", "region:us" ]	null	2024-06-26T11:12:49Z	Entry not found
PradyumSomebody/finetunedLlamaTest2.0	PradyumSomebody	2024-06-26T11:15:53Z	peft	[ "peft", "region:us" ]	null	2024-06-26T11:15:50Z	--- 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 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 - PEFT 0.4.0
MubarakB/wav2vec2-large-xls-r-300m-zu-1hr-v8	MubarakB	2024-06-26T11:17:39Z	null	[ "region:us" ]	null	2024-06-26T11:17:39Z	Entry not found
PrunaAI/sambanovasystems-SambaLingo-Bulgarian-Base-QUANTO-int8bit-smashed	PrunaAI	2024-07-01T07:59:22Z	transformers	[ "transformers", "pruna-ai", "base_model:sambanovasystems/SambaLingo-Bulgarian-Base", "endpoints_compatible", "region:us" ]	null	2024-06-26T11:18:46Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: sambanovasystems/SambaLingo-Bulgarian-Base 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/CP4VSgck) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with 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 sambanovasystems/SambaLingo-Bulgarian-Base 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/sambanovasystems-SambaLingo-Bulgarian-Base-QUANTO-int8bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SambaLingo-Bulgarian-Base") 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 sambanovasystems/SambaLingo-Bulgarian-Base 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/sambanovasystems-SambaLingo-Bulgarian-Base-QUANTO-int4bit-smashed	PrunaAI	2024-07-01T07:59:12Z	transformers	[ "transformers", "pruna-ai", "base_model:sambanovasystems/SambaLingo-Bulgarian-Base", "endpoints_compatible", "region:us" ]	null	2024-06-26T11:18:50Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: sambanovasystems/SambaLingo-Bulgarian-Base 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/CP4VSgck) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with 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 sambanovasystems/SambaLingo-Bulgarian-Base 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/sambanovasystems-SambaLingo-Bulgarian-Base-QUANTO-int4bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SambaLingo-Bulgarian-Base") 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 sambanovasystems/SambaLingo-Bulgarian-Base 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).
CHARKA/Meta-Llama-3-8B-InstructSmallDataV0	CHARKA	2024-06-26T11:22:31Z	null	[ "region:us" ]	null	2024-06-26T11:22:31Z	Entry not found
Masallah/onetry	Masallah	2024-06-26T11:44:04Z	null	[ "region:us" ]	null	2024-06-26T11:25:02Z	Entry not found
MubarakB/wav2vec2-large-xls-r-300m-zulu-vocab-v1	MubarakB	2024-06-26T11:25:17Z	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T11:25:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Grayx/john_paul_van_damme_36	Grayx	2024-06-26T11:25:44Z	null	[ "region:us" ]	null	2024-06-26T11:25:28Z	Entry not found
PrunaAI/sambanovasystems-SambaLingo-Bulgarian-Base-QUANTO-float8bit-smashed	PrunaAI	2024-07-01T08:01:15Z	transformers	[ "transformers", "pruna-ai", "base_model:sambanovasystems/SambaLingo-Bulgarian-Base", "endpoints_compatible", "region:us" ]	null	2024-06-26T11:26:05Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: sambanovasystems/SambaLingo-Bulgarian-Base 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/CP4VSgck) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with 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 sambanovasystems/SambaLingo-Bulgarian-Base 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/sambanovasystems-SambaLingo-Bulgarian-Base-QUANTO-float8bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SambaLingo-Bulgarian-Base") 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 sambanovasystems/SambaLingo-Bulgarian-Base 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).
nglguarino/my-nlp-gemma-model	nglguarino	2024-06-26T11:26:56Z	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/gemma-2b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-26T11:26:33Z	--- base_model: unsloth/gemma-2b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl --- # Uploaded model - Developed by: nglguarino - License: apache-2.0 - Finetuned from model : unsloth/gemma-2b-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
hyungkook/koalpaca-polyglot-12.8b-jun	hyungkook	2024-06-27T00:29:37Z	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T11:27:44Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
SectorZero/SectorZero	SectorZero	2024-06-26T13:33:42Z	null	[ "region:us" ]	null	2024-06-26T11:27:48Z	Entry not found
qwcarol/Teste	qwcarol	2024-06-26T11:29:24Z	null	[ "region:us" ]	null	2024-06-26T11:29:24Z	Entry not found
Vitron/vitron-base	Vitron	2024-07-01T06:00:40Z	transformers	[ "transformers", "llava", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-06-26T11:30:14Z	--- license: apache-2.0 ---
Enkeeper/Mistral_10.7B_TaskInstruct_Unsloth_LORA	Enkeeper	2024-06-26T11:33:09Z	null	[ "safetensors", "license:cc-by-nc-2.0", "region:us" ]	null	2024-06-26T11:30:55Z	--- license: cc-by-nc-2.0 ---
n1ra/phi3-aid-v3	n1ra	2024-06-26T11:40:19Z	transformers	[ "transformers", "tensorboard", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "base_model:microsoft/Phi-3-mini-4k-instruct", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-06-26T11:34:37Z	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers base_model: microsoft/Phi-3-mini-4k-instruct widget: - messages: - role: user content: What is your favorite condiment? license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
