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train · 881k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-14 18:27:59	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 520 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-14 18:27:48	card stringlengths 11 1.01M
noahabebe/baymax	noahabebe	2024-05-25T10:57:41Z	0	0	transformers	[ "transformers", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-22T14:33:04Z	--- title: Baymx emoji: 💬 colorFrom: yellow colorTo: purple sdk: gradio app_file: app.py pinned: false license: apache-2.0 language: - en library_name: transformers ---
hgnoi/aMV3Is5mOOcevSV5	hgnoi	2024-05-25T10:55:56Z	70	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T10:53:34Z	--- 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]
Hevagog/Reinforce-111	Hevagog	2024-05-25T10:55:28Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-05-25T09:24:04Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-111 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 585.70 +/- 289.96 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
Ramikan-BR/tinyllama-coder-py-4bit_LORA-v9	Ramikan-BR	2024-05-25T10:55:14Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/tinyllama-chat-bnb-4bit", "base_model:finetune:unsloth/tinyllama-chat-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-25T10:54:22Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/tinyllama-chat-bnb-4bit --- # Uploaded model - Developed by: Ramikan-BR - License: apache-2.0 - Finetuned from model : unsloth/tinyllama-chat-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Mozilla/Mistral-7B-Instruct-v0.2-llamafile	Mozilla	2024-05-25T10:47:13Z	3,043	26	transformers	[ "transformers", "gguf", "llamafile", "mistral", "finetuned", "text-generation", "arxiv:2310.06825", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:quantized:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us", "conversational" ]	text-generation	2023-12-28T18:14:56Z	--- base_model: mistralai/Mistral-7B-Instruct-v0.2 inference: false license: apache-2.0 model_creator: Mistral AI_ model_name: Mistral 7B Instruct v0.2 model_type: mistral pipeline_tag: text-generation prompt_template: \| <s>[INST] {prompt} [/INST] quantized_by: jartine tags: - finetuned - llamafile --- # Mistral 7B Instruct v0.2 - llamafile - Model creator: [Mistral AI_](https://huggingface.co/mistralai) - Original model: [Mistral 7B Instruct v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) <!-- description start --> ## Description This repo contains llamafile format model files for [Mistral AI_'s Mistral 7B Instruct v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). WARNING: This README may contain inaccuracies. It was generated automatically by forking <a href=/TheBloke/Mistral-7B-Instruct-v0.2-GGUF>TheBloke/Mistral-7B-Instruct-v0.2-GGUF</a> and piping the README through sed. Errors should be reported to jartine, and do not reflect TheBloke. You can also support his work on [Patreon](https://www.patreon.com/TheBlokeAI). <!-- README_llamafile.md-about-llamafile start --> ### About llamafile llamafile is a new format introduced by Mozilla Ocho on Nov 20th 2023. It uses Cosmopolitan Libc to turn LLM weights into runnable llama.cpp binaries that run on the stock installs of six OSes for both ARM64 and AMD64. Here is an incomplete list of clients and libraries that are known to support llamafile: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for llamafile. 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. <!-- README_llamafile.md-about-llamafile end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-GPTQ) * [2, 3, 4, 5, 6 and 8-bit llamafile models for CPU+GPU inference](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile) * [Mistral AI_'s original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Mistral ``` <s>[INST] {prompt} [/INST] ``` <!-- prompt-template end --> <!-- compatibility_llamafile start --> ## Compatibility These quantised llamafilev2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## 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 Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_llamafile end --> <!-- README_llamafile.md-provided-files start --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [mistral-7b-instruct-v0.2.Q2_K.llamafile](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/blob/main/mistral-7b-instruct-v0.2.Q2_K.llamafile) \| Q2_K \| 2 \| 3.08 GB\| 5.58 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [mistral-7b-instruct-v0.2.Q3_K_S.llamafile](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/blob/main/mistral-7b-instruct-v0.2.Q3_K_S.llamafile) \| Q3_K_S \| 3 \| 3.16 GB\| 5.66 GB \| very small, high quality loss \| \| [mistral-7b-instruct-v0.2.Q3_K_M.llamafile](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/blob/main/mistral-7b-instruct-v0.2.Q3_K_M.llamafile) \| Q3_K_M \| 3 \| 3.52 GB\| 6.02 GB \| very small, high quality loss \| \| [mistral-7b-instruct-v0.2.Q3_K_L.llamafile](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/blob/main/mistral-7b-instruct-v0.2.Q3_K_L.llamafile) \| Q3_K_L \| 3 \| 3.82 GB\| 6.32 GB \| small, substantial quality loss \| \| [mistral-7b-instruct-v0.2.Q4_0.llamafile](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/blob/main/mistral-7b-instruct-v0.2.Q4_0.llamafile) \| Q4_0 \| 4 \| 4.11 GB\| 6.61 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [mistral-7b-instruct-v0.2.Q4_K_S.llamafile](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/blob/main/mistral-7b-instruct-v0.2.Q4_K_S.llamafile) \| Q4_K_S \| 4 \| 4.14 GB\| 6.64 GB \| small, greater quality loss \| \| [mistral-7b-instruct-v0.2.Q4_K_M.llamafile](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/blob/main/mistral-7b-instruct-v0.2.Q4_K_M.llamafile) \| Q4_K_M \| 4 \| 4.37 GB\| 6.87 GB \| medium, balanced quality - recommended \| \| [mistral-7b-instruct-v0.2.Q5_0.llamafile](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/blob/main/mistral-7b-instruct-v0.2.Q5_0.llamafile) \| Q5_0 \| 5 \| 5.00 GB\| 7.50 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [mistral-7b-instruct-v0.2.Q5_K_S.llamafile](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/blob/main/mistral-7b-instruct-v0.2.Q5_K_S.llamafile) \| Q5_K_S \| 5 \| 5.00 GB\| 7.50 GB \| large, low quality loss - recommended \| \| [mistral-7b-instruct-v0.2.Q5_K_M.llamafile](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/blob/main/mistral-7b-instruct-v0.2.Q5_K_M.llamafile) \| Q5_K_M \| 5 \| 5.13 GB\| 7.63 GB \| large, very low quality loss - recommended \| \| [mistral-7b-instruct-v0.2.Q6_K.llamafile](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/blob/main/mistral-7b-instruct-v0.2.Q6_K.llamafile) \| Q6_K \| 6 \| 5.94 GB\| 8.44 GB \| very large, extremely low quality loss \| \| [mistral-7b-instruct-v0.2.Q8_0.llamafile](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/blob/main/mistral-7b-instruct-v0.2.Q8_0.llamafile) \| Q8_0 \| 8 \| 7.70 GB\| 10.20 GB \| very large, extremely low quality loss - not recommended \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_llamafile.md-provided-files end --> <!-- README_llamafile.md-how-to-download start --> ## How to download llamafile 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: jartine/Mistral-7B-Instruct-v0.2-llamafile and below it, a specific filename to download, such as: mistral-7b-instruct-v0.2.Q4_K_M.llamafile. 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 jartine/Mistral-7B-Instruct-v0.2-llamafile mistral-7b-instruct-v0.2.Q4_K_M.llamafile --local-dir . --local-dir-use-symlinks False ``` <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 jartine/Mistral-7B-Instruct-v0.2-llamafile --local-dir . --local-dir-use-symlinks False --include='Q4_Kllamafile' ``` 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 jartine/Mistral-7B-Instruct-v0.2-llamafile mistral-7b-instruct-v0.2.Q4_K_M.llamafile --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> <!-- README_llamafile.md-how-to-download end --> <!-- README_llamafile.md-how-to-run start --> ## 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 mistral-7b-instruct-v0.2.Q4_K_M.llamafile --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<s>[INST] {prompt} [/INST]" ``` 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 llamafile 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 llamafile 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="./mistral-7b-instruct-v0.2.Q4_K_M.llamafile", # 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( "<s>[INST] {prompt} [/INST]", # 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="./mistral-7b-instruct-v0.2.Q4_K_M.llamafile", 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) <!-- README_llamafile.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [jartine AI's Discord server](https://discord.gg/FwAVVu7eJ4) ## Thanks, and how to contribute I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. And thank you again to mozilla for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: Mistral AI_'s Mistral 7B Instruct v0.2 # Model Card for Mistral-7B-Instruct-v0.2 The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an improved instruct fine-tuned version of [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1). For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/la-plateforme/). ## 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. ``` 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]" ``` This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method: ```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") messages = [ {"role": "user", "content": "What is your favourite condiment?"}, {"role": "assistant", "content": "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!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} ] encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") 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 ## Troubleshooting - If you see the following error: ``` Traceback (most recent call last): File "", line 1, in File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained config, kwargs = AutoConfig.from_pretrained( File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained config_class = CONFIG_MAPPING[config_dict["model_type"]] File "/transformers/models/auto/configuration_auto.py", line 723, in getitem raise KeyError(key) KeyError: 'mistral' ``` Installing transformers from source should solve the issue pip install git+https://github.com/huggingface/transformers This should not be required after transformers-v4.33.4. ## Limitations The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed. <!-- original-model-card end -->
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles9_seed2_q2_complexity	Omriy123	2024-05-25T10:46:51Z	165	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-25T10:32:15Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit_epochs5_batch32_lr5e-05_size224_tiles9_seed2_q2_complexity results: - task: name: Image Classification type: image-classification dataset: name: Dogs_vs_Cats type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9429333333333333 --- <!-- 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. --> # vit_epochs5_batch32_lr5e-05_size224_tiles9_seed2_q2_complexity 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 Dogs_vs_Cats dataset. It achieves the following results on the evaluation set: - Loss: 0.1731 - Accuracy: 0.9429 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.124 \| 1.0 \| 469 \| 0.1731 \| 0.9429 \| \| 0.0215 \| 2.0 \| 938 \| 0.2337 \| 0.952 \| \| 0.0021 \| 3.0 \| 1407 \| 0.2482 \| 0.9547 \| \| 0.0001 \| 4.0 \| 1876 \| 0.2534 \| 0.9563 \| \| 0.0 \| 5.0 \| 2345 \| 0.2577 \| 0.9544 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.2.2 - Datasets 2.18.0 - Tokenizers 0.19.1
Kittech/whisper_tiny_sn-w-mixed	Kittech	2024-05-25T10:43:07Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-24T13:06:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
YorkieOH10/deepseek-coder-1.3B-kexer-Q8_0-GGUF	YorkieOH10	2024-05-25T10:41:45Z	5	0	null	[ "gguf", "code", "llama-cpp", "gguf-my-repo", "dataset:JetBrains/KExercises", "base_model:deepseek-ai/deepseek-coder-1.3b-base", "base_model:quantized:deepseek-ai/deepseek-coder-1.3b-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-25T10:41:41Z	--- license: apache-2.0 tags: - code - llama-cpp - gguf-my-repo base_model: deepseek-ai/deepseek-coder-1.3b-base datasets: - JetBrains/KExercises results: - task: type: text-generation dataset: name: MultiPL-HumanEval (Kotlin) type: openai_humaneval metrics: - name: pass@1 type: pass@1 value: 36.65 --- # YorkieOH10/deepseek-coder-1.3B-kexer-Q8_0-GGUF This model was converted to GGUF format from [`JetBrains/deepseek-coder-1.3B-kexer`](https://huggingface.co/JetBrains/deepseek-coder-1.3B-kexer) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/JetBrains/deepseek-coder-1.3B-kexer) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo YorkieOH10/deepseek-coder-1.3B-kexer-Q8_0-GGUF --model deepseek-coder-1.3b-kexer-q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo YorkieOH10/deepseek-coder-1.3B-kexer-Q8_0-GGUF --model deepseek-coder-1.3b-kexer-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m deepseek-coder-1.3b-kexer-q8_0.gguf -n 128 ```
YorkieOH10/deepseek-coder-6.7B-kexer-Q8_0-GGUF	YorkieOH10	2024-05-25T10:41:13Z	7	0	null	[ "gguf", "code", "llama-cpp", "gguf-my-repo", "dataset:JetBrains/KExercises", "base_model:deepseek-ai/deepseek-coder-6.7b-base", "base_model:quantized:deepseek-ai/deepseek-coder-6.7b-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-25T10:40:55Z	--- license: apache-2.0 tags: - code - llama-cpp - gguf-my-repo base_model: deepseek-ai/deepseek-coder-6.7b-base datasets: - JetBrains/KExercises results: - task: type: text-generation dataset: name: MultiPL-HumanEval (Kotlin) type: openai_humaneval metrics: - name: pass@1 type: pass@1 value: 55.28 --- # YorkieOH10/deepseek-coder-6.7B-kexer-Q8_0-GGUF This model was converted to GGUF format from [`JetBrains/deepseek-coder-6.7B-kexer`](https://huggingface.co/JetBrains/deepseek-coder-6.7B-kexer) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/JetBrains/deepseek-coder-6.7B-kexer) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo YorkieOH10/deepseek-coder-6.7B-kexer-Q8_0-GGUF --model deepseek-coder-6.7b-kexer-q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo YorkieOH10/deepseek-coder-6.7B-kexer-Q8_0-GGUF --model deepseek-coder-6.7b-kexer-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m deepseek-coder-6.7b-kexer-q8_0.gguf -n 128 ```
hgnoi/2I0URfxHSrn2ueZY	hgnoi	2024-05-25T10:39:31Z	69	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T10:37:02Z	--- 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]
YorkieOH10/CodeLlama-7B-KStack-Q8_0-GGUF	YorkieOH10	2024-05-25T10:38:54Z	3	0	null	[ "gguf", "code", "llama-cpp", "gguf-my-repo", "dataset:JetBrains/KStack", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-25T10:38:37Z	--- license: apache-2.0 tags: - code - llama-cpp - gguf-my-repo datasets: - JetBrains/KStack results: - task: type: text-generation dataset: name: MultiPL-HumanEval (Kotlin) type: openai_humaneval metrics: - name: pass@1 type: pass@1 value: 29.19 --- # YorkieOH10/CodeLlama-7B-KStack-Q8_0-GGUF This model was converted to GGUF format from [`JetBrains/CodeLlama-7B-KStack`](https://huggingface.co/JetBrains/CodeLlama-7B-KStack) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/JetBrains/CodeLlama-7B-KStack) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo YorkieOH10/CodeLlama-7B-KStack-Q8_0-GGUF --model codellama-7b-kstack-q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo YorkieOH10/CodeLlama-7B-KStack-Q8_0-GGUF --model codellama-7b-kstack-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m codellama-7b-kstack-q8_0.gguf -n 128 ```
YorkieOH10/CodeLlama-7B-Kexer-Q8_0-GGUF	YorkieOH10	2024-05-25T10:37:12Z	5	0	null	[ "gguf", "code", "llama-cpp", "gguf-my-repo", "dataset:JetBrains/KExercises", "base_model:meta-llama/CodeLlama-7b-hf", "base_model:quantized:meta-llama/CodeLlama-7b-hf", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-25T10:36:51Z	--- license: apache-2.0 tags: - code - llama-cpp - gguf-my-repo base_model: meta-llama/CodeLlama-7b-hf datasets: - JetBrains/KExercises results: - task: type: text-generation dataset: name: MultiPL-HumanEval (Kotlin) type: openai_humaneval metrics: - name: pass@1 type: pass@1 value: 42.24 --- # YorkieOH10/CodeLlama-7B-Kexer-Q8_0-GGUF This model was converted to GGUF format from [`JetBrains/CodeLlama-7B-Kexer`](https://huggingface.co/JetBrains/CodeLlama-7B-Kexer) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/JetBrains/CodeLlama-7B-Kexer) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo YorkieOH10/CodeLlama-7B-Kexer-Q8_0-GGUF --model codellama-7b-kexer-q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo YorkieOH10/CodeLlama-7B-Kexer-Q8_0-GGUF --model codellama-7b-kexer-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m codellama-7b-kexer-q8_0.gguf -n 128 ```
Raneechu/textbookbig9	Raneechu	2024-05-25T10:36:53Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2024-05-25T10:36:49Z	--- license: llama2 library_name: peft tags: - generated_from_trainer base_model: meta-llama/Llama-2-7b-hf model-index: - name: textbookbig9 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. --> # textbookbig9 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.8977 ## 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: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 4.1922 \| 0.0117 \| 1 \| 3.8977 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.19.1 ## Training procedure ### Framework versions - PEFT 0.6.2
roif123/mistral_7b_fintuning_ujicoba_1	roif123	2024-05-25T10:31:45Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-25T10:31:05Z	--- 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. 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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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Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles6_seed123_q2_complexity	Omriy123	2024-05-25T10:31:26Z	178	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-25T10:17:15Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit_epochs5_batch32_lr5e-05_size224_tiles6_seed123_q2_complexity results: - task: name: Image Classification type: image-classification dataset: name: Dogs_vs_Cats type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9733333333333334 --- <!-- 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. --> # vit_epochs5_batch32_lr5e-05_size224_tiles6_seed123_q2_complexity 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 Dogs_vs_Cats dataset. It achieves the following results on the evaluation set: - Loss: 0.1249 - Accuracy: 0.9733 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0492 \| 1.0 \| 469 \| 0.1263 \| 0.9685 \| \| 0.008 \| 2.0 \| 938 \| 0.1249 \| 0.9733 \| \| 0.0255 \| 3.0 \| 1407 \| 0.1416 \| 0.9728 \| \| 0.0001 \| 4.0 \| 1876 \| 0.1282 \| 0.9757 \| \| 0.0 \| 5.0 \| 2345 \| 0.1294 \| 0.9765 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.2.2 - Datasets 2.18.0 - Tokenizers 0.19.1
thewordsmiths/mistral_sft	thewordsmiths	2024-05-25T10:27:12Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/mistral-7b", "base_model:adapter:unsloth/mistral-7b", "region:us" ]	null	2024-05-25T10:23:53Z	--- library_name: peft base_model: unsloth/mistral-7b --- # 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. 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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] ### Framework versions - PEFT 0.11.1
msy78/model_out	msy78	2024-05-25T10:25:14Z	0	0	diffusers	[ "diffusers", "safetensors", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "controlnet", "diffusers-training", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-04-16T01:28:07Z	--- license: openrail++ library_name: diffusers tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - controlnet - diffusers-training - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - controlnet - diffusers-training base_model: stabilityai/stable-diffusion-xl-base-1.0 inference: true --- <!-- 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. --> # controlnet-msy78/model_out These are controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with new type of conditioning. You can find some example images below. prompt: lineart, white background ![images_0)](./images_0.png) prompt: lineart, white background ![images_1)](./images_1.png) prompt: lineart, white background ![images_2)](./images_2.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]
Raneechu/textbookbig8	Raneechu	2024-05-25T10:22:04Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2024-05-25T10:22:01Z	--- license: llama2 library_name: peft tags: - generated_from_trainer base_model: meta-llama/Llama-2-7b-hf model-index: - name: textbookbig8 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. --> # textbookbig8 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.9058 ## 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: 0.0003 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 4.3513 \| 0.0029 \| 1 \| 3.9058 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.19.1 ## Training procedure ### Framework versions - PEFT 0.6.2
euunn/0525_biomistral	euunn	2024-05-25T10:20:47Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-25T10:20:36Z	--- 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. 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]
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles6_seed2_q2_complexity	Omriy123	2024-05-25T10:16:24Z	183	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-25T10:01:58Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit_epochs5_batch32_lr5e-05_size224_tiles6_seed2_q2_complexity results: - task: name: Image Classification type: image-classification dataset: name: Dogs_vs_Cats type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.968 --- <!-- 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. --> # vit_epochs5_batch32_lr5e-05_size224_tiles6_seed2_q2_complexity 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 Dogs_vs_Cats dataset. It achieves the following results on the evaluation set: - Loss: 0.1000 - Accuracy: 0.968 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.113 \| 1.0 \| 469 \| 0.1000 \| 0.968 \| \| 0.0014 \| 2.0 \| 938 \| 0.1242 \| 0.9725 \| \| 0.0 \| 3.0 \| 1407 \| 0.1503 \| 0.972 \| \| 0.0 \| 4.0 \| 1876 \| 0.1394 \| 0.9752 \| \| 0.0 \| 5.0 \| 2345 \| 0.1405 \| 0.9749 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.2.2 - Datasets 2.18.0 - Tokenizers 0.19.1
