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train · 844k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-26 12:28:48	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 498 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-26 12:28:16	card stringlengths 11 1.01M
aboros98/kepler1	aboros98	2024-02-23T14:29:25Z	107	0	transformers	[ "transformers", "pytorch", "gemma", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T10:50:04Z	\| Metric \| Value \| \|-----------------------\|---------------------------\| \| Average \| - \| \| ARC \| 36.01 \| \| ARC Easy \| 59.60 \| \| BoolQ \| 71.87 \| \| HellaSwag \| 58.07 \| \| OpenBookQA \| 33.80 \| \| PiQA \| 75.24 \| \| Winogrande \| 56.20 \| \|-----------------------\|---------------------------\| \| MMLU \| 38.63 \| \| GSM8K \| \| \| Truthful QA \| 45.76 \|
MaziyarPanahi/Ultra-Smaug-125B-v0.1-GGUF	MaziyarPanahi	2024-02-23T14:27:57Z	34	5	transformers	[ "transformers", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "safetensors", "llama", "text-generation", "license:agpl-3.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us", "base_model:MaziyarPanahi/Ultra-Smaug-125B-v0.1", "base_model:finetune:MaziyarPanahi/Ultra-Smaug-125B-v0.1" ]	text-generation	2024-02-21T09:04:07Z	--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - transformers - safetensors - llama - text-generation - license:agpl-3.0 - autotrain_compatible - endpoints_compatible - text-generation-inference - region:us - text-generation model_name: Ultra-Smaug-125B-v0.1-GGUF base_model: MaziyarPanahi/Ultra-Smaug-125B-v0.1 inference: false model_creator: MaziyarPanahi pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/Ultra-Smaug-125B-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Ultra-Smaug-125B-v0.1-GGUF) - Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi) - Original model: [MaziyarPanahi/Ultra-Smaug-125B-v0.1](https://huggingface.co/MaziyarPanahi/Ultra-Smaug-125B-v0.1) ## Description [MaziyarPanahi/Ultra-Smaug-125B-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Ultra-Smaug-125B-v0.1-GGUF) contains GGUF format model files for [MaziyarPanahi/Ultra-Smaug-125B-v0.1](https://huggingface.co/MaziyarPanahi/Ultra-Smaug-125B-v0.1). ## How to use Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models: ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ### Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: [MaziyarPanahi/Ultra-Smaug-125B-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Ultra-Smaug-125B-v0.1-GGUF) and below it, a specific filename to download, such as: Ultra-Smaug-125B-v0.1-GGUF.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download MaziyarPanahi/Ultra-Smaug-125B-v0.1-GGUF Ultra-Smaug-125B-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` </details> <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download [MaziyarPanahi/Ultra-Smaug-125B-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Ultra-Smaug-125B-v0.1-GGUF) --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download MaziyarPanahi/Ultra-Smaug-125B-v0.1-GGUF Ultra-Smaug-125B-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m Ultra-Smaug-125B-v0.1-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./Ultra-Smaug-125B-v0.1-GGUF.Q4_K_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./Ultra-Smaug-125B-v0.1-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
wjworld/chaoyang_df_0_2_2_colon_slide	wjworld	2024-02-23T14:25:02Z	27	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "stable-diffusion", "stable-diffusion-diffusers", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-02-23T13:41:34Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - stable-diffusion - stable-diffusion-diffusers - text-to-image - dreambooth - stable-diffusion - stable-diffusion-diffusers inference: true base_model: CompVis/stable-diffusion-v1-4 instance_prompt: 'A Photo of a colon section: the expert 1 labels it as normal, expert 2 labels it as adenocarcinoma, expert 3 labels it as adenocarcinoma' --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - wjworld/chaoyang_df_0_2_2_colon_slide This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on A Photo of a colon section: the expert 1 labels it as normal, expert 2 labels it as adenocarcinoma, expert 3 labels it as adenocarcinoma using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
Saimeghana2004/my-pet-dog	Saimeghana2004	2024-02-23T14:24:07Z	0	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-02-23T14:17:28Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Dog Dreambooth model trained by Saimeghana2004 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: GoX19932gAS Sample pictures of this concept: ![0](https://huggingface.co/Saimeghana2004/my-pet-dog/resolve/main/sample_images/xzg_(3).jpg) ![1](https://huggingface.co/Saimeghana2004/my-pet-dog/resolve/main/sample_images/xzg_(2).jpeg) ![2](https://huggingface.co/Saimeghana2004/my-pet-dog/resolve/main/sample_images/xzg_(1).jpeg)
lucarubini/Reinforce-carpole-v1-300iter	lucarubini	2024-02-23T14:21:23Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-02-23T14:21:13Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-carpole-v1-300iter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 479.60 +/- 44.59 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
saurabh-straive/llava_100k_finetuned	saurabh-straive	2024-02-23T14:20:14Z	0	0	null	[ "arxiv:2310.03744", "arxiv:2304.08485", "arxiv:2306.00890", "arxiv:2309.09958", "arxiv:2309.10020", "arxiv:2306.14895", "endpoints_compatible", "region:us" ]	null	2024-02-23T14:05:41Z	# 🌋 LLaVA: Large Language and Vision Assistant Visual instruction tuning towards large language and vision models with GPT-4 level capabilities. [[Project Page](https://llava-vl.github.io/)] [[Demo](https://llava.hliu.cc/)] [[Data](https://github.com/haotian-liu/LLaVA/blob/main/docs/Data.md)] [[Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)] 🤝Community Contributions: [[llama.cpp](https://github.com/ggerganov/llama.cpp/pull/3436)] [[Colab](https://github.com/camenduru/LLaVA-colab)] [[🤗Space](https://huggingface.co/spaces/badayvedat/LLaVA)] Improved Baselines with Visual Instruction Tuning [[Paper](https://arxiv.org/abs/2310.03744)] <br> [Haotian Liu](https://hliu.cc), [Chunyuan Li](https://chunyuan.li/), [Yuheng Li](https://yuheng-li.github.io/), [Yong Jae Lee](https://pages.cs.wisc.edu/~yongjaelee/) Visual Instruction Tuning (NeurIPS 2023, Oral) [[Paper](https://arxiv.org/abs/2304.08485)]<br> [Haotian Liu](https://hliu.cc), [Chunyuan Li](https://chunyuan.li/), [Qingyang Wu](https://scholar.google.ca/citations?user=HDiw-TsAAAAJ&hl=en/), [Yong Jae Lee](https://pages.cs.wisc.edu/~yongjaelee/) (Equal Contribution) <p align="center"> <a href="https://llava.hliu.cc/"><img src="images/llava_logo.png" width="50%"></a> <br> Generated by <a href="https://gligen.github.io/">GLIGEN</a> via "a cute lava llama with glasses" and box prompt </p> ## Release - [10/12] 🔥 Check out the Korean LLaVA (Ko-LLaVA), created by ETRI, who has generously supported our research! [[🤗 Demo](https://huggingface.co/spaces/etri-vilab/Ko-LLaVA)] - [10/12] LLaVA is now supported in [llama.cpp](https://github.com/ggerganov/llama.cpp/pull/3436) with 4-bit / 5-bit quantization support! - [10/11] The training data and scripts of LLaVA-1.5 are released [here](https://github.com/haotian-liu/LLaVA#train), and evaluation scripts are released [here](https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md)! - [10/5] 🔥 LLaVA-1.5 is out! Achieving SoTA on 11 benchmarks, with just simple modifications to the original LLaVA, utilizes all public data, completes training in ~1 day on a single 8-A100 node, and surpasses methods like Qwen-VL-Chat that use billion-scale data. Check out the [technical report](https://arxiv.org/abs/2310.03744), and explore the [demo](https://llava.hliu.cc/)! Models are available in [Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md). - [9/26] LLaVA is improved with reinforcement learning from human feedback (RLHF) to improve fact grounding and reduce hallucination. Check out the new SFT and RLHF checkpoints at project [[LLavA-RLHF]](https://llava-rlhf.github.io/) - [9/22] [LLaVA](https://arxiv.org/abs/2304.08485) is accepted by NeurIPS 2023 as oral presentation, and [LLaVA-Med](https://arxiv.org/abs/2306.00890) is accepted by NeurIPS 2023 Datasets and Benchmarks Track as spotlight presentation. - [9/20] We summarize our empirical study of training 33B and 65B LLaVA models in a [note](https://arxiv.org/abs/2309.09958). Further, if you are interested in the comprehensive review, evolution and trend of multimodal foundation models, please check out our recent survey paper [``Multimodal Foundation Models: From Specialists to General-Purpose Assistants''.](https://arxiv.org/abs/2309.10020) <p align="center"> <img src="https://github.com/Computer-Vision-in-the-Wild/CVinW_Readings/blob/main/images/mfm_evolution.jpeg?raw=true" width=50%/> </p> - [7/19] 🔥 We release a major upgrade, including support for LLaMA-2, LoRA training, 4-/8-bit inference, higher resolution (336x336), and a lot more. We release [LLaVA Bench](https://github.com/haotian-liu/LLaVA/blob/main/docs/LLaVA_Bench.md) for benchmarking open-ended visual chat with results from Bard and Bing-Chat. We also support and verify training with RTX 3090 and RTX A6000. Check out [LLaVA-from-LLaMA-2](https://github.com/haotian-liu/LLaVA/blob/main/docs/LLaVA_from_LLaMA2.md), and our [model zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)! - [6/26] [CVPR 2023 Tutorial](https://vlp-tutorial.github.io/) on Large Multimodal Models: Towards Building and Surpassing Multimodal GPT-4! Please check out [[Slides](https://datarelease.blob.core.windows.net/tutorial/vision_foundation_models_2023/slides/Chunyuan_cvpr2023_tutorial_lmm.pdf)] [[Notes](https://arxiv.org/abs/2306.14895)] [[YouTube](https://youtu.be/mkI7EPD1vp8)] [[Bilibli](https://www.bilibili.com/video/BV1Ng4y1T7v3/)]. - [6/11] We released the preview for the most requested feature: DeepSpeed and LoRA support! Please see documentations [here](./docs/LoRA.md). - [6/1] We released LLaVA-Med: Large Language and Vision Assistant for Biomedicine, a step towards building biomedical domain large language and vision models with GPT-4 level capabilities. Checkout the [paper](https://arxiv.org/abs/2306.00890) and [page](https://github.com/microsoft/LLaVA-Med). - [5/6] We are releasing [LLaVA-Lighting-MPT-7B-preview](https://huggingface.co/liuhaotian/LLaVA-Lightning-MPT-7B-preview), based on MPT-7B-Chat! See [here](#LLaVA-MPT-7b) for more details. - [5/2] 🔥 We are releasing LLaVA-Lighting! Train a lite, multimodal GPT-4 with just $40 in 3 hours! See [here](#train-llava-lightning) for more details. - [4/27] Thanks to the community effort, LLaVA-13B with 4-bit quantization allows you to run on a GPU with as few as 12GB VRAM! Try it out [here](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/llava). - [4/17] 🔥 We released LLaVA: Large Language and Vision Assistant. We propose visual instruction tuning, towards building large language and vision models with GPT-4 level capabilities. Checkout the [paper](https://arxiv.org/abs/2304.08485) and [demo](https://llava.hliu.cc/). <!-- <a href="https://llava.hliu.cc/"><img src="assets/demo.gif" width="70%"></a> --> [![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg)](https://github.com/tatsu-lab/stanford_alpaca/blob/main/LICENSE) [![Data License](https://img.shields.io/badge/Data%20License-CC%20By%20NC%204.0-red.svg)](https://github.com/tatsu-lab/stanford_alpaca/blob/main/DATA_LICENSE) Usage and License Notices: The data and checkpoint is intended and licensed for research use only. They are also restricted to uses that follow the license agreement of LLaMA, Vicuna and GPT-4. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes. ## Contents - [Install](#install) - [LLaVA Weights](#llava-weights) - [Demo](#Demo) - [Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md) - [Dataset](https://github.com/haotian-liu/LLaVA/blob/main/docs/Data.md) - [Train](#train) - [Evaluation](#evaluation) ## Install 1. Clone this repository and navigate to LLaVA folder ```bash git clone https://github.com/haotian-liu/LLaVA.git cd LLaVA ``` 2. Install Package ```Shell conda create -n llava python=3.10 -y conda activate llava pip install --upgrade pip # enable PEP 660 support pip install -e . ``` 3. Install additional packages for training cases ``` pip install ninja pip install flash-attn --no-build-isolation ``` ### Upgrade to latest code base ```Shell git pull pip uninstall transformers pip install -e . ``` ## LLaVA Weights Please check out our [Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md) for all public LLaVA checkpoints, and the instructions of how to use the weights. ## Demo To run our demo, you need to prepare LLaVA checkpoints locally. Please follow the instructions [here](#llava-weights) to download the checkpoints. ### Gradio Web UI To launch a Gradio demo locally, please run the following commands one by one. If you plan to launch multiple model workers to compare between different checkpoints, you only need to launch the controller and the web server ONCE. #### Launch a controller ```Shell python -m llava.serve.controller --host 0.0.0.0 --port 10000 ``` #### Launch a gradio web server. ```Shell python -m llava.serve.gradio_web_server --controller http://localhost:10000 --model-list-mode reload ``` You just launched the Gradio web interface. Now, you can open the web interface with the URL printed on the screen. You may notice that there is no model in the model list. Do not worry, as we have not launched any model worker yet. It will be automatically updated when you launch a model worker. #### Launch a model worker This is the actual worker* that performs the inference on the GPU. Each worker is responsible for a single model specified in `--model-path`. ```Shell python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path liuhaotian/llava-v1.5-13b ``` Wait until the process finishes loading the model and you see "Uvicorn running on ...". Now, refresh your Gradio web UI, and you will see the model you just launched in the model list. You can launch as many workers as you want, and compare between different model checkpoints in the same Gradio interface. Please keep the `--controller` the same, and modify the `--port` and `--worker` to a different port number for each worker. ```Shell python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port <different from 40000, say 40001> --worker http://localhost:<change accordingly, i.e. 40001> --model-path <ckpt2> ``` If you are using an Apple device with an M1 or M2 chip, you can specify the mps device by using the `--device` flag: `--device mps`. #### Launch a model worker (Multiple GPUs, when GPU VRAM <= 24GB) If the VRAM of your GPU is less than 24GB (e.g., RTX 3090, RTX 4090, etc.), you may try running it with multiple GPUs. Our latest code base will automatically try to use multiple GPUs if you have more than one GPU. You can specify which GPUs to use with `CUDA_VISIBLE_DEVICES`. Below is an example of running with the first two GPUs. ```Shell CUDA_VISIBLE_DEVICES=0,1 python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path liuhaotian/llava-v1.5-13b ``` #### Launch a model worker (4-bit, 8-bit inference, quantized) You can launch the model worker with quantized bits (4-bit, 8-bit), which allows you to run the inference with reduced GPU memory footprint, potentially allowing you to run on a GPU with as few as 12GB VRAM. Note that inference with quantized bits may not be as accurate as the full-precision model. Simply append `--load-4bit` or `--load-8bit` to the model worker command that you are executing. Below is an example of running with 4-bit quantization. ```Shell python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path liuhaotian/llava-v1.5-13b --load-4bit ``` #### Launch a model worker (LoRA weights, unmerged) You can launch the model worker with LoRA weights, without merging them with the base checkpoint, to save disk space. There will be additional loading time, while the inference speed is the same as the merged checkpoints. Unmerged LoRA checkpoints do not have `lora-merge` in the model name, and are usually much smaller (less than 1GB) than the merged checkpoints (13G for 7B, and 25G for 13B). To load unmerged LoRA weights, you simply need to pass an additional argument `--model-base`, which is the base LLM that is used to train the LoRA weights. You can check the base LLM of each LoRA weights in the [model zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md). ```Shell python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path liuhaotian/llava-v1-0719-336px-lora-vicuna-13b-v1.3 --model-base lmsys/vicuna-13b-v1.3 ``` ### CLI Inference Chat about images using LLaVA without the need of Gradio interface. It also supports multiple GPUs, 4-bit and 8-bit quantized inference. With 4-bit quantization, for our LLaVA-1.5-7B, it uses less than 8GB VRAM on a single GPU. ```Shell python -m llava.serve.cli \ --model-path liuhaotian/llava-v1.5-7b \ --image-file "https://llava-vl.github.io/static/images/view.jpg" \ --load-4bit ``` <img src="images/demo_cli.gif" width="70%"> ## Train Below is the latest training configuration for LLaVA v1.5. For legacy models, please refer to README of [this](https://github.com/haotian-liu/LLaVA/tree/v1.0.1) version for now. We'll add them in a separate doc later. LLaVA training consists of two stages: (1) feature alignment stage: use our 558K subset of the LAION-CC-SBU dataset to connect a frozen pretrained vision encoder to a frozen LLM; (2) visual instruction tuning stage: use 150K GPT-generated multimodal instruction-following data, plus around 515K VQA data from academic-oriented tasks, to teach the model to follow multimodal instructions. LLaVA is trained on 8 A100 GPUs with 80GB memory. To train on fewer GPUs, you can reduce the `per_device_train_batch_size` and increase the `gradient_accumulation_steps` accordingly. Always keep the global batch size the same: `per_device_train_batch_size` x `gradient_accumulation_steps` x `num_gpus`. ### Hyperparameters We use a similar set of hyperparameters as Vicuna in finetuning. Both hyperparameters used in pretraining and finetuning are provided below. 