Reihaneh/wav2vec2_fy_nl_de_lid_common_voice_52	Reihaneh	2024-06-26T12:33:28Z	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T11:35:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
sgonzalezsilot/whisper-tiny-spanish-es-Nemo_unique_2024-06-26_11-36-34	sgonzalezsilot	2024-06-26T11:36:34Z	null	[ "region:us" ]	null	2024-06-26T11:36:34Z	Entry not found
execbat/q-Taxi-v3	execbat	2024-06-26T11:36:58Z	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-06-26T11:36:56Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 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="execbat/q-Taxi-v3", 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"]) ```
sgonzalezsilot/whisper-tiny-spanish-es-Nemo_unified_2024-06-26_11-36-57	sgonzalezsilot	2024-06-26T11:36:57Z	null	[ "region:us" ]	null	2024-06-26T11:36:57Z	Entry not found
XxLOLxX/donald_duck_LoRA	XxLOLxX	2024-06-26T11:41:03Z	null	[ "tensorboard", "region:us" ]	null	2024-06-26T11:40:25Z	Entry not found
chrismiller/example-model	chrismiller	2024-06-26T11:42:19Z	null	[ "region:us" ]	null	2024-06-26T11:40:34Z	# Example model This is my model card README --- license: mit ---
quakumei/facefusion-model-pack	quakumei	2024-07-01T19:31:21Z	null	[ "region:us" ]	null	2024-06-26T11:41:31Z	Entry not found
DNA-LLM/virus-diffusion-five-d	DNA-LLM	2024-06-26T11:55:39Z	null	[ "tensorboard", "region:us" ]	null	2024-06-26T11:45:25Z	Entry not found
Ankit9263/zephyr-orpo-141b-A35b	Ankit9263	2024-06-26T11:47:34Z	null	[ "region:us" ]	null	2024-06-26T11:47:34Z	Entry not found
impossibleexchange/ummomm	impossibleexchange	2024-06-26T15:44:05Z	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	null	2024-06-26T11:48:29Z	--- 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).
ACCMConseils/LexieTrained	ACCMConseils	2024-06-26T12:58:15Z	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-26T11:48:29Z	--- 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: ACCMConseils - 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)
Prithwi/DreamboothProject	Prithwi	2024-06-26T11:48:57Z	null	[ "region:us" ]	null	2024-06-26T11:48:57Z	Entry not found
mekaneeky/averager-miner-assign	mekaneeky	2024-07-01T14:46:38Z	null	[ "region:us" ]	null	2024-06-26T11:49:13Z	Entry not found
RoGellert/peft-starcoder-lora-a100	RoGellert	2024-06-26T15:37:40Z	null	[ "tensorboard", "safetensors", "region:us" ]	null	2024-06-26T11:50:47Z	Entry not found
HuggingAlone/my_awesome_asr_mind_model	HuggingAlone	2024-06-26T11:53:03Z	null	[ "region:us" ]	null	2024-06-26T11:53:03Z	Entry not found
gaur3009/chatAI	gaur3009	2024-06-26T11:53:50Z	null	[ "license:unknown", "region:us" ]	null	2024-06-26T11:53:05Z	--- license: unknown ---
REILX/llava-1.5-7b-hf-meme-lora	REILX	2024-06-27T01:10:01Z	null	[ "safetensors", "llava", "lora", "en", "zh", "dataset:REILX/text-description-of-the-meme", "license:llama2", "region:us" ]	null	2024-06-26T11:57:19Z	--- license: llama2 datasets: - REILX/text-description-of-the-meme language: - en - zh tags: - llava - lora --- <style> .img-responsive { width: 100%; height: auto; } </style> ### Conclusion While significantly better at understanding and describing emotions and details in images compared to LLaVA-1.5-7b-hf, the fine-tuned model struggles with recognizing text. ### Train Loss <img src="./adapter-module/training_loss.png" alt="loss" class="img-responsive"> ### Test A comparative analysis of emoji in prompts, differents between the original model and its fine-tuned counterpart. </br> Original Model:https://huggingface.co/llava-hf/llava-1.5-7b-hf/</br> <img src="./images/original-01.JPG" alt="meme01" class="img-responsive"> <img src="./images/original-02.JPG" alt="meme02" class="img-responsive"> <img src="./images/original-03.JPG" alt="meme03" class="img-responsive"> Fine-tuned Lora Model:https://huggingface.co/REILX/llava-1.5-7b-hf-meme-lora</br> <img src="./images/lora-01.JPG" alt="meme01" class="img-responsive"> <img src="./images/lora-02.JPG" alt="meme02" class="img-responsive"> <img src="./images/lora-03.JPG" alt="meme03" class="img-responsive"> ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - cutoff_len: 2048 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 8 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 5.0
vibhanshu13/gemma-medical_qa-Finetune	vibhanshu13	2024-06-26T11:57:37Z	null	[ "region:us" ]	null	2024-06-26T11:57:37Z	Entry not found
scrapegraphai/scrapegraphcompanion_finetuning-adapter	scrapegraphai	2024-06-26T11:57:50Z	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-26T11:57:43Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: vinci00 - 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)
ellzo/results	ellzo	2024-06-26T11:59:39Z	null	[ "region:us" ]	null	2024-06-26T11:59:39Z	Entry not found
AudioLLMs/SALMONN_7B	AudioLLMs	2024-06-26T12:01:08Z	null	[ "region:us" ]	null	2024-06-26T11:59:49Z	This is the example model to allow good compatability with AudioBench.
valerielucro/mistral_gsm8k_dpo_cot_beta_0.6	valerielucro	2024-06-26T12:00:22Z	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-26T12:00:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]