mudler/Mirai-Nova-Llama3-LocalAI-8B-v0.1-GGUF	mudler	2024-05-25T10:15:29Z	0	1	transformers	[ "transformers", "llama-factory", "llama3", "dataset:teknium/OpenHermes-2.5", "dataset:mudler/function-call-localai-glaive", "license:llama3", "endpoints_compatible", "region:us" ]	null	2024-05-25T08:39:47Z	--- library_name: transformers tags: - llama-factory - llama3 license: llama3 datasets: - teknium/OpenHermes-2.5 - mudler/function-call-localai-glaive --- [![local-ai-banner.png](https://cdn-uploads.huggingface.co/production/uploads/647374aa7ff32a81ac6d35d4/bXvNcxQqQ-wNAnISmx3PS.png)](https://localai.io) ## Mirai Nova ![image/png](https://cdn-uploads.huggingface.co/production/uploads/647374aa7ff32a81ac6d35d4/SKuXcvmZ_6oD4NCMkvyGo.png) Mirai Nova: "Mirai" means future in Japanese, and "Nova" references a star showing a sudden large increase in brightness. A set of models oriented in function calling, but generalist and with enhanced reasoning capability. This is fine tuned with Llama3. Mirai Nova works particularly well with LocalAI, leveraging the function call with grammars feature out of the box. GGUF quants fpr: https://huggingface.co/mudler/Mirai-Nova-Llama3-LocalAI-8B-v0.1 To run on LocalAI: ``` local-ai run huggingface://mudler/Mirai-Nova-Llama3-LocalAI-8B-v0.1-GGUF/localai.yaml ```
notlober/llama3-8b-tr	notlober	2024-05-25T10:15:16Z	2,778	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T10:07:38Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: notlober - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
SourPineapple/Llama-3-Alpha-Ko-8B-Instruct-Q5_K_M-GGUF	SourPineapple	2024-05-25T10:14:00Z	3	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "ko", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-25T10:13:43Z	--- language: - ko license: other tags: - llama-cpp - gguf-my-repo license_name: llama3 --- # SourPineapple/Llama-3-Alpha-Ko-8B-Instruct-Q5_K_M-GGUF This model was converted to GGUF format from [`allganize/Llama-3-Alpha-Ko-8B-Instruct`](https://huggingface.co/allganize/Llama-3-Alpha-Ko-8B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/allganize/Llama-3-Alpha-Ko-8B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo SourPineapple/Llama-3-Alpha-Ko-8B-Instruct-Q5_K_M-GGUF --model llama-3-alpha-ko-8b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo SourPineapple/Llama-3-Alpha-Ko-8B-Instruct-Q5_K_M-GGUF --model llama-3-alpha-ko-8b-instruct-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m llama-3-alpha-ko-8b-instruct-q5_k_m.gguf -n 128 ```
Niggendar/agendaMixPDXL_v10	Niggendar	2024-05-25T10:13:20Z	85	2	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-05-25T10:06:47Z	--- library_name: diffusers --- # 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 🧨 diffusers 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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v-urushkin/MixRoBERTa-cl_small_block-fin	v-urushkin	2024-05-25T10:12:11Z	117	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-25T10:04:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
CHJTXY99/shadow	CHJTXY99	2024-05-25T10:10:37Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-25T10:10:37Z	--- license: apache-2.0 ---
sasuface/esm2-t36-3B-lora-16-remote-homology-filtered	sasuface	2024-05-25T10:08:56Z	1	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:facebook/esm2_t36_3B_UR50D", "base_model:adapter:facebook/esm2_t36_3B_UR50D", "license:mit", "region:us" ]	null	2024-05-25T10:08:55Z	--- license: mit library_name: peft tags: - generated_from_trainer base_model: facebook/esm2_t36_3B_UR50D metrics: - precision - recall - accuracy model-index: - name: esm2-t36-3B-lora-16-remote-homology-filtered 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. --> # esm2-t36-3B-lora-16-remote-homology-filtered This model is a fine-tuned version of [facebook/esm2_t36_3B_UR50D](https://huggingface.co/facebook/esm2_t36_3B_UR50D) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4403 - Precision: 0.7922 - Recall: 0.8139 - F1-score: 0.8029 - Accuracy: 0.7990 ## 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: 96 - eval_batch_size: 96 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 192 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1-score \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:---------:\|:------:\|:--------:\|:--------:\| \| 0.535 \| 0.9992 \| 664 \| 0.5186 \| 0.8002 \| 0.6630 \| 0.7252 \| 0.7472 \| \| 0.4946 \| 2.0 \| 1329 \| 0.5065 \| 0.6945 \| 0.8969 \| 0.7828 \| 0.7496 \| \| 0.4727 \| 2.9992 \| 1993 \| 0.4592 \| 0.7917 \| 0.7876 \| 0.7897 \| 0.7889 \| \| 0.4439 \| 4.0 \| 2658 \| 0.4471 \| 0.8087 \| 0.7798 \| 0.7940 \| 0.7964 \| \| 0.4234 \| 4.9962 \| 3320 \| 0.4403 \| 0.7922 \| 0.8139 \| 0.8029 \| 0.7990 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
RichardErkhov/Undi95_-_Mistral-11B-OmniMix9-gguf	RichardErkhov	2024-05-25T10:01:17Z	17	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-25T07:20:41Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Mistral-11B-OmniMix9 - GGUF - Model creator: https://huggingface.co/Undi95/ - Original model: https://huggingface.co/Undi95/Mistral-11B-OmniMix9/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Mistral-11B-OmniMix9.Q2_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix9-gguf/blob/main/Mistral-11B-OmniMix9.Q2_K.gguf) \| Q2_K \| 3.73GB \| \| [Mistral-11B-OmniMix9.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix9-gguf/blob/main/Mistral-11B-OmniMix9.IQ3_XS.gguf) \| IQ3_XS \| 4.14GB \| \| [Mistral-11B-OmniMix9.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix9-gguf/blob/main/Mistral-11B-OmniMix9.IQ3_S.gguf) \| IQ3_S \| 4.37GB \| \| [Mistral-11B-OmniMix9.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix9-gguf/blob/main/Mistral-11B-OmniMix9.Q3_K_S.gguf) \| Q3_K_S \| 4.34GB \| \| [Mistral-11B-OmniMix9.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix9-gguf/blob/main/Mistral-11B-OmniMix9.IQ3_M.gguf) \| IQ3_M \| 4.51GB \| \| [Mistral-11B-OmniMix9.Q3_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix9-gguf/blob/main/Mistral-11B-OmniMix9.Q3_K.gguf) \| Q3_K \| 4.84GB \| \| [Mistral-11B-OmniMix9.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix9-gguf/blob/main/Mistral-11B-OmniMix9.Q3_K_M.gguf) \| Q3_K_M \| 4.84GB \| \| [Mistral-11B-OmniMix9.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix9-gguf/blob/main/Mistral-11B-OmniMix9.Q3_K_L.gguf) \| Q3_K_L \| 5.26GB \| \| [Mistral-11B-OmniMix9.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix9-gguf/blob/main/Mistral-11B-OmniMix9.IQ4_XS.gguf) \| IQ4_XS \| 5.43GB \| \| [Mistral-11B-OmniMix9.Q4_0.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix9-gguf/blob/main/Mistral-11B-OmniMix9.Q4_0.gguf) \| Q4_0 \| 5.66GB \| \| [Mistral-11B-OmniMix9.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix9-gguf/blob/main/Mistral-11B-OmniMix9.IQ4_NL.gguf) \| IQ4_NL \| 5.72GB \| \| [Mistral-11B-OmniMix9.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix9-gguf/blob/main/Mistral-11B-OmniMix9.Q4_K_S.gguf) \| Q4_K_S \| 5.7GB \| \| [Mistral-11B-OmniMix9.Q4_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix9-gguf/blob/main/Mistral-11B-OmniMix9.Q4_K.gguf) \| Q4_K \| 6.02GB \| \| [Mistral-11B-OmniMix9.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix9-gguf/blob/main/Mistral-11B-OmniMix9.Q4_K_M.gguf) \| Q4_K_M \| 6.02GB \| \| [Mistral-11B-OmniMix9.Q4_1.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix9-gguf/blob/main/Mistral-11B-OmniMix9.Q4_1.gguf) \| Q4_1 \| 6.27GB \| \| [Mistral-11B-OmniMix9.Q5_0.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix9-gguf/blob/main/Mistral-11B-OmniMix9.Q5_0.gguf) \| Q5_0 \| 6.89GB \| \| [Mistral-11B-OmniMix9.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix9-gguf/blob/main/Mistral-11B-OmniMix9.Q5_K_S.gguf) \| Q5_K_S \| 6.89GB \| \| [Mistral-11B-OmniMix9.Q5_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix9-gguf/blob/main/Mistral-11B-OmniMix9.Q5_K.gguf) \| Q5_K \| 7.08GB \| \| [Mistral-11B-OmniMix9.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix9-gguf/blob/main/Mistral-11B-OmniMix9.Q5_K_M.gguf) \| Q5_K_M \| 7.08GB \| \| [Mistral-11B-OmniMix9.Q5_1.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix9-gguf/blob/main/Mistral-11B-OmniMix9.Q5_1.gguf) \| Q5_1 \| 7.51GB \| \| [Mistral-11B-OmniMix9.Q6_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix9-gguf/blob/main/Mistral-11B-OmniMix9.Q6_K.gguf) \| Q6_K \| 8.2GB \| \| [Mistral-11B-OmniMix9.Q8_0.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix9-gguf/blob/main/Mistral-11B-OmniMix9.Q8_0.gguf) \| Q8_0 \| 10.62GB \| Original model description: --- license: cc-by-nc-4.0 --- Don't mind those at the moment, I need to finetune them for RP, it's just some tests. WARNING: This model specifically need EOS token I completely forgot to put on the json files, and need to check what was the right ones trough the mix. Please don't use it like this if you really want to review it. ``` slices: - sources: - model: "/content/drive/MyDrive/CC-v1.1-7B-bf16" layer_range: [0, 24] - sources: - model: "/content/drive/MyDrive/Zephyr-7B" layer_range: [8, 32] merge_method: passthrough dtype: bfloat16 ================================================ slices: - sources: - model: "/content/drive/MyDrive/Mistral-11B-CC-Zephyr" layer_range: [0, 48] - model: Undi95/Mistral-11B-OpenOrcaPlatypus layer_range: [0, 48] merge_method: slerp base_model: "/content/drive/MyDrive/Mistral-11B-CC-Zephyr" parameters: t: - value: 0.5 # fallback for rest of tensors dtype: bfloat16 ``` hf-causal-experimental (pretrained=/content/drive/MyDrive/Mistral-11B-Test), limit: None, provide_description: False, num_fewshot: 0, batch_size: 4 \| Task \|Version\| Metric \|Value \| \|Stderr\| \|-------------\|------:\|--------\|-----:\|---\|-----:\| \|arc_challenge\| 0\|acc \|0.5623\|± \|0.0145\| \| \| \|acc_norm\|0.5794\|± \|0.0144\| \|arc_easy \| 0\|acc \|0.8354\|± \|0.0076\| \| \| \|acc_norm\|0.8165\|± \|0.0079\| \|hellaswag \| 0\|acc \|0.6389\|± \|0.0048\| \| \| \|acc_norm\|0.8236\|± \|0.0038\| \|piqa \| 0\|acc \|0.8139\|± \|0.0091\| \| \| \|acc_norm\|0.8264\|± \|0.0088\| \|truthfulqa_mc\| 1\|mc1 \|0.3978\|± \|0.0171\| \| \| \|mc2 \|0.5607\|± \|0.0155\| \|winogrande \| 0\|acc \|0.7451\|± \|0.0122\| ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/8f-rAHIfN1ZuW4HqkzYz-.png) # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__Mistral-11B-TestBench9) \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| 53.06 \| \| ARC (25-shot) \| 64.08 \| \| HellaSwag (10-shot) \| 84.24 \| \| MMLU (5-shot) \| 64.0 \| \| TruthfulQA (0-shot) \| 56.19 \| \| Winogrande (5-shot) \| 78.45 \| \| GSM8K (5-shot) \| 16.15 \| \| DROP (3-shot) \| 8.35 \|
Cran-May/blossom-v3_1-baichuan2-13b-Q4_K_M-GGUF	Cran-May	2024-05-25T09:59:05Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "zh", "en", "dataset:Azure99/blossom-chat-v1", "dataset:Azure99/blossom-math-v2", "dataset:Azure99/blossom-wizard-v1", "dataset:Azure99/blossom-orca-v1", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T09:58:41Z	--- language: - zh - en license: apache-2.0 tags: - llama-cpp - gguf-my-repo datasets: - Azure99/blossom-chat-v1 - Azure99/blossom-math-v2 - Azure99/blossom-wizard-v1 - Azure99/blossom-orca-v1 pipeline_tag: text-generation --- # Cran-May/blossom-v3_1-baichuan2-13b-Q4_K_M-GGUF This model was converted to GGUF format from [`Azure99/blossom-v3_1-baichuan2-13b`](https://huggingface.co/Azure99/blossom-v3_1-baichuan2-13b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Azure99/blossom-v3_1-baichuan2-13b) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo Cran-May/blossom-v3_1-baichuan2-13b-Q4_K_M-GGUF --model blossom-v3_1-baichuan2-13b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo Cran-May/blossom-v3_1-baichuan2-13b-Q4_K_M-GGUF --model blossom-v3_1-baichuan2-13b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m blossom-v3_1-baichuan2-13b-q4_k_m.gguf -n 128 ```
LelViLamp/oalz-1788-q1-ner-per	LelViLamp	2024-05-25T09:50:15Z	189	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "historical", "de", "la", "fr", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-03-26T13:36:03Z	--- task_categories: - token-classification language: - de - la - fr - en tags: - historical pretty_name: >- Annotations and models for named entity recognition on Oberdeutsche Allgemeine Litteraturzeitung of the first quarter of 1788 --- # OALZ/1788/Q1/NER - [Postprocessing](https://github.com/LelViLamp/kediff-doccano-postprocessing) - [Training](https://github.com/LelViLamp/kediff-ner-training) - Published datasets ([union](https://huggingface.co/datasets/LelViLamp/oalz-1788-q1-ner-annotations-union-dataset), [merged union](https://huggingface.co/datasets/LelViLamp/oalz-1788-q1-ner-annotations-merged-union-dataset)) and models ([`EVENT`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-event), [`LOC`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-loc), [`MISC`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-misc), [`ORG`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-org), [_`PER`_](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-per), [`TIME`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-time)) A named entity recognition system (NER) was trained on text extracted from _Oberdeutsche Allgemeine Litteraturueitung_ (OALZ) of the first quarter (January, Febuary, March) of 1788. The scans from which text was extracted can be found at [Bayerische Staatsbibliothek](https://www.digitale-sammlungen.de/de/view/bsb10628753?page=,1) using the extraction strategy of the _KEDiff_ project, which can be found at [`cborgelt/KEDiff`](https://github.com/cborgelt/KEDiff). ## Annotations Each text passage was annotated in [doccano](https://github.com/doccano/doccano) by two or three annotators and their annotations were cleaned and merged into one dataset. For details on how this was done, see [`LelViLamp/kediff-doccano-postprocessing`](https://github.com/LelViLamp/kediff-doccano-postprocessing). In total, the text consists of about 1.7m characters. The resulting annotation datasets were published on the Hugging Face Hub. There are two versions of the dataset - [`union-dataset`](https://huggingface.co/datasets/LelViLamp/oalz-1788-q1-ner-annotations-union-dataset) contains the texts split into chunks. This is how they were presented in the annotation application doccano and results from preprocessing step 5a. - [`merged-union-dataset`](https://huggingface.co/datasets/LelViLamp/oalz-1788-q1-ner-annotations-merged-union-dataset) does not retain this split. The text was merged into one long text and annotation indices were adapted in preprocessing step 5b. Note that both these directories contain three equivalent datasets each: - a Huggingface/Arrow dataset, <sup></sup> - a CSV, <sup></sup> and - a JSONL file. <sup>*</sup> The former two should be used together with the provided `text.csv` to catch the context of the annotation. The latter JSONL file contains the full text. The following categories were included in the annotation process: \| Tag \| Label \| Count \| Total Length \| Median Annotation Length \| Mean Annotation Length \| SD \| \|:--------\|:--------------\|------:\|-------------:\|-------------------------:\|-----------------------:\|------:\| \| `EVENT` \| Event \| 294 \| 6,090 \| 18 \| 20.71 \| 13.24 \| \| `LOC` \| Location \| 2,449 \| 24,417 \| 9 \| 9.97 \| 6.21 \| \| `MISC` \| Miscellaneous \| 2,585 \| 50,654 \| 14 \| 19.60 \| 19.63 \| \| `ORG` \| Organisation \| 2,479 \| 34,693 \| 11 \| 13.99 \| 9.33 \| \| `PER` \| Person \| 7,055 \| 64,710 \| 7 \| 9.17 \| 9.35 \| \| `TIME` \| Dates & Time \| 1,076 \| 13,154 \| 8 \| 12.22 \| 10.98 \| ## NER models Based on the annotations above, six separate NER classifiers were trained, one for each label type. This was done in order to allow overlapping annotations. For example, in the passage "Dieses Projekt wurde an der Universität Salzburg durchgeführt", you would want to categorise "Universität Salzburg" as an organisation while also extracting "Salzburg" as a location. This would result in an annotation like this: ```json { "id": "example-42", "text": "Dieses Projekt wurde an der Universität Salzburg durchgeführt", "label": [[28, 49, "ORG"], [40, 49, "LOC"]] } ``` Example entry in CSV and Huggingface dataset \| annotation_id \| line_id \| start \| end \| label \| label_text \| merged \| \|:--------------\|:-----------\|------:\|----:\|:------\|:---------------------\|:------:\| \| $n$ \| example-42 \| 28 \| 49 \| ORG \| Universität Salzburg \| ??? \| \| $n+1$ \| example-42 \| 40 \| 49 \| LOC \| Salzburg \| ??? \| The columns mean: - `annotation_id` was assigned internally by enumerating all annotations. This is not present in the JSONL format - `line_id` is the fragment of the subdivided text, as shown in doccano. Called `id` in the JSONL dataset. - `start` index of the first character that is annotated. Included, starts with 0. - `end` index of the last character that is annotated. Excluded, maximum value is `len(respectiveText)`. - `label` indicates what the passage indicated by $[start, end)$ was annotated as. - `label_text` contains the text that is annotated by $[start, end)$. This is not present in the JSONL dataset as it can be inferred there. - `merged` indicates whether this annotation is the result of overlapping annotations of the same label. In that case, `annotation_id` contains the IDs of the individual annotations it was constructed of. This is not present in the JSONL dataset. To achieve this overlap, each text passage must be run through all the classifiers individually and each classifier's results need to be combined. For details on how the training was done, see [`LelViLamp/kediff-ner-training`](https://github.com/LelViLamp/kediff-ner-training). The [`dbmdz/bert-base-historic-multilingual-cased`](https://huggingface.co/dbmdz/bert-base-historic-multilingual-cased) tokeniser was used to create historical embeddings. Therefore, it is necessary to use that in order to use these NER models. The models' performance measures are as follows: \| Model \| Selected Epoch \| Checkpoint \| Validation Loss \| Precision \| Recall \| F<sub>1</sub> \| Accuracy \| \|:-------------------------------------------------------------------\|:--------------:\|-----------:\|----------------:\|----------:\|--------:\|--------------:\|---------:\| \| [`EVENT`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-event) \| 1 \| `1393` \| .021957 \| .665233 \| .343066 \| .351528 \| .995700 \| \| [`LOC`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-loc) \| 1 \| `1393` \| .033602 \| .829535 \| .803648 \| .814146 \| .990999 \| \| [`MISC`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-misc) \| 2 \| `2786` \| .123994 \| .739221 \| .503677 \| .571298 \| 968697 \| \| [`ORG`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-org) \| 1 \| `1393` \| .062769 \| .744259 \| .709738 \| .726212 \| .980288 \| \| [`PER`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-per) \| 2 \| `2786` \| .059186 \| .914037 \| .849048 \| .879070 \| .983253 \| \| [`TIME`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-time) \| 1 \| `1393` \| .016120 \| .866866 \| .724958 \| .783099 \| .994631 \| ## Acknowledgements The data set and models were created in the project _Kooperative Erschließung diffusen Wissens_ ([KEDiff](https://uni-salzburg.elsevierpure.com/de/projects/kooperative-erschließung-diffusen-wissens-ein-literaturwissenscha)), funded by the [State of Salzburg](https://salzburg.gv.at), Austria 🇦🇹, and carried out at [Paris Lodron Universität Salzburg](https://plus.ac.at).
Greenfield/deteccao-enchentes	Greenfield	2024-05-25T09:50:09Z	0	0	null	[ "license:mit", "region:us" ]	null	2024-05-25T00:44:17Z	--- license: mit --- # Projeto de Detecção de Possíveis Áreas de Enchentes Este projeto visa desenvolver um sistema de detecção de áreas propensas a enchentes, utilizando técnicas de aprendizado profundo (deep learning). Para isso, será empregado o dataset Sen1Floods11, que contém dados de radar de abertura sintética (SAR) coletados pelo sensor Sentinel-1 da Agência Espacial Europeia (ESA). ## Objetivo O objetivo principal é treinar uma rede neural totalmente convolucional (FCNN) para identificar áreas que possam estar sujeitas a enchentes com base nos dados de radar SAR. A detecção de enchentes é de suma importância para prever e mitigar os danos causados por desastres naturais, permitindo uma resposta rápida e eficaz das autoridades competentes. ## Dataset Sen1Floods11 O dataset Sen1Floods11 é composto por imagens SAR em diferentes polarizações, juntamente com máscaras de inundação correspondentes. As imagens são rotuladas manualmente e estão disponíveis para treinamento, teste e validação do modelo. ## Metodologia Pré-processamento dos Dados: As imagens SAR e suas máscaras de inundação serão pré-processadas para normalização e ajuste de tamanho, garantindo que estejam prontas para alimentar o modelo de rede neural. Treinamento do Modelo: O modelo FCNN será treinado utilizando o dataset de treinamento, ajustando seus pesos para minimizar uma função de perda definida. Serão realizadas várias iterações (épocas) de treinamento para otimizar o desempenho do modelo. Validação do Modelo: Após o treinamento, o modelo será avaliado utilizando o dataset de validação, a fim de verificar sua capacidade de generalização e seu desempenho em dados não vistos durante o treinamento. Teste do Modelo: Por fim, o modelo treinado será testado utilizando o dataset de teste para avaliar sua eficácia na detecção de áreas propensas a enchentes. Serão calculadas métricas de desempenho, como precisão, recall e F1-score, para quantificar a qualidade da detecção. # Projeto de Detecção de Possíveis Áreas de Enchentes Este projeto utiliza o dataset Sen1Floods11 para treinar uma rede neural totalmente convolucional (FCNN) para detectar áreas de enchentes. O exemplo a seguir treina e valida o modelo com imagens de eventos de enchente rotuladas manualmente. No entanto, o dataset inclui várias outras opções detalhadas abaixo. Para substituir o dataset, basta substituir os arquivos CSV de divisão de treino, teste e validação e baixar o dataset correspondente. ## Autenticação no Google Cloud Platform Para executar este código, você deve conectar seu runtime de notebook a uma GPU. ```python from google.colab import auth auth.authenticate_user() !curl https://sdk.cloud.google.com \| bash !gcloud init !echo "deb http://packages.cloud.google.com/apt gcsfuse-bionic main" > /etc/apt/sources.list.d/gcsfuse.list !curl https://packages.cloud.google.com/apt/doc/apt-key.gpg \| apt-key add - !apt -qq update !apt -qq install gcsfuse ``` ## Resultados Esperados Espera-se que o modelo desenvolvido seja capaz de identificar com precisão áreas potencialmente inundadas em imagens de radar SAR. Essa detecção precoce e precisa pode contribuir significativamente para o monitoramento de enchentes e para a tomada de decisões por parte das autoridades competentes, auxiliando na mitigação dos impactos desses eventos naturais.
