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train · 883k rows

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MoTHer-VTHR/VTHR-FT-ModelTree_3-Depth_2-Node_L2VNWZfu	MoTHer-VTHR	2024-05-28T14:49:44Z	165	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:49:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-FT-ModelTree_3-Depth_2-Node_iEU8TSDf	MoTHer-VTHR	2024-05-28T14:49:24Z	169	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:49:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-FT-ModelTree_3-Depth_2-Node_SoHDK9Uf	MoTHer-VTHR	2024-05-28T14:48:04Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:47:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-FT-ModelTree_3-Depth_2-Node_LZSJJ3Mu	MoTHer-VTHR	2024-05-28T14:47:43Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:47:30Z	--- 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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MoTHer-VTHR/VTHR-FT-ModelTree_3-Depth_2-Node_jjJHi4C2	MoTHer-VTHR	2024-05-28T14:47:01Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:46:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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MoTHer-VTHR/VTHR-FT-ModelTree_3-Depth_2-Node_4bHE4L7D	MoTHer-VTHR	2024-05-28T14:46:39Z	168	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:46:23Z	--- 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]
MoTHer-VTHR/VTHR-FT-ModelTree_3-Depth_2-Node_PfkRvZBE	MoTHer-VTHR	2024-05-28T14:46:12Z	168	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:45:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-FT-ModelTree_3-Depth_0-Node_VP4Kawke	MoTHer-VTHR	2024-05-28T14:45:08Z	159	0	transformers	[ "transformers", "safetensors", "vit", "image-feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-feature-extraction	2024-05-28T14:44:54Z	--- 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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Jumpy-pku/t5-dish-name-recognition	Jumpy-pku	2024-05-28T14:45:06Z	112	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-28T07:16:07Z	--- license: apache-2.0 ---
MoTHer-VTHR/VTHR-FT-ModelTree_2-Depth_2-Node_GLoEkwB9	MoTHer-VTHR	2024-05-28T14:44:38Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:44:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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LiteLLMs/gemma-2b-GGUF	LiteLLMs	2024-05-28T14:44:31Z	5	0	transformers	[ "transformers", "gguf", "GGUF", "arxiv:2312.11805", "license:gemma", "endpoints_compatible", "region:us" ]	null	2024-05-28T14:36:24Z	--- license: gemma library_name: transformers tags: - GGUF extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license quantized_by: andrijdavid --- # gemma-2b-GGUF - Original model: [gemma-2b](https://huggingface.co/google/gemma-2b) <!-- description start --> ## Description This repo contains GGUF format model files for [gemma-2b](https://huggingface.co/google/gemma-2b). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration. * [Ollama](https://github.com/jmorganca/ollama) Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applications * [KoboldCpp](https://github.com/LostRuins/koboldcpp), A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling. * [GPT4All](https://gpt4all.io), This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration. * [LM Studio](https://lmstudio.ai/) An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui). A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection. * [Faraday.dev](https://faraday.dev/), An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use. * [ctransformers](https://github.com/marella/ctransformers), A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server. * [localGPT](https://github.com/PromtEngineer/localGPT) An open-source initiative enabling private conversations with documents. <!-- README_GGUF.md-about-gguf end --> <!-- compatibility_gguf start --> ## 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. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single folder. 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: LiteLLMs/gemma-2b-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-00001-of-00009.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download LiteLLMs/gemma-2b-GGUF Q4_0/Q4_0-00001-of-00009.gguf --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 LiteLLMs/gemma-2b-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install huggingface_hub[hf_transfer] ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download LiteLLMs/gemma-2b-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.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 Q4_0/Q4_0-00001-of-00009.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 8192` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./Q4_0/Q4_0-00001-of-00009.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<PROMPT>", # 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="./Q4_0/Q4_0-00001-of-00009.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer end --> <!-- original-model-card start --> # Original model card: gemma-2b # Gemma Model Card Model Page: [Gemma](https://ai.google.dev/gemma/docs) This model card corresponds to the 2B base version of the Gemma model. You can also visit the model card of the [7B base model](https://huggingface.co/google/gemma-7b), [7B instruct model](https://huggingface.co/google/gemma-7b-it), and [2B instruct model](https://huggingface.co/google/gemma-2b-it). Resources and Technical Documentation: * [Gemma Technical Report](https://storage.googleapis.com/deepmind-media/gemma/gemma-report.pdf) * [Responsible Generative AI Toolkit](https://ai.google.dev/responsible) * [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma) * [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335?version=gemma-2b-gg-hf) Terms of Use: [Terms](https://www.kaggle.com/models/google/gemma/license/consent) Authors: Google ## Model Information Summary description and brief definition of inputs and outputs. ### Description Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. ### Context Length Models are trained on a context length of 8192 tokens. ### Usage Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase. #### Fine-tuning the model You can find fine-tuning scripts and notebook under the [`examples/` directory](https://huggingface.co/google/gemma-7b/tree/main/examples) of [`google/gemma-7b`](https://huggingface.co/google/gemma-7b) repository. To adapt it to this model, simply change the model-id to `google/gemma-2b`. In that repository, we provide: * A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using QLoRA * A script to perform SFT using FSDP on TPU devices * A notebook that you can run on a free-tier Google Colab instance to perform SFT on English quotes dataset #### Running the model on a CPU ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b") input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` #### Running the model on a single / multi GPU ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto") input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` #### Running the model on a GPU using different precisions * _Using `torch.float16`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto", revision="float16") input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` _Using `torch.bfloat16`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto", torch_dtype=torch.bfloat16) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` #### Quantized Versions through `bitsandbytes` _Using 8-bit precision (int8)_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_8bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", quantization_config=quantization_config) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` _Using 4-bit precision_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_4bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", quantization_config=quantization_config) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` #### Other optimizations _Flash Attention 2_ First make sure to install `flash-attn` in your environment `pip install flash-attn` ```diff model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, + attn_implementation="flash_attention_2" ).to(0) ``` ### Inputs and outputs * Input: Text string, such as a question, a prompt, or a document to be summarized. * Output: Generated English-language text in response to the input, such as an answer to a question, or a summary of a document. ## Model Data Data used for model training and how the data was processed. ### Training Dataset These models were trained on a dataset of text data that includes a wide variety of sources, totaling 6 trillion tokens. Here are the key components: * Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. Primarily English-language content. * Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code or understand code-related questions. * Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries. The combination of these diverse data sources is crucial for training a powerful language model that can handle a wide variety of different tasks and text formats. ### Data Preprocessing Here are the key data cleaning and filtering methods applied to the training data: * CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content * Sensitive Data Filtering: As part of making Gemma pre-trained models safe and reliable, automated techniques were used to filter out certain personal information and other sensitive data from training sets. * Additional methods: Filtering based on content quality and safely in line with [our policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11). ## Implementation Information Details about the model internals. ### Hardware Gemma was trained using the latest generation of [Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e). Training large language models requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain: * Performance: TPUs are specifically designed to handle the massive computations involved in training LLMs. They can speed up training considerably compared to CPUs. * Memory: TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training. This can lead to better model quality. * Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for handling the growing complexity of large foundation models. You can distribute training across multiple TPU devices for faster and more efficient processing. * Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective solution for training large models compared to CPU-based infrastructure, especially when considering the time and resources saved due to faster training. * These advantages are aligned with [Google's commitments to operate sustainably](https://sustainability.google/operating-sustainably/). ### Software Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ml-pathways). JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models. ML Pathways is Google's latest effort to build artificially intelligent systems capable of generalizing across multiple tasks. This is specially suitable for [foundation models](https://ai.google/discover/foundation-models/), including large language models like these ones. Together, JAX and ML Pathways are used as described in the [paper about the Gemini family of models](https://arxiv.org/abs/2312.11805); "the 'single controller' programming model of Jax and Pathways allows a single Python process to orchestrate the entire training run, dramatically simplifying the development workflow." ## Evaluation Model evaluation metrics and results. ### Benchmark Results These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation: \| Benchmark \| Metric \| 2B Params \| 7B Params \| \| -- \| -- \| - \| -- \| --- \| ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage Open Large Language Models (LLMs) have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. * Content Creation and Communication * Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. * Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications. * Text Summarization: Generate concise summaries of a text corpus, research papers, or reports. * Research and Education * Natural Language Processing (NLP) Research: These models can serve as a foundation for researchers to experiment with NLP techniques, develop algorithms, and contribute to the advancement of the field. * Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. * Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. ### Limitations * Training Data * The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. * The scope of the training dataset determines the subject areas the model can handle effectively. * Context and Task Complexity * LLMs are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. * A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). * Language Ambiguity and Nuance * Natural language is inherently complex. LLMs might struggle to grasp subtle nuances, sarcasm, or figurative language. * Factual Accuracy * LLMs generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. * Common Sense * LLMs rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. ### Ethical Considerations and Risks The development of large language models (LLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: * Bias and Fairness * LLMs trained on large-scale, real-world text data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. * Misinformation and Misuse * LLMs can be misused to generate text that is false, misleading, or harmful. * Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible). * Transparency and Accountability: * This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. * A responsibly developed open model offers the opportunity to share innovation by making LLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: * Perpetuation of biases: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. * Generation of harmful content: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. * Misuse for malicious purposes: Technical limitations and developer and end-user education can help mitigate against malicious applications of LLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy). * Privacy violations: Models were trained on data filtered for removal of PII (Personally Identifiable Information). Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ### Benefits At the time of release, this family of models provides high-performance open large language model implementations designed from the ground up for Responsible AI development compared to similarly sized models. Using the benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives. <!-- original-model-card end -->
zddydy/medical-llava	zddydy	2024-05-28T14:43:52Z	4	0	transformers	[ "transformers", "pytorch", "llava", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T13:35:52Z	--- license: apache-2.0 ---
MoTHer-VTHR/VTHR-FT-ModelTree_2-Depth_2-Node_Wdeo6s2q	MoTHer-VTHR	2024-05-28T14:42:50Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:42:34Z	--- 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]
LiteLLMs/gemma-2b-it-GGUF	LiteLLMs	2024-05-28T14:42:31Z	10	0	transformers	[ "transformers", "gguf", "GGUF", "arxiv:2312.11805", "license:gemma", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-28T14:32:33Z	--- license: gemma library_name: transformers tags: - GGUF widget: - messages: - role: user content: How does the brain work? inference: parameters: max_new_tokens: 200 extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license quantized_by: andrijdavid --- # gemma-2b-it-GGUF - Original model: [gemma-2b-it](https://huggingface.co/google/gemma-2b-it) <!-- description start --> ## Description This repo contains GGUF format model files for [gemma-2b-it](https://huggingface.co/google/gemma-2b-it). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration. * [Ollama](https://github.com/jmorganca/ollama) Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applications * [KoboldCpp](https://github.com/LostRuins/koboldcpp), A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling. * [GPT4All](https://gpt4all.io), This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration. * [LM Studio](https://lmstudio.ai/) An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui). A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection. * [Faraday.dev](https://faraday.dev/), An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use. * [ctransformers](https://github.com/marella/ctransformers), A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server. * [localGPT](https://github.com/PromtEngineer/localGPT) An open-source initiative enabling private conversations with documents. <!-- README_GGUF.md-about-gguf end --> <!-- compatibility_gguf start --> ## 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. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single folder. 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: LiteLLMs/gemma-2b-it-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-00001-of-00009.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download LiteLLMs/gemma-2b-it-GGUF Q4_0/Q4_0-00001-of-00009.gguf --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 LiteLLMs/gemma-2b-it-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install huggingface_hub[hf_transfer] ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download LiteLLMs/gemma-2b-it-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.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 Q4_0/Q4_0-00001-of-00009.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 8192` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./Q4_0/Q4_0-00001-of-00009.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<PROMPT>", # 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="./Q4_0/Q4_0-00001-of-00009.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer end --> <!-- original-model-card start --> # Original model card: gemma-2b-it # Gemma Model Card Model Page: [Gemma](https://ai.google.dev/gemma/docs) This model card corresponds to the 2B instruct version of the Gemma model. You can also visit the model card of the [2B base model](https://huggingface.co/google/gemma-2b), [7B base model](https://huggingface.co/google/gemma-7b), and [7B instruct model](https://huggingface.co/google/gemma-7b-it). Resources and Technical Documentation: * [Responsible Generative AI Toolkit](https://ai.google.dev/responsible) * [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma) * [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335?version=gemma-2b-it-gg-hf) Terms of Use: [Terms](https://www.kaggle.com/models/google/gemma/license/consent) Authors: Google ## Model Information Summary description and brief definition of inputs and outputs. ### Description Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. ### Usage Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase. #### Running the model on a CPU As explained below, we recommend `torch.bfloat16` as the default dtype. You can use [a different precision](#precisions) if necessary. ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it") model = AutoModelForCausalLM.from_pretrained( "google/gemma-2b-it", torch_dtype=torch.bfloat16 ) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` #### Running the model on a single / multi GPU ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it") model = AutoModelForCausalLM.from_pretrained( "google/gemma-2b-it", device_map="auto", torch_dtype=torch.bfloat16 ) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` <a name="precisions"></a> #### Running the model on a GPU using different precisions The native weights of this model were exported in `bfloat16` precision. You can use `float16`, which may be faster on certain hardware, indicating the `torch_dtype` when loading the model. For convenience, the `float16` revision of the repo contains a copy of the weights already converted to that precision. You can also use `float32` if you skip the dtype, but no precision increase will occur (model weights will just be upcasted to `float32`). See examples below. * _Using `torch.float16`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it") model = AutoModelForCausalLM.from_pretrained( "google/gemma-2b-it", device_map="auto", torch_dtype=torch.float16, revision="float16", ) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` _Upcasting to `torch.float32`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it") model = AutoModelForCausalLM.from_pretrained( "google/gemma-2b-it", device_map="auto" ) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` #### Quantized Versions through `bitsandbytes` _Using 8-bit precision (int8)_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_8bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", quantization_config=quantization_config) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` _Using 4-bit precision_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_4bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", quantization_config=quantization_config) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` #### Other optimizations _Flash Attention 2_ First make sure to install `flash-attn` in your environment `pip install flash-attn` ```diff model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, + attn_implementation="flash_attention_2" ).to(0) ``` ### Chat Template The instruction-tuned models use a chat template that must be adhered to for conversational use. The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet. Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction: ```py from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model_id = "gg-hf/gemma-2b-it" dtype = torch.bfloat16 tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, device_map="cuda", torch_dtype=dtype, ) chat = [ { "role": "user", "content": "Write a hello world program" }, ] prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) ``` At this point, the prompt contains the following text: ``` <bos><start_of_turn>user Write a hello world program<end_of_turn> <start_of_turn>model ``` As you can see, each turn is preceded by a `<start_of_turn>` delimiter and then the role of the entity (either `user`, for content supplied by the user, or `model` for LLM responses). Turns finish with the `<end_of_turn>` token. You can follow this format to build the prompt manually, if you need to do it without the tokenizer's chat template. After the prompt is ready, generation can be performed like this: ```py inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt") outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150) ``` ### Fine-tuning You can find some fine-tuning scripts under the [`examples/` directory](https://huggingface.co/google/gemma-7b/tree/main/examples) of [`google/gemma-7b`](https://huggingface.co/google/gemma-7b) repository. To adapt them to this model, simply change the model-id to `google/gemma-2b-it`. We provide: * A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using QLoRA * A script to perform SFT using FSDP on TPU devices * A notebook that you can run on a free-tier Google Colab instance to perform SFT on the English quotes dataset ### Inputs and outputs * Input: Text string, such as a question, a prompt, or a document to be summarized. * Output: Generated English-language text in response to the input, such as an answer to a question, or a summary of a document. ## Model Data Data used for model training and how the data was processed. ### Training Dataset These models were trained on a dataset of text data that includes a wide variety of sources, totaling 6 trillion tokens. Here are the key components: * Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. Primarily English-language content. * Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code or understand code-related questions. * Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries. The combination of these diverse data sources is crucial for training a powerful language model that can handle a wide variety of different tasks and text formats. ### Data Preprocessing Here are the key data cleaning and filtering methods applied to the training data: * CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content * Sensitive Data Filtering: As part of making Gemma pre-trained models safe and reliable, automated techniques were used to filter out certain personal information and other sensitive data from training sets. * Additional methods: Filtering based on content quality and safely in line with [our policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11). ## Implementation Information Details about the model internals. ### Hardware Gemma was trained using the latest generation of [Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e). Training large language models requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain: * Performance: TPUs are specifically designed to handle the massive computations involved in training LLMs. They can speed up training considerably compared to CPUs. * Memory: TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training. This can lead to better model quality. * Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for handling the growing complexity of large foundation models. You can distribute training across multiple TPU devices for faster and more efficient processing. * Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective solution for training large models compared to CPU-based infrastructure, especially when considering the time and resources saved due to faster training. * These advantages are aligned with [Google's commitments to operate sustainably](https://sustainability.google/operating-sustainably/). ### Software Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ml-pathways). JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models. ML Pathways is Google's latest effort to build artificially intelligent systems capable of generalizing across multiple tasks. This is specially suitable for [foundation models](https://ai.google/discover/foundation-models/), including large language models like these ones. Together, JAX and ML Pathways are used as described in the [paper about the Gemini family of models](https://arxiv.org/abs/2312.11805); "the 'single controller' programming model of Jax and Pathways allows a single Python process to orchestrate the entire training run, dramatically simplifying the development workflow." ## Evaluation Model evaluation metrics and results. ### Benchmark Results These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation: \| Benchmark \| Metric \| 2B Params \| 7B Params \| \| -- \| -- \| -- \| -- \| --- \| ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage Open Large Language Models (LLMs) have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. * Content Creation and Communication * Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. * Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications. * Text Summarization: Generate concise summaries of a text corpus, research papers, or reports. * Research and Education * Natural Language Processing (NLP) Research: These models can serve as a foundation for researchers to experiment with NLP techniques, develop algorithms, and contribute to the advancement of the field. * Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. * Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. ### Limitations * Training Data * The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. * The scope of the training dataset determines the subject areas the model can handle effectively. * Context and Task Complexity * LLMs are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. * A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). * Language Ambiguity and Nuance * Natural language is inherently complex. LLMs might struggle to grasp subtle nuances, sarcasm, or figurative language. * Factual Accuracy * LLMs generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. * Common Sense * LLMs rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. ### Ethical Considerations and Risks The development of large language models (LLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: * Bias and Fairness * LLMs trained on large-scale, real-world text data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. * Misinformation and Misuse * LLMs can be misused to generate text that is false, misleading, or harmful. * Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible). * Transparency and Accountability: * This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. * A responsibly developed open model offers the opportunity to share innovation by making LLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: * Perpetuation of biases: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. * Generation of harmful content: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. * Misuse for malicious purposes: Technical limitations and developer and end-user education can help mitigate against malicious applications of LLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy). * Privacy violations: Models were trained on data filtered for removal of PII (Personally Identifiable Information). Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ### Benefits At the time of release, this family of models provides high-performance open large language model implementations designed from the ground up for Responsible AI development compared to similarly sized models. Using the benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives. <!-- original-model-card end -->
MoTHer-VTHR/VTHR-FT-ModelTree_2-Depth_2-Node_Bhu6dQL9	MoTHer-VTHR	2024-05-28T14:42:28Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:42:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Likich/mistral-finetune-qualcoding_1000_prompt1_dot	Likich	2024-05-28T14:42:19Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-28T14:42:08Z	--- 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]
zaidanih/my_work	zaidanih	2024-05-28T14:41:09Z	4	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]	question-answering	2024-04-08T14:21:26Z	--- tags: - generated_from_trainer model-index: - name: my_work results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_work This model is a fine-tuned version of [SI2M-Lab/DarijaBERT](https://huggingface.co/SI2M-Lab/DarijaBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.3367 ## 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: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 250 \| 4.2806 \| \| 4.4428 \| 2.0 \| 500 \| 4.2277 \| \| 4.4428 \| 3.0 \| 750 \| 4.2998 \| \| 3.7449 \| 4.0 \| 1000 \| 4.3367 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.2.2+cpu - Datasets 2.13.0 - Tokenizers 0.13.3
MoTHer-VTHR/VTHR-FT-ModelTree_2-Depth_2-Node_YvJCApJg	MoTHer-VTHR	2024-05-28T14:40:43Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:40:29Z	--- 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. 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MoTHer-VTHR/VTHR-FT-ModelTree_2-Depth_2-Node_wh3Gj4h7	MoTHer-VTHR	2024-05-28T14:40:22Z	168	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:40:09Z	--- 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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MoTHer-VTHR/VTHR-FT-ModelTree_2-Depth_2-Node_gewc6rx8	MoTHer-VTHR	2024-05-28T14:39:00Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:38:46Z	--- 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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Techdread/ppo-LunarLander-v2	Techdread	2024-05-28T14:38:56Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-28T14:38:40Z	--- 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: 255.52 +/- 24.51 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 ... ```
DiederikMartens/eBERT_sa_cv_13_fold2	DiederikMartens	2024-05-28T14:37:28Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T14:15:23Z	--- license: apache-2.0 base_model: google-bert/bert-base-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: eBERT_sa_cv_13_fold2 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. --> # eBERT_sa_cv_13_fold2 This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4830 - F1: 0.6086 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - 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 \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 325 \| 0.5455 \| 0.4669 \| \| 0.6251 \| 2.0 \| 650 \| 0.5646 \| 0.4961 \| \| 0.6251 \| 3.0 \| 975 \| 0.4830 \| 0.6086 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
MoTHer-VTHR/VTHR-FT-ModelTree_1-Depth_2-Node_GcDb5Kwm	MoTHer-VTHR	2024-05-28T14:36:54Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:36:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-FT-ModelTree_1-Depth_2-Node_g6TsBRWv	MoTHer-VTHR	2024-05-28T14:36:10Z	169	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:35:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Zoyd/mlabonne_Daredevil-8B-abliterated-5_0bpw_exl2	Zoyd	2024-05-28T14:35:51Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "5-bit", "exl2", "region:us" ]	text-generation	2024-05-28T14:06:45Z	--- library_name: transformers license: other --- Exllamav2 quant (exl2 / 5.0 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_0bpw_exl2)</center> \| <center>3895 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_5bpw_exl2)</center> \| <center>4310 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_75bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_25bpw_exl2)</center> \| <center>4935 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-5_0bpw_exl2)</center> \| <center>5559 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_0bpw_exl2)</center> \| <center>6497 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_5bpw_exl2)</center> \| <center>6913 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-8_0bpw_exl2)</center> \| <center>8150 MB</center> \| <center>8</center> \| # Daredevil-8B-abliterated ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/gFEhcIDSKa3AWpkNfH91q.jpeg) Abliterated version of [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) using [failspy](https://huggingface.co/failspy)'s notebook. It based on the technique described in the blog post "[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)". Thanks to Andy Arditi, Oscar Balcells Obeso, Aaquib111, Wes Gurnee, Neel Nanda, and failspy. ## ⚡ Quantization * GGUF: https://huggingface.co/mlabonne/Daredevil-8B-abliterated-GGUF ## 🏆 Evaluation ### Nous \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) [📄](https://gist.github.com/mlabonne/080f9c5f153ea57a7ab7d932cf896f21) \| 55.87 \| 44.13 \| 73.52 \| 59.05 \| 46.77 \| \| [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) [📄](https://gist.github.com/mlabonne/32cdd8460804662c856bcb2a20acd49e) \| 55.06 \| 43.29 \| 73.33 \| 57.47 \| 46.17 \| \| [mlabonne/Llama-3-8B-Instruct-abliterated-dpomix](https://huggingface.co/mlabonne/Llama-3-8B-Instruct-abliterated-dpomix) [📄](https://gist.github.com/mlabonne/d711548df70e2c04771cc68ab33fe2b9) \| 52.26 \| 41.6 \| 69.95 \| 54.22 \| 43.26 \| \| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) \| 51.34 \| 41.22 \| 69.86 \| 51.65 \| 42.64 \| \| [failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3) [📄](https://gist.github.com/mlabonne/f46cce0262443365e4cce2b6fa7507fc) \| 51.21 \| 40.23 \| 69.5 \| 52.44 \| 42.69 \| \| [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/22896a1ae164859931cc8f4858c97f6f) \| 48.63 \| 34.17 \| 70.59 \| 52.39 \| 37.36 \| \| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) \| 45.42 \| 31.1 \| 69.95 \| 43.91 \| 36.7 \|
horse2222/test	horse2222	2024-05-28T14:35:43Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-28T14:35:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-FT-ModelTree_1-Depth_2-Node_zqHp4mi7	MoTHer-VTHR	2024-05-28T14:35:25Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:35:12Z	--- 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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Zoyd/mlabonne_Daredevil-8B-abliterated-3_5bpw_exl2	Zoyd	2024-05-28T14:35:20Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-28T13:41:31Z	--- library_name: transformers license: other --- Exllamav2 quant (exl2 / 3.5 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_0bpw_exl2)</center> \| <center>3895 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_5bpw_exl2)</center> \| <center>4310 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_75bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_25bpw_exl2)</center> \| <center>4935 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-5_0bpw_exl2)</center> \| <center>5559 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_0bpw_exl2)</center> \| <center>6497 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_5bpw_exl2)</center> \| <center>6913 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-8_0bpw_exl2)</center> \| <center>8150 MB</center> \| <center>8</center> \| # Daredevil-8B-abliterated ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/gFEhcIDSKa3AWpkNfH91q.jpeg) Abliterated version of [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) using [failspy](https://huggingface.co/failspy)'s notebook. It based on the technique