PrunaAI/sambanovasystems-SambaLingo-Bulgarian-Base-bnb-4bit-smashed

PrunaAI

2024-06-26T09:34:50Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "pruna-ai", "base_model:sambanovasystems/SambaLingo-Bulgarian-Base", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2024-06-26T09:32:40Z

--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: sambanovasystems/SambaLingo-Bulgarian-Base 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/CP4VSgck) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) **Frequently Asked Questions** - ***How does the compression work?*** The model is compressed with llm-int8. - ***How does the model quality change?*** The quality of the model output might vary compared to the base model. - ***How is the model efficiency evaluated?*** These results were obtained 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 sambanovasystems/SambaLingo-Bulgarian-Base installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install transformers accelerate bitsandbytes>0.37.0 ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("PrunaAI/sambanovasystems-SambaLingo-Bulgarian-Base-bnb-4bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SambaLingo-Bulgarian-Base") 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 sambanovasystems/SambaLingo-Bulgarian-Base 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).

a414166402/depth-anything-v2-small

a414166402

2024-06-26T09:32:44Z

0

null

[ "region:us" ]

null

2024-06-26T09:32:44Z

Entry not found

bsmani/git-base-caption

bsmani

2024-06-26T09:34:36Z

0

null

[ "region:us" ]

null

2024-06-26T09:34:36Z

Entry not found

n1ra/phi-aid-GGUF

n1ra

2024-06-26T09:36:15Z

0

null

[ "region:us" ]

null

2024-06-26T09:36:15Z

Entry not found

triplee/supernatural_dataset_model

triplee

2024-06-26T09:40:13Z

0

transformers

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

null

2024-06-26T09:37:41Z

--- 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]

lolloawdlnjawndjkw/model

lolloawdlnjawndjkw

2024-06-26T09:37:46Z

0

null

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

null

2024-06-26T09:37:46Z

--- license: apache-2.0 ---

euhaq/repo_name

euhaq

2024-06-26T12:08:14Z

0

null

[ "text-classification", "region:us" ]

text-classification

2024-06-26T09:38:09Z

--- pipeline_tag: text-classification ---

PrunaAI/sambanovasystems-SambaLingo-Bulgarian-Base-HQQ-1bit-smashed

PrunaAI

2024-06-26T09:43:05Z

0

transformers

[ "transformers", "llama", "text-generation", "pruna-ai", "base_model:sambanovasystems/SambaLingo-Bulgarian-Base", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-06-26T09:42:11Z

--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: sambanovasystems/SambaLingo-Bulgarian-Base 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/CP4VSgck) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) **Frequently Asked Questions** - ***How does the compression work?*** The model is compressed with hqq. - ***How does the model quality change?*** The quality of the model output might vary compared to the base model. - ***How is the model efficiency evaluated?*** These results were obtained 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 sambanovasystems/SambaLingo-Bulgarian-Base installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install hqq ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer from hqq.engine.hf import HQQModelForCausalLM from hqq.models.hf.base import AutoHQQHFModel try: model = HQQModelForCausalLM.from_quantized("PrunaAI/sambanovasystems-SambaLingo-Bulgarian-Base-HQQ-1bit-smashed", device_map='auto') except: model = AutoHQQHFModel.from_quantized("PrunaAI/sambanovasystems-SambaLingo-Bulgarian-Base-HQQ-1bit-smashed") tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SambaLingo-Bulgarian-Base") 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 sambanovasystems/SambaLingo-Bulgarian-Base 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/sambanovasystems-SambaLingo-Bulgarian-Base-HQQ-2bit-smashed

PrunaAI

2024-06-26T09:43:38Z

0

transformers

[ "transformers", "llama", "text-generation", "pruna-ai", "base_model:sambanovasystems/SambaLingo-Bulgarian-Base", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-06-26T09:42:12Z

--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: sambanovasystems/SambaLingo-Bulgarian-Base 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/CP4VSgck) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) **Frequently Asked Questions** - ***How does the compression work?*** The model is compressed with hqq. - ***How does the model quality change?*** The quality of the model output might vary compared to the base model. - ***How is the model efficiency evaluated?*** These results were obtained 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 sambanovasystems/SambaLingo-Bulgarian-Base installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install hqq ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer from hqq.engine.hf import HQQModelForCausalLM from hqq.models.hf.base import AutoHQQHFModel try: model = HQQModelForCausalLM.from_quantized("PrunaAI/sambanovasystems-SambaLingo-Bulgarian-Base-HQQ-2bit-smashed", device_map='auto') except: model = AutoHQQHFModel.from_quantized("PrunaAI/sambanovasystems-SambaLingo-Bulgarian-Base-HQQ-2bit-smashed") tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SambaLingo-Bulgarian-Base") 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 sambanovasystems/SambaLingo-Bulgarian-Base 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).

anyMODE/realMODE_900M

anyMODE

2024-06-26T10:07:02Z

0

diffusers

[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "full", "pixart", "pixart sigma", "base_model:ptx0/pixart-900m-1024-ft-large", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2024-06-26T09:46:48Z

--- license: creativeml-openrail-m base_model: ptx0/pixart-900m-1024-ft-large tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - full - pixart - pixart sigma inference: false library_name: diffusers pipeline_tag: text-to-image --- # realMODE PixArt Sigma 900M v1 ## Inference ### ComfyUI - Download model file `transformer/diffusion_pytorch_model.safetensors` and put into `ComfyUI/models/checkpoints` - Use ExtraModels node: https://github.com/city96/ComfyUI_ExtraModels?tab=readme-ov-file#pixart ![image/png](https://cdn-uploads.huggingface.co/production/uploads/643c7e91b409fef15e0bd11b/MJfTShin1fYOOCo4mTv2-.png) ```python import torch from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler from diffusers.models import PixArtTransformer2DModel model_id = "anyMODE/realMODE_900M" negative_prompt = "malformed, disgusting, overexposed, washed-out" pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipeline.transformer = PixArtTransformer2DModel.from_pretrained(model_id, subfolder="transformer", torch_dtype=torch.float16) pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config) pipeline.to('cuda' if torch.cuda.is_available() else 'cpu') prompt = "On the left, there is a red cube. On the right, there is a blue sphere. On top of the red cube is a dog. On top of the blue sphere is a cat" image = pipeline( prompt=prompt, negative_prompt='blurry, cropped, ugly', num_inference_steps=30, generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826), width=1024, height=1024, guidance_scale=5.5, ).images[0] image.save("output.png", format="JPEG") ```