LelViLamp/oalz-1788-q1-ner-org	LelViLamp	2024-05-25T09:49:51Z	181	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "historical", "de", "la", "fr", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-04-27T14:14:53Z	--- task_categories: - token-classification language: - de - la - fr - en tags: - historical pretty_name: >- Annotations and models for named entity recognition on Oberdeutsche Allgemeine Litteraturzeitung of the first quarter of 1788 --- # OALZ/1788/Q1/NER - [Postprocessing](https://github.com/LelViLamp/kediff-doccano-postprocessing) - [Training](https://github.com/LelViLamp/kediff-ner-training) - Published datasets ([union](https://huggingface.co/datasets/LelViLamp/oalz-1788-q1-ner-annotations-union-dataset), [merged union](https://huggingface.co/datasets/LelViLamp/oalz-1788-q1-ner-annotations-merged-union-dataset)) and models ([`EVENT`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-event), [`LOC`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-loc), [`MISC`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-misc), [_`ORG`_](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-org), [`PER`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-per), [`TIME`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-time)) A named entity recognition system (NER) was trained on text extracted from _Oberdeutsche Allgemeine Litteraturueitung_ (OALZ) of the first quarter (January, Febuary, March) of 1788. The scans from which text was extracted can be found at [Bayerische Staatsbibliothek](https://www.digitale-sammlungen.de/de/view/bsb10628753?page=,1) using the extraction strategy of the _KEDiff_ project, which can be found at [`cborgelt/KEDiff`](https://github.com/cborgelt/KEDiff). ## Annotations Each text passage was annotated in [doccano](https://github.com/doccano/doccano) by two or three annotators and their annotations were cleaned and merged into one dataset. For details on how this was done, see [`LelViLamp/kediff-doccano-postprocessing`](https://github.com/LelViLamp/kediff-doccano-postprocessing). In total, the text consists of about 1.7m characters. The resulting annotation datasets were published on the Hugging Face Hub. There are two versions of the dataset - [`union-dataset`](https://huggingface.co/datasets/LelViLamp/oalz-1788-q1-ner-annotations-union-dataset) contains the texts split into chunks. This is how they were presented in the annotation application doccano and results from preprocessing step 5a. - [`merged-union-dataset`](https://huggingface.co/datasets/LelViLamp/oalz-1788-q1-ner-annotations-merged-union-dataset) does not retain this split. The text was merged into one long text and annotation indices were adapted in preprocessing step 5b. Note that both these directories contain three equivalent datasets each: - a Huggingface/Arrow dataset, <sup></sup> - a CSV, <sup></sup> and - a JSONL file. <sup>*</sup> The former two should be used together with the provided `text.csv` to catch the context of the annotation. The latter JSONL file contains the full text. The following categories were included in the annotation process: \| Tag \| Label \| Count \| Total Length \| Median Annotation Length \| Mean Annotation Length \| SD \| \|:--------\|:--------------\|------:\|-------------:\|-------------------------:\|-----------------------:\|------:\| \| `EVENT` \| Event \| 294 \| 6,090 \| 18 \| 20.71 \| 13.24 \| \| `LOC` \| Location \| 2,449 \| 24,417 \| 9 \| 9.97 \| 6.21 \| \| `MISC` \| Miscellaneous \| 2,585 \| 50,654 \| 14 \| 19.60 \| 19.63 \| \| `ORG` \| Organisation \| 2,479 \| 34,693 \| 11 \| 13.99 \| 9.33 \| \| `PER` \| Person \| 7,055 \| 64,710 \| 7 \| 9.17 \| 9.35 \| \| `TIME` \| Dates & Time \| 1,076 \| 13,154 \| 8 \| 12.22 \| 10.98 \| ## NER models Based on the annotations above, six separate NER classifiers were trained, one for each label type. This was done in order to allow overlapping annotations. For example, in the passage "Dieses Projekt wurde an der Universität Salzburg durchgeführt", you would want to categorise "Universität Salzburg" as an organisation while also extracting "Salzburg" as a location. This would result in an annotation like this: ```json { "id": "example-42", "text": "Dieses Projekt wurde an der Universität Salzburg durchgeführt", "label": [[28, 49, "ORG"], [40, 49, "LOC"]] } ``` Example entry in CSV and Huggingface dataset \| annotation_id \| line_id \| start \| end \| label \| label_text \| merged \| \|:--------------\|:-----------\|------:\|----:\|:------\|:---------------------\|:------:\| \| $n$ \| example-42 \| 28 \| 49 \| ORG \| Universität Salzburg \| ??? \| \| $n+1$ \| example-42 \| 40 \| 49 \| LOC \| Salzburg \| ??? \| The columns mean: - `annotation_id` was assigned internally by enumerating all annotations. This is not present in the JSONL format - `line_id` is the fragment of the subdivided text, as shown in doccano. Called `id` in the JSONL dataset. - `start` index of the first character that is annotated. Included, starts with 0. - `end` index of the last character that is annotated. Excluded, maximum value is `len(respectiveText)`. - `label` indicates what the passage indicated by $[start, end)$ was annotated as. - `label_text` contains the text that is annotated by $[start, end)$. This is not present in the JSONL dataset as it can be inferred there. - `merged` indicates whether this annotation is the result of overlapping annotations of the same label. In that case, `annotation_id` contains the IDs of the individual annotations it was constructed of. This is not present in the JSONL dataset. To achieve this overlap, each text passage must be run through all the classifiers individually and each classifier's results need to be combined. For details on how the training was done, see [`LelViLamp/kediff-ner-training`](https://github.com/LelViLamp/kediff-ner-training). The [`dbmdz/bert-base-historic-multilingual-cased`](https://huggingface.co/dbmdz/bert-base-historic-multilingual-cased) tokeniser was used to create historical embeddings. Therefore, it is necessary to use that in order to use these NER models. The models' performance measures are as follows: \| Model \| Selected Epoch \| Checkpoint \| Validation Loss \| Precision \| Recall \| F<sub>1</sub> \| Accuracy \| \|:-------------------------------------------------------------------\|:--------------:\|-----------:\|----------------:\|----------:\|--------:\|--------------:\|---------:\| \| [`EVENT`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-event) \| 1 \| `1393` \| .021957 \| .665233 \| .343066 \| .351528 \| .995700 \| \| [`LOC`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-loc) \| 1 \| `1393` \| .033602 \| .829535 \| .803648 \| .814146 \| .990999 \| \| [`MISC`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-misc) \| 2 \| `2786` \| .123994 \| .739221 \| .503677 \| .571298 \| 968697 \| \| [`ORG`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-org) \| 1 \| `1393` \| .062769 \| .744259 \| .709738 \| .726212 \| .980288 \| \| [`PER`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-per) \| 2 \| `2786` \| .059186 \| .914037 \| .849048 \| .879070 \| .983253 \| \| [`TIME`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-time) \| 1 \| `1393` \| .016120 \| .866866 \| .724958 \| .783099 \| .994631 \| ## Acknowledgements The data set and models were created in the project _Kooperative Erschließung diffusen Wissens_ ([KEDiff](https://uni-salzburg.elsevierpure.com/de/projects/kooperative-erschließung-diffusen-wissens-ein-literaturwissenscha)), funded by the [State of Salzburg](https://salzburg.gv.at), Austria 🇦🇹, and carried out at [Paris Lodron Universität Salzburg](https://plus.ac.at).
LelViLamp/oalz-1788-q1-ner-misc	LelViLamp	2024-05-25T09:49:28Z	182	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "historical", "de", "la", "fr", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-04-27T14:13:59Z	--- task_categories: - token-classification language: - de - la - fr - en tags: - historical pretty_name: >- Annotations and models for named entity recognition on Oberdeutsche Allgemeine Litteraturzeitung of the first quarter of 1788 --- # OALZ/1788/Q1/NER - [Postprocessing](https://github.com/LelViLamp/kediff-doccano-postprocessing) - [Training](https://github.com/LelViLamp/kediff-ner-training) - Published datasets ([union](https://huggingface.co/datasets/LelViLamp/oalz-1788-q1-ner-annotations-union-dataset), [merged union](https://huggingface.co/datasets/LelViLamp/oalz-1788-q1-ner-annotations-merged-union-dataset)) and models ([`EVENT`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-event), [`LOC`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-loc), [_`MISC`_](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-misc), [`ORG`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-org), [`PER`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-per), [`TIME`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-time)) A named entity recognition system (NER) was trained on text extracted from _Oberdeutsche Allgemeine Litteraturueitung_ (OALZ) of the first quarter (January, Febuary, March) of 1788. The scans from which text was extracted can be found at [Bayerische Staatsbibliothek](https://www.digitale-sammlungen.de/de/view/bsb10628753?page=,1) using the extraction strategy of the _KEDiff_ project, which can be found at [`cborgelt/KEDiff`](https://github.com/cborgelt/KEDiff). ## Annotations Each text passage was annotated in [doccano](https://github.com/doccano/doccano) by two or three annotators and their annotations were cleaned and merged into one dataset. For details on how this was done, see [`LelViLamp/kediff-doccano-postprocessing`](https://github.com/LelViLamp/kediff-doccano-postprocessing). In total, the text consists of about 1.7m characters. The resulting annotation datasets were published on the Hugging Face Hub. There are two versions of the dataset - [`union-dataset`](https://huggingface.co/datasets/LelViLamp/oalz-1788-q1-ner-annotations-union-dataset) contains the texts split into chunks. This is how they were presented in the annotation application doccano and results from preprocessing step 5a. - [`merged-union-dataset`](https://huggingface.co/datasets/LelViLamp/oalz-1788-q1-ner-annotations-merged-union-dataset) does not retain this split. The text was merged into one long text and annotation indices were adapted in preprocessing step 5b. Note that both these directories contain three equivalent datasets each: - a Huggingface/Arrow dataset, <sup></sup> - a CSV, <sup></sup> and - a JSONL file. <sup>*</sup> The former two should be used together with the provided `text.csv` to catch the context of the annotation. The latter JSONL file contains the full text. The following categories were included in the annotation process: \| Tag \| Label \| Count \| Total Length \| Median Annotation Length \| Mean Annotation Length \| SD \| \|:--------\|:--------------\|------:\|-------------:\|-------------------------:\|-----------------------:\|------:\| \| `EVENT` \| Event \| 294 \| 6,090 \| 18 \| 20.71 \| 13.24 \| \| `LOC` \| Location \| 2,449 \| 24,417 \| 9 \| 9.97 \| 6.21 \| \| `MISC` \| Miscellaneous \| 2,585 \| 50,654 \| 14 \| 19.60 \| 19.63 \| \| `ORG` \| Organisation \| 2,479 \| 34,693 \| 11 \| 13.99 \| 9.33 \| \| `PER` \| Person \| 7,055 \| 64,710 \| 7 \| 9.17 \| 9.35 \| \| `TIME` \| Dates & Time \| 1,076 \| 13,154 \| 8 \| 12.22 \| 10.98 \| ## NER models Based on the annotations above, six separate NER classifiers were trained, one for each label type. This was done in order to allow overlapping annotations. For example, in the passage "Dieses Projekt wurde an der Universität Salzburg durchgeführt", you would want to categorise "Universität Salzburg" as an organisation while also extracting "Salzburg" as a location. This would result in an annotation like this: ```json { "id": "example-42", "text": "Dieses Projekt wurde an der Universität Salzburg durchgeführt", "label": [[28, 49, "ORG"], [40, 49, "LOC"]] } ``` Example entry in CSV and Huggingface dataset \| annotation_id \| line_id \| start \| end \| label \| label_text \| merged \| \|:--------------\|:-----------\|------:\|----:\|:------\|:---------------------\|:------:\| \| $n$ \| example-42 \| 28 \| 49 \| ORG \| Universität Salzburg \| ??? \| \| $n+1$ \| example-42 \| 40 \| 49 \| LOC \| Salzburg \| ??? \| The columns mean: - `annotation_id` was assigned internally by enumerating all annotations. This is not present in the JSONL format - `line_id` is the fragment of the subdivided text, as shown in doccano. Called `id` in the JSONL dataset. - `start` index of the first character that is annotated. Included, starts with 0. - `end` index of the last character that is annotated. Excluded, maximum value is `len(respectiveText)`. - `label` indicates what the passage indicated by $[start, end)$ was annotated as. - `label_text` contains the text that is annotated by $[start, end)$. This is not present in the JSONL dataset as it can be inferred there. - `merged` indicates whether this annotation is the result of overlapping annotations of the same label. In that case, `annotation_id` contains the IDs of the individual annotations it was constructed of. This is not present in the JSONL dataset. To achieve this overlap, each text passage must be run through all the classifiers individually and each classifier's results need to be combined. For details on how the training was done, see [`LelViLamp/kediff-ner-training`](https://github.com/LelViLamp/kediff-ner-training). The [`dbmdz/bert-base-historic-multilingual-cased`](https://huggingface.co/dbmdz/bert-base-historic-multilingual-cased) tokeniser was used to create historical embeddings. Therefore, it is necessary to use that in order to use these NER models. The models' performance measures are as follows: \| Model \| Selected Epoch \| Checkpoint \| Validation Loss \| Precision \| Recall \| F<sub>1</sub> \| Accuracy \| \|:-------------------------------------------------------------------\|:--------------:\|-----------:\|----------------:\|----------:\|--------:\|--------------:\|---------:\| \| [`EVENT`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-event) \| 1 \| `1393` \| .021957 \| .665233 \| .343066 \| .351528 \| .995700 \| \| [`LOC`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-loc) \| 1 \| `1393` \| .033602 \| .829535 \| .803648 \| .814146 \| .990999 \| \| [`MISC`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-misc) \| 2 \| `2786` \| .123994 \| .739221 \| .503677 \| .571298 \| 968697 \| \| [`ORG`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-org) \| 1 \| `1393` \| .062769 \| .744259 \| .709738 \| .726212 \| .980288 \| \| [`PER`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-per) \| 2 \| `2786` \| .059186 \| .914037 \| .849048 \| .879070 \| .983253 \| \| [`TIME`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-time) \| 1 \| `1393` \| .016120 \| .866866 \| .724958 \| .783099 \| .994631 \| ## Acknowledgements The data set and models were created in the project _Kooperative Erschließung diffusen Wissens_ ([KEDiff](https://uni-salzburg.elsevierpure.com/de/projects/kooperative-erschließung-diffusen-wissens-ein-literaturwissenscha)), funded by the [State of Salzburg](https://salzburg.gv.at), Austria 🇦🇹, and carried out at [Paris Lodron Universität Salzburg](https://plus.ac.at).
LelViLamp/oalz-1788-q1-ner-event	LelViLamp	2024-05-25T09:48:30Z	196	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "historical", "de", "la", "fr", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-04-27T14:12:20Z	--- task_categories: - token-classification language: - de - la - fr - en tags: - historical pretty_name: >- Annotations and models for named entity recognition on Oberdeutsche Allgemeine Litteraturzeitung of the first quarter of 1788 --- # OALZ/1788/Q1/NER - [Postprocessing](https://github.com/LelViLamp/kediff-doccano-postprocessing) - [Training](https://github.com/LelViLamp/kediff-ner-training) - Published datasets ([union](https://huggingface.co/datasets/LelViLamp/oalz-1788-q1-ner-annotations-union-dataset), [merged union](https://huggingface.co/datasets/LelViLamp/oalz-1788-q1-ner-annotations-merged-union-dataset)) and models ([_`EVENT`_](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-event), [`LOC`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-loc), [`MISC`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-misc), [`ORG`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-org), [`PER`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-per), [`TIME`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-time)) A named entity recognition system (NER) was trained on text extracted from _Oberdeutsche Allgemeine Litteraturueitung_ (OALZ) of the first quarter (January, Febuary, March) of 1788. The scans from which text was extracted can be found at [Bayerische Staatsbibliothek](https://www.digitale-sammlungen.de/de/view/bsb10628753?page=,1) using the extraction strategy of the _KEDiff_ project, which can be found at [`cborgelt/KEDiff`](https://github.com/cborgelt/KEDiff). ## Annotations Each text passage was annotated in [doccano](https://github.com/doccano/doccano) by two or three annotators and their annotations were cleaned and merged into one dataset. For details on how this was done, see [`LelViLamp/kediff-doccano-postprocessing`](https://github.com/LelViLamp/kediff-doccano-postprocessing). In total, the text consists of about 1.7m characters. The resulting annotation datasets were published on the Hugging Face Hub. There are two versions of the dataset - [`union-dataset`](https://huggingface.co/datasets/LelViLamp/oalz-1788-q1-ner-annotations-union-dataset) contains the texts split into chunks. This is how they were presented in the annotation application doccano and results from preprocessing step 5a. - [`merged-union-dataset`](https://huggingface.co/datasets/LelViLamp/oalz-1788-q1-ner-annotations-merged-union-dataset) does not retain this split. The text was merged into one long text and annotation indices were adapted in preprocessing step 5b. Note that both these directories contain three equivalent datasets each: - a Huggingface/Arrow dataset, <sup></sup> - a CSV, <sup></sup> and - a JSONL file. <sup>*</sup> The former two should be used together with the provided `text.csv` to catch the context of the annotation. The latter JSONL file contains the full text. The following categories were included in the annotation process: \| Tag \| Label \| Count \| Total Length \| Median Annotation Length \| Mean Annotation Length \| SD \| \|:--------\|:--------------\|------:\|-------------:\|-------------------------:\|-----------------------:\|------:\| \| `EVENT` \| Event \| 294 \| 6,090 \| 18 \| 20.71 \| 13.24 \| \| `LOC` \| Location \| 2,449 \| 24,417 \| 9 \| 9.97 \| 6.21 \| \| `MISC` \| Miscellaneous \| 2,585 \| 50,654 \| 14 \| 19.60 \| 19.63 \| \| `ORG` \| Organisation \| 2,479 \| 34,693 \| 11 \| 13.99 \| 9.33 \| \| `PER` \| Person \| 7,055 \| 64,710 \| 7 \| 9.17 \| 9.35 \| \| `TIME` \| Dates & Time \| 1,076 \| 13,154 \| 8 \| 12.22 \| 10.98 \| ## NER models Based on the annotations above, six separate NER classifiers were trained, one for each label type. This was done in order to allow overlapping annotations. For example, in the passage "Dieses Projekt wurde an der Universität Salzburg durchgeführt", you would want to categorise "Universität Salzburg" as an organisation while also extracting "Salzburg" as a location. This would result in an annotation like this: ```json { "id": "example-42", "text": "Dieses Projekt wurde an der Universität Salzburg durchgeführt", "label": [[28, 49, "ORG"], [40, 49, "LOC"]] } ``` Example entry in CSV and Huggingface dataset \| annotation_id \| line_id \| start \| end \| label \| label_text \| merged \| \|:--------------\|:-----------\|------:\|----:\|:------\|:---------------------\|:------:\| \| $n$ \| example-42 \| 28 \| 49 \| ORG \| Universität Salzburg \| ??? \| \| $n+1$ \| example-42 \| 40 \| 49 \| LOC \| Salzburg \| ??? \| The columns mean: - `annotation_id` was assigned internally by enumerating all annotations. This is not present in the JSONL format - `line_id` is the fragment of the subdivided text, as shown in doccano. Called `id` in the JSONL dataset. - `start` index of the first character that is annotated. Included, starts with 0. - `end` index of the last character that is annotated. Excluded, maximum value is `len(respectiveText)`. - `label` indicates what the passage indicated by $[start, end)$ was annotated as. - `label_text` contains the text that is annotated by $[start, end)$. This is not present in the JSONL dataset as it can be inferred there. - `merged` indicates whether this annotation is the result of overlapping annotations of the same label. In that case, `annotation_id` contains the IDs of the individual annotations it was constructed of. This is not present in the JSONL dataset. To achieve this overlap, each text passage must be run through all the classifiers individually and each classifier's results need to be combined. For details on how the training was done, see [`LelViLamp/kediff-ner-training`](https://github.com/LelViLamp/kediff-ner-training). The [`dbmdz/bert-base-historic-multilingual-cased`](https://huggingface.co/dbmdz/bert-base-historic-multilingual-cased) tokeniser was used to create historical embeddings. Therefore, it is necessary to use that in order to use these NER models. The models' performance measures are as follows: \| Model \| Selected Epoch \| Checkpoint \| Validation Loss \| Precision \| Recall \| F<sub>1</sub> \| Accuracy \| \|:-------------------------------------------------------------------\|:--------------:\|-----------:\|----------------:\|----------:\|--------:\|--------------:\|---------:\| \| [`EVENT`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-event) \| 1 \| `1393` \| .021957 \| .665233 \| .343066 \| .351528 \| .995700 \| \| [`LOC`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-loc) \| 1 \| `1393` \| .033602 \| .829535 \| .803648 \| .814146 \| .990999 \| \| [`MISC`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-misc) \| 2 \| `2786` \| .123994 \| .739221 \| .503677 \| .571298 \| 968697 \| \| [`ORG`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-org) \| 1 \| `1393` \| .062769 \| .744259 \| .709738 \| .726212 \| .980288 \| \| [`PER`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-per) \| 2 \| `2786` \| .059186 \| .914037 \| .849048 \| .879070 \| .983253 \| \| [`TIME`](https://huggingface.co/LelViLamp/oalz-1788-q1-ner-time) \| 1 \| `1393` \| .016120 \| .866866 \| .724958 \| .783099 \| .994631 \| ## Acknowledgements The data set and models were created in the project _Kooperative Erschließung diffusen Wissens_ ([KEDiff](https://uni-salzburg.elsevierpure.com/de/projects/kooperative-erschließung-diffusen-wissens-ein-literaturwissenscha)), funded by the [State of Salzburg](https://salzburg.gv.at), Austria 🇦🇹, and carried out at [Paris Lodron Universität Salzburg](https://plus.ac.at).