1. Pretraining \| Hyperparameter \| Global Batch Size \| Learning rate \| Epochs \| Max length \| Weight decay \| \| --- \| ---: \| ---: \| ---: \| ---: \| ---: \| \| LLaVA-v1.5-13B \| 256 \| 1e-3 \| 1 \| 2048 \| 0 \| 2. Finetuning \| Hyperparameter \| Global Batch Size \| Learning rate \| Epochs \| Max length \| Weight decay \| \| --- \| ---: \| ---: \| ---: \| ---: \| ---: \| \| LLaVA-v1.5-13B \| 128 \| 2e-5 \| 1 \| 2048 \| 0 \| ### Download Vicuna checkpoints (automatically) Our base model Vicuna v1.5, which is an instruction-tuned chatbot, will be downloaded automatically when you run our provided training scripts. No action is needed. ### Pretrain (feature alignment) Please download the 558K subset of the LAION-CC-SBU dataset with BLIP captions we use in the paper [here](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain). Pretrain takes around 5.5 hours for LLaVA-v1.5-13B on 8x A100 (80G), due to the increased resolution to 336px. It takes around 3.5 hours for LLaVA-v1.5-7B. Training script with DeepSpeed ZeRO-2: [`pretrain.sh`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/v1_5/pretrain.sh). - `--mm_projector_type mlp2x_gelu`: the two-layer MLP vision-language connector. - `--vision_tower openai/clip-vit-large-patch14-336`: CLIP ViT-L/14 336px. ### Visual Instruction Tuning 1. Prepare data Please download the annotation of the final mixture our instruction tuning data [llava_v1_5_mix665k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/llava_v1_5_mix665k.json), and download the images from constituting datasets: - COCO: [train2017](http://images.cocodataset.org/zips/train2017.zip) - GQA: [images](https://downloads.cs.stanford.edu/nlp/data/gqa/images.zip) - OCR-VQA: [download script](https://drive.google.com/drive/folders/1_GYPY5UkUy7HIcR0zq3ZCFgeZN7BAfm_?usp=sharing) - TextVQA: [train_val_images](https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip) - VisualGenome: [part1](https://cs.stanford.edu/people/rak248/VG_100K_2/images.zip), [part2](https://cs.stanford.edu/people/rak248/VG_100K_2/images2.zip) After downloading all of them, organize the data as follows in `./playground/data`, ``` ├── coco │ └── train2017 ├── gqa │ └── images ├── ocr_vqa │ └── images ├── textvqa │ └── train_images └── vg ├── VG_100K └── VG_100K_2 ``` 2. Start training! You may download our pretrained projectors in [Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md). It is not recommended to use legacy projectors, as they may be trained with a different version of the codebase, and if any option is off, the model will not function/train as we expected. Visual instruction tuning takes around 20 hours for LLaVA-v1.5-13B on 8x A100 (80G), due to the increased resolution to 336px. It takes around 10 hours for LLaVA-v1.5-7B on 8x A100 (40G). Training script with DeepSpeed ZeRO-3: [`finetune.sh`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/v1_5/finetune.sh). New options to note: - `--mm_projector_type mlp2x_gelu`: the two-layer MLP vision-language connector. - `--vision_tower openai/clip-vit-large-patch14-336`: CLIP ViT-L/14 336px. - `--image_aspect_ratio pad`: this pads the non-square images to square, instead of cropping them; it slightly reduces hallucination. - `--group_by_modality_length True`: this should only be used when your instruction tuning dataset contains both language (e.g. ShareGPT) and multimodal (e.g. LLaVA-Instruct). It makes the training sampler only sample a single modality (either image or language) during training, which we observe to speed up training by ~25%, and does not affect the final outcome. ## Evaluation In LLaVA-1.5, we evaluate models on a diverse set of 12 benchmarks. To ensure the reproducibility, we evaluate the models with greedy decoding. We do not evaluate using beam search to make the inference process consistent with the chat demo of real-time outputs. See [Evaluation.md](https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md). ### GPT-assisted Evaluation Our GPT-assisted evaluation pipeline for multimodal modeling is provided for a comprehensive understanding of the capabilities of vision-language models. Please see our paper for more details. 1. Generate LLaVA responses ```Shell python model_vqa.py \ --model-path ./checkpoints/LLaVA-13B-v0 \ --question-file \ playground/data/coco2014_val_qa_eval/qa90_questions.jsonl \ --image-folder \ /path/to/coco2014_val \ --answers-file \ /path/to/answer-file-our.jsonl ``` 2. Evaluate the generated responses. In our case, [`answer-file-ref.jsonl`](./playground/data/coco2014_val_qa_eval/qa90_gpt4_answer.jsonl) is the response generated by text-only GPT-4 (0314), with the context captions/boxes provided. ```Shell OPENAI_API_KEY="sk-***********************************" python llava/eval/eval_gpt_review_visual.py \ --question playground/data/coco2014_val_qa_eval/qa90_questions.jsonl \ --context llava/eval/table/caps_boxes_coco2014_val_80.jsonl \ --answer-list \ /path/to/answer-file-ref.jsonl \ /path/to/answer-file-our.jsonl \ --rule llava/eval/table/rule.json \ --output /path/to/review.json ``` 3. Summarize the evaluation results ```Shell python summarize_gpt_review.py ``` ## Citation If you find LLaVA useful for your research and applications, please cite using this BibTeX: ```bibtex @misc{liu2023improvedllava, title={Improved Baselines with Visual Instruction Tuning}, author={Liu, Haotian and Li, Chunyuan and Li, Yuheng and Lee, Yong Jae}, publisher={arXiv:2310.03744}, year={2023}, } @misc{liu2023llava, title={Visual Instruction Tuning}, author={Liu, Haotian and Li, Chunyuan and Wu, Qingyang and Lee, Yong Jae}, publisher={arXiv:2304.08485}, year={2023}, } ``` ## Acknowledgement - [Vicuna](https://github.com/lm-sys/FastChat): the codebase we built upon, and our base model Vicuna-13B that has the amazing language capabilities! ## Related Projects - [Instruction Tuning with GPT-4](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) - [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://github.com/microsoft/LLaVA-Med) - [Otter: In-Context Multi-Modal Instruction Tuning](https://github.com/Luodian/Otter) For future project ideas, please check out: - [SEEM: Segment Everything Everywhere All at Once](https://github.com/UX-Decoder/Segment-Everything-Everywhere-All-At-Once) - [Grounded-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything) to detect, segment, and generate anything by marrying [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO) and [Segment-Anything](https://github.com/facebookresearch/segment-anything).
MohamedEsa/mistral-7b	MohamedEsa	2024-02-23T14:15:20Z	77	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-02-19T14:02:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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urubatan/wikineural-multilingual-ner-conll2003-improved	urubatan	2024-02-23T14:13:23Z	0	0	null	[ "license:cc-by-nc-sa-4.0", "region:us" ]	null	2024-02-23T14:13:23Z	--- license: cc-by-nc-sa-4.0 ---
Hasanur525/mt5-deed-sum	Hasanur525	2024-02-23T14:12:05Z	104	0	transformers	[ "transformers", "tensorboard", "safetensors", "mt5", "text2text-generation", "generated_from_trainer", "base_model:Hasanur525/deed_summarization_mt5_version_1", "base_model:finetune:Hasanur525/deed_summarization_mt5_version_1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T14:02:10Z	--- license: apache-2.0 base_model: Hasanur525/deed_summarization_mt5_version_1 tags: - generated_from_trainer metrics: - rouge model-index: - name: mt5-deed-sum 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. --> # mt5-deed-sum This model is a fine-tuned version of [Hasanur525/deed_summarization_mt5_version_1](https://huggingface.co/Hasanur525/deed_summarization_mt5_version_1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4953 - Rouge1: 1.5754 - Rouge2: 1.087 - Rougel: 1.5005 - Rougelsum: 1.4211 - Gen Len: 310.6981 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5000 - num_epochs: 22 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:--------:\| \| 0.0915 \| 1.0 \| 375 \| 0.5844 \| 0.7311 \| 0.4193 \| 0.7311 \| 0.7311 \| 289.3396 \| \| 0.9545 \| 2.0 \| 750 \| 0.5858 \| 0.6289 \| 0.444 \| 0.6289 \| 0.6289 \| 291.5912 \| \| 0.8026 \| 3.0 \| 1125 \| 0.5817 \| 1.1119 \| 0.6733 \| 1.067 \| 1.0428 \| 295.0692 \| \| 0.2525 \| 4.0 \| 1500 \| 0.5698 \| 0.7311 \| 0.4193 \| 0.7311 \| 0.7311 \| 299.7987 \| \| 1.5794 \| 5.0 \| 1875 \| 0.5685 \| 0.8096 \| 0.4733 \| 0.7714 \| 0.7549 \| 286.0126 \| \| 0.0558 \| 6.0 \| 2250 \| 0.5701 \| 0.5003 \| 0.3431 \| 0.5003 \| 0.4785 \| 301.6855 \| \| 0.4973 \| 7.0 \| 2625 \| 0.5521 \| 1.1281 \| 0.7349 \| 0.9983 \| 0.9983 \| 295.0692 \| \| 1.1935 \| 8.0 \| 3000 \| 0.5661 \| 1.3444 \| 0.9964 \| 1.2673 \| 1.2213 \| 324.3648 \| \| 0.0752 \| 9.0 \| 3375 \| 0.5531 \| 1.4883 \| 1.0199 \| 1.4252 \| 1.3979 \| 301.0377 \| \| 0.216 \| 10.0 \| 3750 \| 0.5573 \| 1.5516 \| 1.0371 \| 1.5047 \| 1.4656 \| 319.195 \| \| 0.3619 \| 11.0 \| 4125 \| 0.5571 \| 1.2368 \| 0.8055 \| 1.2326 \| 1.2146 \| 294.4717 \| \| 0.1881 \| 12.0 \| 4500 \| 0.5293 \| 1.2922 \| 0.941 \| 1.2149 \| 1.2084 \| 305.9057 \| \| 0.2247 \| 13.0 \| 4875 \| 0.5340 \| 1.0581 \| 0.594 \| 0.9989 \| 0.987 \| 306.3774 \| \| 0.0715 \| 14.0 \| 5250 \| 0.5211 \| 1.2905 \| 0.8861 \| 1.259 \| 1.2143 \| 321.6226 \| \| 0.1851 \| 15.0 \| 5625 \| 0.5231 \| 1.4625 \| 0.9737 \| 1.3919 \| 1.3637 \| 318.4969 \| \| 0.5285 \| 16.0 \| 6000 \| 0.5154 \| 1.1892 \| 0.8552 \| 1.1401 \| 1.1061 \| 313.2138 \| \| 0.0482 \| 17.0 \| 6375 \| 0.5032 \| 1.1826 \| 0.8687 \| 1.1554 \| 1.1554 \| 327.1824 \| \| 0.0733 \| 18.0 \| 6750 \| 0.5193 \| 1.6133 \| 1.1373 \| 1.5626 \| 1.5085 \| 317.8113 \| \| 0.2814 \| 19.0 \| 7125 \| 0.5007 \| 1.5689 \| 1.1133 \| 1.5189 \| 1.4606 \| 307.7421 \| \| 0.0672 \| 20.0 \| 7500 \| 0.4959 \| 1.5754 \| 1.078 \| 1.489 \| 1.4166 \| 316.6164 \| \| 0.2456 \| 21.0 \| 7875 \| 0.4966 \| 1.5754 \| 1.087 \| 1.5005 \| 1.4211 \| 314.3396 \| \| 0.0405 \| 22.0 \| 8250 \| 0.4953 \| 1.5754 \| 1.087 \| 1.5005 \| 1.4211 \| 310.6981 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0.dev20230811+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
orkidea/whisper-small-guc	orkidea	2024-02-23T14:11:17Z	81	1	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "trascript", "ASR", "wayuunaiki", "guc", "dataset:orkidea/wayuu_CO_train", "license:afl-3.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-10-11T05:21:42Z	--- license: afl-3.0 datasets: - orkidea/wayuu_CO_train language: - guc metrics: - wer pipeline_tag: automatic-speech-recognition finetuned_from": openai/whisper-small tags: - trascript - ASR - wayuunaiki --- # Model Background This model has been trained on a unique dataset derived from parsed audio and textual data. It's important to note that the dataset originates from recordings and transcriptions of the Bible in Wayuunaiki. Due to proprietary restrictions, the dataset cannot be shared publicly. Wayuunaiki is the native language of the Wayuu people, predominantly spoken by communities in Colombia and Venezuela. It's a part of the larger Arawakan language family. In the present day, there are a significant number of speakers in both Colombia and Venezuela, making it one of the more widely spoken indigenous languages in the region. This model represents an initial endeavor in the journey of developing transcription models specifically for indigenous languages. The creation and improvement of such models have profound societal implications. It not only helps in preserving and promoting indigenous languages but also serves as a valuable asset for linguistic studies, helping scholars and communities alike in understanding and promoting the rich cultural tapestry of indigenous languages. ## Training Dataset Details The dataset consists of 1,835 audio recordings, each accompanied by its respective transcription. The lexical corpus encompasses approximately 3,000 unique words. - Total Audio Duration: 6241.65 seconds (approximately 1.7 hours) - Average Audio Duration: 3.41 seconds This collection of data serves as a foundational resource for understanding and processing the Wayuunaiki language. [The test dataset](https://huggingface.co/datasets/orkidea/wayuu_CO_test) can be used under the principles of '[fair use](https://en.wikipedia.org/wiki/Fair_use)' copyright. # Model Accuracy Warning While this model has shown promising results, it's essential to be aware of its limitations: - Based on the training and validation data, the model has a Word Error Rate (WER) of around 36%. This indicates that while it can capture the essence of most spoken content, errors can still occur. - The model particularly struggles with long vowels, leading to occasional transcription inaccuracies in such instances. - This iteration serves as a starting point and can be instrumental in refining future models. It is efficient in capturing the bulk of words, but like any machine learning model, it's not infallible. Recommendation: Any transcription produced by this model should undergo subsequent validation and correction to ensure accuracy. This model is an excellent tool for initial drafts but must be used judiciously. # Test it yourself \| Transcription \| Audio Link \| \|---------------\|------------\| \| iseeichi chi wayuu aneekünakai nütüma Maleiwa süpüla nuꞌutünajachin aaꞌin süpüla nülaꞌajaainjatüin saainjala wayuu süpüshua sainküin mmakat \| [Listen here](https://storage.googleapis.com/audio-guc/audio/test/85.wav) \| \| maa akaapüꞌü tü anneerü oꞌutünapüꞌükat aaꞌin watüma wayakana judíokana shiiꞌiree sülaꞌajaanüin waainjala \| [Listen here](https://storage.googleapis.com/audio-guc/audio/test/86.wav) \| The table provides sample transcriptions alongside their corresponding audio links. These examples give users an opportunity to listen to the audios and evaluate the transcription performance of the model firsthand. By exploring these samples, users can better understand the strengths and potential areas of refinement for the model, especially concerning specific nuances in the Wayuunaiki language. # Model Description This model is a speech recognition system trained on a dataset to transcribe audio into text. The model underwent training for 4,000 steps, achieving remarkable improvements in loss metrics during its training journey. ## Training Statistics - Initial Training Loss (Step 1000): 0.016 - Final Training Loss (Step 4000): 0.000200 - Average Training Loss: 0.161 ## Validation Statistics (at the end of training) - Validation Loss: 0.567 - Word Error Rate (WER): 36.3% ## Performance Metrics - Training Runtime: 13,696.0441 seconds - Samples Processed Per Second: 4.673 - Steps Processed Per Second: 0.292 The model demonstrated promising potential with a consistent reduction in the training loss and a competitive Word Error Rate (WER) during validation.