djbp/swin-tiny-patch4-window7-224-image-classification

djbp

2024-06-26T09:48:39Z

0

null

[ "region:us" ]

null

2024-06-26T09:48:39Z

Entry not found

Studeni/llama-3-8b-bnb-4bit-robot-instruct-v2

Studeni

2024-06-26T09:49:46Z

0

null

[ "region:us" ]

null

2024-06-26T09:49:46Z

Entry not found

PrunaAI/sambanovasystems-SambaLingo-Bulgarian-Base-HQQ-4bit-smashed

PrunaAI

2024-06-26T09:52:18Z

0

transformers

[ "transformers", "llama", "text-generation", "pruna-ai", "base_model:sambanovasystems/SambaLingo-Bulgarian-Base", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-06-26T09:50:25Z

--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: sambanovasystems/SambaLingo-Bulgarian-Base 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/CP4VSgck) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) **Frequently Asked Questions** - ***How does the compression work?*** The model is compressed with hqq. - ***How does the model quality change?*** The quality of the model output might vary compared to the base model. - ***How is the model efficiency evaluated?*** These results were obtained 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 sambanovasystems/SambaLingo-Bulgarian-Base installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install hqq ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer from hqq.engine.hf import HQQModelForCausalLM from hqq.models.hf.base import AutoHQQHFModel try: model = HQQModelForCausalLM.from_quantized("PrunaAI/sambanovasystems-SambaLingo-Bulgarian-Base-HQQ-4bit-smashed", device_map='auto') except: model = AutoHQQHFModel.from_quantized("PrunaAI/sambanovasystems-SambaLingo-Bulgarian-Base-HQQ-4bit-smashed") tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SambaLingo-Bulgarian-Base") 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 sambanovasystems/SambaLingo-Bulgarian-Base 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).

Vash1989/SimoPresLora

Vash1989

2024-06-26T09:53:05Z

0

null

[ "tensorboard", "region:us" ]

null

2024-06-26T09:53:02Z

Entry not found

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

amiune

2024-06-26T09:53:50Z

0

null

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

reinforcement-learning

2024-06-26T09:53:48Z

--- 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="amiune/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"]) ```

amiune/q-taxi-v3

amiune

2024-06-26T09:55:11Z

0

null

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

reinforcement-learning

2024-06-26T09:55:09Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 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="amiune/q-taxi-v3", 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"]) ```

ArtNikNov/Piknik_Picnic_Edmund_Shklyarskiy

ArtNikNov

2024-06-28T13:20:17Z

0

null

[ "region:us" ]

null

2024-06-26T09:55:10Z

Entry not found

PradyumSomebody/finetunedLlamaTest9

PradyumSomebody

2024-06-26T09:56:16Z

0

peft

[ "peft", "region:us" ]

null

2024-06-26T09:56:11Z

--- 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 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 - PEFT 0.4.0

bsmani/git-base-caption1

bsmani

2024-06-26T09:57:25Z

0

null

[ "region:us" ]

null

2024-06-26T09:57:25Z

Entry not found

shubh-2896/RAG_Financial_Document

shubh-2896

2024-06-26T09:57:54Z

0

null

[ "region:us" ]

null

2024-06-26T09:57:54Z

Entry not found

PrunaAI/sambanovasystems-SambaLingo-Bulgarian-Base-QUANTO-int2bit-smashed

PrunaAI

2024-07-01T08:00:46Z

0

transformers

[ "transformers", "pruna-ai", "base_model:sambanovasystems/SambaLingo-Bulgarian-Base", "endpoints_compatible", "region:us" ]

null

2024-06-26T09:57:58Z

--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: sambanovasystems/SambaLingo-Bulgarian-Base 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/CP4VSgck) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) **Frequently Asked Questions** - ***How does the compression work?*** The model is compressed with 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 sambanovasystems/SambaLingo-Bulgarian-Base 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/sambanovasystems-SambaLingo-Bulgarian-Base-QUANTO-int2bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SambaLingo-Bulgarian-Base") 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 sambanovasystems/SambaLingo-Bulgarian-Base 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).

bezzam/digicam-celeba-unet2M-mwdn-6M

bezzam

2024-06-26T09:59:53Z

0

null

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

null

2024-06-26T09:59:25Z

--- license: mit ---

Pandita-IA/ppo-LunarLanderScratch-v2

Pandita-IA

2024-06-26T10:46:01Z

0

null

[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]

reinforcement-learning

2024-06-26T10:01:30Z

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -32.16 +/- 82.95 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 2000000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'Pandita-IA/ppo-LunarLanderScratch-v2' 'batch_size': 512 'minibatch_size': 128} ```

Abhilash-12/youtube-video-summarizer

Abhilash-12

2024-06-26T10:02:52Z

0

null

[ "region:us" ]

null

2024-06-26T10:02:52Z

Entry not found

rhemzypm/BERT_klasifikasi_risiko

rhemzypm

2024-06-27T08:25:17Z

0

null

[ "region:us" ]

null

2024-06-26T10:04:02Z

metadata title: Risk Classification IndoBERT emoji: 🌍 colorFrom: indigo colorTo: indigo sdk: gradio sdk_version: 3.45.2 app_file: app.py pinned: false license: mit

benmajor27/whisper-small-hun

benmajor27

2024-06-26T10:06:40Z

0

null

[ "region:us" ]

null

2024-06-26T10:06:40Z

Entry not found

xahilmalik/llama-3-8b-chat-acebot-4

xahilmalik

2024-06-26T10:08:37Z

0

transformers

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

null

2024-06-26T10:08:29Z

--- 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]