described in the blog post "[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)". Thanks to Andy Arditi, Oscar Balcells Obeso, Aaquib111, Wes Gurnee, Neel Nanda, and failspy. ## ⚡ Quantization * GGUF: https://huggingface.co/mlabonne/Daredevil-8B-abliterated-GGUF ## 🏆 Evaluation ### Nous \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) [📄](https://gist.github.com/mlabonne/080f9c5f153ea57a7ab7d932cf896f21) \| 55.87 \| 44.13 \| 73.52 \| 59.05 \| 46.77 \| \| [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) [📄](https://gist.github.com/mlabonne/32cdd8460804662c856bcb2a20acd49e) \| 55.06 \| 43.29 \| 73.33 \| 57.47 \| 46.17 \| \| [mlabonne/Llama-3-8B-Instruct-abliterated-dpomix](https://huggingface.co/mlabonne/Llama-3-8B-Instruct-abliterated-dpomix) [📄](https://gist.github.com/mlabonne/d711548df70e2c04771cc68ab33fe2b9) \| 52.26 \| 41.6 \| 69.95 \| 54.22 \| 43.26 \| \| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) \| 51.34 \| 41.22 \| 69.86 \| 51.65 \| 42.64 \| \| [failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3) [📄](https://gist.github.com/mlabonne/f46cce0262443365e4cce2b6fa7507fc) \| 51.21 \| 40.23 \| 69.5 \| 52.44 \| 42.69 \| \| [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/22896a1ae164859931cc8f4858c97f6f) \| 48.63 \| 34.17 \| 70.59 \| 52.39 \| 37.36 \| \| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) \| 45.42 \| 31.1 \| 69.95 \| 43.91 \| 36.7 \|
MoTHer-VTHR/VTHR-FT-ModelTree_1-Depth_2-Node_hpgQiK4Q	MoTHer-VTHR	2024-05-28T14:35:03Z	168	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:34:47Z	--- 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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Zoyd/mlabonne_Daredevil-8B-abliterated-6_5bpw_exl2	Zoyd	2024-05-28T14:34:52Z	6	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-28T14:13:47Z	--- library_name: transformers license: other --- Exllamav2 quant (exl2 / 6.5 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_0bpw_exl2)</center> \| <center>3895 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_5bpw_exl2)</center> \| <center>4310 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_75bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_25bpw_exl2)</center> \| <center>4935 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-5_0bpw_exl2)</center> \| <center>5559 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_0bpw_exl2)</center> \| <center>6497 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_5bpw_exl2)</center> \| <center>6913 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-8_0bpw_exl2)</center> \| <center>8150 MB</center> \| <center>8</center> \| # Daredevil-8B-abliterated ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/gFEhcIDSKa3AWpkNfH91q.jpeg) Abliterated version of [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) using [failspy](https://huggingface.co/failspy)'s notebook. It based on the technique described in the blog post "[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)". Thanks to Andy Arditi, Oscar Balcells Obeso, Aaquib111, Wes Gurnee, Neel Nanda, and failspy. ## ⚡ Quantization * GGUF: https://huggingface.co/mlabonne/Daredevil-8B-abliterated-GGUF ## 🏆 Evaluation ### Nous \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) [📄](https://gist.github.com/mlabonne/080f9c5f153ea57a7ab7d932cf896f21) \| 55.87 \| 44.13 \| 73.52 \| 59.05 \| 46.77 \| \| [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) [📄](https://gist.github.com/mlabonne/32cdd8460804662c856bcb2a20acd49e) \| 55.06 \| 43.29 \| 73.33 \| 57.47 \| 46.17 \| \| [mlabonne/Llama-3-8B-Instruct-abliterated-dpomix](https://huggingface.co/mlabonne/Llama-3-8B-Instruct-abliterated-dpomix) [📄](https://gist.github.com/mlabonne/d711548df70e2c04771cc68ab33fe2b9) \| 52.26 \| 41.6 \| 69.95 \| 54.22 \| 43.26 \| \| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) \| 51.34 \| 41.22 \| 69.86 \| 51.65 \| 42.64 \| \| [failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3) [📄](https://gist.github.com/mlabonne/f46cce0262443365e4cce2b6fa7507fc) \| 51.21 \| 40.23 \| 69.5 \| 52.44 \| 42.69 \| \| [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/22896a1ae164859931cc8f4858c97f6f) \| 48.63 \| 34.17 \| 70.59 \| 52.39 \| 37.36 \| \| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) \| 45.42 \| 31.1 \| 69.95 \| 43.91 \| 36.7 \|
Zoyd/mlabonne_Daredevil-8B-abliterated-2_5bpw_exl2	Zoyd	2024-05-28T14:34:34Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-28T13:30:38Z	--- library_name: transformers license: other --- Exllamav2 quant (exl2 / 2.5 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_0bpw_exl2)</center> \| <center>3895 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_5bpw_exl2)</center> \| <center>4310 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_75bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_25bpw_exl2)</center> \| <center>4935 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-5_0bpw_exl2)</center> \| <center>5559 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_0bpw_exl2)</center> \| <center>6497 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_5bpw_exl2)</center> \| <center>6913 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-8_0bpw_exl2)</center> \| <center>8150 MB</center> \| <center>8</center> \| # Daredevil-8B-abliterated ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/gFEhcIDSKa3AWpkNfH91q.jpeg) Abliterated version of [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) using [failspy](https://huggingface.co/failspy)'s notebook. It based on the technique described in the blog post "[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)". Thanks to Andy Arditi, Oscar Balcells Obeso, Aaquib111, Wes Gurnee, Neel Nanda, and failspy. ## ⚡ Quantization * GGUF: https://huggingface.co/mlabonne/Daredevil-8B-abliterated-GGUF ## 🏆 Evaluation ### Nous \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) [📄](https://gist.github.com/mlabonne/080f9c5f153ea57a7ab7d932cf896f21) \| 55.87 \| 44.13 \| 73.52 \| 59.05 \| 46.77 \| \| [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) [📄](https://gist.github.com/mlabonne/32cdd8460804662c856bcb2a20acd49e) \| 55.06 \| 43.29 \| 73.33 \| 57.47 \| 46.17 \| \| [mlabonne/Llama-3-8B-Instruct-abliterated-dpomix](https://huggingface.co/mlabonne/Llama-3-8B-Instruct-abliterated-dpomix) [📄](https://gist.github.com/mlabonne/d711548df70e2c04771cc68ab33fe2b9) \| 52.26 \| 41.6 \| 69.95 \| 54.22 \| 43.26 \| \| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) \| 51.34 \| 41.22 \| 69.86 \| 51.65 \| 42.64 \| \| [failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3) [📄](https://gist.github.com/mlabonne/f46cce0262443365e4cce2b6fa7507fc) \| 51.21 \| 40.23 \| 69.5 \| 52.44 \| 42.69 \| \| [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/22896a1ae164859931cc8f4858c97f6f) \| 48.63 \| 34.17 \| 70.59 \| 52.39 \| 37.36 \| \| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) \| 45.42 \| 31.1 \| 69.95 \| 43.91 \| 36.7 \|
MoTHer-VTHR/VTHR-FT-ModelTree_1-Depth_2-Node_AqZcPQjB	MoTHer-VTHR	2024-05-28T14:34:19Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:34:06Z	--- 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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Zoyd/mlabonne_Daredevil-8B-abliterated-8_0bpw_exl2	Zoyd	2024-05-28T14:34:06Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "exl2", "region:us" ]	text-generation	2024-05-28T14:26:36Z	--- library_name: transformers license: other --- Exllamav2 quant (exl2 / 8.0 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_0bpw_exl2)</center> \| <center>3895 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_5bpw_exl2)</center> \| <center>4310 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_75bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_25bpw_exl2)</center> \| <center>4935 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-5_0bpw_exl2)</center> \| <center>5559 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_0bpw_exl2)</center> \| <center>6497 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_5bpw_exl2)</center> \| <center>6913 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-8_0bpw_exl2)</center> \| <center>8150 MB</center> \| <center>8</center> \| # Daredevil-8B-abliterated ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/gFEhcIDSKa3AWpkNfH91q.jpeg) Abliterated version of [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) using [failspy](https://huggingface.co/failspy)'s notebook. It based on the technique described in the blog post "[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)". Thanks to Andy Arditi, Oscar Balcells Obeso, Aaquib111, Wes Gurnee, Neel Nanda, and failspy. ## ⚡ Quantization * GGUF: https://huggingface.co/mlabonne/Daredevil-8B-abliterated-GGUF ## 🏆 Evaluation ### Nous \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) [📄](https://gist.github.com/mlabonne/080f9c5f153ea57a7ab7d932cf896f21) \| 55.87 \| 44.13 \| 73.52 \| 59.05 \| 46.77 \| \| [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) [📄](https://gist.github.com/mlabonne/32cdd8460804662c856bcb2a20acd49e) \| 55.06 \| 43.29 \| 73.33 \| 57.47 \| 46.17 \| \| [mlabonne/Llama-3-8B-Instruct-abliterated-dpomix](https://huggingface.co/mlabonne/Llama-3-8B-Instruct-abliterated-dpomix) [📄](https://gist.github.com/mlabonne/d711548df70e2c04771cc68ab33fe2b9) \| 52.26 \| 41.6 \| 69.95 \| 54.22 \| 43.26 \| \| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) \| 51.34 \| 41.22 \| 69.86 \| 51.65 \| 42.64 \| \| [failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3) [📄](https://gist.github.com/mlabonne/f46cce0262443365e4cce2b6fa7507fc) \| 51.21 \| 40.23 \| 69.5 \| 52.44 \| 42.69 \| \| [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/22896a1ae164859931cc8f4858c97f6f) \| 48.63 \| 34.17 \| 70.59 \| 52.39 \| 37.36 \| \| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) \| 45.42 \| 31.1 \| 69.95 \| 43.91 \| 36.7 \|