usvsnsp/code-vs-nl	usvsnsp	2024-02-23T14:09:51Z	123	2	transformers	[ "transformers", "pytorch", "tensorboard", "onnx", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:bookcorpus", "dataset:codeparrot/github-code", "base_model:distilbert/distilbert-base-uncased", "base_model:quantized:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-01-25T15:34:59Z	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - bookcorpus - codeparrot/github-code metrics: - accuracy - f1 base_model: distilbert-base-uncased model-index: - name: code-vs-nl 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. --> # code-vs-nl This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on [bookcorpus](https://huggingface.co/datasets/bookcorpus) for text and [codeparrot/github-code](https://huggingface.co/datasets/codeparrot/github-code) for code datasets. It achieves the following results on the evaluation set: - Loss: 0.5180 - Accuracy: 0.9951 - F1 Score: 0.9950 ## Model description As it's a finetuned model, it's architecture is same as distilbert-base-uncased for Sequence Classification ## Intended uses & limitations Can be used to classify documents into text and code ## Training and evaluation data It is a mix of above two datasets, equally random sampled ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-07 - train_batch_size: 256 - eval_batch_size: 1024 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 Score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:--------:\| \| 0.5732 \| 0.07 \| 500 \| 0.5658 \| 0.9934 \| 0.9934 \| \| 0.5254 \| 0.14 \| 1000 \| 0.5180 \| 0.9951 \| 0.9950 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
ferrazzipietro/Llama-2-13b-chat-hf_adapters_en.layer1_4_torch.bfloat16_16_32_0.05_2_0.0002	ferrazzipietro	2024-02-23T14:09:18Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T14:08:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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OmarHaroon01/t5_pretrain_small_dataset_with_tokenizer_30	OmarHaroon01	2024-02-23T14:07:10Z	105	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T09:35:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Khadidja22/my_awesome_food_model	Khadidja22	2024-02-23T14:05:13Z	190	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-02-23T13:56:16Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_awesome_food_model 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. --> # my_awesome_food_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6057 - Accuracy: 0.89 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.6504 \| 0.99 \| 62 \| 2.4984 \| 0.827 \| \| 1.8086 \| 2.0 \| 125 \| 1.7755 \| 0.86 \| \| 1.5975 \| 2.98 \| 186 \| 1.6057 \| 0.89 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
MichaelKim/dacon_comp_hansoldeco_v2	MichaelKim	2024-02-23T13:57:38Z	127	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-generation", "generated_from_trainer", "base_model:heegyu/polyglot-ko-1.3b-chat", "base_model:finetune:heegyu/polyglot-ko-1.3b-chat", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T13:54:31Z	--- base_model: heegyu/polyglot-ko-1.3b-chat tags: - generated_from_trainer model-index: - name: dacon_comp_hansoldeco_v2 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. --> # dacon_comp_hansoldeco_v2 This model is a fine-tuned version of [heegyu/polyglot-ko-1.3b-chat](https://huggingface.co/heegyu/polyglot-ko-1.3b-chat) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 2 \| nan \| ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1
allstax/AI-G-Expander-v5-fp16	allstax	2024-02-23T13:42:58Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T13:27:28Z	--- 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]
antoniomae/voice-cloaning	antoniomae	2024-02-23T13:35:45Z	0	0	pyannote-audio	[ "pyannote-audio", "legal", "text-to-audio", "pt", "dataset:allenai/dolma", "license:creativeml-openrail-m", "region:us" ]	text-to-audio	2024-02-23T13:33:21Z	--- license: creativeml-openrail-m datasets: - allenai/dolma language: - pt metrics: - code_eval library_name: pyannote-audio pipeline_tag: text-to-audio tags: - legal ---
lvcalucioli/phi2_question-answering	lvcalucioli	2024-02-23T13:32:44Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-02-21T02:37:04Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/phi-2 model-index: - name: phi2_question-answering 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. --> # phi2_question-answering This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.16.1 - Tokenizers 0.15.2
KapilPathak/whisper-small-hi	KapilPathak	2024-02-23T13:32:24Z	98	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-02-23T07:02:43Z	--- language: - hi license: apache-2.0 base_model: openai/whisper-small tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small Hi - Kapil Pathak results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: hi split: test args: 'config: hi, split: test' metrics: - name: Wer type: wer value: 33.22610683145687 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Hi - Kapil Pathak This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4305 - Wer: 33.2261 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.0878 \| 2.44 \| 1000 \| 0.2926 \| 35.2154 \| \| 0.0193 \| 4.89 \| 2000 \| 0.3489 \| 33.7129 \| \| 0.002 \| 7.33 \| 3000 \| 0.4076 \| 33.1880 \| \| 0.0004 \| 9.78 \| 4000 \| 0.4305 \| 33.2261 \| ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
WaadSairolaas/Tiny-Assembely0.0	WaadSairolaas	2024-02-23T13:31:06Z	0	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "llama", "trl", "sft", "en", "base_model:cognitivecomputations/TinyDolphin-2.8-1.1b", "base_model:finetune:cognitivecomputations/TinyDolphin-2.8-1.1b", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-02-23T13:31:00Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: cognitivecomputations/TinyDolphin-2.8-1.1b --- # Uploaded model - Developed by: WaadSairolaas - License: apache-2.0 - Finetuned from model : cognitivecomputations/TinyDolphin-2.8-1.1b 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)
davidpedem/french_simplified	davidpedem	2024-02-23T13:31:05Z	104	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "simplification", "generated_from_trainer", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T13:16:23Z	--- license: apache-2.0 base_model: google/flan-t5-base tags: - simplification - generated_from_trainer metrics: - bleu model-index: - name: french_simplified 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. --> # french_simplified This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0115 - Bleu: 31.2501 - Gen Len: 17.8806 ## 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: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\| \| No log \| 1.0 \| 34 \| 1.0348 \| 31.1209 \| 17.8955 \| \| No log \| 2.0 \| 68 \| 1.0115 \| 31.2501 \| 17.8806 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
Hemant06/speecht5_finetuned_LJ_nl	Hemant06	2024-02-23T13:21:53Z	77	0	transformers	[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2024-02-23T05:56:40Z	--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer model-index: - name: speecht5_finetuned_LJ_nl 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. --> # speecht5_finetuned_LJ_nl This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 300 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
WaadSairolaas/Tiny-Assembely	WaadSairolaas	2024-02-23T13:21:09Z	0	0	peft	[ "peft", "pytorch", "safetensors", "llama", "arxiv:1910.09700", "base_model:cognitivecomputations/TinyDolphin-2.8-1.1b", "base_model:adapter:cognitivecomputations/TinyDolphin-2.8-1.1b", "region:us" ]	null	2024-02-23T13:02:48Z	--- library_name: peft base_model: cognitivecomputations/TinyDolphin-2.8-1.1b --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2
justinlamlamlam/open_orca_chat	justinlamlamlam	2024-02-23T13:06:59Z	106	0	transformers	[ "transformers", "safetensors", "opt", "text-generation", "generated_from_trainer", "base_model:Open-Orca/Mistral-7B-OpenOrca", "base_model:finetune:Open-Orca/Mistral-7B-OpenOrca", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-22T23:26:45Z	--- license: apache-2.0 base_model: Open-Orca/Mistral-7B-OpenOrca tags: - generated_from_trainer model-index: - name: open_orca_chat 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. --> # open_orca_chat This model is a fine-tuned version of [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 1000 ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.2 - Datasets 2.15.0 - Tokenizers 0.15.1
FreedomIntelligence/MindedWheeler	FreedomIntelligence	2024-02-23T12:55:55Z	149	1	transformers	[ "transformers", "safetensors", "qwen", "text-generation", "custom_code", "license:apache-2.0", "autotrain_compatible", "region:us" ]	text-generation	2024-02-23T11:19:21Z	--- license: apache-2.0 --- # MindedWheeler Embody_AI with car as Demo ![MindedWheeler](assets/MindedWheeler.png) <p align="center"> 🌐 <a href="https://github.com/FreedomIntelligence/MindedWheeler" target="_blank">Website</a> • 🤗 <a href="" target="_blank">Model</a> </p> ## 🌈 Update * [2024.02.23] 🎉🎉🎉 MindedWheeler is published！🎉🎉🎉 ## 🤖 Model Training Data ``` User：快速向左转 RobotAI: (1.0, -0.3) ... ``` - The two float are in range [-1,1] - The first float is speed, the second is direction (negative means left, positive means right). ## 🤖 Communication Protocol - 0x02, 0x02, 0x01, 8, data_buf; (See detail in [code](https://github.com/FreedomIntelligence/MindedWheeler/blob/main/qwen.cpp#L151)) ## ℹ️ Usage 1. DownLoad 🤗 [Model](https://huggingface.co/FreedomIntelligence/MindedWheeler) get model.bin. ``` cd MindedWheeler git submodule update --init --recursive python qwen_cpp/convert.py -i {Model_Path} -t {type} -o robot1_8b-ggml.bin ``` You are free to try any of the below quantization types by specifying -t <type>: - q4_0: 4-bit integer quantization with fp16 scales. - q4_1: 4-bit integer quantization with fp16 scales and minimum values. - q5_0: 5-bit integer quantization with fp16 scales. - q5_1: 5-bit integer quantization with fp16 scales and minimum values. - q8_0: 8-bit integer quantization with fp16 scales. - f16: half precision floating point weights without quantization. - f32: single precision floating point weights without quantization. 2. Install package serial.tar.gz ``` cd serial cmake .. & make & sudo make install ``` 3. Compile the project using CMake: ``` cmake -B build cmake --build build -j --config Release ``` 4. Now you may chat and control your AI car with the quantized RobotAI model by running: - qwen.tiktoken is in the model directory ``` ./build/bin/main -m robot1_8b-ggml.bin --tiktoken qwen.tiktoken -p 请快速向前 ``` To run the model in interactive mode, add the -i flag. For example: ``` ./build/bin/main -m robot1_8b-ggml.bin --tiktoken qwen.tiktoken -i ``` In interactive mode, your chat history will serve as the context for the next-round conversation. ## 🥸 To do list - Continue to create data and train a robust model - Add ASR and TTS - ... ## ✨ Citation Please use the following citation if you intend to use our dataset for training or evaluation: ``` @misc{MindedWheeler, title={MindedWheeler: Embody_AI with car as Demo}, author={Xidong Wang, Yuan Shen}, year = {2024}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/FreedomIntelligence/MindedWheeler}}, } ``` ## 🤖 Acknowledgement - We thank [Qwen.cpp](https://github.com/QwenLM/qwen.cpp.git) and [llama.cpp](https://github.com/ggerganov/llama.cpp) for their excellent work.