Amirreza12/amirreza

Amirreza12

2024-06-26T10:10:01Z

0

null

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

null

2024-06-26T10:10:00Z

--- license: openrail ---

abhishektandon/sd-naruto-model

abhishektandon

2024-06-26T10:11:29Z

0

null

[ "region:us" ]

null

2024-06-26T10:11:29Z

Entry not found

txt2shivam/gpt2

txt2shivam

2024-06-26T10:12:44Z

0

null

[ "region:us" ]

null

2024-06-26T10:12:44Z

Entry not found

fearvel/kemono-jihen-kabane-pony-xl

fearvel

2024-06-26T10:14:56Z

0

null

[ "stable-diffusion", "text-to-image", "StableDiffusionPipeline", "lora", "license:other", "region:us" ]

text-to-image

2024-06-26T10:14:53Z

--- tags: - stable-diffusion - text-to-image - StableDiffusionPipeline - lora license: other --- ## Model ![pipeline](00053-2041176827.png)

hieupth/blueforest

hieupth

2024-06-28T13:36:04Z

0

null

[ "onnx", "region:us" ]

null

2024-06-26T10:15:41Z

Entry not found

abhishektandon/naruto-lora

abhishektandon

2024-06-26T10:15:51Z

0

null

[ "region:us" ]

null

2024-06-26T10:15:51Z

Entry not found

EdwardSpaeth/my-awesome-model

EdwardSpaeth

2024-06-26T10:17:12Z

0

transformers

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

null

2024-06-26T10:17:01Z

--- 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]

sergeipetrov/swin2SR-classical-sr-x2-64-IE-distr

sergeipetrov

2024-06-26T12:51:40Z

0

null

[ "endpoints_compatible", "region:us" ]

null

2024-06-26T10:17:10Z

Entry not found

JulietteAV/XLM-Letters-Wiki

JulietteAV

2024-06-26T10:20:18Z

0

null

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

null

2024-06-26T10:20:18Z

--- license: unknown ---

BluBry/NeoModel

BluBry

2024-06-26T10:21:54Z

0

null

[ "license:llama2", "region:us" ]

null

2024-06-26T10:21:54Z

--- license: llama2 ---

jddllwqa/Qwen-Qwen1.5-0.5B-1719397490

jddllwqa

2024-06-26T10:24:50Z

0

null

[ "region:us" ]

null

2024-06-26T10:24:50Z

Entry not found

mohan11111/tinyllama8bit80k_new-GGUF

mohan11111

2024-06-26T10:25:18Z

0

null

[ "region:us" ]

null

2024-06-26T10:25:17Z

Entry not found

Boostaro155/Pharma42

Boostaro155

2024-06-26T10:27:21Z

0

null

[ "region:us" ]

null

2024-06-26T10:26:30Z

# Pharma Flex XR Deutschland : Pharma Flex Erfahrungen Test, Einnahme Zutaten Preis Pharma Flex XR Deutschland Produkte wie Pharma Flex Rx werden mit einer Vielzahl wichtiger Verbindungen hergestellt, die zusammenwirken, um Gelenkschmerzen und Entzündungen zu lindern. Wir haben jedoch festgestellt, dass Pharma Flex Rx einige wichtige Inhaltsstoffe fehlen, die dieses Nahrungsergänzungsmittel viel wirksamer machen würden. ## **[Klicken Sie hier, um Pharma Flex XR jetzt auf der offiziellen Website zu kaufen](https://deutschlandbuzz.de/pharma-flex-xr-de)** ## Beschreibung PharmaFlex Rx wurde für diejenigen entwickelt, die eine natürliche Lösung zur Linderung von Gelenkbeschwerden, zur Unterstützung der Muskelregeneration, zur Beschleunigung der Gelenkreparatur und zur Stärkung des Bindegewebes suchen. Dieses einzigartige Nahrungsergänzungsmittel wurde entwickelt, um die Herausforderungen von Gelenkproblemen anzugehen und bietet die Möglichkeit, ein aktives Leben ohne Schmerzen zu führen. Wenn Sie unter Gelenkproblemen leiden und nach einer wirksamen Lösung suchen, könnte PharmaFlex Rx die Antwort sein. Anwendungsgebiete Gelenkschmerzen Arthritis Arthrose Gelenksteifheit Einschränkung der Mobilität Schwellungen und Entzündungen der Gelenke Knorpelschäden Muskelschwäche und -schmerzen in der Umgebung der Gelenke ## PharmaFlex Rx – Wirkungsweise Die Wirkung von PharmaFlex Rx beruht auf einer einzigartigen Formel aus natürlichen Inhaltsstoffen, die synergistisch wirken, um die Gelenke zu unterstützen. PharmaFlex Rx – Inhaltsstoffe und Wirkstoff Glucosaminsulfat Kurkuma-Extrakt MSM (Methylsulfonylmethan) Bromelain PharmaFlex Rx – Effekte – Auswirkungen Verringerung der Gelenkschmerzen: Die Therapie hilft dabei, Gelenkschmerzen zu lindern, sodass die Betroffenen weniger Schmerzen verspüren. Verbesserte Mobilität: Durch die Behandlung wird die Beweglichkeit der Gelenke erhöht, was eine bessere Bewegungsfreiheit ermöglicht. Beschleunigte Gelenkreparatur: Der Heilungsprozess der Gelenke wird durch die Therapie beschleunigt, sodass Verletzungen schneller heilen. Stärkung des Bindegewebes: Die Therapie stärkt das Bindegewebe, was zur Stabilität und Unterstützung der Gelenke beiträgt. ## **[Klicken Sie hier, um Pharma Flex XR jetzt auf der offiziellen Website zu kaufen](https://deutschlandbuzz.de/pharma-flex-xr-de)**