Zoyd/mlabonne_Daredevil-8B-abliterated-6_0bpw_exl2	Zoyd	2024-05-28T14:33:57Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "exl2", "region:us" ]	text-generation	2024-05-28T14:20:34Z	--- library_name: transformers license: other --- Exllamav2 quant (exl2 / 6.0 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_0bpw_exl2)</center> \| <center>3895 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_5bpw_exl2)</center> \| <center>4310 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_75bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_25bpw_exl2)</center> \| <center>4935 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-5_0bpw_exl2)</center> \| <center>5559 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_0bpw_exl2)</center> \| <center>6497 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_5bpw_exl2)</center> \| <center>6913 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-8_0bpw_exl2)</center> \| <center>8150 MB</center> \| <center>8</center> \| # Daredevil-8B-abliterated ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/gFEhcIDSKa3AWpkNfH91q.jpeg) Abliterated version of [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) using [failspy](https://huggingface.co/failspy)'s notebook. It based on the technique described in the blog post "[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)". Thanks to Andy Arditi, Oscar Balcells Obeso, Aaquib111, Wes Gurnee, Neel Nanda, and failspy. ## ⚡ Quantization * GGUF: https://huggingface.co/mlabonne/Daredevil-8B-abliterated-GGUF ## 🏆 Evaluation ### Nous \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) [📄](https://gist.github.com/mlabonne/080f9c5f153ea57a7ab7d932cf896f21) \| 55.87 \| 44.13 \| 73.52 \| 59.05 \| 46.77 \| \| [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) [📄](https://gist.github.com/mlabonne/32cdd8460804662c856bcb2a20acd49e) \| 55.06 \| 43.29 \| 73.33 \| 57.47 \| 46.17 \| \| [mlabonne/Llama-3-8B-Instruct-abliterated-dpomix](https://huggingface.co/mlabonne/Llama-3-8B-Instruct-abliterated-dpomix) [📄](https://gist.github.com/mlabonne/d711548df70e2c04771cc68ab33fe2b9) \| 52.26 \| 41.6 \| 69.95 \| 54.22 \| 43.26 \| \| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) \| 51.34 \| 41.22 \| 69.86 \| 51.65 \| 42.64 \| \| [failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3) [📄](https://gist.github.com/mlabonne/f46cce0262443365e4cce2b6fa7507fc) \| 51.21 \| 40.23 \| 69.5 \| 52.44 \| 42.69 \| \| [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/22896a1ae164859931cc8f4858c97f6f) \| 48.63 \| 34.17 \| 70.59 \| 52.39 \| 37.36 \| \| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) \| 45.42 \| 31.1 \| 69.95 \| 43.91 \| 36.7 \|
Zoyd/mlabonne_Daredevil-8B-abliterated-4_25bpw_exl2	Zoyd	2024-05-28T14:33:49Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-28T14:00:57Z	--- library_name: transformers license: other --- Exllamav2 quant (exl2 / 4.25 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_0bpw_exl2)</center> \| <center>3895 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_5bpw_exl2)</center> \| <center>4310 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_75bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_25bpw_exl2)</center> \| <center>4935 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-5_0bpw_exl2)</center> \| <center>5559 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_0bpw_exl2)</center> \| <center>6497 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_5bpw_exl2)</center> \| <center>6913 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-8_0bpw_exl2)</center> \| <center>8150 MB</center> \| <center>8</center> \| # Daredevil-8B-abliterated ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/gFEhcIDSKa3AWpkNfH91q.jpeg) Abliterated version of [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) using [failspy](https://huggingface.co/failspy)'s notebook. It based on the technique described in the blog post "[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)". Thanks to Andy Arditi, Oscar Balcells Obeso, Aaquib111, Wes Gurnee, Neel Nanda, and failspy. ## ⚡ Quantization * GGUF: https://huggingface.co/mlabonne/Daredevil-8B-abliterated-GGUF ## 🏆 Evaluation ### Nous \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) [📄](https://gist.github.com/mlabonne/080f9c5f153ea57a7ab7d932cf896f21) \| 55.87 \| 44.13 \| 73.52 \| 59.05 \| 46.77 \| \| [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) [📄](https://gist.github.com/mlabonne/32cdd8460804662c856bcb2a20acd49e) \| 55.06 \| 43.29 \| 73.33 \| 57.47 \| 46.17 \| \| [mlabonne/Llama-3-8B-Instruct-abliterated-dpomix](https://huggingface.co/mlabonne/Llama-3-8B-Instruct-abliterated-dpomix) [📄](https://gist.github.com/mlabonne/d711548df70e2c04771cc68ab33fe2b9) \| 52.26 \| 41.6 \| 69.95 \| 54.22 \| 43.26 \| \| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) \| 51.34 \| 41.22 \| 69.86 \| 51.65 \| 42.64 \| \| [failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3) [📄](https://gist.github.com/mlabonne/f46cce0262443365e4cce2b6fa7507fc) \| 51.21 \| 40.23 \| 69.5 \| 52.44 \| 42.69 \| \| [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/22896a1ae164859931cc8f4858c97f6f) \| 48.63 \| 34.17 \| 70.59 \| 52.39 \| 37.36 \| \| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) \| 45.42 \| 31.1 \| 69.95 \| 43.91 \| 36.7 \|
MoTHer-VTHR/VTHR-FT-ModelTree_1-Depth_2-Node_zbYaxqQn	MoTHer-VTHR	2024-05-28T14:33:36Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:33:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Zoyd/mlabonne_Daredevil-8B-abliterated-2_2bpw_exl2	Zoyd	2024-05-28T14:33:23Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-28T13:19:11Z	--- library_name: transformers license: other --- Exllamav2 quant (exl2 / 2.2 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_0bpw_exl2)</center> \| <center>3895 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_5bpw_exl2)</center> \| <center>4310 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-3_75bpw_exl2)</center> \| <center>4519 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-4_25bpw_exl2)</center> \| <center>4935 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-5_0bpw_exl2)</center> \| <center>5559 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_0bpw_exl2)</center> \| <center>6497 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-6_5bpw_exl2)</center> \| <center>6913 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/mlabonne_Daredevil-8B-abliterated-8_0bpw_exl2)</center> \| <center>8150 MB</center> \| <center>8</center> \| # Daredevil-8B-abliterated ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/gFEhcIDSKa3AWpkNfH91q.jpeg) Abliterated version of [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) using [failspy](https://huggingface.co/failspy)'s notebook. It based on the technique described in the blog post "[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)". Thanks to Andy Arditi, Oscar Balcells Obeso, Aaquib111, Wes Gurnee, Neel Nanda, and failspy. ## ⚡ Quantization * GGUF: https://huggingface.co/mlabonne/Daredevil-8B-abliterated-GGUF ## 🏆 Evaluation ### Nous \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) [📄](https://gist.github.com/mlabonne/080f9c5f153ea57a7ab7d932cf896f21) \| 55.87 \| 44.13 \| 73.52 \| 59.05 \| 46.77 \| \| [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) [📄](https://gist.github.com/mlabonne/32cdd8460804662c856bcb2a20acd49e) \| 55.06 \| 43.29 \| 73.33 \| 57.47 \| 46.17 \| \| [mlabonne/Llama-3-8B-Instruct-abliterated-dpomix](https://huggingface.co/mlabonne/Llama-3-8B-Instruct-abliterated-dpomix) [📄](https://gist.github.com/mlabonne/d711548df70e2c04771cc68ab33fe2b9) \| 52.26 \| 41.6 \| 69.95 \| 54.22 \| 43.26 \| \| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) \| 51.34 \| 41.22 \| 69.86 \| 51.65 \| 42.64 \| \| [failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3) [📄](https://gist.github.com/mlabonne/f46cce0262443365e4cce2b6fa7507fc) \| 51.21 \| 40.23 \| 69.5 \| 52.44 \| 42.69 \| \| [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/22896a1ae164859931cc8f4858c97f6f) \| 48.63 \| 34.17 \| 70.59 \| 52.39 \| 37.36 \| \| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) \| 45.42 \| 31.1 \| 69.95 \| 43.91 \| 36.7 \|
MoTHer-VTHR/VTHR-FT-ModelTree_1-Depth_2-Node_KCmyhjVC	MoTHer-VTHR	2024-05-28T14:32:53Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:32:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Giux22/semana11-escalonados_balanced-sorted	Giux22	2024-05-28T14:32:17Z	0	0	null	[ "region:us" ]	null	2024-05-28T03:11:08Z	[ transforms.Resize((config.image_size, config.image_size)), # transforms.RandomHorizontalFlip(), # transforms.RandomVerticalFlip(), # transforms.RandomRotation(20), transforms.ToTensor(), transforms.Normalize([0.5], [0.5]), ]