rizputra/sealion7b-sharded	rizputra	2024-02-23T12:44:49Z	6	0	transformers	[ "transformers", "safetensors", "mpt", "text-generation", "custom_code", "arxiv:2101.09635", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T10:12:02Z	--- license: mit --- This is a version of the [sealion7b](https://huggingface.co/aisingapore/sealion7b) model, sharded to 2 GB chunks. Please refer to the previously linked repo for details on usage/implementation/etc. This model was downloaded from the original repo and is redistributed under the same license. # SEA-LION SEA-LION is a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for the Southeast Asia (SEA) region. The size of the models range from 3 billion to 7 billion parameters. This is the card for the SEA-LION 7B base model. SEA-LION stands for <i>Southeast Asian Languages In One Network</i>. ## Model Details ### Model Description The SEA-LION model is a significant leap forward in the field of Natural Language Processing, specifically trained to understand the SEA regional context. SEA-LION is built on the robust MPT architecture and has a vocabulary size of 256K. For tokenization, the model employs our custom SEABPETokenizer, which is specially tailored for SEA languages, ensuring optimal model performance. The training data for SEA-LION encompasses 980B tokens. - Developed by: Products Pillar, AI Singapore - Funded by: Singapore NRF - Model type: Decoder - Languages: English, Chinese, Indonesian, Malay, Thai, Vietnamese, Filipino, Tamil, Burmese, Khmer, Lao - License: MIT License ### Performance Benchmarks SEA-LION has an average performance on general tasks in English (as measured by Hugging Face's LLM Leaderboard): \| Model \| ARC \| HellaSwag \| MMLU \| TruthfulQA \| Average \| \|-------------\|:-----:\|:---------:\|:-----:\|:----------:\|:-------:\| \| SEA-LION 7B \| 39.93 \| 68.51 \| 26.87 \| 35.09 \| 42.60 \| ## Training Details ### Data SEA-LION was trained on 980B tokens of the following data: \| Data Source \| Unique Tokens \| Multiplier \| Total Tokens \| Percentage \| \|---------------------------\|:-------------:\|:----------:\|:------------:\|:----------:\| \| RefinedWeb - English \| 571.3B \| 1 \| 571.3B \| 58.20% \| \| mC4 - Chinese \| 91.2B \| 1 \| 91.2B \| 9.29% \| \| mC4 - Indonesian \| 3.68B \| 4 \| 14.7B \| 1.50% \| \| mC4 - Malay \| 0.72B \| 4 \| 2.9B \| 0.29% \| \| mC4 - Filipino \| 1.32B \| 4 \| 5.3B \| 0.54% \| \| mC4 - Burmese \| 1.2B \| 4 \| 4.9B \| 0.49% \| \| mC4 - Vietnamese \| 63.4B \| 1 \| 63.4B \| 6.46% \| \| mC4 - Thai \| 5.8B \| 2 \| 11.6B \| 1.18% \| \| WangChanBERTa - Thai \| 5B \| 2 \| 10B \| 1.02% \| \| mC4 - Lao \| 0.27B \| 4 \| 1.1B \| 0.12% \| \| mC4 - Khmer \| 0.97B \| 4 \| 3.9B \| 0.40% \| \| mC4 - Tamil \| 2.55B \| 4 \| 10.2B \| 1.04% \| \| the Stack - Python \| 20.9B \| 2 \| 41.8B \| 4.26% \| \| the Stack - Javascript \| 55.6B \| 1 \| 55.6B \| 5.66% \| \| the Stack - Shell \| 1.2B5 \| 2 \| 2.5B \| 0.26% \| \| the Stack - SQL \| 6.4B \| 2 \| 12.8B \| 1.31% \| \| the Stack - Markdown \| 26.6B \| 1 \| 26.6B \| 2.71% \| \| RedPajama - StackExchange \| 21.2B \| 1 \| 21.2B \| 2.16% \| \| RedPajama - ArXiv \| 30.6B \| 1 \| 30.6B \| 3.12% \| ### Infrastructure SEA-LION was trained using [MosaicML Composer](https://github.com/mosaicml/composer) on the following hardware: \| Training Details \| SEA-LION 7B \| \|----------------------\|:------------:\| \| AWS EC2 p4d.24xlarge \| 32 instances \| \| Nvidia A100 40GB GPU \| 256 \| \| Training Duration \| 22 days \| ### Configuration \| HyperParameter \| SEA-LION 7B \| \|-------------------\|:------------------:\| \| Precision \| bfloat16 \| \| Optimizer \| decoupled_adamw \| \| Scheduler \| cosine_with_warmup \| \| Learning Rate \| 6.0e-5 \| \| Global Batch Size \| 2048 \| \| Micro Batch Size \| 4 \| ## Technical Specifications ### Model Architecture and Objective SEA-LION is a decoder model using the MPT architecture. \| Parameter \| SEA-LION 7B \| \|-----------------\|:-----------:\| \| Layers \| 32 \| \| d_model \| 4096 \| \| head_dim \| 32 \| \| Vocabulary \| 256000 \| \| Sequence Length \| 2048 \| ### Tokenizer Details We sample 20M lines from the training data to train the tokenizer.<br> The framework for training is [SentencePiece](https://github.com/google/sentencepiece).<br> The tokenizer type is Byte-Pair Encoding (BPE). ## The Team Lam Wen Zhi Clarence<br> Leong Wei Qi<br> Li Yier<br> Liu Bing Jie Darius<br> Lovenia Holy<br> Montalan Jann Railey<br> Ng Boon Cheong Raymond<br> Ngui Jian Gang<br> Nguyen Thanh Ngan<br> Ong Tat-Wee David<br> Rengarajan Hamsawardhini<br> Susanto Yosephine<br> Tai Ngee Chia<br> Tan Choon Meng<br> Teo Jin Howe<br> Teo Eng Sipp Leslie<br> Teo Wei Yi<br> Tjhi William<br> Yeo Yeow Tong<br> Yong Xianbin<br> ## Acknowledgements AI Singapore is a national programme supported by the National Research Foundation, Singapore and hosted by the National University of Singapore. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore. ## Contact For more info, please contact us using this [SEA-LION Inquiry Form](https://forms.gle/sLCUVb95wmGf43hi6) [Link to SEA-LION's GitHub repository](https://github.com/aisingapore/sealion) ## Disclaimer This the repository for the base model. The model has _not_ been aligned for safety. Developers and users should perform their own safety fine-tuning and related security measures. In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights and codes. ## References ```bibtex @misc{lowphansirikul2021wangchanberta, title={WangchanBERTa: Pretraining transformer-based Thai Language Models}, author={Lalita Lowphansirikul and Charin Polpanumas and Nawat Jantrakulchai and Sarana Nutanong}, year={2021}, eprint={2101.09635}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
myrtotsok/distilbert-base-uncased-EO-intent-classifier-5	myrtotsok	2024-02-23T12:40:58Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T12:40:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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next-tat/tat-llm-13b-lora	next-tat	2024-02-23T12:40:32Z	0	1	null	[ "en", "arxiv:2401.13223", "license:llama2", "region:us" ]	null	2024-02-20T03:53:27Z	--- language: - en license: llama2 --- # TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data Paper: https://arxiv.org/abs/2401.13223 Code: https://github.com/fengbinzhu/TAT-LLM ## Introduction We present TAT-LLM, a specialized language model crafted through the innovative Step-wise Pipeline approach, focusing on the nuanced realm of tabular and textual question answering (QA). This model is the fruit of rigorously fine-tuning the LLaMA 2 architecture with a novel dataset, autonomously generated from expertly annotated resources. TAT-LLM stands at the intersection of tabular comprehension and textual analysis, engineered to excel by embodying three fundamental phases: Extraction, Reasoning, and Execution. Our empirical findings illuminate TAT-LLM's remarkable capability to eclipse traditional benchmarks, surmounting even the most advanced models and colossal language models such as GPT-4 across a suite of demanding financial QA tasks like FinQA, TAT-QA, and TAT-DQA. This endeavor not only sets a new standard for task-specific language models but also paves the way for future explorations in optimizing smaller models for highly specialized functions. \| Model \| Size \| FINQA \| TATQA \| TATDQA \| \| --- \| --- \| --- \| --- \| --- \| \| GPT-3.5-Turbo \| - \| 58.00 \| 59.47 \| 52.74 \| \| GPT-4 \| - \| 63.91 \| 71.92 \| 64.46 \| \| [TAT-LLM-7B-LORA](https://huggingface.co/next-tat/tat-llm-7b-lora) \| 7B \| 65.13 \| 76.49 \| 71.38 \| \| [TAT-LLM-7B-FFT](https://huggingface.co/next-tat/tat-llm-7b-fft) \| 7B \| 69.75 \| 76.91 \| 72.64 \| \| [TAT-LLM-13B-LORA](https://huggingface.co/next-tat/tat-llm-13b-lora) \| 13B \| 71.93 \| 77.51 \| 72.22 \| \| [TAT-LLM-13B-FFT](https://huggingface.co/next-tat/tat-llm-13b-fft) \| 13B \| 72.97 \| 78.41 \| 73.18 \| \| [TAT-LLM-70B-LORA](https://huggingface.co/next-tat/tat-llm-70b-lora) \| 70B \| 76.81 \| 81.42 \| 76.55 \| \| [TAT-LLM-70B-FFT](https://huggingface.co/next-tat/tat-llm-70b-fft) \| 70B \| 76.11 \| 82.20 \| 76.97 \| ## Training We train our TAT-LLM model in various sizes, including 7B, 13B, and 70B, using different methods such as parameter-efficient fine-tuning and full-parameter fine-tuning of LLaMA 2 on a combination of financial data from the FinQA, TAT-QA, and TAT-DQA training sets([🤗HuggingFace Repo](https://huggingface.co/datasets/next-tat/tat-llm-instructions)). To refine accuracy, we introduce an External Executor, enhancing the model by processing intermediate outputs to derive conclusive answers. Please refer to the [paper](https://arxiv.org/abs/2401.13223) for more details. ## Inference & Evaluation Please refer to code [here](https://github.com/fengbinzhu/TAT-LLM) ## Citation If you find this model helpful, please consider citing our paper: ``` @misc{zhu2024tatllm, title={TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data}, author={Fengbin Zhu and Ziyang Liu and Fuli Feng and Chao Wang and Moxin Li and Tat-Seng Chua}, year={2024}, eprint={2401.13223}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
next-tat/tat-llm-70b-fft	next-tat	2024-02-23T12:39:40Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "en", "arxiv:2401.13223", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-21T03:31:45Z	--- language: - en license: llama2 --- # TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data Paper: https://arxiv.org/abs/2401.13223 Code: https://github.com/fengbinzhu/TAT-LLM ## Introduction We present TAT-LLM, a specialized language model crafted through the innovative Step-wise Pipeline approach, focusing on the nuanced realm of tabular and textual question answering (QA). This model is the fruit of rigorously fine-tuning the LLaMA 2 architecture with a novel dataset, autonomously generated from expertly annotated resources. TAT-LLM stands at the intersection of tabular comprehension and textual analysis, engineered to excel by embodying three fundamental phases: Extraction, Reasoning, and Execution. Our empirical findings illuminate TAT-LLM's remarkable capability to eclipse traditional benchmarks, surmounting even the most advanced models and colossal language models such as GPT-4 across a suite of demanding financial QA tasks like FinQA, TAT-QA, and TAT-DQA. This endeavor not only sets a new standard for task-specific language models but also paves the way for future explorations in optimizing smaller models for highly specialized functions. \| Model \| Size \| FINQA \| TATQA \| TATDQA \| \| --- \| --- \| --- \| --- \| --- \| \| GPT-3.5-Turbo \| - \| 58.00 \| 59.47 \| 52.74 \| \| GPT-4 \| - \| 63.91 \| 71.92 \| 64.46 \| \| [TAT-LLM-7B-LORA](https://huggingface.co/next-tat/tat-llm-7b-lora) \| 7B \| 65.13 \| 76.49 \| 71.38 \| \| [TAT-LLM-7B-FFT](https://huggingface.co/next-tat/tat-llm-7b-fft) \| 7B \| 69.75 \| 76.91 \| 72.64 \| \| [TAT-LLM-13B-LORA](https://huggingface.co/next-tat/tat-llm-13b-lora) \| 13B \| 71.93 \| 77.51 \| 72.22 \| \| [TAT-LLM-13B-FFT](https://huggingface.co/next-tat/tat-llm-13b-fft) \| 13B \| 72.97 \| 78.41 \| 73.18 \| \| [TAT-LLM-70B-LORA](https://huggingface.co/next-tat/tat-llm-70b-lora) \| 70B \| 76.81 \| 81.42 \| 76.55 \| \| [TAT-LLM-70B-FFT](https://huggingface.co/next-tat/tat-llm-70b-fft) \| 70B \| 76.11 \| 82.20 \| 76.97 \| ## Training We train our TAT-LLM model in various sizes, including 7B, 13B, and 70B, using different methods such as parameter-efficient fine-tuning and full-parameter fine-tuning of LLaMA 2 on a combination of financial data from the FinQA, TAT-QA, and TAT-DQA training sets([🤗HuggingFace Repo](https://huggingface.co/datasets/next-tat/tat-llm-instructions)). To refine accuracy, we introduce an External Executor, enhancing the model by processing intermediate outputs to derive conclusive answers. Please refer to the [paper](https://arxiv.org/abs/2401.13223) for more details. ## Inference & Evaluation Please refer to code [here](https://github.com/fengbinzhu/TAT-LLM) ## Citation If you find this model helpful, please consider citing our paper: ``` @misc{zhu2024tatllm, title={TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data}, author={Fengbin Zhu and Ziyang Liu and Fuli Feng and Chao Wang and Moxin Li and Tat-Seng Chua}, year={2024}, eprint={2401.13223}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
next-tat/tat-llm-7b-fft	next-tat	2024-02-23T12:38:44Z	86	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "en", "arxiv:2401.13223", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-20T07:46:17Z	--- language: - en license: llama2 --- # TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data Paper: https://arxiv.org/abs/2401.13223 Code: https://github.com/fengbinzhu/TAT-LLM ## Introduction We present TAT-LLM, a specialized language model crafted through the innovative Step-wise Pipeline approach, focusing on the nuanced realm of tabular and textual question answering (QA). This model is the fruit of rigorously fine-tuning the LLaMA 2 architecture with a novel dataset, autonomously generated from expertly annotated resources. TAT-LLM stands at the intersection of tabular comprehension and textual analysis, engineered to excel by embodying three fundamental phases: Extraction, Reasoning, and Execution. Our empirical findings illuminate TAT-LLM's remarkable capability to eclipse traditional benchmarks, surmounting even the most advanced models and colossal language models such as GPT-4 across a suite of demanding financial QA tasks like FinQA, TAT-QA, and TAT-DQA. This endeavor not only sets a new standard for task-specific language models but also paves the way for future explorations in optimizing smaller models for highly specialized functions. \| Model \| Size \| FINQA \| TATQA \| TATDQA \| \| --- \| --- \| --- \| --- \| --- \| \| GPT-3.5-Turbo \| - \| 58.00 \| 59.47 \| 52.74 \| \| GPT-4 \| - \| 63.91 \| 71.92 \| 64.46 \| \| [TAT-LLM-7B-LORA](https://huggingface.co/next-tat/tat-llm-7b-lora) \| 7B \| 65.13 \| 76.49 \| 71.38 \| \| [TAT-LLM-7B-FFT](https://huggingface.co/next-tat/tat-llm-7b-fft) \| 7B \| 69.75 \| 76.91 \| 72.64 \| \| [TAT-LLM-13B-LORA](https://huggingface.co/next-tat/tat-llm-13b-lora) \| 13B \| 71.93 \| 77.51 \| 72.22 \| \| [TAT-LLM-13B-FFT](https://huggingface.co/next-tat/tat-llm-13b-fft) \| 13B \| 72.97 \| 78.41 \| 73.18 \| \| [TAT-LLM-70B-LORA](https://huggingface.co/next-tat/tat-llm-70b-lora) \| 70B \| 76.81 \| 81.42 \| 76.55 \| \| [TAT-LLM-70B-FFT](https://huggingface.co/next-tat/tat-llm-70b-fft) \| 70B \| 76.11 \| 82.20 \| 76.97 \| ## Training We train our TAT-LLM model in various sizes, including 7B, 13B, and 70B, using different methods such as parameter-efficient fine-tuning and full-parameter fine-tuning of LLaMA 2 on a combination of financial data from the FinQA, TAT-QA, and TAT-DQA training sets([🤗HuggingFace Repo](https://huggingface.co/datasets/next-tat/tat-llm-instructions)). To refine accuracy, we introduce an External Executor, enhancing the model by processing intermediate outputs to derive conclusive answers. Please refer to the [paper](https://arxiv.org/abs/2401.13223) for more details. ## Inference & Evaluation Please refer to code [here](https://github.com/fengbinzhu/TAT-LLM) ## Citation If you find this model helpful, please consider citing our paper: ``` @misc{zhu2024tatllm, title={TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data}, author={Fengbin Zhu and Ziyang Liu and Fuli Feng and Chao Wang and Moxin Li and Tat-Seng Chua}, year={2024}, eprint={2401.13223}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