alokhom/aimodel

alokhom

2024-06-26T10:27:59Z

0

null

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

null

2024-06-26T10:27:59Z

--- license: mit ---

rithwik19/scootervqa_blip2

rithwik19

2024-06-26T11:46:19Z

0

null

[ "safetensors", "region:us" ]

null

2024-06-26T10:28:59Z

--- {} ---

Grayx/john_paul_van_damme_32

Grayx

2024-06-26T10:30:45Z

0

null

[ "region:us" ]

null

2024-06-26T10:30:32Z

Entry not found

ldd12/flowmodel

ldd12

2024-06-26T10:31:08Z

0

null

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

null

2024-06-26T10:31:08Z

--- license: apache-2.0 ---

Grayx/john_paul_van_damme_33

Grayx

2024-06-26T10:33:05Z

0

null

[ "region:us" ]

null

2024-06-26T10:32:50Z

Entry not found

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

execbat

2024-06-26T10:35:51Z

0

null

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

reinforcement-learning

2024-06-26T10:35:49Z

--- 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="execbat/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"]) ```

CileCe/cars

CileCe

2024-06-26T10:38:09Z

0

null

[ "region:us" ]

null

2024-06-26T10:38:09Z

Entry not found

Grayx/john_paul_van_damme_34

Grayx

2024-06-26T10:40:09Z

0

null

[ "region:us" ]

null

2024-06-26T10:39:58Z

Entry not found

Abolfazl87/shaadmeehr

Abolfazl87

2024-06-26T10:40:29Z

0

null

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

null

2024-06-26T10:40:29Z

--- license: openrail ---

florinshen/Gamba

florinshen

2024-06-27T09:23:26Z

0

null

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

null

2024-06-26T10:40:32Z

--- license: cc ---

Hoshiraku/llama-7b-Preprocessed

Hoshiraku

2024-06-26T10:47:01Z

0

null

[ "region:us" ]

null

2024-06-26T10:47:01Z

Entry not found

rydevdev/example-model

rydevdev

2024-06-26T11:04:46Z

0

null

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

null

2024-06-26T10:48:09Z

--- license: mit ---

Ram2085/example-model

Ram2085

2024-06-26T10:57:29Z

0

null

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

null

2024-06-26T10:57:29Z

--- license: mit ---

patruff/probablyBadFineTune11B

patruff

2024-06-26T10:59:59Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:Sao10K/Fimbulvetr-11B-v2", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-06-26T10:59:26Z

--- base_model: Sao10K/Fimbulvetr-11B-v2 language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** patruff - **License:** apache-2.0 - **Finetuned from model :** Sao10K/Fimbulvetr-11B-v2 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)

cherifkhalifah/Tounsify-v0.3

cherifkhalifah

2024-06-26T11:00:53Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "marian", "text2text-generation", "generated_from_trainer", "base_model:marefa-nlp/marefa-mt-en-ar", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2024-06-26T11:00:02Z

--- base_model: marefa-nlp/marefa-mt-en-ar license: apache-2.0 metrics: - bleu tags: - generated_from_trainer model-index: - name: Tounsify-v0.3 results: [] ---  # Tounsify-v0.3 This model is a fine-tuned version of [marefa-nlp/marefa-mt-en-ar](https://huggingface.co/marefa-nlp/marefa-mt-en-ar) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7357 - Bleu: 14.3631 - Gen Len: 7.5667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 8 | 3.9993 | 8.8788 | 9.4 | | No log | 2.0 | 16 | 3.7357 | 14.3631 | 7.5667 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

psiborgtechnologies/smart-agriculture-system

psiborgtechnologies

2024-06-26T11:05:26Z

0

null

[ "region:us" ]

null

2024-06-26T11:00:35Z

PsiBorg's smart agriculture system represents a revolutionary leap in farming technology, designed to enhance productivity, sustainability, and efficiency. At its core, the system integrates advanced IoT sensors that monitor soil moisture, temperature, and nutrient levels in real-time. These sensors provide farmers with precise data, enabling them to make informed decisions about irrigation, fertilization, and crop management. Farmers can access all this information through a user-friendly mobile app, which provides alerts and actionable insights, ensuring they stay connected to their fields from anywhere. The app also offers weather forecasting and market trends analysis to help farmers plan their activities and sales strategies more effectively. PsiBorg's smart agriculture system not only increases productivity and reduces costs but also promotes sustainable farming practices by optimizing resource use and minimizing environmental impact. With its cutting-edge technology, PsiBorg is paving the way for the future of agriculture. Read More: https://psiborg.in/smart-agricultural-system/

athena913/test_model

athena913

2024-06-26T11:00:40Z

0

null

[ "region:us" ]

null

2024-06-26T11:00:40Z

Entry not found

Ganeshkumar34/llama3-8b-kovaion_cpp_f16

Ganeshkumar34

2024-06-26T11:00:48Z

0

transformers

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

null

2024-06-26T11:00:48Z

--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf --- # Uploaded model - **Developed by:** Ganeshkumar34 - **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)

Grayx/john_paul_van_damme_35

Grayx

2024-06-26T11:05:18Z

0

null

[ "region:us" ]

null

2024-06-26T11:05:07Z

Entry not found

rahiakela/layoutlmv3-finance-doc-classification

rahiakela

2024-06-26T11:10:42Z

0

null

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

null

2024-06-26T11:10:42Z

--- license: apache-2.0 ---

ViniciusWellington/teste

ViniciusWellington

2024-06-26T11:11:35Z

0

null

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

null

2024-06-26T11:11:35Z

--- license: apache-2.0 ---

Kathernie/vasista_m-tamil-r_moe

Kathernie

2024-07-02T03:55:58Z

0

null

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

null

2024-06-26T11:12:49Z

Entry not found

PradyumSomebody/finetunedLlamaTest2.0

PradyumSomebody

2024-06-26T11:15:53Z

0

peft

[ "peft", "region:us" ]

null

2024-06-26T11:15:50Z

--- 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 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 - PEFT 0.4.0