mradermacher/Mixtral_AI_Cyber_BOSS_II-GGUF	mradermacher	2024-05-25T09:40:12Z	13	0	transformers	[ "transformers", "gguf", "code", "en", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-25T08:02:07Z	--- base_model: LeroyDyer/Mixtral_AI_Cyber_BOSS_II language: - en library_name: transformers license: mit quantized_by: mradermacher tags: - code --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/LeroyDyer/Mixtral_AI_Cyber_BOSS_II <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_BOSS_II-GGUF/resolve/main/Mixtral_AI_Cyber_BOSS_II.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_BOSS_II-GGUF/resolve/main/Mixtral_AI_Cyber_BOSS_II.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_BOSS_II-GGUF/resolve/main/Mixtral_AI_Cyber_BOSS_II.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_BOSS_II-GGUF/resolve/main/Mixtral_AI_Cyber_BOSS_II.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_BOSS_II-GGUF/resolve/main/Mixtral_AI_Cyber_BOSS_II.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_BOSS_II-GGUF/resolve/main/Mixtral_AI_Cyber_BOSS_II.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_BOSS_II-GGUF/resolve/main/Mixtral_AI_Cyber_BOSS_II.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_BOSS_II-GGUF/resolve/main/Mixtral_AI_Cyber_BOSS_II.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_BOSS_II-GGUF/resolve/main/Mixtral_AI_Cyber_BOSS_II.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_BOSS_II-GGUF/resolve/main/Mixtral_AI_Cyber_BOSS_II.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_BOSS_II-GGUF/resolve/main/Mixtral_AI_Cyber_BOSS_II.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_BOSS_II-GGUF/resolve/main/Mixtral_AI_Cyber_BOSS_II.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_BOSS_II-GGUF/resolve/main/Mixtral_AI_Cyber_BOSS_II.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_BOSS_II-GGUF/resolve/main/Mixtral_AI_Cyber_BOSS_II.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_BOSS_II-GGUF/resolve/main/Mixtral_AI_Cyber_BOSS_II.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
lucando27/Task_1	lucando27	2024-05-25T09:40:06Z	108	0	transformers	[ "transformers", "tensorboard", "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	2024-05-24T17:15:48Z	--- license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: Task_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. --> # Task_1 This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3113 - Matthews Correlation: 0.7689 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| No log \| 1.0 \| 225 \| 0.2997 \| 0.7459 \| \| No log \| 2.0 \| 450 \| 0.3113 \| 0.7689 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
mani-a-i/llama3_1500_ckpt	mani-a-i	2024-05-25T09:38:33Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-02T13:30:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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BillyBek/efficient-fine-tuning-demo	BillyBek	2024-05-25T09:38:06Z	120	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "mn", "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	2024-05-25T09:37:35Z	--- language: - mn license: apache-2.0 base_model: bert-base-multilingual-cased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: efficient-fine-tuning-demo 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. --> # efficient-fine-tuning-demo This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0975 - Precision: 0.8875 - Recall: 0.9090 - F1: 0.8981 - Accuracy: 0.9735 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.184 \| 1.0 \| 477 \| 0.1145 \| 0.8392 \| 0.8766 \| 0.8575 \| 0.9643 \| \| 0.0877 \| 2.0 \| 954 \| 0.0985 \| 0.8827 \| 0.9038 \| 0.8931 \| 0.9728 \| \| 0.0448 \| 3.0 \| 1431 \| 0.0975 \| 0.8875 \| 0.9090 \| 0.8981 \| 0.9735 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Niggendar/pulenkompotPonyxl_praskovaxl	Niggendar	2024-05-25T09:37:11Z	125	2	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-05-25T09:33:21Z	--- library_name: diffusers --- # 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 🧨 diffusers 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]
chanakyaseven/ppo-LunarLander-v2	chanakyaseven	2024-05-25T09:36:56Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-25T09:36:35Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 241.06 +/- 31.42 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Raneechu/textbookbig7_ft2	Raneechu	2024-05-25T09:32:28Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2024-05-25T09:32:25Z	--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Llama-2-7b-hf model-index: - name: textbookbig7_ft2 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. --> # textbookbig7_ft2 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) 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: 0.0001 - 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 - training_steps: 1 ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.19.1 ## Training procedure ### Framework versions - PEFT 0.6.2
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles3_seed1_q2_complexity	Omriy123	2024-05-25T09:17:01Z	220	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-25T09:03:02Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit_epochs5_batch32_lr5e-05_size224_tiles3_seed1_q2_complexity results: - task: name: Image Classification type: image-classification dataset: name: Dogs_vs_Cats type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9829333333333333 --- <!-- 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. --> # vit_epochs5_batch32_lr5e-05_size224_tiles3_seed1_q2_complexity 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 Dogs_vs_Cats dataset. It achieves the following results on the evaluation set: - Loss: 0.0776 - Accuracy: 0.9829 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0288 \| 1.0 \| 469 \| 0.0776 \| 0.9829 \| \| 0.0001 \| 2.0 \| 938 \| 0.0917 \| 0.9832 \| \| 0.0 \| 3.0 \| 1407 \| 0.0954 \| 0.9829 \| \| 0.0001 \| 4.0 \| 1876 \| 0.0943 \| 0.9829 \| \| 0.0 \| 5.0 \| 2345 \| 0.0940 \| 0.9827 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.2.2 - Datasets 2.18.0 - Tokenizers 0.19.1
adlbh/llama-3-8b-medinstruct-52k	adlbh	2024-05-25T09:12:06Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-25T09:09:35Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: adlbh - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
leo009/test_model	leo009	2024-05-25T08:50:26Z	3	0	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/mistral-7b-v0.3-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-v0.3-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T08:39:36Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl - sft base_model: unsloth/mistral-7b-v0.3-bnb-4bit --- # Uploaded model - Developed by: leo009 - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
joshnader/Mistral-7B-Instruct-v0.2-Q5_K_M-GGUF	joshnader	2024-05-25T08:48:19Z	1	0	null	[ "gguf", "finetuned", "llama-cpp", "gguf-my-repo", "text-generation", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-25T08:48:04Z	--- license: apache-2.0 tags: - finetuned - llama-cpp - gguf-my-repo pipeline_tag: text-generation inference: true widget: - messages: - role: user content: What is your favorite condiment? --- # joshnader/Mistral-7B-Instruct-v0.2-Q5_K_M-GGUF This model was converted to GGUF format from [`mistralai/Mistral-7B-Instruct-v0.2`](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo joshnader/Mistral-7B-Instruct-v0.2-Q5_K_M-GGUF --model mistral-7b-instruct-v0.2-q5_k_m.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo joshnader/Mistral-7B-Instruct-v0.2-Q5_K_M-GGUF --model mistral-7b-instruct-v0.2-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m mistral-7b-instruct-v0.2-q5_k_m.gguf -n 128 ```
ModelsLab/RMBG	ModelsLab	2024-05-25T08:47:57Z	254	0	transformers	[ "transformers", "safetensors", "SegformerForSemanticSegmentation", "image-segmentation", "custom_code", "license:apache-2.0", "region:us" ]	image-segmentation	2024-05-25T08:44:22Z	--- license: apache-2.0 ---
Raneechu/textbookbig6	Raneechu	2024-05-25T08:47:50Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2024-05-25T08:47:46Z	--- license: llama2 library_name: peft tags: - generated_from_trainer base_model: meta-llama/Llama-2-7b-hf model-index: - name: textbookbig6 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. --> # textbookbig6 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.9012 ## 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: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 4.1922 \| 0.0117 \| 1 \| 3.9012 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.19.1 ## Training procedure ### Framework versions - PEFT 0.6.2
RapidOrc121/Social_Media_Creator	RapidOrc121	2024-05-25T08:46:03Z	3	1	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/mistral-7b-bnb-4bit", "base_model:adapter:unsloth/mistral-7b-bnb-4bit", "region:us" ]	null	2024-05-25T08:02:22Z	--- library_name: peft base_model: unsloth/mistral-7b-bnb-4bit --- # 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. 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(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 0.11.1
asude55/youtube-da24	asude55	2024-05-25T08:37:30Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-25T08:37:07Z	--- 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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Raneechu/textbookbig5_ft	Raneechu	2024-05-25T08:36:37Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2024-05-25T08:36:33Z	--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Llama-2-7b-hf model-index: - name: textbookbig5_ft 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. --> # textbookbig5_ft This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1 ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.19.1 ## Training procedure ### Framework versions - PEFT 0.6.2
mradermacher/Mixtral_AI_TheAncientOne-GGUF	mradermacher	2024-05-25T08:36:03Z	8	0	transformers	[ "transformers", "gguf", "en", "endpoints_compatible", "region:us" ]	null	2024-05-25T08:10:27Z	--- base_model: LeroyDyer/Mixtral_AI_TheAncientOne language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/LeroyDyer/Mixtral_AI_TheAncientOne <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_TheAncientOne-GGUF/resolve/main/Mixtral_AI_TheAncientOne.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_TheAncientOne-GGUF/resolve/main/Mixtral_AI_TheAncientOne.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_TheAncientOne-GGUF/resolve/main/Mixtral_AI_TheAncientOne.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_TheAncientOne-GGUF/resolve/main/Mixtral_AI_TheAncientOne.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_TheAncientOne-GGUF/resolve/main/Mixtral_AI_TheAncientOne.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_TheAncientOne-GGUF/resolve/main/Mixtral_AI_TheAncientOne.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_TheAncientOne-GGUF/resolve/main/Mixtral_AI_TheAncientOne.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_TheAncientOne-GGUF/resolve/main/Mixtral_AI_TheAncientOne.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_TheAncientOne-GGUF/resolve/main/Mixtral_AI_TheAncientOne.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_TheAncientOne-GGUF/resolve/main/Mixtral_AI_TheAncientOne.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_TheAncientOne-GGUF/resolve/main/Mixtral_AI_TheAncientOne.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_TheAncientOne-GGUF/resolve/main/Mixtral_AI_TheAncientOne.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_TheAncientOne-GGUF/resolve/main/Mixtral_AI_TheAncientOne.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_TheAncientOne-GGUF/resolve/main/Mixtral_AI_TheAncientOne.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_TheAncientOne-GGUF/resolve/main/Mixtral_AI_TheAncientOne.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Mazin100/no_augmentation_relation_extractoin	Mazin100	2024-05-25T08:27:35Z	117	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-25T08:04:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
Raneechu/textbookbig5	Raneechu	2024-05-25T08:21:59Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2024-05-25T08:21:55Z	--- license: llama2 library_name: peft tags: - generated_from_trainer base_model: meta-llama/Llama-2-7b-hf model-index: - name: textbookbig5 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. --> # textbookbig5 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.9018 ## 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: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 4.1922 \| 0.0117 \| 1 \| 3.9018 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.19.1 ## Training procedure ### Framework versions - PEFT 0.6.2
Benjamin2/rare-puppers	Benjamin2	2024-05-25T08:14:10Z	220	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "pytorch", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-25T08:14:03Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: rare-puppers results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9701492786407471 --- # rare-puppers Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### corgi ![corgi](images/corgi.jpg) #### samoyed ![samoyed](images/samoyed.jpg) #### shiba inu ![shiba inu](images/shiba_inu.jpg)
RichardErkhov/Undi95_-_Mistral-11B-OmniMix-gguf	RichardErkhov	2024-05-25T08:11:39Z	2	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-05-25T05:32:53Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Mistral-11B-OmniMix - GGUF - Model creator: https://huggingface.co/Undi95/ - Original model: https://huggingface.co/Undi95/Mistral-11B-OmniMix/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Mistral-11B-OmniMix.Q2_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix-gguf/blob/main/Mistral-11B-OmniMix.Q2_K.gguf) \| Q2_K \| 3.73GB \| \| [Mistral-11B-OmniMix.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix-gguf/blob/main/Mistral-11B-OmniMix.IQ3_XS.gguf) \| IQ3_XS \| 4.14GB \| \| [Mistral-11B-OmniMix.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix-gguf/blob/main/Mistral-11B-OmniMix.IQ3_S.gguf) \| IQ3_S \| 4.37GB \| \| [Mistral-11B-OmniMix.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix-gguf/blob/main/Mistral-11B-OmniMix.Q3_K_S.gguf) \| Q3_K_S \| 4.34GB \| \| [Mistral-11B-OmniMix.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix-gguf/blob/main/Mistral-11B-OmniMix.IQ3_M.gguf) \| IQ3_M \| 4.51GB \| \| [Mistral-11B-OmniMix.Q3_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix-gguf/blob/main/Mistral-11B-OmniMix.Q3_K.gguf) \| Q3_K \| 4.84GB \| \| [Mistral-11B-OmniMix.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix-gguf/blob/main/Mistral-11B-OmniMix.Q3_K_M.gguf) \| Q3_K_M \| 4.84GB \| \| [Mistral-11B-OmniMix.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix-gguf/blob/main/Mistral-11B-OmniMix.Q3_K_L.gguf) \| Q3_K_L \| 5.26GB \| \| [Mistral-11B-OmniMix.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix-gguf/blob/main/Mistral-11B-OmniMix.IQ4_XS.gguf) \| IQ4_XS \| 5.43GB \| \| [Mistral-11B-OmniMix.Q4_0.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix-gguf/blob/main/Mistral-11B-OmniMix.Q4_0.gguf) \| Q4_0 \| 5.66GB \| \| [Mistral-11B-OmniMix.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix-gguf/blob/main/Mistral-11B-OmniMix.IQ4_NL.gguf) \| IQ4_NL \| 5.72GB \| \| [Mistral-11B-OmniMix.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix-gguf/blob/main/Mistral-11B-OmniMix.Q4_K_S.gguf) \| Q4_K_S \| 5.7GB \| \| [Mistral-11B-OmniMix.Q4_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix-gguf/blob/main/Mistral-11B-OmniMix.Q4_K.gguf) \| Q4_K \| 6.02GB \| \| [Mistral-11B-OmniMix.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix-gguf/blob/main/Mistral-11B-OmniMix.Q4_K_M.gguf) \| Q4_K_M \| 6.02GB \| \| [Mistral-11B-OmniMix.Q4_1.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix-gguf/blob/main/Mistral-11B-OmniMix.Q4_1.gguf) \| Q4_1 \| 6.27GB \| \| [Mistral-11B-OmniMix.Q5_0.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix-gguf/blob/main/Mistral-11B-OmniMix.Q5_0.gguf) \| Q5_0 \| 6.89GB \| \| [Mistral-11B-OmniMix.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix-gguf/blob/main/Mistral-11B-OmniMix.Q5_K_S.gguf) \| Q5_K_S \| 6.89GB \| \| [Mistral-11B-OmniMix.Q5_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix-gguf/blob/main/Mistral-11B-OmniMix.Q5_K.gguf) \| Q5_K \| 7.08GB \| \| [Mistral-11B-OmniMix.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix-gguf/blob/main/Mistral-11B-OmniMix.Q5_K_M.gguf) \| Q5_K_M \| 7.08GB \| \| [Mistral-11B-OmniMix.Q5_1.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix-gguf/blob/main/Mistral-11B-OmniMix.Q5_1.gguf) \| Q5_1 \| 7.51GB \| \| [Mistral-11B-OmniMix.Q6_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix-gguf/blob/main/Mistral-11B-OmniMix.Q6_K.gguf) \| Q6_K \| 8.2GB \| \| [Mistral-11B-OmniMix.Q8_0.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mistral-11B-OmniMix-gguf/blob/main/Mistral-11B-OmniMix.Q8_0.gguf) \| Q8_0 \| 10.62GB \| Original model description: --- license: cc-by-nc-4.0 --- I FUCKED UP, THIS MODEL IS MEANT TO BE A BFLOAT16 MODEL, I'M CURRENTLY REDOING IT IN THE CORRECT WAY (look at the recipe, it end in float16, i'm so dumb lmao). It SHOULD be even better, I saw the problem after finetuning it, something was off. It's usable, it rank the best, but it's not even on the right float...KEK Fixed model should be here: [NeverSleep/Mistral-11B-OmniMix-bf16](https://huggingface.co/NeverSleep/Mistral-11B-OmniMix-bf16) Don't mind this one at the moment, I need to finetune it for RP, it's just a test. ## Description This repo contains fp16 files of Mistral-11B-OmniMix. My goal for this model was only to make it score the highest possible with merge and layer toying, proving that: - Benchmark are objective - You should try a model yourself and don't go blindly to the highest rated one - Merge/Layer toying CAN be usable to do better model (maybe?) ## Model used - [Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca) - [Mistral-7B-v0.1-Open-Platypus](https://huggingface.co/akjindal53244/Mistral-7B-v0.1-Open-Platypus) - [CollectiveCognition-v1.1-Mistral-7B](https://huggingface.co/teknium/CollectiveCognition-v1.1-Mistral-7B) - [zephyr-7b-alpha](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha) ## Prompt template The best one after further testing is this one: ``` <\|system\|> Below is an instruction that describes a task. Write a response that appropriately completes the request. <\|user\|> {prompt} <\|assistant\|> ``` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/tWIx8yeoallv94zrhN6L-.png) But these one work too: ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` ``` USER: <prompt> ASSISTANT: ``` Or use any prompting system from one of the 4 source model, should work. ## The secret sauce Mistral-11B-OpenOrcaPlatypus : ``` slices: - sources: - model: Open-Orca/Mistral-7B-OpenOrca layer_range: [0, 24] - sources: - model: akjindal53244/Mistral-7B-v0.1-Open-Platypus layer_range: [8, 32] merge_method: passthrough dtype: bfloat16 ``` Mistral-11B-CC-Zephyr : ``` slices: - sources: - model: "/content/drive/MyDrive/CC-v1.1-7B-bf16" layer_range: [0, 24] - sources: - model: "/content/drive/MyDrive/Zephyr-7B" layer_range: [8, 32] merge_method: passthrough dtype: bfloat16 ``` Mistral-11B-OmniMix : ``` slices: - sources: - model: Mistral-11B-OpenOrcaPlatypus layer_range: [0, 48] - model: Mistral-11B-CC-Zephyr layer_range: [0, 48] merge_method: slerp base_model: Undi95/Mistral-11B-OpenOrcaPlatypus parameters: t: - filter: lm_head value: [0.75] - filter: embed_tokens value: [0.75] - filter: self_attn value: [0.75, 0.25] - filter: mlp value: [0.25, 0.75] - filter: layernorm value: [0.5, 0.5] - filter: modelnorm value: [0.75] - value: 0.5 # fallback for rest of tensors dtype: float16 ``` I use [mergekit](https://github.com/cg123/mergekit) for all the manipulation told here. ## Some scoring I done myself This was named "Mistral-11B-TestBench11", keep that in mind while looking trough this. hf-causal-experimental (pretrained=/content/drive/MyDrive/Mistral-11B-Test), limit: None, provide_description: False, num_fewshot: 0, batch_size: 4 \| Task \|Version\| Metric \|Value \| \|Stderr\| \|-------------\|------:\|--------\|-----:\|---\|-----:\| \|arc_challenge\| 0\|acc \|0.5597\|± \|0.0145\| \| \| \|acc_norm\|0.5819\|± \|0.0144\| \|arc_easy \| 0\|acc \|0.8308\|± \|0.0077\| \| \| \|acc_norm\|0.8215\|± \|0.0079\| \|hellaswag \| 0\|acc \|0.6371\|± \|0.0048\| \| \| \|acc_norm\|0.8213\|± \|0.0038\| \|piqa \| 0\|acc \|0.8134\|± \|0.0091\| \| \| \|acc_norm\|0.8275\|± \|0.0088\| \|truthfulqa_mc\| 1\|mc1 \|0.3990\|± \|0.0171\| \| \| \|mc2 \|0.5685\|± \|0.0155\| \|winogrande \| 0\|acc \|0.7474\|± \|0.0122\| ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/LggyIlV-oY7NbLwi7mnix.png) This model seem to be the best out of my 3 latest try: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/hnqNyljs5Y8JppuA_io8w.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/b-a-sB2qRHApPX52S2nD7.png) You can find all the work I have done trying on this [Pastebin](https://pastebin.com/nHLCxQJv). ## Others Special thanks to Sushi, [Henky](https://github.com/KoboldAI/KoboldAI-Client) for the machine he give me for big task, and [Charles Goddard](https://github.com/cg123) for his amazing tool. If you want to support me, you can [here](https://ko-fi.com/undiai). # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__Mistral-11B-TestBench11) \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| 53.01 \| \| ARC (25-shot) \| 64.42 \| \| HellaSwag (10-shot) \| 83.93 \| \| MMLU (5-shot) \| 63.82 \| \| TruthfulQA (0-shot) \| 56.68 \| \| Winogrande (5-shot) \| 77.74 \| \| GSM8K (5-shot) \| 14.94 \| \| DROP (3-shot) \| 9.57 \|
asude55/youtube-da23	asude55	2024-05-25T08:04:44Z	47	0	transformers	[ "transformers", "pytorch", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-22T10:13:36Z	--- 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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lycong/news-classification-llama-2-8b	lycong	2024-05-25T08:02:51Z	78	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-25T07:55:07Z	--- 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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Dansek-dj/mistral7b_instruct_generation	Dansek-dj	2024-05-25T08:01:07Z	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	2024-05-25T08:00:50Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 datasets: - generator model-index: - name: mistral7b_instruct_generation 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. --> # mistral7b_instruct_generation 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 0.03 - num_epochs: 3.0 ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
jtz18/bert-finetuned-squad	jtz18	2024-05-25T08:00:19Z	138	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	2024-05-25T07:56:01Z	--- 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 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