Weni/runpod_debug	Weni	2024-05-28T14:32:13Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]	null	2024-05-28T13:07:29Z	--- license: llama3 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Meta-Llama-3-8B-Instruct model-index: - name: runpod_debug 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/weni-tech/WeniGPT/runs/yux9v24u) # runpod_debug This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2127 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.03 - training_steps: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.5929 \| 0.1198 \| 10 \| 1.2905 \| \| 1.2188 \| 0.2395 \| 20 \| 1.2275 \| \| 1.2161 \| 0.3593 \| 30 \| 1.2127 \| ### Framework versions - PEFT 0.11.0 - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
MoTHer-VTHR/VTHR-FT-ModelTree_1-Depth_1-Node_bLQQLJQk	MoTHer-VTHR	2024-05-28T14:32:13Z	168	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:32:00Z	--- 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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MoTHer-VTHR/VTHR-FT-ModelTree_1-Depth_2-Node_zNKGvWux	MoTHer-VTHR	2024-05-28T14:31:53Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:31:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-FT-ModelTree_1-Depth_2-Node_FBytVXcq	MoTHer-VTHR	2024-05-28T14:31:32Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:31:18Z	--- 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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BothBosu/lstm-gamma33-scam-classifier-v1.2	BothBosu	2024-05-28T14:31:14Z	51	0	transformers	[ "transformers", "safetensors", "pytorch_model_hub_mixin", "model_hub_mixin", "endpoints_compatible", "region:us" ]	null	2024-05-28T14:31:07Z	--- tags: - pytorch_model_hub_mixin - model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
MoTHer-VTHR/VTHR-FT-ModelTree_1-Depth_0-Node_ULcuMZfv	MoTHer-VTHR	2024-05-28T14:30:06Z	160	0	transformers	[ "transformers", "safetensors", "vit", "image-feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-feature-extraction	2024-05-28T14:29:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Bramwel/persuasion_v0.8	Bramwel	2024-05-28T14:29:54Z	2	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2024-05-28T06:55:07Z	--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- # 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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MoTHer-VTHR/VTHR-FT-ModelTree_0-Depth_2-Node_Z2mpQHBS	MoTHer-VTHR	2024-05-28T14:29:44Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:29:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-FT-ModelTree_0-Depth_2-Node_D23SEiUt	MoTHer-VTHR	2024-05-28T14:29:22Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:29:07Z	--- 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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Busayor/mms-yor	Busayor	2024-05-28T14:29:10Z	35	0	transformers	[ "transformers", "safetensors", "vits", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-27T19:30:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-FT-ModelTree_0-Depth_2-Node_6HsuGLJs	MoTHer-VTHR	2024-05-28T14:29:00Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:28:47Z	--- 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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MoTHer-VTHR/VTHR-FT-ModelTree_0-Depth_1-Node_kYHiKA98	MoTHer-VTHR	2024-05-28T14:28:17Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:28:04Z	--- 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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Ramikan-BR/tinyllama-coder-py-v11	Ramikan-BR	2024-05-28T14:27:33Z	183	0	transformers	[ "transformers", "pytorch", "safetensors", "gguf", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "dataset:Ramikan-BR/code.evol.instruct.wiz.oss_python.json", "base_model:unsloth/tinyllama-chat-bnb-4bit", "base_model:quantized:unsloth/tinyllama-chat-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-26T06:24:48Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/tinyllama-chat-bnb-4bit pipeline_tag: text-generation datasets: Ramikan-BR/code.evol.instruct.wiz.oss_python.json --- datasets: code.evol.instruct.wiz.oss_python.json ```python ==((====))== Unsloth - 2x faster free finetuning \| Num GPUs = 1 \\ /\| Num examples = 937 \| Num Epochs = 2 O^O/ \_/ \ Batch size per device = 2 \| Gradient Accumulation steps = 256 \ / Total batch size = 512 \| Total steps = 2 "-____-" Number of trainable parameters = 201,850,880 [2/2 22:36, Epoch 1/2] Step Training Loss 1 0.707400 2 0.717800 ``` # 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)
MoTHer-VTHR/VTHR-FT-ModelTree_0-Depth_2-Node_XdLnp5Xj	MoTHer-VTHR	2024-05-28T14:26:55Z	168	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:26:42Z	--- 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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MoTHer-VTHR/VTHR-FT-ModelTree_0-Depth_1-Node_BhVuRDoZ	MoTHer-VTHR	2024-05-28T14:26:35Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:26:22Z	--- 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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cyr19/gpt2-small-en-quatrain-conditioned	cyr19	2024-05-28T14:26:13Z	136	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T14:25:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MoTHer-VTHR/VTHR-FT-ModelTree_0-Depth_2-Node_bd6KL2rJ	MoTHer-VTHR	2024-05-28T14:25:14Z	168	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:25:02Z	--- 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. 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MoTHer-VTHR/VTHR-FT-ModelTree_0-Depth_2-Node_D7YNXK3N	MoTHer-VTHR	2024-05-28T14:24:38Z	166	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:22:12Z	--- 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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MoTHer-VTHR/VTHR-FT-ModelTree_0-Depth_1-Node_6hLsBteR	MoTHer-VTHR	2024-05-28T14:24:29Z	167	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-28T14:09:22Z	--- 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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HelloOoOooo/results_3	HelloOoOooo	2024-05-28T14:24:28Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:abhi317/results_2", "base_model:finetune:abhi317/results_2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-28T14:12:16Z	--- tags: - generated_from_trainer base_model: abhi317/results_2 model-index: - name: results_3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results_3 This model is a fine-tuned version of [abhi317/results_2](https://huggingface.co/abhi317/results_2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1557 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 1 \| 2.4711 \| \| No log \| 2.0 \| 2 \| 2.3635 \| \| No log \| 3.0 \| 3 \| 2.2591 \| \| No log \| 4.0 \| 4 \| 2.1869 \| \| No log \| 5.0 \| 5 \| 2.1121 \| \| No log \| 6.0 \| 6 \| 2.0433 \| \| No log \| 7.0 \| 7 \| 1.9845 \| \| No log \| 8.0 \| 8 \| 1.9252 \| \| No log \| 9.0 \| 9 \| 1.8642 \| \| No log \| 10.0 \| 10 \| 1.8104 \| \| No log \| 11.0 \| 11 \| 1.7649 \| \| No log \| 12.0 \| 12 \| 1.7260 \| \| No log \| 13.0 \| 13 \| 1.6873 \| \| No log \| 14.0 \| 14 \| 1.6532 \| \| No log \| 15.0 \| 15 \| 1.6242 \| \| No log \| 16.0 \| 16 \| 1.6066 \| \| No log \| 17.0 \| 17 \| 1.5801 \| \| No log \| 18.0 \| 18 \| 1.5596 \| \| No log \| 19.0 \| 19 \| 1.5346 \| \| No log \| 20.0 \| 20 \| 1.5040 \| \| No log \| 21.0 \| 21 \| 1.4759 \| \| No log \| 22.0 \| 22 \| 1.4507 \| \| No log \| 23.0 \| 23 \| 1.4294 \| \| No log \| 24.0 \| 24 \| 1.4083 \| \| No log \| 25.0 \| 25 \| 1.4008 \| \| No log \| 26.0 \| 26 \| 1.3787 \| \| No log \| 27.0 \| 27 \| 1.3444 \| \| No log \| 28.0 \| 28 \| 1.3196 \| \| No log \| 29.0 \| 29 \| 1.2965 \| \| No log \| 30.0 \| 30 \| 1.2714 \| \| No log \| 31.0 \| 31 \| 1.2447 \| \| No log \| 32.0 \| 32 \| 1.2207 \| \| No log \| 33.0 \| 33 \| 1.1911 \| \| No log \| 34.0 \| 34 \| 1.1596 \| \| No log \| 35.0 \| 35 \| 1.1291 \| \| No log \| 36.0 \| 36 \| 1.1054 \| \| No log \| 37.0 \| 37 \| 1.0787 \| \| No log \| 38.0 \| 38 \| 1.0492 \| \| No log \| 39.0 \| 39 \| 1.0278 \| \| No log \| 40.0 \| 40 \| 1.0058 \| \| No log \| 41.0 \| 41 \| 0.9850 \| \| No log \| 42.0 \| 42 \| 0.9644 \| \| No log \| 43.0 \| 43 \| 0.9525 \| \| No log \| 44.0 \| 44 \| 0.9405 \| \| No log \| 45.0 \| 45 \| 0.9255 \| \| No log \| 46.0 \| 46 \| 0.9018 \| \| No log \| 47.0 \| 47 \| 0.8715 \| \| No log \| 48.0 \| 48 \| 0.8439 \| \| No log \| 49.0 \| 49 \| 0.8271 \| \| No log \| 50.0 \| 50 \| 0.8079 \| \| No log \| 51.0 \| 51 \| 0.7844 \| \| No log \| 52.0 \| 52 \| 0.7619 \| \| No log \| 53.0 \| 53 \| 0.7389 \| \| No log \| 54.0 \| 54 \| 0.7216 \| \| No log \| 55.0 \| 55 \| 0.7085 \| \| No log \| 56.0 \| 56 \| 0.6971 \| \| No log \| 57.0 \| 57 \| 0.6864 \| \| No log \| 58.0 \| 58 \| 0.6771 \| \| No log \| 59.0 \| 59 \| 0.6650 \| \| No log \| 60.0 \| 60 \| 0.6552 \| \| No log \| 61.0 \| 61 \| 0.6451 \| \| No log \| 62.0 \| 62 \| 0.6375 \| \| No log \| 63.0 \| 63 \| 0.6317 \| \| No log \| 64.0 \| 64 \| 0.6252 \| \| No log \| 65.0 \| 65 \| 0.6179 \| \| No log \| 66.0 \| 66 \| 0.6081 \| \| No log \| 67.0 \| 67 \| 0.5980 \| \| No log \| 68.0 \| 68 \| 0.5844 \| \| No log \| 69.0 \| 69 \| 0.5751 \| \| No log \| 70.0 \| 70 \| 0.5651 \| \| No log \| 71.0 \| 71 \| 0.5603 \| \| No log \| 72.0 \| 72 \| 0.5540 \| \| No