MubarakB/wav2vec2-large-xls-r-300m-zu-1hr-v8

MubarakB

2024-06-26T11:17:39Z

0

null

[ "region:us" ]

null

2024-06-26T11:17:39Z

Entry not found

PrunaAI/sambanovasystems-SambaLingo-Bulgarian-Base-QUANTO-int8bit-smashed

PrunaAI

2024-07-01T07:59:22Z

0

transformers

[ "transformers", "pruna-ai", "base_model:sambanovasystems/SambaLingo-Bulgarian-Base", "endpoints_compatible", "region:us" ]

null

2024-06-26T11:18:46Z

--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: sambanovasystems/SambaLingo-Bulgarian-Base 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/CP4VSgck) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) **Frequently Asked Questions** - ***How does the compression work?*** The model is compressed with 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 sambanovasystems/SambaLingo-Bulgarian-Base 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/sambanovasystems-SambaLingo-Bulgarian-Base-QUANTO-int8bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SambaLingo-Bulgarian-Base") 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 sambanovasystems/SambaLingo-Bulgarian-Base 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/sambanovasystems-SambaLingo-Bulgarian-Base-QUANTO-int4bit-smashed

PrunaAI

2024-07-01T07:59:12Z

0

transformers

[ "transformers", "pruna-ai", "base_model:sambanovasystems/SambaLingo-Bulgarian-Base", "endpoints_compatible", "region:us" ]

null

2024-06-26T11:18:50Z

--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: sambanovasystems/SambaLingo-Bulgarian-Base 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/CP4VSgck) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) **Frequently Asked Questions** - ***How does the compression work?*** The model is compressed with 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 sambanovasystems/SambaLingo-Bulgarian-Base 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/sambanovasystems-SambaLingo-Bulgarian-Base-QUANTO-int4bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SambaLingo-Bulgarian-Base") 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 sambanovasystems/SambaLingo-Bulgarian-Base 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).

CHARKA/Meta-Llama-3-8B-InstructSmallDataV0

CHARKA

2024-06-26T11:22:31Z

0

null

[ "region:us" ]

null

2024-06-26T11:22:31Z

Entry not found

Masallah/onetry

Masallah

2024-06-26T11:44:04Z

0

null

[ "region:us" ]

null

2024-06-26T11:25:02Z

Entry not found

MubarakB/wav2vec2-large-xls-r-300m-zulu-vocab-v1

MubarakB

2024-06-26T11:25:17Z

0

transformers

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

null

2024-06-26T11:25:17Z

--- 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]

Grayx/john_paul_van_damme_36

Grayx

2024-06-26T11:25:44Z

0

null

[ "region:us" ]

null

2024-06-26T11:25:28Z

Entry not found

PrunaAI/sambanovasystems-SambaLingo-Bulgarian-Base-QUANTO-float8bit-smashed

PrunaAI

2024-07-01T08:01:15Z

0

transformers

[ "transformers", "pruna-ai", "base_model:sambanovasystems/SambaLingo-Bulgarian-Base", "endpoints_compatible", "region:us" ]

null

2024-06-26T11:26:05Z

--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: sambanovasystems/SambaLingo-Bulgarian-Base 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/CP4VSgck) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) **Frequently Asked Questions** - ***How does the compression work?*** The model is compressed with 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 sambanovasystems/SambaLingo-Bulgarian-Base 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/sambanovasystems-SambaLingo-Bulgarian-Base-QUANTO-float8bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SambaLingo-Bulgarian-Base") 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 sambanovasystems/SambaLingo-Bulgarian-Base 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).

nglguarino/my-nlp-gemma-model

nglguarino

2024-06-26T11:26:56Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/gemma-2b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-06-26T11:26:33Z

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

hyungkook/koalpaca-polyglot-12.8b-jun

hyungkook

2024-06-27T00:29:37Z

0

transformers

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

null

2024-06-26T11:27:44Z

--- 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]

SectorZero/SectorZero

SectorZero

2024-06-26T13:33:42Z

0

null

[ "region:us" ]

null

2024-06-26T11:27:48Z

Entry not found

qwcarol/Teste

qwcarol

2024-06-26T11:29:24Z

0

null

[ "region:us" ]

null

2024-06-26T11:29:24Z

Entry not found

Vitron/vitron-base

Vitron

2024-07-01T06:00:40Z

0

transformers

[ "transformers", "llava", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-06-26T11:30:14Z

--- license: apache-2.0 ---

Enkeeper/Mistral_10.7B_TaskInstruct_Unsloth_LORA

Enkeeper

2024-06-26T11:33:09Z

0

null

[ "safetensors", "license:cc-by-nc-2.0", "region:us" ]

null

2024-06-26T11:30:55Z

--- license: cc-by-nc-2.0 ---

n1ra/phi3-aid-v3

n1ra

2024-06-26T11:40:19Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "base_model:microsoft/Phi-3-mini-4k-instruct", "license:other", "endpoints_compatible", "region:us" ]

text-generation

2024-06-26T11:34:37Z

--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers base_model: microsoft/Phi-3-mini-4k-instruct widget: - messages: - role: user content: What is your favorite condiment? license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```