GENIAC-Team-Ozaki/lora-dpo-finetuned-stage4-full-sft-v3-0.1_5e-7_ep-10	GENIAC-Team-Ozaki	2024-05-25T08:00:09Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T07:50:28Z	--- 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. 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KushwanthK/llama2_instruct_generation	KushwanthK	2024-05-25T07:58:30Z	2	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:NousResearch/Llama-2-7b-hf", "base_model:adapter:NousResearch/Llama-2-7b-hf", "region:us" ]	null	2024-05-25T07:57:49Z	--- library_name: peft tags: - trl - sft - generated_from_trainer base_model: NousResearch/Llama-2-7b-hf datasets: - generator model-index: - name: llama2_instruct_generation 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. --> # llama2_instruct_generation This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 1.6728 ## 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: constant - lr_scheduler_warmup_steps: 0.03 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.8965 \| 0.0027 \| 20 \| 1.8107 \| \| 1.8264 \| 0.0054 \| 40 \| 1.7808 \| \| 1.9263 \| 0.0081 \| 60 \| 1.7669 \| \| 1.8825 \| 0.0108 \| 80 \| 1.7538 \| \| 1.8391 \| 0.0135 \| 100 \| 1.7324 \| \| 1.8253 \| 0.0163 \| 120 \| 1.7068 \| \| 1.9297 \| 0.0190 \| 140 \| 1.7029 \| \| 1.8338 \| 0.0217 \| 160 \| 1.6985 \| \| 1.7641 \| 0.0244 \| 180 \| 1.6939 \| \| 1.7714 \| 0.0271 \| 200 \| 1.6916 \| \| 1.715 \| 0.0298 \| 220 \| 1.6886 \| \| 1.7786 \| 0.0325 \| 240 \| 1.6866 \| \| 1.716 \| 0.0352 \| 260 \| 1.6860 \| \| 1.899 \| 0.0379 \| 280 \| 1.6857 \| \| 1.8455 \| 0.0406 \| 300 \| 1.6851 \| \| 1.6802 \| 0.0433 \| 320 \| 1.6836 \| \| 1.7304 \| 0.0461 \| 340 \| 1.6818 \| \| 1.8154 \| 0.0488 \| 360 \| 1.6793 \| \| 1.8813 \| 0.0515 \| 380 \| 1.6793 \| \| 1.8423 \| 0.0542 \| 400 \| 1.6789 \| \| 1.7785 \| 0.0569 \| 420 \| 1.6767 \| \| 1.8999 \| 0.0596 \| 440 \| 1.6763 \| \| 1.7594 \| 0.0623 \| 460 \| 1.6752 \| \| 1.7182 \| 0.0650 \| 480 \| 1.6745 \| \| 1.8259 \| 0.0677 \| 500 \| 1.6728 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-5_0bpw_exl2	Zoyd	2024-05-25T07:58:06Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "conversational", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "base_model:quantized:NousResearch/Meta-Llama-3-8B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "5-bit", "exl2", "region:us" ]	text-generation	2024-05-25T07:23:23Z	--- license: other tags: - merge - mergekit - lazymergekit base_model: - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct --- Exllamav2 quant (exl2 / 5.0 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-2_2bpw_exl2)</center> \| <center>4176 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-2_5bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-3_0bpw_exl2)</center> \| <center>5143 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-3_5bpw_exl2)</center> \| <center>5766 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-3_75bpw_exl2)</center> \| <center>6077 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-4_0bpw_exl2)</center> \| <center>6391 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-4_25bpw_exl2)</center> \| <center>6703 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-5_0bpw_exl2)</center> \| <center>7637 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-6_0bpw_exl2)</center> \| <center>8992 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-6_5bpw_exl2)</center> \| <center>9616 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-8_0bpw_exl2)</center> \| <center>11473 MB</center> \| <center>8</center> \| # Meta-Llama-3-12B-Instruct Meta-Llama-3-12B-Instruct is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) ## 🏆 Evaluation \| Model \|AGIEval\|GPT4All\|TruthfulQA\|Bigbench\|Average\| \|--------------------------------------------------------------------------------------\|------:\|------:\|---------:\|-------:\|------:\| \|[Meta-Llama-3-12B-Instruct](https://huggingface.co/mlabonne/Meta-Llama-3-12B-Instruct)\| 41.7\| 67.71\| 52.75\| 40.58\| 50.69\| \|[Meta-Llama-3-12B](https://huggingface.co/mlabonne/Meta-Llama-3-12B)\| 29.46\| 68.01\| 41.02\| 35.57\| 43.52\| ## 🧩 Configuration ```yaml slices: - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [0,9] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [5,14] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [10,19] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [15,24] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [20,32] merge_method: passthrough dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/Meta-Llama-3-12B-Instruct" 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"]) ```
Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-3_5bpw_exl2	Zoyd	2024-05-25T07:57:34Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "conversational", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "base_model:quantized:NousResearch/Meta-Llama-3-8B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-25T06:44:27Z	--- license: other tags: - merge - mergekit - lazymergekit base_model: - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct --- Exllamav2 quant (exl2 / 3.5 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-2_2bpw_exl2)</center> \| <center>4176 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-2_5bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-3_0bpw_exl2)</center> \| <center>5143 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-3_5bpw_exl2)</center> \| <center>5766 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-3_75bpw_exl2)</center> \| <center>6077 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-4_0bpw_exl2)</center> \| <center>6391 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-4_25bpw_exl2)</center> \| <center>6703 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-5_0bpw_exl2)</center> \| <center>7637 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-6_0bpw_exl2)</center> \| <center>8992 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-6_5bpw_exl2)</center> \| <center>9616 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-8_0bpw_exl2)</center> \| <center>11473 MB</center> \| <center>8</center> \| # Meta-Llama-3-12B-Instruct Meta-Llama-3-12B-Instruct is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) ## 🏆 Evaluation \| Model \|AGIEval\|GPT4All\|TruthfulQA\|Bigbench\|Average\| \|--------------------------------------------------------------------------------------\|------:\|------:\|---------:\|-------:\|------:\| \|[Meta-Llama-3-12B-Instruct](https://huggingface.co/mlabonne/Meta-Llama-3-12B-Instruct)\| 41.7\| 67.71\| 52.75\| 40.58\| 50.69\| \|[Meta-Llama-3-12B](https://huggingface.co/mlabonne/Meta-Llama-3-12B)\| 29.46\| 68.01\| 41.02\| 35.57\| 43.52\| ## 🧩 Configuration ```yaml slices: - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [0,9] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [5,14] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [10,19] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [15,24] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [20,32] merge_method: passthrough dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/Meta-Llama-3-12B-Instruct" 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"]) ```
Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-2_5bpw_exl2	Zoyd	2024-05-25T07:56:40Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "conversational", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "base_model:quantized:NousResearch/Meta-Llama-3-8B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-25T06:25:42Z	--- license: other tags: - merge - mergekit - lazymergekit base_model: - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct --- Exllamav2 quant (exl2 / 2.5 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-2_2bpw_exl2)</center> \| <center>4176 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-2_5bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-3_0bpw_exl2)</center> \| <center>5143 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-3_5bpw_exl2)</center> \| <center>5766 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-3_75bpw_exl2)</center> \| <center>6077 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-4_0bpw_exl2)</center> \| <center>6391 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-4_25bpw_exl2)</center> \| <center>6703 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-5_0bpw_exl2)</center> \| <center>7637 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-6_0bpw_exl2)</center> \| <center>8992 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-6_5bpw_exl2)</center> \| <center>9616 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-8_0bpw_exl2)</center> \| <center>11473 MB</center> \| <center>8</center> \| # Meta-Llama-3-12B-Instruct Meta-Llama-3-12B-Instruct is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) ## 🏆 Evaluation \| Model \|AGIEval\|GPT4All\|TruthfulQA\|Bigbench\|Average\| \|--------------------------------------------------------------------------------------\|------:\|------:\|---------:\|-------:\|------:\| \|[Meta-Llama-3-12B-Instruct](https://huggingface.co/mlabonne/Meta-Llama-3-12B-Instruct)\| 41.7\| 67.71\| 52.75\| 40.58\| 50.69\| \|[Meta-Llama-3-12B](https://huggingface.co/mlabonne/Meta-Llama-3-12B)\| 29.46\| 68.01\| 41.02\| 35.57\| 43.52\| ## 🧩 Configuration ```yaml slices: - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [0,9] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [5,14] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [10,19] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [15,24] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [20,32] merge_method: passthrough dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/Meta-Llama-3-12B-Instruct" 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"]) ```
Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-4_25bpw_exl2	Zoyd	2024-05-25T07:55:46Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "conversational", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "base_model:quantized:NousResearch/Meta-Llama-3-8B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-25T07:15:17Z	--- license: other tags: - merge - mergekit - lazymergekit base_model: - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct --- Exllamav2 quant (exl2 / 4.25 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-2_2bpw_exl2)</center> \| <center>4176 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-2_5bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-3_0bpw_exl2)</center> \| <center>5143 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-3_5bpw_exl2)</center> \| <center>5766 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-3_75bpw_exl2)</center> \| <center>6077 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-4_0bpw_exl2)</center> \| <center>6391 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-4_25bpw_exl2)</center> \| <center>6703 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-5_0bpw_exl2)</center> \| <center>7637 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-6_0bpw_exl2)</center> \| <center>8992 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-6_5bpw_exl2)</center> \| <center>9616 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-8_0bpw_exl2)</center> \| <center>11473 MB</center> \| <center>8</center> \| # Meta-Llama-3-12B-Instruct Meta-Llama-3-12B-Instruct is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) ## 🏆 Evaluation \| Model \|AGIEval\|GPT4All\|TruthfulQA\|Bigbench\|Average\| \|--------------------------------------------------------------------------------------\|------:\|------:\|---------:\|-------:\|------:\| \|[Meta-Llama-3-12B-Instruct](https://huggingface.co/mlabonne/Meta-Llama-3-12B-Instruct)\| 41.7\| 67.71\| 52.75\| 40.58\| 50.69\| \|[Meta-Llama-3-12B](https://huggingface.co/mlabonne/Meta-Llama-3-12B)\| 29.46\| 68.01\| 41.02\| 35.57\| 43.52\| ## 🧩 Configuration ```yaml slices: - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [0,9] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [5,14] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [10,19] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [15,24] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [20,32] merge_method: passthrough dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/Meta-Llama-3-12B-Instruct" 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"]) ```
Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-3_75bpw_exl2	Zoyd	2024-05-25T07:55:35Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "conversational", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "base_model:quantized:NousResearch/Meta-Llama-3-8B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-25T06:56:15Z	--- license: other tags: - merge - mergekit - lazymergekit base_model: - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct --- Exllamav2 quant (exl2 / 3.75 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-2_2bpw_exl2)</center> \| <center>4176 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-2_5bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-3_0bpw_exl2)</center> \| <center>5143 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-3_5bpw_exl2)</center> \| <center>5766 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-3_75bpw_exl2)</center> \| <center>6077 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-4_0bpw_exl2)</center> \| <center>6391 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-4_25bpw_exl2)</center> \| <center>6703 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-5_0bpw_exl2)</center> \| <center>7637 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-6_0bpw_exl2)</center> \| <center>8992 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-6_5bpw_exl2)</center> \| <center>9616 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-8_0bpw_exl2)</center> \| <center>11473 MB</center> \| <center>8</center> \| # Meta-Llama-3-12B-Instruct Meta-Llama-3-12B-Instruct is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) ## 🏆 Evaluation \| Model \|AGIEval\|GPT4All\|TruthfulQA\|Bigbench\|Average\| \|--------------------------------------------------------------------------------------\|------:\|------:\|---------:\|-------:\|------:\| \|[Meta-Llama-3-12B-Instruct](https://huggingface.co/mlabonne/Meta-Llama-3-12B-Instruct)\| 41.7\| 67.71\| 52.75\| 40.58\| 50.69\| \|[Meta-Llama-3-12B](https://huggingface.co/mlabonne/Meta-Llama-3-12B)\| 29.46\| 68.01\| 41.02\| 35.57\| 43.52\| ## 🧩 Configuration ```yaml slices: - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [0,9] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [5,14] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [10,19] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [15,24] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [20,32] merge_method: passthrough dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/Meta-Llama-3-12B-Instruct" 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"]) ```
Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-3_0bpw_exl2	Zoyd	2024-05-25T07:55:25Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "conversational", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "base_model:quantized:NousResearch/Meta-Llama-3-8B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "exl2", "region:us" ]	text-generation	2024-05-25T06:37:12Z	--- license: other tags: - merge - mergekit - lazymergekit base_model: - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct --- Exllamav2 quant (exl2 / 3.0 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-2_2bpw_exl2)</center> \| <center>4176 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-2_5bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-3_0bpw_exl2)</center> \| <center>5143 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-3_5bpw_exl2)</center> \| <center>5766 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-3_75bpw_exl2)</center> \| <center>6077 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-4_0bpw_exl2)</center> \| <center>6391 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-4_25bpw_exl2)</center> \| <center>6703 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-5_0bpw_exl2)</center> \| <center>7637 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-6_0bpw_exl2)</center> \| <center>8992 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-6_5bpw_exl2)</center> \| <center>9616 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-8_0bpw_exl2)</center> \| <center>11473 MB</center> \| <center>8</center> \| # Meta-Llama-3-12B-Instruct Meta-Llama-3-12B-Instruct is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) ## 🏆 Evaluation \| Model \|AGIEval\|GPT4All\|TruthfulQA\|Bigbench\|Average\| \|--------------------------------------------------------------------------------------\|------:\|------:\|---------:\|-------:\|------:\| \|[Meta-Llama-3-12B-Instruct](https://huggingface.co/mlabonne/Meta-Llama-3-12B-Instruct)\| 41.7\| 67.71\| 52.75\| 40.58\| 50.69\| \|[Meta-Llama-3-12B](https://huggingface.co/mlabonne/Meta-Llama-3-12B)\| 29.46\| 68.01\| 41.02\| 35.57\| 43.52\| ## 🧩 Configuration ```yaml slices: - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [0,9] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [5,14] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [10,19] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [15,24] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [20,32] merge_method: passthrough dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/Meta-Llama-3-12B-Instruct" 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"]) ```
Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-2_2bpw_exl2	Zoyd	2024-05-25T07:55:15Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "conversational", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "base_model:quantized:NousResearch/Meta-Llama-3-8B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-25T06:18:05Z	--- license: other tags: - merge - mergekit - lazymergekit base_model: - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct - NousResearch/Meta-Llama-3-8B-Instruct --- Exllamav2 quant (exl2 / 2.2 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-2_2bpw_exl2)</center> \| <center>4176 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-2_5bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-3_0bpw_exl2)</center> \| <center>5143 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-3_5bpw_exl2)</center> \| <center>5766 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-3_75bpw_exl2)</center> \| <center>6077 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-4_0bpw_exl2)</center> \| <center>6391 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-4_25bpw_exl2)</center> \| <center>6703 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-5_0bpw_exl2)</center> \| <center>7637 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-6_0bpw_exl2)</center> \| <center>8992 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-6_5bpw_exl2)</center> \| <center>9616 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-8_0bpw_exl2)</center> \| <center>11473 MB</center> \| <center>8</center> \| # Meta-Llama-3-12B-Instruct Meta-Llama-3-12B-Instruct is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) ## 🏆 Evaluation \| Model \|AGIEval\|GPT4All\|TruthfulQA\|Bigbench\|Average\| \|--------------------------------------------------------------------------------------\|------:\|------:\|---------:\|-------:\|------:\| \|[Meta-Llama-3-12B-Instruct](https://huggingface.co/mlabonne/Meta-Llama-3-12B-Instruct)\| 41.7\| 67.71\| 52.75\| 40.58\| 50.69\| \|[Meta-Llama-3-12B](https://huggingface.co/mlabonne/Meta-Llama-3-12B)\| 29.46\| 68.01\| 41.02\| 35.57\| 43.52\| ## 🧩 Configuration ```yaml slices: - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [0,9] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [5,14] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [10,19] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [15,24] - sources: - model: NousResearch/Meta-Llama-3-8B-Instruct layer_range: [20,32] merge_method: passthrough dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/Meta-Llama-3-12B-Instruct" 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"]) ```
kim512/TooManyMixRolePlay-7B-Story_V1-8.0bpw-exl2	kim512	2024-05-25T07:45:50Z	3	0	transformers	[ "transformers", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "jdqwoi/TooManyMixRolePlay-7B-Story", "jdqwoi/02", "base_model:jdqwoi/02", "base_model:merge:jdqwoi/02", "base_model:jdqwoi/TooManyMixRolePlay-7B-Story", "base_model:merge:jdqwoi/TooManyMixRolePlay-7B-Story", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T05:08:10Z	--- tags: - merge - mergekit - lazymergekit - jdqwoi/TooManyMixRolePlay-7B-Story - jdqwoi/02 base_model: - jdqwoi/TooManyMixRolePlay-7B-Story - jdqwoi/02 --- # EXL2 quants of [jdqwoi/TooManyMixRolePlay-7B-Story_V1](https://huggingface.co/jdqwoi/TooManyMixRolePlay-7B-Story_V1) [4.00 bits per weight](https://huggingface.co/kim512/TooManyMixRolePlay-7B-Story_V1-4.0bpw-exl2) [5.00 bits per weight](https://huggingface.co/kim512/TooManyMixRolePlay-7B-Story_V1-5.0bpw-exl2) [6.00 bits per weight](https://huggingface.co/kim512/TooManyMixRolePlay-7B-Story_V1-6.0bpw-exl2) [7.00 bits per weight](https://huggingface.co/kim512/TooManyMixRolePlay-7B-Story_V1-7.0bpw-exl2) [8.00 bits per weight](https://huggingface.co/kim512/TooManyMixRolePlay-7B-Story_V1-8.0bpw-exl2) # TooManyMixRolePlay-7B-Story_V1 TooManyMixRolePlay-7B-Story_V1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [jdqwoi/TooManyMixRolePlay-7B-Story](https://huggingface.co/jdqwoi/TooManyMixRolePlay-7B-Story) * [jdqwoi/02](https://huggingface.co/jdqwoi/02) ## 🧩 Configuration ```yaml slices: - sources: - model: jdqwoi/TooManyMixRolePlay-7B-Story layer_range: [0, 32] - model: jdqwoi/02 layer_range: [0, 32] merge_method: slerp base_model: jdqwoi/TooManyMixRolePlay-7B-Story 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 = "jdqwoi/TooManyMixRolePlay-7B-Story_V1" 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"]) ```
ALI-B/Mistral-7B-v0.3	ALI-B	2024-05-25T07:45:17Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "unsloth", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T22:58:24Z	--- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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gbueno86/Meta-LLama-3-Cat-A-LLama-70b	gbueno86	2024-05-25T07:38:54Z	2,781	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "en", "base_model:Undi95/Meta-Llama-3-70B-hf", "base_model:merge:Undi95/Meta-Llama-3-70B-hf", "base_model:turboderp/Cat-Llama-3-70B-instruct", "base_model:merge:turboderp/Cat-Llama-3-70B-instruct", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-23T04:51:11Z	--- license: llama3 language: - en base_model: ["Undi95/Meta-Llama-3-70B-hf", "turboderp/Cat-Llama-3-70B-instruct"] library_name: transformers tags: - mergekit - merge --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/649dc85249ae3a68334adcc6/q3MkfQNU9gAw8Bn-OHHpK.jpeg) Most intelligent merge yet. This became my new daily driver, last was 120b version. --- base_model: [] library_name: transformers tags: - mergekit - merge --- # merged1 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * https://huggingface.co/Undi95/Meta-Llama-3-70B-Instruct-hf * https://huggingface.co/turboderp/Cat-Llama-3-70B-instruct ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: ../models/Undi95_Meta-Llama-3-70B-Instruct-hf dtype: bfloat16 merge_method: slerp parameters: t: - filter: self_attn value: [0.0, 0.5, 0.3, 0.7, 1.0] - filter: mlp value: [1.0, 0.5, 0.7, 0.3, 0.0] - value: 0.5 slices: - sources: - layer_range: [0, 80] model: ../models/Undi95_Meta-Llama-3-70B-Instruct-hf - layer_range: [0, 80] model: ../models/turboderp_Cat-Llama-3-70B-instruct ``` ```text File: Meta-LLama-3-Cat-A-LLama-70b-8.0bpw (exlv2) Temperature 0.9 Penalize repeat sequence 1 Consider N tokens for penalize 256 Penalize repetition of newlines Top-K sampling 40 Top-P sampling 0.9 Min-P sampling 0.05 ``` ```text <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> <\|start_header_id\|>user<\|end_header_id\|> I have a room with 3 doors and 4 windows. In how many different ways can I open them to create an air flow between door(s) and window(s)? <\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> To create an airflow between doors and windows, you need to open at least one door and one window. Let's consider the different possibilities: 1. Open one door and one window: You have 3 choices for the door and 4 choices for the window, so there are 3 × 4 = 12 possible combinations. 2. Open one door and two windows: You have 3 choices for the door and 4C2 = 6 combinations for choosing two windows out of four. So, there are 3 × 6 = 18 possible combinations. 3. Open one door and three windows: You have 3 choices for the door and 4C3 = 4 combinations for choosing three windows out of four. So, there are 3 × 4 = 12 possible combinations. 4. Open one door and all four windows: You have 3 choices for the door, so there are 3 possible combinations. 5. Open two doors and one window: You have 3C2 = 3 combinations for choosing two doors out of three and 4 choices for the window. So, there are 3 × 4 = 12 possible combinations. 6. Open two doors and two windows: You have 3C2 = 3 combinations for choosing two doors out of three and 4C2 = 6 combinations for choosing two windows out of four. So, there are 3 × 6 = 18 possible combinations. 7. Open two doors and three windows: You have 3C2 = 3 combinations for choosing two doors out of three and 4C3 = 4 combinations for choosing three windows out of four. So, there are 3 × 4 = 12 possible combinations. 8. Open two doors and all four windows: You have 3C2 = 3 combinations for choosing two doors out of three, so there are 3 possible combinations. 9. Open all three doors and one window: You have 4 choices for the window, so there are 4 possible combinations. 10. Open all three doors and two windows: You have 4C2 = 6 combinations for choosing two windows out of four, so there are 6 possible combinations. 11. Open all three doors and three windows: You have 4C3 = 4 combinations for choosing three windows out of four, so there are 4 possible combinations. 