ThuyNT03/CS505_COQE_viT5_Prompting5_ASPOL_vcheck2	ThuyNT03	2024-02-23T12:25:50Z	104	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "base_model:finetune:VietAI/vit5-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T11:24:19Z	--- license: mit base_model: VietAI/vit5-large tags: - generated_from_trainer model-index: - name: CS505_COQE_viT5_Prompting5_ASPOL_vcheck2 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. --> # CS505_COQE_viT5_Prompting5_ASPOL_vcheck2 This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1
wyvernwaves/phi-1_5-finetuned-py	wyvernwaves	2024-02-23T12:24:04Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "phi", "generated_from_trainer", "custom_code", "base_model:microsoft/phi-1_5", "base_model:adapter:microsoft/phi-1_5", "license:mit", "region:us" ]	null	2024-02-23T12:12:56Z	--- license: mit library_name: peft tags: - generated_from_trainer base_model: microsoft/phi-1_5 model-index: - name: phi-1_5-finetuned-py 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. --> # phi-1_5-finetuned-py This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 1000 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
sayhan/gemma-7b-GGUF-quantized	sayhan	2024-02-23T12:15:08Z	158	0	transformers	[ "transformers", "gguf", "text-generation", "en", "base_model:google/gemma-7b", "base_model:quantized:google/gemma-7b", "license:other", "region:us" ]	text-generation	2024-02-21T15:34:23Z	--- base_model: google/gemma-7b language: - en pipeline_tag: text-generation license: other model_type: gemma library_name: transformers inference: false --- ![image/webp](https://cdn-uploads.huggingface.co/production/uploads/65aa2d4b356bf23b4a4da247/NQAvp6NRHlNILyWWFlrA7.webp) ## Google Gemma 7B - Model creator: [Google](https://huggingface.co/google) - Original model: [gemma-7b-it](https://huggingface.co/google/gemma-7b) - [Terms of use](https://www.kaggle.com/models/google/gemma/license/consent) <!-- description start --> ## Description This repo contains GGUF format model files for [Google's Gemma 7B](https://huggingface.co/google/gemma-7b) ## Original model - Developed by: [Google](https://huggingface.co/google) ### Description Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. ## Quantizon types \| quantization method \| bits \| size \| description \| recommended \| \|---------------------\|------\|----------\|-----------------------------------------------------\|-------------\| \| Q2_K \| 2 \| 3.09 \| very small, very high quality loss \| ❌ \| \| Q3_K_S \| 3 \| 3.68 GB \| very small, high quality loss \| ❌ \| \| Q3_K_L \| 3 \| 4.4 GB \| small, substantial quality loss \| ❌ \| \| Q4_0 \| 4 \| 4.81 GB \| legacy; small, very high quality loss \| ❌ \| \| Q4_K_S \| 4 \| 4.84 GB \| medium, balanced quality \| ✅ \| \| Q4_K_M \| 4 \| 5.13 GB \| medium, balanced quality \| ✅ \| \| Q5_0 \| 5 \| 5.88 GB \| legacy; medium, balanced quality \| ❌ \| \| Q5_K_S \| 5 \| 5.88 GB \| large, low quality loss \| ✅ \| \| Q5_K_M \| 5 \| 6.04 GB \| large, very low quality loss \| ✅ \| \| Q6_K \| 6 \| 7.01 GB \| very large, extremely low quality loss \| ❌ \| \| Q8_0 \| 8 \| 9.08 GB \| very large, extremely low quality loss \| ❌ \| \| FP16 \| 16 \| 17.1 GB \| enormous, negligible quality loss \| ❌ \| ## Usage You can use this model with the latest builds of LM Studio and llama.cpp. If you're new to the world of _large language models_, I recommend starting with LM Studio. <!-- description end -->
ThuyNT03/CS505_COQE_viT5_Prompting5_ASPOL_vcheck1	ThuyNT03	2024-02-23T12:14:10Z	104	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "base_model:finetune:VietAI/vit5-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T11:12:59Z	--- license: mit base_model: VietAI/vit5-large tags: - generated_from_trainer model-index: - name: CS505_COQE_viT5_Prompting5_ASPOL_vcheck1 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. --> # CS505_COQE_viT5_Prompting5_ASPOL_vcheck1 This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1
fia24/filtered_annotated100k_byt5	fia24	2024-02-23T12:11:34Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/byt5-small", "base_model:finetune:google/byt5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T10:05:00Z	--- license: apache-2.0 base_model: google/byt5-small tags: - generated_from_trainer model-index: - name: filtered_annotated100k_byt5 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. --> # filtered_annotated100k_byt5 This model is a fine-tuned version of [google/byt5-small](https://huggingface.co/google/byt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0404 - eval_Val Accuracy: 0.5118 - eval_gen_len: 16.6927 - eval_runtime: 289.0028 - eval_samples_per_second: 33.232 - eval_steps_per_second: 1.042 - epoch: 3.0 - step: 3603 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 200 ### Framework versions - Transformers 4.37.2 - Pytorch 1.13.1+cu117 - Datasets 2.17.1 - Tokenizers 0.15.2
theBrokenCat/ArturoSprint	theBrokenCat	2024-02-23T12:10:31Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dataset:theBrokenCat/SprintDataset-0.1", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-02-23T09:20:07Z	--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 datasets: - theBrokenCat/SprintDataset-0.1 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- # Text-to-image finetuning - theBrokenCat/ArturoSprint This pipeline was finetuned from CompVis/stable-diffusion-v1-4 on the theBrokenCat/SprintDataset-0.1 dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['Un prompt de ejemplo']: ![val_imgs_grid](./val_imgs_grid.png) ## Pipeline usage You can use the pipeline like so: ```python from diffusers import DiffusionPipeline import torch pipeline = DiffusionPipeline.from_pretrained("theBrokenCat/ArturoSprint", torch_dtype=torch.float16) prompt = "Un prompt de ejemplo" image = pipeline(prompt).images[0] image.save("my_image.png") ``` ## Training info These are the key hyperparameters used during training: * Epochs: 13 * Learning rate: 1e-05 * Batch size: 1 * Gradient accumulation steps: 4 * Image resolution: 512 * Mixed-precision: None
HektorOrion/mbart-neutralization	HektorOrion	2024-02-23T12:10:19Z	103	0	transformers	[ "transformers", "tensorboard", "safetensors", "mbart", "text2text-generation", "simplification", "generated_from_trainer", "base_model:facebook/mbart-large-50", "base_model:finetune:facebook/mbart-large-50", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-21T13:50:13Z	--- license: mit base_model: facebook/mbart-large-50 tags: - simplification - generated_from_trainer metrics: - bleu model-index: - name: mbart-neutralization 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. --> # mbart-neutralization This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0220 - Bleu: 98.2132 - Gen Len: 18.5417 ## 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: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\| \| No log \| 1.0 \| 440 \| 0.0490 \| 96.2659 \| 19.0104 \| \| 0.2462 \| 2.0 \| 880 \| 0.0220 \| 98.2132 \| 18.5417 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
Alegal/ppo-Huggy	Alegal	2024-02-23T12:08:59Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-02-23T12:06:03Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Alegal/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
GauravGrow/AbuseXLMR_Tamil	GauravGrow	2024-02-23T12:06:08Z	108	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-23T12:05:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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sayhan/gemma-7b-it-GGUF-quantized	sayhan	2024-02-23T12:05:37Z	346	19	transformers	[ "transformers", "gguf", "text-generation", "en", "base_model:google/gemma-7b-it", "base_model:quantized:google/gemma-7b-it", "license:other", "region:us", "conversational" ]	text-generation	2024-02-21T15:28:05Z	--- base_model: google/gemma-7b-it language: - en pipeline_tag: text-generation license: other model_type: gemma library_name: transformers inference: false --- ![image/webp](https://cdn-uploads.huggingface.co/production/uploads/65aa2d4b356bf23b4a4da247/NQAvp6NRHlNILyWWFlrA7.webp) ## Google Gemma 7B Instruct - Model creator: [Google](https://huggingface.co/google) - Original model: [gemma-7b-it](https://huggingface.co/google/gemma-7b-it) - [Terms of use](https://www.kaggle.com/models/google/gemma/license/consent) <!-- description start --> ## Description This repo contains GGUF format model files for [Google's Gemma 7B Instruct](https://huggingface.co/google/gemma-7b-it) ## Original model - Developed by: [Google](https://huggingface.co/google) ### Description Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. ## Quantizon types \| quantization method \| bits \| size \| description \| recommended \| \|---------------------\|------\|----------\|-----------------------------------------------------\|-------------\| \| Q3_K_S \| 3 \| 3.68 GB \| very small, high quality loss \| ❌ \| \| Q3_K_L \| 3 \| 4.4 GB \| small, substantial quality loss \| ❌ \| \| Q4_0 \| 4 \| 4.81 GB \| legacy; small, very high quality loss \| ❌ \| \| Q4_K_M \| 4 \| 5.13 GB \| medium, balanced quality \| ✅ \| \| Q5_0 \| 5 \| 5.88 GB \| legacy; medium, balanced quality \| ❌ \| \| Q5_K_S \| 5 \| 5.88 GB \| large, low quality loss \| ✅ \| \| Q5_K_M \| 5 \| 6.04 GB \| large, very low quality loss \| ✅ \| \| Q6_K \| 6 \| 7.01 GB \| very large, extremely low quality loss \| ❌ \| \| Q8_0 \| 8 \| 9.08 GB \| very large, extremely low quality loss \| ❌ \| \| FP16 \| 16 \| 17.1 GB \| enormous, negligible quality loss \| ❌ \| ## Usage You can use this model with the latest builds of LM Studio and llama.cpp. If you're new to the world of _large language models_, I recommend starting with LM Studio. <!-- description end -->
ondinehunter/bert-finetuned-squad	ondinehunter	2024-02-23T12:03:41Z	105	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "question-answering", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-02-23T09:46:47Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer model-index: - name: bert-finetuned-squad 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. --> # bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
JDWebProgrammer/Mistral-CultriX-slerp	JDWebProgrammer	2024-02-23T11:56:05Z	9	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "mistralai/Mistral-7B-v0.1", "CultriX/NeuralTrix-7B-dpo", "base_model:CultriX/NeuralTrix-7B-dpo", "base_model:merge:CultriX/NeuralTrix-7B-dpo", "base_model:mistralai/Mistral-7B-v0.1", "base_model:merge:mistralai/Mistral-7B-v0.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-14T02:55:13Z	--- tags: - merge - mergekit - lazymergekit - mistralai/Mistral-7B-v0.1 - CultriX/NeuralTrix-7B-dpo base_model: - mistralai/Mistral-7B-v0.1 - CultriX/NeuralTrix-7B-dpo --- # Mistral-CultriX-slerp Research & Development for AutoSynthetix AI 🌐 Website https://autosynthetix.com/ 📨 Discord https://discord.gg/pAKqENStQr 📦 GitHub https://github.com/jdwebprogrammer 📦 GitLab https://gitlab.com/jdwebprogrammer 🏆 Patreon https://patreon.com/jdwebprogrammer 📷 YouTube https://www.youtube.com/@jdwebprogrammer 📺 Twitch https://www.twitch.tv/jdwebprogrammer 🐦 Twitter(X) https://twitter.com/jdwebprogrammer * License includes the license of the model derivatives: - MergeKit LGPL-3.0 https://github.com/arcee-ai/mergekit?tab=LGPL-3.0-1-ov-file#readme - Mistral Apache 2.0 https://huggingface.co/mistralai/Mistral-7B-v0.1 - CultriX Apache 2.0 https://huggingface.co/CultriX/NeuralTrix-7B-dpo Mistral-CultriX-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) * [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo) ## 🧩 Configuration ```yaml slices: - sources: - model: mistralai/Mistral-7B-v0.1 layer_range: [0, 32] - model: CultriX/NeuralTrix-7B-dpo layer_range: [0, 32] merge_method: slerp base_model: mistralai/Mistral-7B-v0.1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "JDWebProgrammer/Mistral-CultriX-slerp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
magixn/Reinforce-Pixelcopter-PLE-v0	magixn	2024-02-23T11:44:55Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-02-23T11:44:51Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 24.60 +/- 14.77 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
bmateusz/ddpm-butterflies-128	bmateusz	2024-02-23T11:36:36Z	3	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "dataset:huggan/smithsonian_butterflies_subset", "license:apache-2.0", "region:us" ]	null	2024-02-22T15:51:19Z	--- license: apache-2.0 library_name: diffusers datasets: - huggan/smithsonian_butterflies_subset ---
oraul/Stability_CelebsHAQ_v1	oraul	2024-02-23T11:25:59Z	42	0	diffusers	[ "diffusers", "safetensors", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-30T03:13:38Z	# Stable Diffusion: Stable Diffusion is a generative artificial intelligence (generative AI) model that produces unique photorealistic images from text and image prompts. The model is based on diffusion technology and uses latent space. This significantly reduces processing requirements, and you can run the model on desktops or laptops equipped with GPUs. Stable Diffusion can be fine-tuned to meet your specific needs with as little as five images through transfer learning. # Model info: The model is trained on aorund 100 images of actor Brad Pitt. The oraul/Stability_CelebsHAQ_v1 is a Stable Diffusion model that has been fine-tuned by using stabilityai/stable-diffusion-xl-base-1.0 as a base model # Usage: !pip install -U -qq git+https://github.com/huggingface/diffusers.git -q !pip install -qq accelerate transformers ftfy -q !pip install transformers -q from diffusers import StableDiffusionPipeline import torch model = "oraul/Stability_CelebsHAQ_v1" pipeline = StableDiffusionPipeline.from_pretrained(model, torch_dtype=torch.float16).to("cuda") prompt = "how the personal batch2_proj_11 was looked at the age of 2 years" # here, batch2_proj_11 is the placeholder that identifies unique style in the model. please add it into the angel brackets image = pipeline(prompt).images[0] image