Reihaneh/wav2vec2_fy_nl_de_lid_common_voice_52

Reihaneh

2024-06-26T12:33:28Z

0

transformers

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

null

2024-06-26T11:35:39Z

--- 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]

sgonzalezsilot/whisper-tiny-spanish-es-Nemo_unique_2024-06-26_11-36-34

sgonzalezsilot

2024-06-26T11:36:34Z

0

null

[ "region:us" ]

null

2024-06-26T11:36:34Z

Entry not found

execbat/q-Taxi-v3

execbat

2024-06-26T11:36:58Z

0

null

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

reinforcement-learning

2024-06-26T11:36:56Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 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="execbat/q-Taxi-v3", 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"]) ```

sgonzalezsilot/whisper-tiny-spanish-es-Nemo_unified_2024-06-26_11-36-57

sgonzalezsilot

2024-06-26T11:36:57Z

0

null

[ "region:us" ]

null

2024-06-26T11:36:57Z

Entry not found

XxLOLxX/donald_duck_LoRA

XxLOLxX

2024-06-26T11:41:03Z

0

null

[ "tensorboard", "region:us" ]

null

2024-06-26T11:40:25Z

Entry not found

chrismiller/example-model

chrismiller

2024-06-26T11:42:19Z

0

null

[ "region:us" ]

null

2024-06-26T11:40:34Z

# Example model This is my model card README --- license: mit ---

quakumei/facefusion-model-pack

quakumei

2024-07-01T19:31:21Z

0

null

[ "region:us" ]

null

2024-06-26T11:41:31Z

Entry not found

DNA-LLM/virus-diffusion-five-d

DNA-LLM

2024-06-26T11:55:39Z

0

null

[ "tensorboard", "region:us" ]

null

2024-06-26T11:45:25Z

Entry not found

Ankit9263/zephyr-orpo-141b-A35b

Ankit9263

2024-06-26T11:47:34Z

0

null

[ "region:us" ]

null

2024-06-26T11:47:34Z

Entry not found

impossibleexchange/ummomm

impossibleexchange

2024-06-26T15:44:05Z

0

null

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

null

2024-06-26T11:48:29Z

--- 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).

ACCMConseils/LexieTrained

ACCMConseils

2024-06-26T12:58:15Z

0

transformers

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

null

2024-06-26T11:48:29Z

--- 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:** ACCMConseils - **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)

Prithwi/DreamboothProject

Prithwi

2024-06-26T11:48:57Z

0

null

[ "region:us" ]

null

2024-06-26T11:48:57Z

Entry not found

mekaneeky/averager-miner-assign

mekaneeky

2024-07-01T14:46:38Z

0

null

[ "region:us" ]

null

2024-06-26T11:49:13Z

Entry not found

RoGellert/peft-starcoder-lora-a100

RoGellert

2024-06-26T15:37:40Z

0

null

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

null

2024-06-26T11:50:47Z

Entry not found

HuggingAlone/my_awesome_asr_mind_model

HuggingAlone

2024-06-26T11:53:03Z

0

null

[ "region:us" ]

null

2024-06-26T11:53:03Z

Entry not found

gaur3009/chatAI

gaur3009

2024-06-26T11:53:50Z

0

null

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

null

2024-06-26T11:53:05Z

--- license: unknown ---

REILX/llava-1.5-7b-hf-meme-lora

REILX

2024-06-27T01:10:01Z

0

null

[ "safetensors", "llava", "lora", "en", "zh", "dataset:REILX/text-description-of-the-meme", "license:llama2", "region:us" ]

null

2024-06-26T11:57:19Z

--- license: llama2 datasets: - REILX/text-description-of-the-meme language: - en - zh tags: - llava - lora --- <style> .img-responsive { width: 100%; height: auto; } </style> ### Conclusion While significantly better at understanding and describing emotions and details in images compared to LLaVA-1.5-7b-hf, the fine-tuned model struggles with recognizing text. ### Train Loss <img src="./adapter-module/training_loss.png" alt="loss" class="img-responsive"> ### Test A comparative analysis of emoji in prompts, differents between the original model and its fine-tuned counterpart. </br> Original Model:https://huggingface.co/llava-hf/llava-1.5-7b-hf/</br> <img src="./images/original-01.JPG" alt="meme01" class="img-responsive"> <img src="./images/original-02.JPG" alt="meme02" class="img-responsive"> <img src="./images/original-03.JPG" alt="meme03" class="img-responsive"> Fine-tuned Lora Model:https://huggingface.co/REILX/llava-1.5-7b-hf-meme-lora</br> <img src="./images/lora-01.JPG" alt="meme01" class="img-responsive"> <img src="./images/lora-02.JPG" alt="meme02" class="img-responsive"> <img src="./images/lora-03.JPG" alt="meme03" class="img-responsive"> ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - cutoff_len: 2048 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 8 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 5.0

vibhanshu13/gemma-medical_qa-Finetune

vibhanshu13

2024-06-26T11:57:37Z

0

null

[ "region:us" ]

null

2024-06-26T11:57:37Z

Entry not found

scrapegraphai/scrapegraphcompanion_finetuning-adapter

scrapegraphai

2024-06-26T11:57:50Z

0

transformers

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

null

2024-06-26T11:57:43Z

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - **Developed by:** vinci00 - **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)

ellzo/results

ellzo

2024-06-26T11:59:39Z

0

null

[ "region:us" ]

null

2024-06-26T11:59:39Z

Entry not found

AudioLLMs/SALMONN_7B

AudioLLMs

2024-06-26T12:01:08Z

0

null

[ "region:us" ]

null

2024-06-26T11:59:49Z

This is the example model to allow good compatability with AudioBench.

valerielucro/mistral_gsm8k_dpo_cot_beta_0.6

valerielucro

2024-06-26T12:00:22Z

0

transformers

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

null

2024-06-26T12:00:15Z

--- 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]