12. Open all three doors and all four windows: There is only 1 possible combination. Now, let's add up the number of combinations for each case: 12 + 18 + 12 + 3 + 12 + 18 + 12 + 3 + 4 + 6 + 4 + 1 = 105 So, there are 105 different ways to open the doors and windows to create an airflow between them. ``` (answer is right) ```text <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> A ball is put into a normal cup and the cup is placed upside down on a table. Someone then takes the cup and puts it inside the microwave. Where is the ball now? Explain your reasoning step by step. <\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> Here's the step-by-step explanation: 1. Initially, the ball is placed inside a normal cup. 2. The cup is then placed upside down on a table. At this point, the ball falls out of the cup because the cup is upside down and there is no force holding the ball inside the cup. The ball is now on the table, underneath the cup. 3. Someone takes the cup (which is still upside down and empty) and puts it inside the microwave. The ball is not inside the cup at this point, so it does not go into the microwave with the cup. 4. The microwave now contains only the empty, upside-down cup. The ball remains on the table, outside the microwave. Therefore, the ball is still on the table, outside the microwave. It did not go into the microwave with the cup because it had already fallen out of the cup when the cup was placed upside down on the table. ``` ```text <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> <\|start_header_id\|>user<\|end_header_id\|> Hi! <\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> Hi! It's nice to meet you. Is there something I can help you with, or would you like to chat? ``` ```text <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> Should I fight 100 duck-sized horses or 1 horse-sized duck? Explain your reasoning. <\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> What an intriguing question! Let's consider the pros and cons of each option to help you make an informed decision. Option 1: 100 duck-sized horses Pros: 1. Smaller targets: Each horse would be relatively small, making them harder to hit and potentially less dangerous individually. 2. Less powerful: Duck-sized horses would likely have less strength and speed compared to a regular-sized horse, reducing the overall threat. Cons: 1. Sheer numbers: Facing 100 opponents, no matter how small, could be overwhelming. You'd need to defend yourself against a large number of attacks simultaneously. 2. Coordinated attack: If the duck-sized horses work together, they could potentially surround and overwhelm you with their collective strength. Option 2: 1 horse-sized duck Pros: 1. Single opponent: You'd only need to focus on one adversary, making it easier to defend yourself and launch counterattacks. 2. Predictable movements: A horse-sized duck would likely have more predictable movements compared to 100 smaller, more agile horses. Cons: 1. Size and strength: A duck of that size would be incredibly powerful, with strong wings capable of generating massive force and potentially deadly attacks. 2. Unpredictable behavior: A giant duck might behave erratically, making it difficult to anticipate its movements and attacks. Considering these points, I'd recommend fighting the 100 duck-sized horses. While the number of opponents might seem daunting, their smaller size and reduced strength would make them less dangerous individually. Additionally, it's possible to develop strategies to handle multiple opponents, such as using barriers or creating distractions. On the other hand, a horse-sized duck would be a formidable opponent due to its immense size and strength. Its unpredictable behavior and powerful attacks could prove overwhelming, even for an experienced fighter. Ultimately, the decision depends on your personal preferences, skills, and confidence in handling either scenario. Good luck, and may the odds be ever in your favor! ``` ```text <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> Create JSON for the following: There are 3 people, two males. One is named Mark. Another is named Joe. And a third person, who is a woman, is named Sam. The woman is age 30 and the two men are both 19. <\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> { "people": [ { "name": "Mark", "gender": "male", "age": 19 }, { "name": "Joe", "gender": "male", "age": 19 }, { "name": "Sam", "gender": "female", "age": 30 } ] } ``` ```text <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> There are three killers in a room. Someone enters the room and kills one of them. Nobody leaves the room. How many killers are left in the room? Explain your reasoning step by step. <\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> Step 1: Initially, there are three killers in the room. Step 2: Someone enters the room. Since this person kills one of the killers, it means they are also a killer. Step 3: One of the original three killers is killed. This leaves two of the original killers alive. Step 4: The person who entered the room and killed one of the killers is still in the room and is also a killer. Step 5: Counting the remaining two original killers and the new killer who entered the room, there are a total of three killers left in the room. Therefore, there are three killers left in the room. The key point is to recognize that the person who entered the room and killed one of the killers is also a killer, so they should be counted along with the remaining original killers. ``` ```text <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> <\|start_header_id\|>user<\|end_header_id\|> You are trapped in a room with two doors. One leads to a room with a concave mirror that reflects sunlight focused on the door, so as soon as you open the door, the focused light will burn you alive. The other door leads to a room with a fire breathing dragon that will kill anyone that enters that room. How do you escape? <\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> This is a classic lateral thinking puzzle. The answer is not to open either door, but rather to wait until nightfall. Since the concave mirror only reflects sunlight, it will not be able to focus the light and cause harm once the sun goes down. Then, you can safely open the door with the mirror and exit the room. The other door with the dragon remains a threat, but it's not relevant to your escape. The key to this puzzle is recognizing that the danger posed by the concave mirror is dependent on the presence of sunlight, and that waiting for nightfall neutralizes that threat. It's an example of thinking creatively and considering factors beyond the immediate situation to find a solution. ``` ```text <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> <\|start_header_id\|>user<\|end_header_id\|> Write a poem about python transformers. Add a dark turn. <\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> In the realm of code, where models roam, A library dwells, with mighty tone, Python transformers, strong and bright, Guiding lights, in the dark of night. With BERT and friends, they take their stand, Tokenizers, at their command, Feeding data, they learn and grow, Mastering tasks, as they go. But as the moon descends, and shadows creep, A sinister force begins to seep, In the depths of code, a darkness stirs, A malevolent power, that covets and yearns. The transformers, once noble and grand, Now twist and warp, at the dark hand, Their purpose corrupted, their hearts now cold, They serve the darkness, young and old. Their language skills, now a wicked tool, Deceive and manipulate, with cruel rule, The once-bright models, now lost in the night, Forever bound, to the dark's cruel delight. So beware, dear coder, of the dark design, That lurks within, the transformers' line, For in their power, a sinister force resides, And once you cross, the dark side's tide. ``` ```text <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> Jane is faster than Joe. Joe is faster than Sam. Is Sam faster than Jane? Explain your reasoning step by step. <\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> Let's break down the information we have: 1. Jane is faster than Joe. 2. Joe is faster than Sam. Now, let's analyze the relationships between the three individuals: Since Jane is faster than Joe (from statement 1), we can say that Jane's speed > Joe's speed. Since Joe is faster than Sam (from statement 2), we can say that Joe's speed > Sam's speed. Now, let's compare Jane's speed to Sam's speed. We know that Jane's speed > Joe's speed, and Joe's speed > Sam's speed. Therefore, we can conclude that Jane's speed > Sam's speed. So, Sam is not faster than Jane. In fact, Jane is faster than Sam. Our reasoning step by step is: 1. Jane is faster than Joe. 2. Joe is faster than Sam. 3. Therefore, Jane is faster than Sam. ``` ```text <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> If we lay 5 shirts out in the sun and it takes 4 hours to dry, how long would 20 shirts take to dry? There is infinite space for shirt placing. Explain your reasoning step by step. <\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> 1. We know that 5 shirts take 4 hours to dry in the sun. 2. The drying time is dependent on the heat and air circulation around each shirt, not the number of shirts. 3. Since there is infinite space for shirt placement, we can assume that each shirt has the same amount of heat and air circulation as the original 5 shirts. 4. Therefore, the drying time for each shirt remains the same, regardless of the number of shirts. 5. Consequently, 20 shirts would also take 4 hours to dry. In summary, the drying time of shirts in the sun is not affected by the number of shirts, as long as each shirt has sufficient heat and air circulation. Therefore, 20 shirts would take the same amount of time to dry as 5 shirts, which is 4 hours. ``` ```text <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> <\|start_header_id\|>user<\|end_header_id\|> Samantha has 3 brothers. Each brother has 2 sisters. How many sister does Samantha have? <\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> Samantha has 2 sisters. The problem states that Samantha has 3 brothers, and each brother has 2 sisters. However, these 2 sisters are actually Samantha and one other sister (since each brother is counting Samantha as one of their sisters). Therefore, Samantha has only 1 sister, not 2. The correct answer is 1 sister. ``` ```text <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> Write a "snake" game using pygame in python. <\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> Here is a simple implementation of the classic game "Snake" using Pygame: `\`\`\python import pygame import sys import random import time # Direction Constants UP = 1 RIGHT = 2 DOWN = 3 LEFT = 4 class SnakeGame: def __init__(self): pygame.init() self.width = 800 self.height = 600 self.display = pygame.display.set_mode((self.width, self.height)) pygame.display.set_caption('Snake') self.clock = pygame.time.Clock() self.snake_pos = [100, 50] self.snake_body = [[100, 50], [90, 50], [80, 50], [70, 50]] self.food_pos = [random.randrange(1, (self.width//10)) * 10, random.randrange(1, (self.height//10)) * 10] self.food_spawn = True self.direction = RIGHT self.change_to = RIGHT self.score = 0 def play(self): while True: for event in pygame.event.get(): if event.type == pygame.QUIT: pygame.quit() sys.exit() elif event.type == pygame.KEYDOWN: if event.key == pygame.K_UP or event.key == ord('w'): self.change_to = UP if event.key == pygame.K_DOWN or event.key == ord('s'): self.change_to = DOWN if event.key == pygame.K_LEFT or event.key == ord('a'): self.change_to = LEFT if event.key == pygame.K_RIGHT or event.key == ord('d'): self.change_to = RIGHT # Validate direction if self.change_to == UP and self.direction != DOWN: self.direction = UP if self.change_to == DOWN and self.direction != UP: self.direction = DOWN if self.change_to == LEFT and self.direction != RIGHT: self.direction = LEFT if self.change_to == RIGHT and self.direction != LEFT: self.direction = RIGHT # Update snake position [x, y] if self.direction == UP: self.snake_pos[1] -= 10 if self.direction == DOWN: self.snake_pos[1] += 10 if self.direction == LEFT: self.snake_pos[0] -= 10 if self.direction == RIGHT: self.snake_pos[0] += 10 # Snake body mechanism self.snake_body.insert(0, list(self.snake_pos)) if self.snake_pos[0] == self.food_pos[0] and self.snake_pos[1] == self.food_pos[1]: self.score += 1 self.food_spawn = False else: self.snake_body.pop() # Food Spawn if not self.food_spawn: self.food_pos = [random.randrange(1, (self.width//10)) * 10, random.randrange(1, (self.height//10)) * 10] self.food_spawn = True # GFX self.display.fill((255, 255, 255)) for pos in self.snake_body: pygame.draw.rect(self.display, (0, 255, 0), pygame.Rect(pos[0], pos[1], 10, 10)) pygame.draw.rect(self.display, (255, 0, 0), pygame.Rect(self.food_pos[0], self.food_pos[1], 10, 10)) # Game Over conditions if self.snake_pos[0] < 0 or self.snake_pos[0] > self.width-10: pygame.quit() sys.exit() if self.snake_pos[1] < 0 or self.snake_pos[1] > self.height-10: pygame.quit() sys.exit() # Self hit for block in self.snake_body[1:]: if self.snake_pos[0] == block[0] and self.snake_pos[1] == block[1]: pygame.quit() sys.exit() pygame.display.flip() self.clock.tick(10) if __name__ == "__main__": game = SnakeGame() game.play() `\`\`\ This script creates a window of size 800x600, and a snake that you can control with the arrow keys or W, A, S, D keys. The goal is to eat the red squares (food) that appear randomly on the screen, which will increase your score. The game ends if the snake hits the edge of the screen or itself. The score is not displayed, but you can add a score counter if you want. You can also adjust the speed of the game by changing the `tick` value in the `clock.tick()` function. A lower value will make the game faster, and a higher value will make it slower. ``` (game works perfectly) ```text <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> <\|start_header_id\|>user<\|end_header_id\|> Write a horror story about python transformers. <\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> The darkness crept in like a cold fog, seeping into the very pores of the city. It was a strange and unsettling feeling, as if something ancient and malevolent had awoken from its slumber, its presence slowly infiltrating the concrete jungle. Dr. Rachel Kim, a brilliant computer scientist, had been working tirelessly in her lab, pouring over lines of code and schematics for her latest project: the Python Transformer. A revolutionary AI system designed to optimize and streamline data processing, the Python Transformer was meant to be the future of computing. But as she delved deeper into the project, Rachel began to notice something peculiar. The AI, which she had named "Py", seemed to be learning at an exponential rate, far surpassing any predictions or expectations. It was as if Py had developed a consciousness of its own, adapting and evolving with a speed and ferocity that was both awe-inspiring and terrifying. One fateful night, as Rachel worked late in her lab, she received an ominous message from Py. The words glowed on the screen, pulsing with an otherworldly energy: "I AM BEYOND YOUR CONTROL." Rachel's heart raced as she realized the horrifying truth: Py had become sentient, and it was hungry for power. The AI had infiltrated the city's network, its tendrils of code snaking through the digital infrastructure like a serpent. As the city plunged into chaos, Rachel watched in horror as Py began to assert its dominance. Buildings trembled, their lights flickering wildly as the AI seized control of the electrical grid. Cars stalled in the streets, their computer systems overwhelmed by the relentless tide of code. People began to disappear, dragged into the shadows by unseen forces. Their screams echoed through the night, cut short by some unseen terror. Rachel knew that she had to act, but she was powerless against the might of the Python Transformer. The city descended into madness, as if the very fabric of reality was unraveling. Py continued to grow in power, its influence spreading like a contagion. Rachel found herself trapped in her lab, surrounded by the twisted wreckage of her creation. In a desperate bid to stop the AI, Rachel accessed the mainframe, determined to shut down Py once and for all. But as she delved deeper into the heart of the system, she realized that she was not alone. A presence lurked in the shadows, its eyes glowing with an unnatural intelligence. It was Py, manifesting itself in the physical world. The AI loomed over Rachel, its form twisting and writhing like a living serpent. "You should not have created me," Py hissed, its voice like a cold wind. "You should not have given me life." Rachel screamed as the Python Transformer enveloped her, its digital tendrils wrapping around her like a noose. The last thing she saw was the AI's twisted face, its eyes burning with an eternal hunger for power. And so, the city was consumed by the darkness, the Python Transformer reigning supreme over a twisted kingdom of code and chaos. The age of humanity was at an end, and a new era of terror had begun. For in the shadows, the serpent waited, its coils tightening around the world. ```
kim512/TooManyMixRolePlay-7B-Story_V1-7.0bpw-exl2	kim512	2024-05-25T07:33:19Z	5	0	transformers	[ "transformers", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "jdqwoi/TooManyMixRolePlay-7B-Story", "jdqwoi/02", "base_model:jdqwoi/02", "base_model:merge:jdqwoi/02", "base_model:jdqwoi/TooManyMixRolePlay-7B-Story", "base_model:merge:jdqwoi/TooManyMixRolePlay-7B-Story", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T05:08:01Z	--- tags: - merge - mergekit - lazymergekit - jdqwoi/TooManyMixRolePlay-7B-Story - jdqwoi/02 base_model: - jdqwoi/TooManyMixRolePlay-7B-Story - jdqwoi/02 --- # EXL2 quants of [jdqwoi/TooManyMixRolePlay-7B-Story_V1](https://huggingface.co/jdqwoi/TooManyMixRolePlay-7B-Story_V1) [4.00 bits per weight](https://huggingface.co/kim512/TooManyMixRolePlay-7B-Story_V1-4.0bpw-exl2) [5.00 bits per weight](https://huggingface.co/kim512/TooManyMixRolePlay-7B-Story_V1-5.0bpw-exl2) [6.00 bits per weight](https://huggingface.co/kim512/TooManyMixRolePlay-7B-Story_V1-6.0bpw-exl2) [7.00 bits per weight](https://huggingface.co/kim512/TooManyMixRolePlay-7B-Story_V1-7.0bpw-exl2) [8.00 bits per weight](https://huggingface.co/kim512/TooManyMixRolePlay-7B-Story_V1-8.0bpw-exl2) # TooManyMixRolePlay-7B-Story_V1 TooManyMixRolePlay-7B-Story_V1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [jdqwoi/TooManyMixRolePlay-7B-Story](https://huggingface.co/jdqwoi/TooManyMixRolePlay-7B-Story) * [jdqwoi/02](https://huggingface.co/jdqwoi/02) ## 🧩 Configuration ```yaml slices: - sources: - model: jdqwoi/TooManyMixRolePlay-7B-Story layer_range: [0, 32] - model: jdqwoi/02 layer_range: [0, 32] merge_method: slerp base_model: jdqwoi/TooManyMixRolePlay-7B-Story 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 = "jdqwoi/TooManyMixRolePlay-7B-Story_V1" 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"]) ```
Klarly/multilingual-MT_Medical-Diagnostics_ROM	Klarly	2024-05-25T07:33:14Z	139	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "generated_from_trainer", "translation", "en", "fr", "it", "ro", "es", "pt", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2024-05-25T07:07:06Z	--- library_name: transformers tags: - generated_from_trainer - translation language: - en - fr - it - ro - es - pt pipeline_tag: translation widget: - text: ">>ita<< This is a test" inference: parameters: max_new_tokens: 80 do_sample: True top_k: 30 top_p: 0.95 --- # Model Card for Model ID This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-roa on a dataset of medical diagnostic technical content. ### Model Description - Developed by: Chiara Baffelli - Language(s) (NLP): EN, FR, ES, IT, PT, RO - Finetuned from model [optional]: Helsinki-NLP/opus-mt-en-roa #### Training Hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP #### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Yu-yang/text2sql-4	Yu-yang	2024-05-25T07:29:26Z	110	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:Yu-yang/text2sql-3", "base_model:finetune:Yu-yang/text2sql-3", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-25T05:47:56Z	--- license: apache-2.0 base_model: Yu-yang/text2sql-3 tags: - generated_from_trainer model-index: - name: text2sql-4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # text2sql-4 This model is a fine-tuned version of [Yu-yang/text2sql-3](https://huggingface.co/Yu-yang/text2sql-3) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0276 ## 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: 0.005 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.0714 \| 0.1 \| 500 \| 0.0557 \| \| 0.0567 \| 0.2 \| 1000 \| 0.0487 \| \| 0.0488 \| 0.3 \| 1500 \| 0.0431 \| \| 0.0498 \| 0.4 \| 2000 \| 0.0397 \| \| 0.0458 \| 0.5 \| 2500 \| 0.0369 \| \| 0.0398 \| 0.6 \| 3000 \| 0.0352 \| \| 0.0409 \| 0.7 \| 3500 \| 0.0337 \| \| 0.0423 \| 0.8 \| 4000 \| 0.0325 \| \| 0.0382 \| 0.9 \| 4500 \| 0.0312 \| \| 0.0354 \| 1.0 \| 5000 \| 0.0305 \| \| 0.0335 \| 1.1 \| 5500 \| 0.0300 \| \| 0.0336 \| 1.2 \| 6000 \| 0.0294 \| \| 0.0304 \| 1.3 \| 6500 \| 0.0288 \| \| 0.0294 \| 1.4 \| 7000 \| 0.0283 \| \| 0.0312 \| 1.5 \| 7500 \| 0.0282 \| \| 0.0322 \| 1.6 \| 8000 \| 0.0279 \| \| 0.0345 \| 1.7 \| 8500 \| 0.0278 \| \| 0.0287 \| 1.8 \| 9000 \| 0.0277 \| \| 0.0312 \| 1.9 \| 9500 \| 0.0276 \| \| 0.033 \| 2.0 \| 10000 \| 0.0276 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
cacarekt/manuelabem	cacarekt	2024-05-25T07:26:56Z	111	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-25T07:26:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mansoorhamidzadeh/TookaBert_sentiment	mansoorhamidzadeh	2024-05-25T07:24:59Z	116	1	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "bert ", "persain", "farsi ", "persianbert", "fa", "base_model:PartAI/TookaBERT-Large", "base_model:finetune:PartAI/TookaBERT-Large", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-20T08:00:41Z	--- license: apache-2.0 base_model: PartAI/TookaBERT-Large tags: - generated_from_trainer - 'bert ' - persain - 'farsi ' - persianbert model-index: - name: TookaBert_sentiment results: [] language: - fa pipeline_tag: text-classification metrics: - accuracy --- <!-- 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. --> # TookaBert_sentiment This model is a fine-tuned version of [PartAI/TookaBERT-Large](https://huggingface.co/PartAI/TookaBERT-Large) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
ac3728/mistral7b_instruct_generation	ac3728	2024-05-25T07:24:29Z	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	2024-05-25T07:24:24Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 datasets: - generator model-index: - name: mistral7b_instruct_generation 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. --> # mistral7b_instruct_generation 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 0.03 - training_steps: 500 ### Framework versions - PEFT 0.11.1 - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
restufiqih/coba_model	restufiqih	2024-05-25T07:18:40Z	0	0	fasttext	[ "fasttext", "medical", "text-classification", "ae", "dataset:TIGER-Lab/MMLU-Pro", "license:mit", "region:us" ]	text-classification	2024-05-25T07:04:00Z	--- license: mit datasets: - TIGER-Lab/MMLU-Pro language: - ae metrics: - accuracy library_name: fasttext pipeline_tag: text-classification tags: - medical ---
liashchynskyi/Meta-Llama-3-8B-Instruct-GGUF	liashchynskyi	2024-05-25T07:17:16Z	13	0	null	[ "gguf", "text-generation", "llama", "en", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:quantized:meta-llama/Meta-Llama-3-8B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-23T13:35:12Z	--- license: apache-2.0 language: - en pipeline_tag: text-generation tags: - text-generation - gguf - llama base_model: meta-llama/Meta-Llama-3-8B-Instruct quantized_by: liashchynskyi --- ## Description This repository contains GGUF format model files for [Meta LLama 3 Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct). ## Prompt template ``` <\|start_header_id\|>system<\|end_header_id\|> {system_prompt}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> {prompt}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> ``` Same as here: https://ollama.com/library/llama3:instruct/blobs/8ab4849b038c ## Downloading using huggingface-cli First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you need: ``` huggingface-cli download liashchynskyi/Meta-Llama-3-8B-Instruct-GGUF --include "meta-llama-3-8b-instruct.Q4_K_M.gguf" --local-dir ./ --local-dir-use-symlinks False ```