NickyNicky/gemma-2b-it_oasst2_chatML_Cluster_3_V1	NickyNicky	2024-02-23T11:25:34Z	108	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "bg", "ca", "cs", "da", "de", "en", "es", "fr", "hr", "hu", "it", "nl", "pl", "pt", "ro", "ru", "sl", "sr", "sv", "uk", "dataset:OpenAssistant/oasst2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T09:17:37Z	--- license: apache-2.0 datasets: - OpenAssistant/oasst2 language: - bg - ca - cs - da - de - en - es - fr - hr - hu - it - nl - pl - pt - ro - ru - sl - sr - sv - uk library_name: transformers widget: - text: \| <bos><start_of_turn>system You are a helpful AI assistant.<end_of_turn> <start_of_turn>user What is the meaning of life in the current time?<end_of_turn> <start_of_turn>model --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/641b435ba5f876fe30c5ae0a/YXqUXFjX8uIJT-mdOnM1h.png) ``` reference data model: datasets: - lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk" link: https://huggingface.co/datasets/NickyNicky/oasst2_clusters model: - google/gemma-2b-it Link: https://huggingface.co/google/gemma-2b-it Epoch: 7 future experts: Cluster_3 Eval model: - link: soon ``` ## ```Python !python -m pip install --upgrade pip !pip install "torch>=2.1.1" -U !pip install torchaudio==2.2.0 !pip install -q datasets trl peft bitsandbytes sentencepiece wandb !pip install -q accelerate safetensors deepspeed !pip install -q scipy ninja -U !pip install -q -U transformers==4.38.0 ``` ## Version ```py import torch torch.__version__ #OUTPUTS: ('2.2.0+cu121' ) ``` ## How to use ```py from transformers import ( AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, HfArgumentParser, TrainingArguments, pipeline, logging, GenerationConfig, TextIteratorStreamer, ) from transformers import StoppingCriteria, StoppingCriteriaList import torch model_id='NickyNicky/gemma-2b-it_oasst2_chatML_Cluster_3_V1' model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2", # load_in_4bit=True, # low_cpu_mem_usage= True, ) max_length=2055 print("max_length",max_length) tokenizer = AutoTokenizer.from_pretrained(model_id, # use_fast = False, max_length=max_length,) class ListOfTokensStoppingCriteria(StoppingCriteria): """ Clase para definir un criterio de parada basado en una lista de tokens específicos. """ def __init__(self, tokenizer, stop_tokens): self.tokenizer = tokenizer # Codifica cada token de parada y guarda sus IDs en una lista self.stop_token_ids_list = [tokenizer.encode(stop_token, add_special_tokens=False) for stop_token in stop_tokens] def __call__(self, input_ids, scores, **kwargs): # Verifica si los últimos tokens generados coinciden con alguno de los conjuntos de tokens de parada for stop_token_ids in self.stop_token_ids_list: len_stop_tokens = len(stop_token_ids) if len(input_ids[0]) >= len_stop_tokens: if input_ids[0, -len_stop_tokens:].tolist() == stop_token_ids: return True return False # Uso del criterio de parada personalizado stop_tokens = ["<end_of_turn>"] # Lista de tokens de parada # Inicializa tu criterio de parada con el tokenizer y la lista de tokens de parada stopping_criteria = ListOfTokensStoppingCriteria(tokenizer, stop_tokens) # Añade tu criterio de parada a una StoppingCriteriaList stopping_criteria_list = StoppingCriteriaList([stopping_criteria]) #EXAMPLE #1 txt="""<bos><start_of_turn>system You are a helpful AI assistant.<end_of_turn> <start_of_turn>user Me dices los diferentes tipos de reciclaje que suelen existir en las ciudades europeas<end_of_turn> <start_of_turn>model """ #EXAMPLE #2 txt="""<bos><start_of_turn>system You are a helpful AI assistant.<end_of_turn> <start_of_turn>user What is the meaning of life in the current time?<end_of_turn> <start_of_turn>model """ inputs = tokenizer.encode(txt, return_tensors="pt", add_special_tokens=False).to("cuda:0") max_new_tokens=1000 generation_config = GenerationConfig( max_new_tokens=max_new_tokens, temperature=0.55, #top_p=0.9, #top_k=len_tokens, repetition_penalty=1.1, do_sample=True, ) outputs = model.generate(generation_config=generation_config, input_ids=inputs, stopping_criteria=stopping_criteria_list,) tokenizer.decode(outputs[0], skip_special_tokens=False) #True ```
thyagarajanc7/thyagarajanc77-gpt-neox	thyagarajanc7	2024-02-23T11:24:46Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T11:24:44Z	--- 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. 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elinaparajuli/T5_Finetuned-finetuned	elinaparajuli	2024-02-23T11:16:46Z	104	0	transformers	[ "transformers", "pytorch", "tf", "tensorboard", "rust", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T10:50:45Z	--- license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer model-index: - name: T5_Finetuned-finetuned 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. --> # T5_Finetuned-finetuned This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2568 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 39 \| 0.3849 \| \| No log \| 2.0 \| 78 \| 0.2738 \| \| No log \| 3.0 \| 117 \| 0.2568 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
Tempo14/parameter-mini-lds_cpu	Tempo14	2024-02-23T11:06:11Z	10	0	setfit	[ "setfit", "safetensors", "bert", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:sentence-transformers/all-MiniLM-L6-v2", "base_model:finetune:sentence-transformers/all-MiniLM-L6-v2", "region:us" ]	text-classification	2024-02-23T11:06:08Z	--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer metrics: - accuracy widget: [] pipeline_tag: text-classification inference: true base_model: sentence-transformers/all-MiniLM-L6-v2 --- # SetFit with sentence-transformers/all-MiniLM-L6-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - Maximum Sequence Length: 256 tokens <!-- - Number of Classes: Unknown --> <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("Tempo14/parameter-mini-lds_cpu") # Run inference preds = model("I loved the spiderman movie!") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Framework Versions - Python: 3.10.12 - SetFit: 1.0.3 - Sentence Transformers: 2.3.1 - Transformers: 4.37.2 - PyTorch: 2.1.0+cu121 - Datasets: 2.17.1 - Tokenizers: 0.15.2 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
Tempo14/contradiction-psb-lds_cpu	Tempo14	2024-02-23T11:06:01Z	6	0	setfit	[ "setfit", "safetensors", "mpnet", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:AI-Growth-Lab/PatentSBERTa", "base_model:finetune:AI-Growth-Lab/PatentSBERTa", "region:us" ]	text-classification	2024-02-23T11:05:46Z	--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer metrics: - accuracy widget: [] pipeline_tag: text-classification inference: true base_model: AI-Growth-Lab/PatentSBERTa --- # SetFit with AI-Growth-Lab/PatentSBERTa This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [AI-Growth-Lab/PatentSBERTa](https://huggingface.co/AI-Growth-Lab/PatentSBERTa) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: [AI-Growth-Lab/PatentSBERTa](https://huggingface.co/AI-Growth-Lab/PatentSBERTa) - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - Maximum Sequence Length: 512 tokens <!-- - Number of Classes: Unknown --> <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("Tempo14/contradiction-psb-lds_cpu") # Run inference preds = model("I loved the spiderman movie!") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Framework Versions - Python: 3.10.12 - SetFit: 1.0.3 - Sentence Transformers: 2.3.1 - Transformers: 4.37.2 - PyTorch: 2.1.0+cu121 - Datasets: 2.17.1 - Tokenizers: 0.15.2 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
Shreyagnani/roberta-large-peft-p-tuning	Shreyagnani	2024-02-23T11:05:05Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-14T10:23:40Z	--- 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]
FlagAlpha/Llama2-Chinese-13b-Chat	FlagAlpha	2024-02-23T11:03:22Z	1,854	274	transformers	[ "transformers", "pytorch", "llama", "text-generation", "question-answering", "zh", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	question-answering	2023-07-24T12:10:46Z	--- developers: [https://huggingface.co/FlagAlphaAI] license: apache-2.0 language: - zh - en pipeline_tag: question-answering library_name: transformers --- # Llama2中文社区 --- ## Llama2中文微调参数由于Llama2本身的中文对齐较弱，我们采用中文指令集，对meta-llama/Llama-2-13b-chat-hf进行LoRA微调，使其具备较强的中文对话能力。 🎯 该版本为LoRA中文微调参数FlagAlpha/Llama2-Chinese-13b-Chat-LoRA和meta-llama/Llama-2-13b-chat-hf参数结合后的版本，可直接使用 --- ## 🚀 社区地址： Github：[Llama-Chinese](https://github.com/LlamaFamily/Llama-Chinese) 在线体验链接：[llama.family](https://llama.family/) ## 🔥 社区介绍欢迎来到Llama2中文社区！我们是一个专注于Llama2模型在中文方面的优化和上层建设的高级技术社区。基于大规模中文数据，从预训练开始对Llama2模型进行中文能力的持续迭代升级。我们热忱欢迎对大模型LLM充满热情的开发者和研究者加入我们的行列。 ## 🐼 社区资源 - Llama2在线体验链接[llama.family](https://llama.family/)，同时包含Meta原版和中文微调版本！ - Llama2 Chat模型的[中文问答能力评测](https://github.com/LlamaFamily/Llama-Chinese/tree/main#-%E6%A8%A1%E5%9E%8B%E8%AF%84%E6%B5%8B)！ - [社区飞书知识库](https://chinesellama.feishu.cn/wiki/space/7257824476874768388?ccm_open_type=lark_wiki_spaceLink)，欢迎大家一起共建！
FlagAlpha/Llama2-Chinese-13b-Chat-LoRA	FlagAlpha	2024-02-23T11:02:53Z	0	17	transformers	[ "transformers", "question-answering", "zh", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-07-24T12:05:28Z	--- developers: [https://huggingface.co/FlagAlphaAI] license: apache-2.0 language: - zh - en pipeline_tag: question-answering library_name: transformers --- # Llama2中文社区 --- ## Llama2中文微调参数由于Llama2本身的中文对齐较弱，我们采用中文指令集，对meta-llama/Llama-2-13b-chat-hf进行LoRA微调，使其具备较强的中文对话能力。 🎯 该版本仅包含LoRA中文微调参数，需要与基础的meta-llama/Llama-2-13b-chat-hf模型结合使用 --- ## 🚀 社区地址： Github：[Llama-Chinese](https://github.com/LlamaFamily/Llama-Chinese) 在线体验链接：[llama.family](https://llama.family/) ## 🔥 社区介绍欢迎来到Llama2中文社区！我们是一个专注于Llama2模型在中文方面的优化和上层建设的高级技术社区。基于大规模中文数据，从预训练开始对Llama2模型进行中文能力的持续迭代升级。我们热忱欢迎对大模型LLM充满热情的开发者和研究者加入我们的行列。 ## 🐼 社区资源 - Llama2在线体验链接[llama.family](https://llama.family/)，同时包含Meta原版和中文微调版本！ - Llama2 Chat模型的[中文问答能力评测](https://github.com/LlamaFamily/Llama-Chinese/tree/main#-%E6%A8%A1%E5%9E%8B%E8%AF%84%E6%B5%8B)！ - [社区飞书知识库](https://chinesellama.feishu.cn/wiki/space/7257824476874768388?ccm_open_type=lark_wiki_spaceLink)，欢迎大家一起共建！
FlagAlpha/Llama2-Chinese-7b-Chat	FlagAlpha	2024-02-23T11:02:23Z	1,702	215	transformers	[ "transformers", "pytorch", "llama", "text-generation", "question-answering", "zh", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	question-answering	2023-07-23T10:12:21Z	--- developers: [https://huggingface.co/FlagAlphaAI] license: apache-2.0 language: - zh - en pipeline_tag: question-answering library_name: transformers --- # Llama2中文社区 --- ## Llama2中文微调参数由于Llama2本身的中文对齐较弱，我们采用中文指令集，对meta-llama/Llama-2-7b-chat-hf进行LoRA微调，使其具备较强的中文对话能力。 🎯 该版本为LoRA中文微调参数FlagAlpha/Llama2-Chinese-7b-Chat-LoRA和meta-llama/Llama-2-7b-chat-hf参数结合后的版本，可直接使用 --- ## 🚀 社区地址： Github：[Llama-Chinese](https://github.com/LlamaFamily/Llama-Chinese) 在线体验链接：[llama.family](https://llama.family/) ## 🔥 社区介绍欢迎来到Llama2中文社区！我们是一个专注于Llama2模型在中文方面的优化和上层建设的高级技术社区。基于大规模中文数据，从预训练开始对Llama2模型进行中文能力的持续迭代升级。我们热忱欢迎对大模型LLM充满热情的开发者和研究者加入我们的行列。 ## 🐼 社区资源 - Llama2在线体验链接[llama.family](https://llama.family/)，同时包含Meta原版和中文微调版本！ - Llama2 Chat模型的[中文问答能力评测](https://github.com/LlamaFamily/Llama-Chinese/tree/main#-%E6%A8%A1%E5%9E%8B%E8%AF%84%E6%B5%8B)！ - [社区飞书知识库](https://chinesellama.feishu.cn/wiki/space/7257824476874768388?ccm_open_type=lark_wiki_spaceLink)，欢迎大家一起共建！
FlagAlpha/Llama2-Chinese-7b-Chat-LoRA	FlagAlpha	2024-02-23T11:01:52Z	0	19	transformers	[ "transformers", "question-answering", "zh", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-07-23T10:02:22Z	--- developers: [https://huggingface.co/FlagAlphaAI] license: apache-2.0 language: - zh - en pipeline_tag: question-answering library_name: transformers --- # Llama2中文社区 --- ## Llama2中文微调参数由于Llama2本身的中文对齐较弱，我们采用中文指令集，对meta-llama/Llama-2-7b-chat-hf进行LoRA微调，使其具备较强的中文对话能力。 🎯 该版本仅包含LoRA中文微调参数，需要与基础的meta-llama/Llama-2-7b-chat-hf模型结合使用 --- ## 🚀 社区地址： Github：[Llama-Chinese](https://github.com/LlamaFamily/Llama-Chinese) 在线体验链接：[llama.family](https://llama.family/) ## 🔥 社区介绍欢迎来到Llama2中文社区！我们是一个专注于Llama2模型在中文方面的优化和上层建设的高级技术社区。基于大规模中文数据，从预训练开始对Llama2模型进行中文能力的持续迭代升级。我们热忱欢迎对大模型LLM充满热情的开发者和研究者加入我们的行列。 ## 🐼 社区资源 - Llama2在线体验链接[llama.family](https://llama.family/)，同时包含Meta原版和中文微调版本！ - Llama2 Chat模型的[中文问答能力评测](https://github.com/LlamaFamily/Llama-Chinese/tree/main#-%E6%A8%A1%E5%9E%8B%E8%AF%84%E6%B5%8B)！ - [社区飞书知识库](https://chinesellama.feishu.cn/wiki/space/7257824476874768388?ccm_open_type=lark_wiki_spaceLink)，欢迎大家一起共建！
FlagAlpha/Llama2-Chinese-13b-Chat-4bit	FlagAlpha	2024-02-23T11:01:13Z	8	59	transformers	[ "transformers", "llama", "text-generation", "question-answering", "zh", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	question-answering	2023-07-26T09:44:35Z	--- developers: [https://huggingface.co/FlagAlphaAI] license: apache-2.0 language: - zh - en pipeline_tag: question-answering library_name: transformers --- # Llama2中文社区 --- ## Llama2中文微调参数由于Llama2本身的中文对齐较弱，我们采用中文指令集，对meta-llama/Llama-2-13b-chat-hf进行LoRA微调，使其具备较强的中文对话能力。 🎯 该版本为中文微调参数FlagAlpha/Llama2-Chinese-13b-Chat进行4 bit量化后的版本，可直接使用 --- ## 🚀 社区地址： Github：[Llama-Chinese](https://github.com/LlamaFamily/Llama-Chinese) 在线体验链接：[llama.family](https://llama.family/) ## 🔥 社区介绍欢迎来到Llama2中文社区！我们是一个专注于Llama2模型在中文方面的优化和上层建设的高级技术社区。基于大规模中文数据，从预训练开始对Llama2模型进行中文能力的持续迭代升级。我们热忱欢迎对大模型LLM充满热情的开发者和研究者加入我们的行列。 ## 🐼 社区资源 - Llama2在线体验链接[llama.family](https://llama.family/)，同时包含Meta原版和中文微调版本！ - Llama2 Chat模型的[中文问答能力评测](https://github.com/LlamaFamily/Llama-Chinese/tree/main#-%E6%A8%A1%E5%9E%8B%E8%AF%84%E6%B5%8B)！ - [社区飞书知识库](https://chinesellama.feishu.cn/wiki/space/7257824476874768388?ccm_open_type=lark_wiki_spaceLink)，欢迎大家一起共建！
captainbob321/captainbobs-model-pack	captainbob321	2024-02-23T10:47:23Z	0	1	null	[ "license:openrail", "region:us" ]	null	2023-08-13T07:37:49Z	--- license: openrail --- I Introduce you to my model pack! Where it can be Futurama-related, Dumb Ways to Die-related, Mr. Men-releated, anything! These are free to use, as long you give credit (The CN Turkey announcer may sound bad, though. So, you've been warned)
emmabedna/bert-base-multilingual-cased-finetuned-language_classification	emmabedna	2024-02-23T10:41:19Z	105	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-02-23T09:54:15Z	--- license: apache-2.0 base_model: bert-base-multilingual-cased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-multilingual-cased-finetuned-language_classification 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. --> # bert-base-multilingual-cased-finetuned-language_classification This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0876 - Precision: 0.8954 - Recall: 0.9009 - F1: 0.8982 - Accuracy: 0.9785 ## 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: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 150 \| 0.1431 \| 0.8415 \| 0.8390 \| 0.8402 \| 0.9649 \| \| No log \| 2.0 \| 300 \| 0.0927 \| 0.8878 \| 0.8905 \| 0.8892 \| 0.9767 \| \| No log \| 3.0 \| 450 \| 0.0876 \| 0.8954 \| 0.9009 \| 0.8982 \| 0.9785 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
likhith231/vit-base-patch16-224-in21k-lora	likhith231	2024-02-23T10:35:25Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T10:35:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
sharren/skincancerclassification	sharren	2024-02-23T10:31:35Z	191	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-02-23T10:31:17Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - generated_from_trainer metrics: - accuracy model-index: - name: skincancerclassification 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. --> # skincancerclassification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the SkinCancerClassification dataset. It achieves the following results on the evaluation set: - Loss: 0.4969 - Accuracy: 0.8273 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7407 \| 0.31 \| 100 \| 0.7736 \| 0.7369 \| \| 0.662 \| 0.62 \| 200 \| 0.6556 \| 0.7649 \| \| 0.8726 \| 0.93 \| 300 \| 0.6127 \| 0.7783 \| \| 0.5618 \| 1.25 \| 400 \| 0.6259 \| 0.7728 \| \| 0.5202 \| 1.56 \| 500 \| 0.6421 \| 0.7958 \| \| 0.4177 \| 1.87 \| 600 \| 0.5702 \| 0.7913 \| \| 0.2606 \| 2.18 \| 700 \| 0.5213 \| 0.8268 \| \| 0.2529 \| 2.49 \| 800 \| 0.4969 \| 0.8273 \| \| 0.2153 \| 2.8 \| 900 \| 0.5216 \| 0.8233 \| \| 0.0772 \| 3.12 \| 1000 \| 0.5125 \| 0.8402 \| \| 0.0485 \| 3.43 \| 1100 \| 0.5232 \| 0.8492 \| \| 0.081 \| 3.74 \| 1200 \| 0.5426 \| 0.8567 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
vardhanam/male_model_xyz_sdxl_lora	vardhanam	2024-02-23T10:31:03Z	2	1	diffusers	[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2024-02-23T10:30:56Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: A photo of male_model_xyz tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.