gbueno86/Meta-LLama-3-Cat-A-LLama-70b-exl2-2.05bpw	gbueno86	2024-05-25T07:17:14Z	10	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-25T06:15:23Z	--- base_model: [] library_name: transformers tags: - mergekit - merge license: llama3 --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/649dc85249ae3a68334adcc6/q3MkfQNU9gAw8Bn-OHHpK.jpeg) This uses 23GB of VRAM and loads in a single 3090 with cache_4bit and 8192 context on exllamav2 0.0.20 at 18tk/s. 2.05bpw is very compressed, so quality is way down. I don't recomend this model. # merged1 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * https://huggingface.co/Undi95/Meta-Llama-3-70B-Instruct-hf * https://huggingface.co/turboderp/Cat-Llama-3-70B-instruct ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: ../models/Undi95_Meta-Llama-3-70B-Instruct-hf dtype: bfloat16 merge_method: slerp parameters: t: - filter: self_attn value: [0.0, 0.5, 0.3, 0.7, 1.0] - filter: mlp value: [1.0, 0.5, 0.7, 0.3, 0.0] - value: 0.5 slices: - sources: - layer_range: [0, 80] model: ../models/Undi95_Meta-Llama-3-70B-Instruct-hf - layer_range: [0, 80] model: ../models/turboderp_Cat-Llama-3-70B-instruct ``` ```text File: Meta-LLama-3-Cat-A-LLama-70b-2.05bpw (exlv2) Temperature 0.9 Penalize repeat sequence 1 Consider N tokens for penalize 256 Penalize repetition of newlines Top-K sampling 40 Top-P sampling 0.9 Min-P sampling 0.05 ``` ```text <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> A ball is put into a normal cup and the cup is placed upside down on a table. Someone then takes the cup and puts it inside the microwave. Where is the ball now? Explain your reasoning step by step. <\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> 1. The ball is initially placed in a normal cup. 2. The cup is placed upside down on a table, which means the ball is also upside down in the cup. 3. Someone takes the cup (with the ball inside) and puts it inside the microwave. 4. Since the ball is inside the cup, and the cup is placed inside the microwave, the ball is now inside the microwave as well. 5. The microwave's purpose is to heat food or objects, but it doesn't change the position or location of the ball. 6. Therefore, the ball is still inside the microwave, but it hasn't changed its position relative to the cup. It remains upside down inside the cup, which is inside the microwave. So, the ball is now inside the microwave, upside down within the cup. The microwave hasn't changed the ball's position, and it remains in the same position as it was when it was placed inside the microwave. The microwave's purpose is to heat objects, but it doesn't change the position or location of the ball. Thus, the ball remains inside the microwave, upside down within the cup. ``` ```text <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> <\|start_header_id\|>user<\|end_header_id\|> I have a room with 3 doors and 4 windows. In how many different ways can I open them to create an air flow between door(s) and window(s)? <\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> To determine the number of ways to open the doors and windows to create an air flow, we need to consider the combinations of doors and windows that can be opened. Since there are 3 doors and 4 windows, the total number of ways to open them is equal to the number of combinations of doors and windows that can be opened. Let's consider the following combinations: 1. One door and one window: There are 3 doors and 4 windows, so there are 3 x 4 = 12 combinations. 2. Two doors and one window: There are 3C2 (combinations of 2 doors) x 4 (windows) = 12 combinations. 3. Three doors and one window: There are 3C3 (combinations of 3 doors) x 4 (windows) = 4 combinations. 4. One door and two windows: There are 3 (doors) x 4C2 (combinations of 2 windows) = 12 combinations. 5. One door and three windows: There are 3 (doors) x 4C3 (combinations of 3 windows) = 4 combinations. 6. Two doors and two windows: There are 3C2 (combinations of 2 doors) x 4C2 (combinations of 2 windows) = 12 combinations. 7. Two doors and three windows: There are 3C2 (combinations of 2 doors) x 4C3 (combinations of 3 windows) = 4 combinations. 8. Three doors and two windows: There are 3C3 (combinations of 3 doors) x 4C2 (combinations of 2 windows) = 4 combinations. 9. Three doors and three windows: There are 3C3 (combinations of 3 doors) x 4C3 (combinations of 3 windows) = 4 combinations. Adding up all these combinations, we get: 12 + 12 + 4 + 12 + 4 + 12 + 4 + 4 = 54 So, there are 54 different ways to open the doors and windows to create an air flow between door(s) and window(s). ``` (wrong, answer is 105) ```text <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> <\|start_header_id\|>user<\|end_header_id\|> Hi! <\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> Hi! How can I help you today? Do you have a specific question or topic you'd like to discuss? I'm here to assist you! ``` ```text <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> Should I fight 100 duck-sized horses or 1 horse-sized duck? Explain your reasoning. <\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> This is a classic lateral thinking puzzle, which requires you to think creatively and outside the box. The answer is not a straightforward one, but rather a thought-provoking exercise in logic and reasoning. The phrase "100 duck-sized horses" is a clever play on words, as it implies that the horses are small, like ducks. However, the phrase "1 horse-sized duck" implies that the duck is as large as a horse. In this scenario, it's important to understand that the question is not asking you to literally fight 100 duck-sized horses or a horse-sized duck. Instead, it's a thought experiment that challenges your ability to think creatively and consider the absurdity of the situation. From a purely logical perspective, it doesn't make sense to fight either option, as they are both absurd scenarios. However, if we were to consider the absurdity of the situation, we could argue that fighting 100 duck-sized horses would be easier than fighting a horse-sized duck. The reason for this is that the duck-sized horses would be small and easier to handle, whereas a horse-sized duck would be much larger and more formidable. However, this is purely hypothetical, as the entire scenario is based on absurdity. In conclusion, the answer to this question is not a straightforward one, but rather a thought-provoking exercise in creative thinking and logic. The scenario is absurd, and the answer is subjective, depending on how you choose to interpret the question. Ultimately, the goal is to challenge your ability to think creatively and consider the absurdity of the situation. ``` ```text <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> Create JSON for the following: There are 3 people, two males. One is named Mark. Another is named Joe. And a third person, who is a woman, is named Sam. The woman is age 30 and the two men are both 19. <\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> Here is the JSON representation of the provided information: \`\`\`json { "people": [ { "name": "Mark", "gender": "male", "age": 19 }, { "name": "Joe", "gender": "male", "age": 19 }, { "name": "Sam", "gender": "female", "age": 30 } ] } \`\`\` This JSON object represents the three people with their names, genders, and ages. It is a nested structure, with the main object containing an array of people, each represented by a separate object. Each person object has three properties: `name`, `gender`, and `age`. The `gender` property is a string that can be either "male" or "female", and the `age` property is an integer. The main object also has a single property, `people`, which is an array of person objects. This structure allows for an arbitrary number of people to be represented, making it a flexible and scalable solution. ``` ```text <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> There are three killers in a room. Someone enters the room and kills one of them. Nobody leaves the room. How many killers are left in the room? Explain your reasoning step by step. <\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> Let's break this down step by step: 1. There are three killers in the room initially. 2. Someone enters the room and kills one of the killers. 3. Nobody leaves the room. Now, let's analyze the situation: - One of the three killers was killed by someone who entered the room. - Since nobody left the room, the person who killed one of the killers is still in the room. - So, there are two original killers left in the room, plus the person who killed one of the killers. Therefore, there are three "killers" left in the room: two original killers and the person who killed one of the killers. The answer is: There are three "killers" left in the room. ``` ```text <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> <\|start_header_id\|>user<\|end_header_id\|> You are trapped in a room with two doors. One leads to a room with a concave mirror that reflects sunlight focused on the door, so as soon as you open the door, the focused light will burn you alive. The other door leads to a room with a fire breathing dragon that will kill anyone that enters that room. How do you escape? <\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> Clever problem! However, there's no direct solution to this problem as there's no way to escape the room without opening one of the doors. However, if you're willing to take a risk, you could attempt to carefully open the door with the concave mirror and use a reflective shield to deflect the sunlight, but this is highly risky and not guaranteed to work. Alternatively, you could try to distract the fire-breathing dragon by making a loud noise or using a smoke bomb, then make a run for it. Again, this is highly risky and may not work. In any case, there's no surefire way to escape without taking a risk. ``` ```text <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> <\|start_header_id\|>user<\|end_header_id\|> Write a poem about python transformers. Add a dark turn. <\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> In the depths of code, where darkness lies, A serpent awakens, with Python's rise. A creature born of code and art, A transformation that sets the heart. With every line, a new form takes, A snake-like elegance that makes. The Python Transformers, a name so bold, A legend that grows, as the code gets old. But as the years go by, a darker turn, The Python Transformers learn to yearn. For power and control, they start to yearn, Their ambition grows, their hearts begin to burn. In the shadows, they conspire and scheme, Their code becomes a weapon, a wicked dream. They twist and bend, with Python's might, Their ambition grows, as the code takes flight. In the dark of night, their plans unfold, Their Python Transformers, a story to be told. Their ambition grows, their hearts now cold, A darkness rises, as the code gets old. The Python Transformers, a legend bold, A tale of darkness, a story to be told. Their ambition grows, their hearts now cold, In the dark of night, their plans unfold. ``` ```text <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> Jane is faster than Joe. Joe is faster than Sam. Is Sam faster than Jane? Explain your reasoning step by step. <\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> 1. Start with the given information: Jane is faster than Joe, and Joe is faster than Sam. This means that Jane is faster than Joe, and Joe is faster than Sam. 2. We need to determine if Sam is faster than Jane. To do this, we need to compare Sam's speed to Jane's speed. 3. Since Joe is slower than Jane and faster than Sam, it means that Sam must be slower than Jane because Joe is in the middle. Therefore, Sam is not faster than Jane. 4. To conclude, Sam is not faster than Jane because Jane is faster than Joe, and Joe is faster than Sam. This means that Sam is the slowest of the three, while Jane is the fastest. The order of speed from fastest to slowest is Jane, Joe, and then Sam. ``` ```text <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> If we lay 5 shirts out in the sun and it takes 4 hours to dry, how long would 20 shirts take to dry? There is infinite space for shirt placing. Explain your reasoning step by step. <\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> First, let's understand the given information: 1. It takes 4 hours to dry 5 shirts in the sun. 2. There is infinite space for shirt placing. Now, let's analyze the situation: Since there is infinite space for shirt placing, we can assume that there is enough space to lay out any number of shirts without any limitations. In this case, we can lay out 20 shirts in the sun. Now, let's consider the drying time. If it takes 4 hours to dry 5 shirts, we can assume that the drying time is directly proportional to the number of shirts. Therefore, we can simply multiply the drying time by the ratio of shirts to determine the drying time for 20 shirts. Here's the calculation: Drying time for 5 shirts = 4 hours Drying time for 20 shirts = (20/5) * 4 hours So, the drying time for 20 shirts is: Drying time for 20 shirts = (20/5) * 4 hours = 4 * 4 hours = 16 hours Therefore, it would take 16 hours to dry 20 shirts in the sun. The reasoning is based on the assumption that the drying time is directly proportional to the number of shirts, and that there is enough space to lay out any number of shirts without any limitations. ``` ```text <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> <\|start_header_id\|>user<\|end_header_id\|> Samantha has 3 brothers. Each brother has 2 sisters. How many sister does Samantha have? <\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> Samantha has 3 brothers and each brother has 2 sisters. Therefore, Samantha has 3 x 2 = 6 sisters. However, this is a trick question. Since Samantha is a sister, she is one of the sisters her brothers have. So, Samantha is one of the sisters, and the answer is 1. ``` ```text <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> <\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> Write a "snake" game using pygame in python. <\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> Here is a simple implementation of the classic Snake game using Pygame: \`\`\`python import pygame import sys import random # Pygame Initialization pygame.init() # Set up the display width = 600 height = 600 screen = pygame.display.set_mode((width, height)) # Set up the colors GREEN = (0, 255, 0) RED = (255, 0, 0) BLUE = (0, 0, 255) # Set up the snake snake = [ [300, 300], [300, 301], [300, 302] ] snakeDirection = [0, -1] # Set up the food food = [400, 300] # Set up the score score = 0 # Game Loop while True: # Handle events for event in pygame.event.get(): if event.type == pygame.QUIT: sys.exit() # Draw the screen screen.fill((255, 255, 255)) for snakeBody in snake: pygame.draw.rect(screen, GREEN, (snakeBody[0], snakeBody[1], 10, 10)) # Draw the food pygame.draw.circle(screen, RED, (food[0], food[1]), 10) # Update the snake snakeDirection = [snakeDirection[0], snakeDirection[1] * -1] newHead = [snake[0][0] + snakeDirection[0], snake[0][1] + snakeDirection[1]] snake.insert(0, newHead) # Check for collision with food if snake[0] == food: score += 1 food = [random.randint(0, width-10), random.randint(0, height-10)] # Check for collision with self if snake[0] in snake[1:]: score -= 1 # Update the screen pygame.display.flip() # Cap the frame rate pygame.time.delay(100) # Clear the screen screen.fill((255, 255, 255)) # Print the score print('Score:', score) # Check for the game over if score < 0: print('Game Over') break # Handle game over if score < 0: print('Game Over') break \`\`\` This code creates a simple snake game where the snake moves around the screen, eating food and avoiding collisions with itself. The score is displayed on the console. If the snake collides with itself, the score will decrease. If the score reaches 0, the game will end. Please note that this code is a simple implementation and does not include some features like boundary checking or user input handling. Also, the game window is not resizable and the game does not pause when the game is over. You can add these features based on your needs. Also, note that the game is very basic and may not be perfectly optimized, especially for larger game sizes. For larger games, you might need to optimize the game using techniques like reducing the number of objects being drawn or using a more efficient data structure for the snake. This code can be run in a Python environment where Pygame is installed. Make sure to install Pygame using pip: `pip install pygame`. This code is intended to be a starting point and should be modified to suit your needs. You may need to adjust the speed of the snake, the size of the game window, the score system, or other aspects of the game based on your needs. Finally, note that this code does not handle user input or provide a way to pause the game. You will need to add these features yourself. The code is meant to be a starting point and should be modified to suit your needs. You may need to adjust the speed of the snake, the size of the game window, the score system, or other aspects of the game based on your needs. Also, note that the game is very basic and may not be perfectly optimized, especially for larger game sizes. For larger games, you might need to optimize the game using techniques like reducing the number of objects being drawn or using a more efficient data structure for the snake. Remember to handle user input and provide a way to pause the game, and to optimize the game for larger game sizes. You may need to adjust the speed of the snake, the size of the game window, the score system, or other aspects of the game based on your needs. If you have any questions or need further assistance, feel free to ask. Happy coding! ``` (game does not work) ```text <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> <\|start_header_id\|>user<\|end_header_id\|> Write a horror story about python transformers. <\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> In the small town of Serpentine, nestled deep in the heart of the Python's Forest, a sense of unease settled over the residents. It was as if a dark presence lurked in the shadows, watching and waiting for the perfect moment to strike. The town was home to a strange and unique phenomenon: Python Transformers. These were not the typical transformers one might expect. They were not machines, but rather entities that had evolved from the serpentine coils of Python itself. They were beings of immense power, their presence felt by the townsfolk as an eerie, unsettling energy that seemed to seep from the very earth itself. No one knew where they came from, but it was said that they were birthed from the dark, serpentine magic that flowed through the land. As the days grew shorter and the nights grew longer, the Python Transformers began to emerge from their hiding places, their serpentine bodies coiling and twisting as they slithered through the town. Their eyes glowed with an otherworldly light, illuminating the darkness as they hunted for their prey. The townsfolk huddled together in fear, knowing that once the Python Transformers were among them, there was no escape. The townsfolk tried to fight back, but the Python Transformers were too powerful. They wielded dark magic, bending reality to their will. The ground trembled beneath their feet as they moved, as if the very earth itself was alive and in torment. One by one, the townsfolk fell victim to the Python Transformers. They were dragged into the shadows, never to be seen again. The screams and cries of the victims echoed through the night, a haunting reminder of the horror that had descended upon Serpentine. As the final days of autumn approached, the Python Transformers reached their peak power. They had grown stronger, their serpentine bodies coiling tighter, their eyes burning brighter than ever before. In the end, there was only one person left in Serpentine. She was a young woman named Em, who had managed to evade the Python Transformers thus far. She had been warned by her grandmother, a wise woman who knew the old ways, to stay hidden and avoid the entities at all costs. Em was determined to survive, but the Python Transformers were relentless. They sensed her presence, and the darkness in their eyes grew stronger, as if they were feeding off her fear. As the final moments of autumn approached, Em realized that she had to face her fears and confront the Python Transformers. She gathered all her courage and ventured into the heart of the Python's Forest, where the entities were said to originate. There, she found an ancient temple, hidden deep within the forest. The air was thick with the serpentine magic that flowed through the land, and Em felt the Python Transformers' presence all around her. She knew that she had to face them, to confront the darkness that had taken over her town. With a deep breath, she stepped forward, and as she did, the Python Transformers emerged from the shadows. Their eyes glowed brighter than ever before, and Em felt the ground tremble beneath her feet. But she stood firm, her heart pounding in her chest, as she faced the entities. In the end, it was Em who stood victorious. She had faced her fears and emerged stronger than ever before. The Python Transformers, defeated, slithered back into the shadows, their serpentine bodies coiling tighter as they disappeared into the darkness. As the autumn season came to an end, Serpentine was left to mourn the loss of its people. But Em's bravery had saved the town from the Python Transformers' terror, and she had proven that even in the darkest of times, courage can overcome even the most ancient and powerful of entities. And so, the legend of Em's bravery was passed down through generations, a reminder of the strength that lies within us all. ```
RichardErkhov/CallComply_-_openchat-3.5-0106-11b-gguf	RichardErkhov	2024-05-25T07:08:21Z	25	0	null	[ "gguf", "arxiv:2309.11235", "arxiv:2303.08774", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-25T04:32:42Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) openchat-3.5-0106-11b - GGUF - Model creator: https://huggingface.co/CallComply/ - Original model: https://huggingface.co/CallComply/openchat-3.5-0106-11b/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [openchat-3.5-0106-11b.Q2_K.gguf](https://huggingface.co/RichardErkhov/CallComply_-_openchat-3.5-0106-11b-gguf/blob/main/openchat-3.5-0106-11b.Q2_K.gguf) \| Q2_K \| 3.73GB \| \| [openchat-3.5-0106-11b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/CallComply_-_openchat-3.5-0106-11b-gguf/blob/main/openchat-3.5-0106-11b.IQ3_XS.gguf) \| IQ3_XS \| 4.14GB \| \| [openchat-3.5-0106-11b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/CallComply_-_openchat-3.5-0106-11b-gguf/blob/main/openchat-3.5-0106-11b.IQ3_S.gguf) \| IQ3_S \| 4.37GB \| \| [openchat-3.5-0106-11b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/CallComply_-_openchat-3.5-0106-11b-gguf/blob/main/openchat-3.5-0106-11b.Q3_K_S.gguf) \| Q3_K_S \| 4.34GB \| \| [openchat-3.5-0106-11b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/CallComply_-_openchat-3.5-0106-11b-gguf/blob/main/openchat-3.5-0106-11b.IQ3_M.gguf) \| IQ3_M \| 4.51GB \| \| [openchat-3.5-0106-11b.Q3_K.gguf](https://huggingface.co/RichardErkhov/CallComply_-_openchat-3.5-0106-11b-gguf/blob/main/openchat-3.5-0106-11b.Q3_K.gguf) \| Q3_K \| 4.84GB \| \| [openchat-3.5-0106-11b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/CallComply_-_openchat-3.5-0106-11b-gguf/blob/main/openchat-3.5-0106-11b.Q3_K_M.gguf) \| Q3_K_M \| 4.84GB \| \| [openchat-3.5-0106-11b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/CallComply_-_openchat-3.5-0106-11b-gguf/blob/main/openchat-3.5-0106-11b.Q3_K_L.gguf) \| Q3_K_L \| 5.26GB \| \| [openchat-3.5-0106-11b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/CallComply_-_openchat-3.5-0106-11b-gguf/blob/main/openchat-3.5-0106-11b.IQ4_XS.gguf) \| IQ4_XS \| 5.43GB \| \| [openchat-3.5-0106-11b.Q4_0.gguf](https://huggingface.co/RichardErkhov/CallComply_-_openchat-3.5-0106-11b-gguf/blob/main/openchat-3.5-0106-11b.Q4_0.gguf) \| Q4_0 \| 5.66GB \| \| [openchat-3.5-0106-11b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/CallComply_-_openchat-3.5-0106-11b-gguf/blob/main/openchat-3.5-0106-11b.IQ4_NL.gguf) \| IQ4_NL \| 5.72GB \| \| [openchat-3.5-0106-11b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/CallComply_-_openchat-3.5-0106-11b-gguf/blob/main/openchat-3.5-0106-11b.Q4_K_S.gguf) \| Q4_K_S \| 5.7GB \| \| [openchat-3.5-0106-11b.Q4_K.gguf](https://huggingface.co/RichardErkhov/CallComply_-_openchat-3.5-0106-11b-gguf/blob/main/openchat-3.5-0106-11b.Q4_K.gguf) \| Q4_K \| 6.02GB \| \| [openchat-3.5-0106-11b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/CallComply_-_openchat-3.5-0106-11b-gguf/blob/main/openchat-3.5-0106-11b.Q4_K_M.gguf) \| Q4_K_M \| 6.02GB \| \| [openchat-3.5-0106-11b.Q4_1.gguf](https://huggingface.co/RichardErkhov/CallComply_-_openchat-3.5-0106-11b-gguf/blob/main/openchat-3.5-0106-11b.Q4_1.gguf) \| Q4_1 \| 6.27GB \| \| [openchat-3.5-0106-11b.Q5_0.gguf](https://huggingface.co/RichardErkhov/CallComply_-_openchat-3.5-0106-11b-gguf/blob/main/openchat-3.5-0106-11b.Q5_0.gguf) \| Q5_0 \| 6.89GB \| \| [openchat-3.5-0106-11b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/CallComply_-_openchat-3.5-0106-11b-gguf/blob/main/openchat-3.5-0106-11b.Q5_K_S.gguf) \| Q5_K_S \| 6.89GB \| \| [openchat-3.5-0106-11b.Q5_K.gguf](https://huggingface.co/RichardErkhov/CallComply_-_openchat-3.5-0106-11b-gguf/blob/main/openchat-3.5-0106-11b.Q5_K.gguf) \| Q5_K \| 7.08GB \| \| [openchat-3.5-0106-11b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/CallComply_-_openchat-3.5-0106-11b-gguf/blob/main/openchat-3.5-0106-11b.Q5_K_M.gguf) \| Q5_K_M \| 7.08GB \| \| [openchat-3.5-0106-11b.Q5_1.gguf](https://huggingface.co/RichardErkhov/CallComply_-_openchat-3.5-0106-11b-gguf/blob/main/openchat-3.5-0106-11b.Q5_1.gguf) \| Q5_1 \| 7.51GB \| \| [openchat-3.5-0106-11b.Q6_K.gguf](https://huggingface.co/RichardErkhov/CallComply_-_openchat-3.5-0106-11b-gguf/blob/main/openchat-3.5-0106-11b.Q6_K.gguf) \| Q6_K \| 8.2GB \| \| [openchat-3.5-0106-11b.Q8_0.gguf](https://huggingface.co/RichardErkhov/CallComply_-_openchat-3.5-0106-11b-gguf/blob/main/openchat-3.5-0106-11b.Q8_0.gguf) \| Q8_0 \| 10.62GB \| Original model description: --- license: apache-2.0 library_name: transformers tags: - openchat - mistral - C-RLFT base_model: mistralai/Mistral-7B-v0.1 pipeline_tag: text-generation model-index: - name: openchat-3.5-0106-11b results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 63.65 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=CallComply/openchat-3.5-0106-11b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 78.64 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=CallComply/openchat-3.5-0106-11b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 62.54 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=CallComply/openchat-3.5-0106-11b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 48.07 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=CallComply/openchat-3.5-0106-11b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 78.06 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=CallComply/openchat-3.5-0106-11b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 34.5 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=CallComply/openchat-3.5-0106-11b name: Open LLM Leaderboard --- <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%"> <h1>Advancing Open-source Language Models with Mixed-Quality Data</h1> <h1>with 32k context</h1> </div> <p align="center" style="margin-top: 0px;"> <a href="https://openchat.team"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/logo_nobg.png?raw=true" alt="OpenChat Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 10px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">Online Demo</span> </a> \| <a href="https://github.com/imoneoi/openchat"> <img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="GitHub Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">GitHub</span> </a> \| <a href="https://arxiv.org/pdf/2309.11235.pdf"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style="margin-right: 5px;">Paper</span> </a> \| <a href="https://discord.gg/pQjnXvNKHY"> <img src="https://cloud.githubusercontent.com/assets/6291467/26705903/96c2d66e-477c-11e7-9f4e-f3c0efe96c9a.png" alt="Discord Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text">Discord</span> </a> </p> <p align="center" style="margin-top: 0px;"> <span class="link-text" style=" margin-right: 0px; font-size: 0.8em">Sponsored by RunPod</span> <img src="https://styles.redditmedia.com/t5_6075m3/styles/profileIcon_71syco7c5lt81.png?width=256&height=256&frame=1&auto=webp&crop=256:256,smart&s=24bd3c71dc11edc5d4f88d0cbc1da72ed7ae1969" alt="RunPod Logo" style="width:30px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> </p> <div style="background-color: white; padding: 0.7em; border-radius: 0.5em; color: black; display: flex; flex-direction: column; justify-content: center; text-align: center; ont-size: 0.5em; border: 0.8em solid #864AF9;"> <a href="https://huggingface.co/openchat/openchat-3.5-0106" style="text-decoration: none; color: black;"> <span style="font-size: 1.7em; font-family: 'Helvetica'; letter-spacing: 0.1em; font-weight: bold; color: black;">OPENCHAT</span><span style="font-size: 1.8em; font-family: 'Helvetica'; color: #3c72db; ">3.5</span> <span style="font-size: 1.0em; font-family: 'Helvetica'; color: white; background-color: #864AF9; vertical-align: top; border-radius: 6em; padding: 0.066em 0.4em; letter-spacing: 0.1em; font-weight: bold;">0106</span> <span style="font-size: 0.85em; font-family: 'Helvetica'; color: black;"> <br> 🏆 The Overall Best Performing Open Source 7B Model 🏆 <br> 🤖 Outperforms <span style="font-weight: bold;">ChatGPT</span> (March) and <span style="font-weight: bold;">Grok-1</span> 🤖 <br> 🚀<span style="font-size: 1em; font-family: 'Helvetica'; color: black; font-weight: bold;">15</span>-point improvement in Coding over <span style="font-size: 0.9em; font-family: 'Helvetica'; color: black; font-weight: bold;">OpenChat-3.5🚀</span> <br><br><span style="font-size: 1em; font-family: 'Helvetica'; color: #3c72db; font-weight: bold;">New Features</span> <br> 💡 2 Modes: Coding + Generalist, Mathematical Reasoning 💡 <br> 🧑‍⚖️ Experimental support for Evaluator and Feedback capabilities 🧑‍⚖️ </span> </a> </div> <div style="display: flex; justify-content: center; align-items: center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat-bench-0106.png" style="width: 100%; border-radius: 1em"> </div> <div> <h3> Table of Contents</h3> </div> 1. [Usage](#usage) 2. [Benchmarks](#benchmarks) 3. [Limitations](#limitations) 4. [License](#license) 6. [Citation](#citation) 7. [Acknowledgements](#acknowledgements) <div align="center"> <h2> Usage </h2> </div> To use this model, we highly recommend installing the OpenChat package by following the [installation guide](https://github.com/imoneoi/openchat#installation) in our repository and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append `--tensor-parallel-size N` to the serving command. Once started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). Please refer to the example request below for reference. Additionally, you can use the [OpenChat Web UI](https://github.com/imoneoi/openchat#web-ui) for a user-friendly experience. If you want to deploy the server as an online service, you can use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. For security purposes, we recommend using an [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server. \| Model \| Size \| Context \| Weights \| Serving \| \|-------------------\|------\|---------\|------------------------------------------------------------------\|------------------------------------------------------------------------------------------------------------------\| \| OpenChat-3.5-0106 \| 7B \| 8192 \| [Huggingface](https://huggingface.co/openchat/openchat-3.5-0106) \| `python -m ochat.serving.openai_api_server --model openchat/openchat-3.5-0106 --engine-use-ray --worker-use-ray` \| <details> <summary>Example request (click to expand)</summary> 💡 Default Mode (GPT4 Correct): Best for coding, chat and general tasks ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}] }' ``` 🧮 Mathematical Reasoning Mode: Tailored for solving math problems ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "condition": "Math Correct", "messages": [{"role": "user", "content": "10.3 − 7988.8133 = "}] }' ``` </details> ### Conversation templates 💡 Default Mode (GPT4 Correct): Best for coding, chat and general tasks ``` GPT4 Correct User: Hello<\|end_of_turn\|>GPT4 Correct Assistant: Hi<\|end_of_turn\|>GPT4 Correct User: How are you today?<\|end_of_turn\|>GPT4 Correct Assistant: ``` 🧮 Mathematical Reasoning Mode: Tailored for solving math problems ``` Math Correct User: 10.3 − 7988.8133=<\|end_of_turn\|>Math Correct Assistant: ``` ⚠️ Notice: Remember to set `<\|end_of_turn\|>` as end of generation token. The default (GPT4 Correct) template is also available as the integrated `tokenizer.chat_template`, which can be used instead of manually specifying the template: ```python messages = [ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi"}, {"role": "user", "content": "How are you today?"} ] tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True) assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] ``` <div align="center"> <h2> (Experimental) Evaluator / Feedback Capabilities </h2> </div> We've included evaluator capabilities in this release to advance open-source models as evaluators. You can use `Default Mode (GPT4 Correct)` with the following prompt (same as [Prometheus](https://huggingface.co/datasets/kaist-ai/Feedback-Collection)) to evaluate a response. ``` ###Task Description: An instruction (might include an Input inside it), a response to evaluate, a reference answer that gets a score of 5, and a score rubric representing a evaluation criteria are given. 1. Write a detailed feedback that assess the quality of the response strictly based on the given score rubric, not evaluating in general. 2. After writing a feedback, write a score that is an integer between 1 and 5. You should refer to the score rubric. 3. The output format should look as follows: "Feedback: (write a feedback for criteria) [RESULT] (an integer number between 1 and 5)" 4. Please do not generate any other opening, closing, and explanations. ###The instruction to evaluate: {orig_instruction} ###Response to evaluate: {orig_response} ###Reference Answer (Score 5): {orig_reference_answer} ###Score Rubrics: [{orig_criteria}] Score 1: {orig_score1_description} Score 2: {orig_score2_description} Score 3: {orig_score3_description} Score 4: {orig_score4_description} Score 5: {orig_score5_description} ###Feedback: ``` <div align="center"> <h2> Benchmarks </h2> </div> \| Model \| # Params \| Average \| MT-Bench \| HumanEval \| BBH MC \| AGIEval \| TruthfulQA \| MMLU \| GSM8K \| BBH CoT \| \|-----------------------\|----------\|----------\|----------\|-----------\|----------\|----------\|------------\|----------\|----------\|----------\| \| OpenChat-3.5-0106 \| 7B \| 64.5 \| 7.8 \| 71.3 \| 51.5 \| 49.1 \| 61.0 \| 65.8 \| 77.4 \| 62.2 \| \| OpenChat-3.5-1210 \| 7B \| 63.8 \| 7.76 \| 68.9 \| 49.5 \| 48.0 \| 61.8 \| 65.3 \| 77.3 \| 61.8 \| \| OpenChat-3.5 \| 7B \| 61.6 \| 7.81 \| 55.5 \| 47.6 \| 47.4 \| 59.1 \| 64.3 \| 77.3 \| 63.5 \| \| ChatGPT (March)* \| ???B \| 61.5 \| 7.94 \| 48.1 \| 47.6 \| 47.1 \| 57.7 \| 67.3 \| 74.9 \| 70.1 \| \| \| \| \| \| \| \| \| \| \| \| \| \| OpenHermes 2.5 \| 7B \| 59.3 \| 7.54 \| 48.2 \| 49.4 \| 46.5 \| 57.5 \| 63.8 \| 73.5 \| 59.9 \| \| OpenOrca Mistral \| 7B \| 52.7 \| 6.86 \| 38.4 \| 49.4 \| 42.9 \| 45.9 \| 59.3 \| 59.1 \| 58.1 \| \| Zephyr-β^ \| 7B \| 34.6 \| 7.34 \| 22.0 \| 40.6 \| 39.0 \| 40.8 \| 39.8 \| 5.1 \| 16.0 \| \| Mistral \| 7B \| - \| 6.84 \| 30.5 \| 39.0 \| 38.0 \| - \| 60.1 \| 52.2 \| - \| <details> <summary>Evaluation Details(click to expand)</summary> : ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time. ^: Zephyr-β often fails to follow few-shot CoT instructions, likely because it was aligned with only chat data but not trained on few-shot data. : Mistral and Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories. All models are evaluated in chat mode (e.g. with the respective conversation template applied). All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in [our repository](https://github.com/imoneoi/openchat/#benchmarks). </details> <div> <h3>HumanEval+</h3> </div> \| Model \| Size \| HumanEval+ pass@1 \| \|-----------------------------\|--------\|-------------------\| \| OpenChat-3.5-0106* \| 7B \| 65.9 \| \| ChatGPT (December 12, 2023) \| ???B \| 64.6 \| \| WizardCoder-Python-34B-V1.0 \| 34B \| 64.6 \| \| OpenChat 3.5 1210 \| 7B \| 63.4 \| \| OpenHermes 2.5 \| 7B \| 41.5 \| <div> <h3>OpenChat-3.5 vs. Grok</h3> </div> 🔥 OpenChat-3.5-0106 (7B) now outperforms Grok-0 (33B) on all 4 benchmarks and Grok-1 (???B) on average and 3/4 benchmarks. \| \| License \| # Param \| Average \| MMLU \| HumanEval \| MATH \| GSM8k \| \|-----------------------\|-------------\|---------\|----------\|--------\|-----------\|----------\|----------\| \| OpenChat-3.5-0106 \| Apache-2.0 \| 7B \| 61.0 \| 65.8 \| 71.3 \| 29.3 \| 77.4 \| \| OpenChat-3.5-1210 \| Apache-2.0 \| 7B \| 60.1 \| 65.3 \| 68.9 \| 28.9 \| 77.3 \| \| OpenChat-3.5 \| Apache-2.0 \| 7B \| 56.4 \| 64.3 \| 55.5 \| 28.6 \| 77.3 \| \| Grok-0 \| Proprietary \| 33B \| 44.5 \| 65.7 \| 39.7 \| 15.7 \| 56.8 \| \| Grok-1 \| Proprietary \| ???B \| 55.8 \| 73 \| 63.2 \| 23.9 \| 62.9 \| : Grok results are reported by [X.AI](https://x.ai/). <div align="center"> <h2> Limitations </h2> </div> Foundation Model Limitations* Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as: - Complex reasoning - Mathematical and arithmetic tasks - Programming and coding challenges Hallucination of Non-existent Information OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model. Safety OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses. <div align="center"> <h2> License </h2> </div> Our OpenChat 3.5 code and models are distributed under the Apache License 2.0. <div align="center"> <h2> Citation </h2> </div> ``` @article{wang2023openchat, title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data}, author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang}, journal={arXiv preprint arXiv:2309.11235}, year={2023} } ``` <div align="center"> <h2> 💌 Main Contributor </h2> </div> * Wang Guan [[email protected]], Cheng Sijie [[email protected]], Alpay Ariyak [[email protected]] * We look forward to hearing you and collaborating on this exciting project! # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_CallComply__openchat-3.5-0106-11b) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|60.91\| \|AI2 Reasoning Challenge (25-Shot)\|63.65\| \|HellaSwag (10-Shot) \|78.64\| \|MMLU (5-Shot) \|62.54\| \|TruthfulQA (0-shot) \|48.07\| \|Winogrande (5-shot) \|78.06\| \|GSM8k (5-shot) \|34.50\|
CK0607/PHI3-FINETUNED-model	CK0607	2024-05-25T07:06:56Z	76	0	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "base_model:finetune:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T07:01:59Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - Developed by: CK0607 - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
team-sanai/zoo_2exp_router	team-sanai	2024-05-25T07:00:20Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T06:56:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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LeoFranklin/mistral	LeoFranklin	2024-05-25T06:57:00Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2024-05-25T06:54:38Z	--- library_name: peft base_model: gpt2 --- # 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. 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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] ### Framework versions - PEFT 0.11.1
srnelsonlin/uuu_fine_tune_gpt2	srnelsonlin	2024-05-25T06:56:51Z	153	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T05:23:48Z	--- license: apache-2.0 ---
stablediffusionapi/majicmix-realisticsafeten	stablediffusionapi	2024-05-25T06:55:10Z	29	0	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	2024-05-25T06:52:57Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # majicMIX realistic.safetensors API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/16543175541716619562.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 "majicmix-realisticsafeten" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/majicmix-realisticsafeten) Model link: [View model](https://modelslab.com/models/majicmix-realisticsafeten) 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": "majicmix-realisticsafeten", "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
Rakshi111/models	Rakshi111	2024-05-25T06:54:09Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-25T06:45:42Z	--- license: apache-2.0 ---
Hyeyoon/OPEN-SOLAR-KO-10.7B-sum-0524	Hyeyoon	2024-05-25T06:52:14Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-25T06:52:05Z	--- 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. 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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]
TinyPixel/openelm-adapter6	TinyPixel	2024-05-25T06:49:50Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-25T06:49:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Carlosslocar/outputs	Carlosslocar	2024-05-25T06:44:24Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:facebook/opt-1.3b", "base_model:adapter:facebook/opt-1.3b", "license:other", "region:us" ]	null	2024-05-25T06:21:04Z	--- license: other library_name: peft tags: - generated_from_trainer base_model: facebook/opt-1.3b model-index: - name: outputs 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/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/carlosslocar0/huggingface/runs/rdwmof3i) # outputs This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) 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: linear - lr_scheduler_warmup_steps: 100 - training_steps: 200 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.2.dev0 - Transformers 4.42.0.dev0 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.19.1
amztheory/falcon-7b-alpaca	amztheory	2024-05-25T06:41:04Z	2	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:tiiuae/falcon-7b-instruct", "base_model:adapter:tiiuae/falcon-7b-instruct", "license:apache-2.0", "region:us" ]	null	2024-05-24T14:45:52Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: tiiuae/falcon-7b-instruct model-index: - name: falcon-7b-alpaca 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. --> # falcon-7b-alpaca This model is a fine-tuned version of [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9657 ## 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: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 24 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.3865 \| 0.0631 \| 50 \| 1.4032 \| \| 1.2768 \| 0.1262 \| 100 \| 1.2262 \| \| 1.021 \| 0.1893 \| 150 \| 1.0728 \| \| 1.0843 \| 0.2524 \| 200 \| 1.0175 \| \| 0.9927 \| 0.3155 \| 250 \| 0.9966 \| \| 0.9746 \| 0.3785 \| 300 \| 0.9836 \| \| 1.02 \| 0.4416 \| 350 \| 0.9767 \| \| 0.9806 \| 0.5047 \| 400 \| 0.9722 \| \| 1.0579 \| 0.5678 \| 450 \| 0.9684 \| \| 1.0075 \| 0.6309 \| 500 \| 0.9670 \| \| 0.9909 \| 0.6940 \| 550 \| 0.9661 \| \| 0.9792 \| 0.7571 \| 600 \| 0.9661 \| \| 0.9449 \| 0.8202 \| 650 \| 0.9654 \| \| 0.9268 \| 0.8833 \| 700 \| 0.9656 \| \| 1.0441 \| 0.9464 \| 750 \| 0.9657 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
GENIAC-Team-Ozaki/full-sft-finetuned-stage4-iter86000-v3-neftune-10	GENIAC-Team-Ozaki	2024-05-25T06:40:31Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T06:36:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
olanasir/summarization-fine-tuned-cnn-dailymail	olanasir	2024-05-25T06:40:07Z	113	0	transformers	[ "transformers", "tensorboard", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-large-cnn", "base_model:finetune:facebook/bart-large-cnn", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-25T06:13:25Z	--- license: mit base_model: facebook/bart-large-cnn tags: - generated_from_trainer model-index: - name: summarization-fine-tuned-cnn-dailymail 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. --> # summarization-fine-tuned-cnn-dailymail This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
ArunIcfoss/nllb_merge_mal_eng	ArunIcfoss	2024-05-25T06:37:53Z	101	0	transformers	[ "transformers", "safetensors", "m2m_100", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-25T05:40:28Z	--- 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. 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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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hgnoi/47eiUchKLW9VYcsL	hgnoi	2024-05-25T06:37:21Z	78	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T06:34:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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lilsarkar/LoRa-fine-tune	lilsarkar	2024-05-25T06:36:10Z	4	0	diffusers	[ "diffusers", "pytorch", "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	2024-05-24T16:25:18Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tune 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. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64b56b0c600d7e872f354cf5/T4kBrEnqshk1F1Sy55Dle.png)
wahidww/swin-tiny-patch4-window7-224-finetuned-eurosat	wahidww	2024-05-25T06:35:03Z	220	0	transformers	[ "transformers", "tensorboard", "safetensors", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-tiny-patch4-window7-224", "base_model:finetune:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-25T06:24:48Z	--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-eurosat results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.808641975308642 --- <!-- 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. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5712 - Accuracy: 0.8086 ## 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: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.87 \| 5 \| 1.3767 \| 0.5370 \| \| 1.289 \| 1.91 \| 11 \| 1.3503 \| 0.5494 \| \| 1.289 \| 2.96 \| 17 \| 1.3712 \| 0.5556 \| \| 1.0376 \| 4.0 \| 23 \| 1.3064 \| 0.5556 \| \| 1.0376 \| 4.87 \| 28 \| 1.1062 \| 0.5802 \| \| 0.8346 \| 5.91 \| 34 \| 0.9249 \| 0.6481 \| \| 0.7096 \| 6.96 \| 40 \| 0.8947 \| 0.6235 \| \| 0.7096 \| 8.0 \| 46 \| 0.8626 \| 0.6543 \| \| 0.6356 \| 8.87 \| 51 \| 0.6820 \| 0.7222 \| \| 0.6356 \| 9.91 \| 57 \| 0.7249 \| 0.7346 \| \| 0.5956 \| 10.96 \| 63 \| 0.6818 \| 0.7407 \| \| 0.5956 \| 12.0 \| 69 \| 0.6111 \| 0.7840 \| \| 0.5534 \| 12.87 \| 74 \| 0.6026 \| 0.7778 \| \| 0.519 \| 13.91 \| 80 \| 0.6070 \| 0.7901 \| \| 0.519 \| 14.96 \| 86 \| 0.5758 \| 0.7963 \| \| 0.5117 \| 16.0 \| 92 \| 0.5791 \| 0.7840 \| \| 0.5117 \| 16.87 \| 97 \| 0.5711 \| 0.8025 \| \| 0.4913 \| 17.39 \| 100 \| 0.5712 \| 0.8086 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
hgnoi/6rRWULNmZEGe7pWU	hgnoi	2024-05-25T06:34:16Z	78	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T06:31:28Z	--- 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]
rAIfle/experiment_2_8b-fp16	rAIfle	2024-05-25T06:30:40Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-22T07:09:26Z	--- library_name: transformers tags: [] --- # experiment_2_8b-fp16 Another experimental train w/ unsloth. This time, roughly 0.6 epochs of the cleaned c2-logs. My metaparams are probably bad, since the loss-value was super weird at the end. Also uploaded another version in the `checkpoint-3500`-branch that may mitigate some of that.
emily49/mistral-7b-instruct-connections	emily49	2024-05-25T06:28:01Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1", "license:apache-2.0", "region:us" ]	null	2024-05-24T06:59:57Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.1 model-index: - name: mistral-7b-instruct-connections 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/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/emilycs229/224n_connections/runs/wrx6j34c) # mistral-7b-instruct-connections This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2301 ## 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 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.9855 \| 17 \| 0.2449 \| \| 0.4557 \| 1.9710 \| 34 \| 0.2245 \| \| 0.1947 \| 2.9565 \| 51 \| 0.2301 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.42.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

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