jasivan/flan-t5-base-FERMAT	jasivan	2024-02-23T10:29:09Z	159	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "en", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T10:15:44Z	--- license: mit language: - en ---
stablediffusionapi/honey-2d	stablediffusionapi	2024-02-23T10:22:51Z	26	1	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-02-23T10:20:41Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # Honey 2D API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/44440291708683579.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "honey-2d" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs) Try model for free: [Generate Images](https://modelslab.com/models/honey-2d) Model link: [View model](https://modelslab.com/models/honey-2d) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "honey-2d", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN
modelsexperimentmaterial/phi_dpo_v1	modelsexperimentmaterial	2024-02-23T10:21:13Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T10:09:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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dummydoctor/Pallas-0.5-LASER-0.1-btk	dummydoctor	2024-02-23T10:08:03Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-02-23T08:33:57Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: mistralai/Mistral-7B-v0.1 model-index: - name: Pallas-0.5-LASER-0.1-btk 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. --> # Pallas-0.5-LASER-0.1-btk This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.2
AfterRain007/cryptobertRefined	AfterRain007	2024-02-23T10:07:52Z	112	2	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "Crypto", "Bitcoin", "Sentiment Analysis", "RoBERTa", "NLP", "Cryptocurrency", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-02T09:52:57Z	--- license: apache-2.0 language: - en metrics: - accuracy pipeline_tag: text-classification tags: - Crypto - Bitcoin - Sentiment Analysis - RoBERTa - NLP - Cryptocurrency --- # CryptoBERTRefined CryptoBERTRefined is a fine tuned model from [CryptoBERT by Elkulako](https://huggingface.co/ElKulako/cryptobert) model. # Classification Example Input: ```python !pip -q install transformers from transformers import TextClassificationPipeline, AutoModelForSequenceClassification, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("AfterRain007/cryptobertRefined", use_fast=True) model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels = 3) pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, max_length=128, truncation=True, padding = 'max_length') post_3 = "Because Forex Markets have years of solidity and millions in budget, not to mention that they use their own datacenters. These lame cryptomarkets are all supported by some Amazon-cloud-style system. They delegate and delegate their security and in the end, get buttfucked..." post_2 = "Russian crypto market worth $500B despite bad regulation, says exec https://t.co/MZFoZIr2cN #CryptoCurrencies #Bitcoin #Technical Analysis" post_1 = "I really wouldn't be asking strangers such an important question. I'm sure you'd get well meaning answers but you probably need professional advice." df_posts = [post_1, post_2, post_3] preds = pipe(df_posts) print(preds) ``` Output: ```python [{'label': 'Neutral', 'score': 0.8427615165710449}, {'label': 'Bullish', 'score': 0.5444369912147522}, {'label': 'Bearish', 'score': 0.8388379812240601}] ``` # Training Corpus Total of 3.803 text have been labelled manually to fine tune the model, with consideration of non-duplicate and a minimum of 4 words after cleaning. The following website were used for our training dataset: 1. Bitcoin tweet dataset from [Kaggle Datasets](https://www.kaggle.com/datasets/kaushiksuresh147/bitcoin-tweets) (Randomly picked). 2. Labelled crypto sentiment dataset from [SurgeAI](https://www.surgehq.ai/datasets/crypto-sentiment-dataset). 3. Reddit thread r/Bitcoin with the topic "Daily Discussion" (Randomly picked) Data augmentation was also performed to enrich the dataset, Back-Translation was used with Google Translate API on 10 language ('it', 'fr', "sv", "da", 'pt', 'id', 'pl', 'hr', "bg", "fi"). # Source Code See [Github](https://github.com/AfterRain007/cryptobertRefined) for the source code to finetune cryptoBERT model into cryptoBERTRefined. # Credit Credit where credit is due, thank you for all! 1. Muhaza Liebenlito, M.Si and Prof. Dr. Nur Inayah, M.Si. as my academic advisor. 2. Risky Amalia Marhariyadi for helping labelling the dataset. 3. SurgeAI for the dataset. 4. Mikolaj Kulakowski and Flavius Frasincar for the original CryptoBERT model. 5. Kaushik Suresh for the bitcoin tweets.
hongce-tech/dpo-tinyllama_fac	hongce-tech	2024-02-23T10:04:48Z	94	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T10:02:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Coletomyo/whisper-large-v2	Coletomyo	2024-02-23T10:03:13Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "mn", "dataset:mozilla-foundation/common_voice_16_0", "base_model:openai/whisper-large-v2", "base_model:finetune:openai/whisper-large-v2", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-02-21T10:40:25Z	--- language: - mn license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_16_0 metrics: - wer base_model: openai/whisper-large-v2 model-index: - name: Whisper Large Mongolian results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: Common Voice 16.0 type: mozilla-foundation/common_voice_16_0 config: mn split: None args: 'config: mn, split: test' metrics: - type: wer value: 37.23357981731187 name: Wer --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Large Mongolian This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 16.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4028 - Wer: 37.2336 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.3446 \| 0.99 \| 1000 \| 0.4391 \| 51.4572 \| \| 0.1481 \| 1.98 \| 2000 \| 0.3765 \| 42.2412 \| \| 0.076 \| 2.97 \| 3000 \| 0.3830 \| 39.0822 \| \| 0.0149 \| 3.96 \| 4000 \| 0.4028 \| 37.2336 \| ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
mesolitica/gemma-7B-8192-fpf	mesolitica	2024-02-23T09:50:42Z	11	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "ms", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-22T15:13:33Z	--- language: - ms --- # Full Parameter Finetuning Gemma 7B on Malaysian text README at https://github.com/huseinzol05/malaya/tree/5.1/session/gemma WandB, https://wandb.ai/huseinzol05/finetune-gemma-7b?workspace=user-huseinzol05
FCLi/speecht5_finetuned_voxpopuli_nl	FCLi	2024-02-23T09:48:20Z	71	0	transformers	[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2024-02-22T05:38:04Z	--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer model-index: - name: speecht5_finetuned_voxpopuli_nl 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. --> # speecht5_finetuned_voxpopuli_nl This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6587 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.3903 \| 380.95 \| 1000 \| 0.6587 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.0.1 - Datasets 2.17.1 - Tokenizers 0.15.2
manimaranpa07/my_idea_classification_model_trial_1	manimaranpa07	2024-02-23T09:45:53Z	106	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-20T07:28:19Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_idea_classification_model_trial_1 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. --> # my_idea_classification_model_trial_1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2931 - Accuracy: 0.9211 ## 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: 6 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 20 \| 0.4372 \| 0.8158 \| \| No log \| 2.0 \| 40 \| 0.3864 \| 0.8158 \| \| No log \| 3.0 \| 60 \| 0.3451 \| 0.8947 \| \| No log \| 4.0 \| 80 \| 0.3106 \| 0.8947 \| \| No log \| 5.0 \| 100 \| 0.2940 \| 0.9211 \| \| No log \| 6.0 \| 120 \| 0.2931 \| 0.9211 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu118 - Datasets 2.17.0 - Tokenizers 0.15.2
Astral7/roberta-large-finetuned-ner	Astral7	2024-02-23T09:31:46Z	59	0	transformers	[ "transformers", "tf", "roberta", "token-classification", "generated_from_keras_callback", "base_model:FacebookAI/roberta-large", "base_model:finetune:FacebookAI/roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-02-19T15:11:23Z	--- license: mit base_model: FacebookAI/roberta-large tags: - generated_from_keras_callback model-index: - name: ner-test results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> ## roberta-large-finetuned-ner ## Model description roberta-large-finetuned-ner is a fine-tuned Roberta model that is ready to use for Named Entity Recognition. It has been trained to recognize eight types of entities: Geographical, Organization, Person, Geopolitical Entity, Time indicator, Artifact, Event, Natural Phenomenon. This model is a fine-tuned version of [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on an [Named Entity Recognition (NER) Corpus dataset](https://www.kaggle.com/datasets/naseralqaydeh/named-entity-recognition-ner-corpus). It achieves the following results on the evaluation set: - Train Loss: 0.1164 - Validation Loss: 0.0878 - Train Precision: 0.8442 - Train Recall: 0.8358 - Train F1: 0.8400 - Train Accuracy: 0.9718 - Epoch: 0 ## Intended uses & limitations #### How to use: You can use this model with Transformers pipeline for NER. ```python from transformers import AutoTokenizer, TFAutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("Astral7/roberta-large-finetuned-ner",add_prefix_space=True) model = TFAutoModelForTokenClassification.from_pretrained("Astral7/roberta-large-finetuned-ner") nlp_pipe = pipeline("token-classification", model=model,tokenizer=tokenizer ) example = "My name is Clara and I live in Berkeley, California." results=nlp_pipe(example) print(results) ``` #### Limitations: This model is limited by its training dataset of Annotated Corpus for Named Entity Recognition is annotated Corpus for Named Entity Recognition using GMB(Groningen Meaning Bank) corpus for entity classification with enhanced and popular features by Natural Language Processing applied to the data set. ## Training and evaluation data This model was fine-tuned on Corpus for [Named Entity Recognition Dataset](https://www.kaggle.com/datasets/naseralqaydeh/named-entity-recognition-ner-corpus). As in the dataset, each token will be classified as one of the following classes: Abbreviation\|Description -\|- B-eve\| Event I-eve \| Event B-org \| Organization I-org \| Organization B-gpe \| Geopolitical Entity I-gpe \| Geopolitical Entity B-geo \| Geographical I-geo \| Geographical B-nat \| Natural Phenomenon I-nat \| Natural Phenomenon B-per \| Person I-per \| Person B-art \| Art I-art \| Art B-tim \| Time I-tim \| Time ## Training procedure This model was trained on a single T4 GPU. ### Training hyperparameters The following hyperparameters were used during training: - optimizer: { "name": "AdamWeightDecay", "learning_rate": { "module": "keras.optimizers.schedules", "class_name": "PolynomialDecay", "config": { "initial_learning_rate": 2e-05, "decay_steps": 4795, "end_learning_rate": 0.0, "power": 1.0, "cycle": False, "name": None, }, "registered_name": None, }, "decay": 0.0, "beta_1": 0.9, "beta_2": 0.999, "epsilon": 1e-08, "amsgrad": False, "weight_decay_rate": 0.01, } - - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Precision \| Train Recall \| Train F1 \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:---------------:\|:------------:\|:--------:\|:--------------:\|:-----:\| \| 0.1164 \| 0.0878 \| 0.8442 \| 0.8358 \| 0.8400 \| 0.9718 \| 0 \| ### Framework versions - Transformers 4.35.2 - TensorFlow 2.15.0 - Datasets 2.17.1 - Tokenizers 0.15.2
jinyuan22/gemma-Code-Instruct-Finetune-test	jinyuan22	2024-02-23T09:26:17Z	106	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T09:19:44Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ThuyNT03/CS505_COQE_viT5_Prompting5_ASPOL_vcheck	ThuyNT03	2024-02-23T09:19:39Z	99	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "base_model:finetune:VietAI/vit5-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T08:17:01Z	--- license: mit base_model: VietAI/vit5-large tags: - generated_from_trainer model-index: - name: CS505_COQE_viT5_Prompting5_ASPOL_vcheck 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. --> # CS505_COQE_viT5_Prompting5_ASPOL_vcheck This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1
mb7419/mistral-7b-legal-ft-merged	mb7419	2024-02-23T09:19:34Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T09:10:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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angel450/OPEN-SOLAR-10.7B-kra-v1	angel450	2024-02-23T09:19:17Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-22T10:59:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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chatbotNZ/mtext-150224-merged	chatbotNZ	2024-02-23T09:16:14Z	3	0	peft	[ "peft", "pytorch", "gguf", "llama", "axolotl", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "endpoints_compatible", "region:us" ]	null	2024-02-22T08:57:14Z	--- library_name: peft tags: - axolotl - generated_from_trainer base_model: meta-llama/Llama-2-7b-hf model-index: - name: mtext-150224_2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: meta-llama/Llama-2-7b-hf base_model_config: meta-llama/Llama-2-7b-hf model_type: LlamaForCausalLM tokenizer_type: LlamaTokenizer is_llama_derived_model: true load_in_8bit: false load_in_4bit: true strict: false datasets: - path: ascherrer/mtext-data-150224_2 type: completion field: text dataset_prepared_path: last_run_prepared hub_model_id: ascherrer/mtext-150224_2 val_set_size: 0.01 output_dir: ./qlora-out adapter: qlora lora_model_dir: sequence_len: 4096 sample_packing: true eval_sample_packing: false pad_to_sequence_len: true lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_modules: lora_target_linear: true lora_fan_in_fan_out: wandb_project: "machine-de-textes" wandb_entity: wandb_watch: wandb_run_id: wandb_log_model: "checkpoint" lora_modules_to_save: - embed_tokens - lm_head gradient_accumulation_steps: 4 micro_batch_size: 2 num_epochs: 3 optimizer: paged_adamw_32bit lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: true fp16: false tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 eval_steps: 20 eval_table_size: 5 save_steps: debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: bos_token: "<s>" eos_token: "</s>" unk_token: "<unk>" tokens: # these are delimiters - "<\|s\|>" - "<\|e\|>" ``` </details><br> # mtext-150224_2 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0540 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.8407 \| 0.42 \| 20 \| 2.1162 \| \| 1.6743 \| 0.84 \| 40 \| 2.0768 \| \| 1.5006 \| 1.24 \| 60 \| 2.0654 \| \| 1.5812 \| 1.65 \| 80 \| 2.0598 \| \| 1.5619 \| 2.05 \| 100 \| 2.0535 \| \| 1.5251 \| 2.47 \| 120 \| 2.0537 \| \| 1.5473 \| 2.89 \| 140 \| 2.0540 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.1.2+cu118 - Datasets 2.17.0 - Tokenizers 0.15.0
deepaknh/falcon7b-FineTuningQLORA_IncrementTrainingVersion3	deepaknh	2024-02-23T09:15:16Z	1	0	peft	[ "peft", "arxiv:1910.09700", "base_model:ybelkada/falcon-7b-sharded-bf16", "base_model:adapter:ybelkada/falcon-7b-sharded-bf16", "region:us" ]	null	2024-02-23T09:14:46Z	--- library_name: peft base_model: ybelkada/falcon-7b-sharded-bf16 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - 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 Data 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 Data 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] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - 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: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.1
aegon-h/gemma-7b	aegon-h	2024-02-23T09:12:34Z	7	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-02-23T09:12:34Z	--- library_name: transformers tags: [] extra_gated_heading: "Access Gemma on Hugging Face" extra_gated_prompt: "To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately." extra_gated_button_content: "Acknowledge license" license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms inference: false --- # Gemma Model Card Original Model Page: [Gemma](https://ai.google.dev/gemma/docs) This model card corresponds to the 7B base version of the Gemma model. Original Resources and Technical Documentation: * [Responsible Generative AI Toolkit](https://ai.google.dev/responsible) * [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma) * [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335) Terms of Use: [Terms](https://www.kaggle.com/models/google/gemma/license/consent) Original Authors: Google ## Model Information Summary description and brief definition of inputs and outputs. ### Description Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. ### Usage Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase. #### Running the model on a CPU ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b") model = AutoModelForCausalLM.from_pretrained("google/gemma-7b") input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` #### Running the model on a single / multi GPU ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b") model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto") input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` #### Running the model on a GPU using different precisions * _Using `torch.float16`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b") model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto", torch_dtype=torch.float16) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` _Using `torch.bfloat16`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b") model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto", torch_dtype=torch.bfloat16) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` #### Quantized Versions through `bitsandbytes` _Using 8-bit precision (int8)_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_8bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b") model = AutoModelForCausalLM.from_pretrained(google/gemma-7b", quantization_config=quantization_config) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` _Using 4-bit precision_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_4bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b") model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", quantization_config=quantization_config) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` #### Other optimizations _Flash Attention 2_ First make sure to install `flash-attn` in your environment `pip install flash-attn` ```diff model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, + attn_implementation="flash_attention_2" ).to(0) ``` ### Inputs and outputs * Input: Text string, such as a question, a prompt, or a document to be summarized. * Output: Generated English-language text in response to the input, such as an answer to a question, or a summary of a document. ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage Open Large Language Models (LLMs) have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. * Content Creation and Communication * Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. * Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications. * Text Summarization: Generate concise summaries of a text corpus, research papers, or reports. * Research and Education * Natural Language Processing (NLP) Research: These models can serve as a foundation for researchers to experiment with NLP techniques, develop algorithms, and contribute to the advancement of the field. * Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. * Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. ### Limitations * Training Data * The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. * The scope of the training dataset determines the subject areas the model can handle effectively. * Context and Task Complexity * LLMs are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. * A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). * Language Ambiguity and Nuance * Natural language is inherently complex. LLMs might struggle to grasp subtle nuances, sarcasm, or figurative language. * Factual Accuracy * LLMs generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. * Common Sense * LLMs rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. ### Ethical Considerations and Risks The development of large language models (LLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: * Bias and Fairness * LLMs trained on large-scale, real-world text data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. * Misinformation and Misuse * LLMs can be misused to generate text that is false, misleading, or harmful. * Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible). * Transparency and Accountability: * This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. * A responsibly developed open model offers the opportunity to share innovation by making LLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: * Perpetuation of biases: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. * Generation of harmful content: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. * Misuse for malicious purposes: Technical limitations and developer and end-user education can help mitigate against malicious applications of LLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy). * Privacy violations: Models were trained on data filtered for removal of PII (Personally Identifiable Information). Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ### Benefits At the time of release, this family of models provides high-performance open large language model implementations designed from the ground up for Responsible AI development compared to similarly sized models. Using the benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives.
dongtan/output	dongtan	2024-02-23T09:07:24Z	3	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "lora", "stable-diffusion", "stable-diffusion-diffusers", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-02-23T06:05:19Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - diffusers - lora - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora - stable-diffusion - stable-diffusion-diffusers inference: true base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of yjmki man --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # LoRA DreamBooth - dongtan/output These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of yjmki man using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
DocCheck/test_model2	DocCheck	2024-02-23T09:05:32Z	0	0	null	[ "license:other", "region:us" ]	null	2024-02-23T09:05:32Z	--- license: other license_name: cc-by-sa-4.0 license_link: https://creativecommons.org/licenses/by-sa/4.0/legalcode.txt ---
izumi04/Orion-14B-Chat-GGUF	izumi04	2024-02-23T09:04:46Z	7	0	null	[ "gguf", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2024-02-23T08:40:42Z	--- license: other license_name: orion-14b-lincense license_link: >- https://huggingface.co/OrionStarAI/Orion-14B-Chat/blob/main/ModelsCommunityLicenseAgreement ---
anrouxel/MedicApp	anrouxel	2024-02-23T09:01:53Z	0	1	null	[ "fr", "license:eupl-1.1", "region:us" ]	null	2024-02-22T22:06:42Z	--- license: eupl-1.1 language: - fr --- <h1 align="center"> <img src="MedicApp.png" alt="MedicAppAssets" height="200"> <br /> </h1> <p align="center"><b>This project is licensed under the EUPL 1.1.</b></p> ## Authors * Antonin Rouxel ([anrouxel](https://github.com/anrouxel))
RafSarLop/mbart-neutralization	RafSarLop	2024-02-23T09:01:11Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "mbart", "text2text-generation", "simplification", "generated_from_trainer", "base_model:facebook/mbart-large-50", "base_model:finetune:facebook/mbart-large-50", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-18T17:23:38Z	--- license: mit base_model: facebook/mbart-large-50 tags: - simplification - generated_from_trainer model-index: - name: mbart-neutralization 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. --> # mbart-neutralization This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
Spico/internlm2-7b-hf-llama	Spico	2024-02-23T08:58:12Z	2	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T08:36:18Z	# InternLM2-7B in LLaMA format with tokenizer fixed for llama.cpp [#612](https://github.com/InternLM/InternLM/issues/612)
Sevewing/pokemon-lora	Sevewing	2024-02-23T08:45:51Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-02-22T10:04:46Z	--- license: creativeml-openrail-m library_name: diffusers tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true base_model: runwayml/stable-diffusion-v1-5 --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # LoRA text2image fine-tuning - Sevewing/pokemon-lora These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
hythyt/rl_course_vizdoom_health_gathering_supreme	hythyt	2024-02-23T08:45:33Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-23T08:34:02Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 7.92 +/- 3.80 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r hythyt/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
Ayus077BCT014Bhandari/vartat5-using-100K-plus-8	Ayus077BCT014Bhandari	2024-02-23T08:45:01Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T01:34:05Z	--- 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. 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antonioglass/real-or-anime-age	antonioglass	2024-02-23T08:40:12Z	92	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "autotrain", "dataset:real-or-anime-age/autotrain-data", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-02-23T08:39:51Z	--- tags: - autotrain - image-classification widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace datasets: - real-or-anime-age/autotrain-data --- # Model Trained Using AutoTrain - Problem type: Image Classification ## Validation Metricsg loss: 0.4898335635662079 f1_macro: 0.7652092723432848 f1_micro: 0.7869487485101311 f1_weighted: 0.7873025165202922 precision_macro: 0.7664681493316189 precision_micro: 0.7869487485101311 precision_weighted: 0.7878104453780785 recall_macro: 0.7640948741007194 recall_micro: 0.7869487485101311 recall_weighted: 0.7869487485101311 accuracy: 0.7869487485101311
QEQ1996/1990	QEQ1996	2024-02-23T08:33:52Z	2	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-02-23T08:33:05Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: '-' output: url: >- images/2613E9E280A3B7C4848E3BF4EC5060815F8D08009B4E3EC536D9EEEC25C02145.jpeg base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: null license: creativeml-openrail-m --- # 1990 <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/QEQ1996/1990/tree/main) them in the Files & versions tab.
xiaoyizhu/test-exl2	xiaoyizhu	2024-02-23T08:19:22Z	2	0	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "finetuned", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-02-23T06:52:48Z	--- license: apache-2.0 pipeline_tag: text-generation tags: - finetuned --- https://huggingface.co/turboderp/Mistral-7B-instruct-exl2/tree/2.5bpw # Model Card for Mistral-7B-Instruct-v0.1 The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) generative text model using a variety of publicly available conversation datasets. For full details of this model please read our [release blog post](https://mistral.ai/news/announcing-mistral-7b/) ## Instruction format In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[\INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id. E.g. ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") text = "<s>[INST] What is your favourite condiment? [/INST]" "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> " "[INST] Do you have mayonnaise recipes? [/INST]" encodeds = tokenizer(text, return_tensors="pt", add_special_tokens=False) model_inputs = encodeds.to(device) model.to(device) generated_ids = model.generate(**model_inputs, max_new_tokens=1000, do_sample=True) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0]) ``` ## Model Architecture This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices: - Grouped-Query Attention - Sliding-Window Attention - Byte-fallback BPE tokenizer ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
ryusangwon/2711_Llama-2-7b-hf	ryusangwon	2024-02-23T08:19:07Z	1	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:samsum", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2024-02-23T08:19:02Z	--- base_model: meta-llama/Llama-2-7b-hf tags: - generated_from_trainer datasets: - samsum model-index: - name: 2711_Llama-2-7b-hf results: [] library_name: peft --- <!-- 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. --> # 2711_Llama-2-7b-hf This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the samsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 1 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.4.0 - Transformers 4.36.2 - Pytorch 2.0.1+cu117 - Datasets 2.15.0 - Tokenizers 0.15.0
ryusangwon/5192_Llama-2-7b-hf	ryusangwon	2024-02-23T08:18:44Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:samsum", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2024-02-23T08:18:39Z	--- base_model: meta-llama/Llama-2-7b-hf tags: - generated_from_trainer datasets: - samsum model-index: - name: 5192_Llama-2-7b-hf results: [] library_name: peft --- <!-- 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. --> # 5192_Llama-2-7b-hf This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the samsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 100 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.4.0 - Transformers 4.36.2 - Pytorch 2.0.1+cu117 - Datasets 2.15.0 - Tokenizers 0.15.0
Erikellerx/videomae-base-finetuned-ucf101	Erikellerx	2024-02-23T08:16:33Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "base_model:finetune:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2024-02-23T06:11:59Z	--- license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-ucf101 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # videomae-base-finetuned-ucf101 This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0721 - Accuracy: 0.9793 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 3328 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.0148 \| 0.25 \| 833 \| 1.1001 \| 0.6825 \| \| 0.2036 \| 1.25 \| 1666 \| 0.4224 \| 0.8734 \| \| 0.1442 \| 2.25 \| 2499 \| 0.1231 \| 0.9660 \| \| 0.073 \| 3.25 \| 3328 \| 0.0721 \| 0.9793 \| ### Framework versions - Transformers 4.37.1 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.1
silk-road/Haruhi-Zero-Gemma-2B-0_5	silk-road	2024-02-23T08:15:43Z	8	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T08:07:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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fzzhang/mistralv1_s_tuneonGSM8k_quantized_merged_on_quantized	fzzhang	2024-02-23T08:15:42Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-02-23T08:13:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Shaleen123/mistral1-6b	Shaleen123	2024-02-23T08:15:25Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T08:15:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Shaleen123/mistral-6b	Shaleen123	2024-02-23T08:15:15Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T08:01:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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deepaknh/falcon7b-FineTuningQLORA_IncrementTrainingVersion1	deepaknh	2024-02-23T07:59:51Z	5	0	peft	[ "peft", "pytorch", "falcon", "arxiv:1910.09700", "base_model:ybelkada/falcon-7b-sharded-bf16", "base_model:adapter:ybelkada/falcon-7b-sharded-bf16", "region:us" ]	null	2024-02-23T07:50:36Z	--- library_name: peft base_model: ybelkada/falcon-7b-sharded-bf16 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - 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: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.1
BloodMichael/autotrain-heuwk-z28ee	BloodMichael	2024-02-23T07:57:12Z	5	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "autotrain", "dataset:autotrain-heuwk-z28ee/autotrain-data", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-23T07:56:56Z	--- tags: - autotrain - text-classification widget: - text: "I love AutoTrain" datasets: - autotrain-heuwk-z28ee/autotrain-data --- # Model Trained Using AutoTrain - Problem type: Text Classification ## Validation Metrics loss: 1.9699126482009888 f1_macro: 0.0 f1_micro: 0.0 f1_weighted: 0.0 precision_macro: 0.0 precision_micro: 0.0 precision_weighted: 0.0 recall_macro: 0.0 recall_micro: 0.0 recall_weighted: 0.0 accuracy: 0.0

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