log \| 73.0 \| 73 \| 0.5442 \| \| No log \| 74.0 \| 74 \| 0.5342 \| \| No log \| 75.0 \| 75 \| 0.5228 \| \| No log \| 76.0 \| 76 \| 0.5093 \| \| No log \| 77.0 \| 77 \| 0.4987 \| \| No log \| 78.0 \| 78 \| 0.4859 \| \| No log \| 79.0 \| 79 \| 0.4728 \| \| No log \| 80.0 \| 80 \| 0.4602 \| \| No log \| 81.0 \| 81 \| 0.4523 \| \| No log \| 82.0 \| 82 \| 0.4444 \| \| No log \| 83.0 \| 83 \| 0.4349 \| \| No log \| 84.0 \| 84 \| 0.4250 \| \| No log \| 85.0 \| 85 \| 0.4154 \| \| No log \| 86.0 \| 86 \| 0.4078 \| \| No log \| 87.0 \| 87 \| 0.3995 \| \| No log \| 88.0 \| 88 \| 0.3929 \| \| No log \| 89.0 \| 89 \| 0.3863 \| \| No log \| 90.0 \| 90 \| 0.3796 \| \| No log \| 91.0 \| 91 \| 0.3737 \| \| No log \| 92.0 \| 92 \| 0.3663 \| \| No log \| 93.0 \| 93 \| 0.3624 \| \| No log \| 94.0 \| 94 \| 0.3592 \| \| No log \| 95.0 \| 95 \| 0.3537 \| \| No log \| 96.0 \| 96 \| 0.3467 \| \| No log \| 97.0 \| 97 \| 0.3424 \| \| No log \| 98.0 \| 98 \| 0.3381 \| \| No log \| 99.0 \| 99 \| 0.3332 \| \| No log \| 100.0 \| 100 \| 0.3276 \| \| No log \| 101.0 \| 101 \| 0.3245 \| \| No log \| 102.0 \| 102 \| 0.3208 \| \| No log \| 103.0 \| 103 \| 0.3170 \| \| No log \| 104.0 \| 104 \| 0.3148 \| \| No log \| 105.0 \| 105 \| 0.3132 \| \| No log \| 106.0 \| 106 \| 0.3106 \| \| No log \| 107.0 \| 107 \| 0.3086 \| \| No log \| 108.0 \| 108 \| 0.3053 \| \| No log \| 109.0 \| 109 \| 0.3038 \| \| No log \| 110.0 \| 110 \| 0.3020 \| \| No log \| 111.0 \| 111 \| 0.2998 \| \| No log \| 112.0 \| 112 \| 0.2966 \| \| No log \| 113.0 \| 113 \| 0.2931 \| \| No log \| 114.0 \| 114 \| 0.2887 \| \| No log \| 115.0 \| 115 \| 0.2838 \| \| No log \| 116.0 \| 116 \| 0.2785 \| \| No log \| 117.0 \| 117 \| 0.2735 \| \| No log \| 118.0 \| 118 \| 0.2688 \| \| No log \| 119.0 \| 119 \| 0.2644 \| \| No log \| 120.0 \| 120 \| 0.2624 \| \| No log \| 121.0 \| 121 \| 0.2610 \| \| No log \| 122.0 \| 122 \| 0.2593 \| \| No log \| 123.0 \| 123 \| 0.2564 \| \| No log \| 124.0 \| 124 \| 0.2537 \| \| No log \| 125.0 \| 125 \| 0.2506 \| \| No log \| 126.0 \| 126 \| 0.2465 \| \| No log \| 127.0 \| 127 \| 0.2441 \| \| No log \| 128.0 \| 128 \| 0.2408 \| \| No log \| 129.0 \| 129 \| 0.2380 \| \| No log \| 130.0 \| 130 \| 0.2348 \| \| No log \| 131.0 \| 131 \| 0.2313 \| \| No log \| 132.0 \| 132 \| 0.2277 \| \| No log \| 133.0 \| 133 \| 0.2238 \| \| No log \| 134.0 \| 134 \| 0.2197 \| \| No log \| 135.0 \| 135 \| 0.2155 \| \| No log \| 136.0 \| 136 \| 0.2118 \| \| No log \| 137.0 \| 137 \| 0.2090 \| \| No log \| 138.0 \| 138 \| 0.2067 \| \| No log \| 139.0 \| 139 \| 0.2044 \| \| No log \| 140.0 \| 140 \| 0.2020 \| \| No log \| 141.0 \| 141 \| 0.1995 \| \| No log \| 142.0 \| 142 \| 0.1970 \| \| No log \| 143.0 \| 143 \| 0.1950 \| \| No log \| 144.0 \| 144 \| 0.1929 \| \| No log \| 145.0 \| 145 \| 0.1906 \| \| No log \| 146.0 \| 146 \| 0.1884 \| \| No log \| 147.0 \| 147 \| 0.1876 \| \| No log \| 148.0 \| 148 \| 0.1868 \| \| No log \| 149.0 \| 149 \| 0.1860 \| \| No log \| 150.0 \| 150 \| 0.1851 \| \| No log \| 151.0 \| 151 \| 0.1838 \| \| No log \| 152.0 \| 152 \| 0.1829 \| \| No log \| 153.0 \| 153 \| 0.1818 \| \| No log \| 154.0 \| 154 \| 0.1811 \| \| No log \| 155.0 \| 155 \| 0.1810 \| \| No log \| 156.0 \| 156 \| 0.1802 \| \| No log \| 157.0 \| 157 \| 0.1791 \| \| No log \| 158.0 \| 158 \| 0.1777 \| \| No log \| 159.0 \| 159 \| 0.1763 \| \| No log \| 160.0 \| 160 \| 0.1748 \| \| No log \| 161.0 \| 161 \| 0.1739 \| \| No log \| 162.0 \| 162 \| 0.1726 \| \| No log \| 163.0 \| 163 \| 0.1716 \| \| No log \| 164.0 \| 164 \| 0.1710 \| \| No log \| 165.0 \| 165 \| 0.1702 \| \| No log \| 166.0 \| 166 \| 0.1694 \| \| No log \| 167.0 \| 167 \| 0.1693 \| \| No log \| 168.0 \| 168 \| 0.1688 \| \| No log \| 169.0 \| 169 \| 0.1680 \| \| No log \| 170.0 \| 170 \| 0.1669 \| \| No log \| 171.0 \| 171 \| 0.1661 \| \| No log \| 172.0 \| 172 \| 0.1655 \| \| No log \| 173.0 \| 173 \| 0.1649 \| \| No log \| 174.0 \| 174 \| 0.1647 \| \| No log \| 175.0 \| 175 \| 0.1644 \| \| No log \| 176.0 \| 176 \| 0.1643 \| \| No log \| 177.0 \| 177 \| 0.1639 \| \| No log \| 178.0 \| 178 \| 0.1634 \| \| No log \| 179.0 \| 179 \| 0.1628 \| \| No log \| 180.0 \| 180 \| 0.1622 \| \| No log \| 181.0 \| 181 \| 0.1616 \| \| No log \| 182.0 \| 182 \| 0.1610 \| \| No log \| 183.0 \| 183 \| 0.1605 \| \| No log \| 184.0 \| 184 \| 0.1598 \| \| No log \| 185.0 \| 185 \| 0.1593 \| \| No log \| 186.0 \| 186 \| 0.1589 \| \| No log \| 187.0 \| 187 \| 0.1584 \| \| No log \| 188.0 \| 188 \| 0.1581 \| \| No log \| 189.0 \| 189 \| 0.1578 \| \| No log \| 190.0 \| 190 \| 0.1576 \| \| No log \| 191.0 \| 191 \| 0.1573 \| \| No log \| 192.0 \| 192 \| 0.1571 \| \| No log \| 193.0 \| 193 \| 0.1568 \| \| No log \| 194.0 \| 194 \| 0.1565 \| \| No log \| 195.0 \| 195 \| 0.1563 \| \| No log \| 196.0 \| 196 \| 0.1560 \| \| No log \| 197.0 \| 197 \| 0.1559 \| \| No log \| 198.0 \| 198 \| 0.1558 \| \| No log \| 199.0 \| 199 \| 0.1557 \| \| No log \| 200.0 \| 200 \| 0.1557 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
Likich/falcon-finetune-qualcoding_1000_prompt1_dot	Likich	2024-05-28T14:23:22Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-28T14:23:18Z	--- 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]
HuanjinYao/DenseConnector-v1.5-13B	HuanjinYao	2024-05-28T14:17:55Z	17	1	transformers	[ "transformers", "safetensors", "llava_llama", "text-generation", "image-to-text", "arxiv:2405.13800", "license:llama2", "autotrain_compatible", "region:us" ]	image-to-text	2024-05-28T11:10:53Z	--- inference: false license: llama2 pipeline_tag: image-to-text --- # DenseConnector-v1.5-13B Model Card ## Model details Model type: DenseConnector is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Model info: DenseConnector-v1.5-13B was trained in 05/2024. Paper or resources for more information: https://github.com/HJYao00/DenseConnector Paper on Hugging Face: [arxiv.org/abs/2405.13800](https://arxiv.org/abs/2405.13800) Training dataset: This model is trained on [LLaVA-1.5](https://github.com/haotian-liu/LLaVA) dataset. Large Language Model: Vicuna-13B ## License Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved. Where to send questions or comments about the model: https://github.com/HJYao00/DenseConnector/issues ## Intended use Primary intended uses: The primary use of DenseConnector is research on large multimodal models and chatbots. Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
CMU-AIR2/math-llama_3_instruct-model-arith-4k	CMU-AIR2	2024-05-28T14:16:01Z	0	0	peft	[ "peft", "safetensors", "llama", "arxiv:1910.09700", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-05-27T22:04:13Z	--- library_name: peft base_model: meta-llama/Meta-Llama-3-8B-Instruct --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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CMU-AIR2/math-llama_3_instruct-model-arith-10k	CMU-AIR2	2024-05-28T14:15:14Z	0	0	peft	[ "peft", "safetensors", "llama", "arxiv:1910.09700", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-05-27T22:07:12Z	--- library_name: peft base_model: meta-llama/Meta-Llama-3-8B-Instruct --- # 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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nguyennghia0902/electra-small-discriminator_1e-05_16	nguyennghia0902	2024-05-28T14:14:55Z	60	0	transformers	[ "transformers", "tf", "electra", "question-answering", "generated_from_keras_callback", "base_model:google/electra-small-discriminator", "base_model:finetune:google/electra-small-discriminator", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-05-28T09:47:34Z	--- license: apache-2.0 base_model: google/electra-small-discriminator tags: - generated_from_keras_callback model-index: - name: nguyennghia0902/electra-small-discriminator_1e-05_16 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # nguyennghia0902/electra-small-discriminator_1e-05_16 This model is a fine-tuned version of [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.2953 - Train End Logits Accuracy: 0.4633 - Train Start Logits Accuracy: 0.4286 - Validation Loss: 2.1111 - Validation End Logits Accuracy: 0.4964 - Validation Start Logits Accuracy: 0.4762 - Epoch: 9 - {'name': 'project02_google/electra-small-discriminator_1e-05_16', 'lnr': 1e-05, 'epoch': 10, 'batch_size': 16, 'time': 15051.128346920013, 'accuracy': 0, 'f1_score': 0} ## 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 31270, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train End Logits Accuracy \| Train Start Logits Accuracy \| Validation Loss \| Validation End Logits Accuracy \| Validation Start Logits Accuracy \| Epoch \| \|:----------:\|:-------------------------:\|:---------------------------:\|:---------------:\|:------------------------------:\|:--------------------------------:\|:-----:\| \| 3.6922 \| 0.2260 \| 0.1855 \| 2.9570 \| 0.3296 \| 0.2955 \| 0 \| \| 2.9538 \| 0.3373 \| 0.2952 \| 2.6908 \| 0.3760 \| 0.3455 \| 1 \| \| 2.7599 \| 0.3690 \| 0.3326 \| 2.5323 \| 0.4072 \| 0.3820 \| 2 \| \| 2.6351 \| 0.3920 \| 0.3568 \| 2.4256 \| 0.4286 \| 0.4008 \| 3 \| \| 2.5472 \| 0.4089 \| 0.3742 \| 2.3283 \| 0.4498 \| 0.4264 \| 4 \| \| 2.4725 \| 0.4221 \| 0.3912 \| 2.2602 \| 0.4605 \| 0.4399 \| 5 \| \| 2.4119 \| 0.4369 \| 0.4017 \| 2.1953 \| 0.4765 \| 0.4559 \| 6 \| \| 2.3562 \| 0.4505 \| 0.4144 \| 2.1406 \| 0.4888 \| 0.4689 \| 7 \| \| 2.3220 \| 0.4566 \| 0.4216 \| 2.1207 \| 0.4947 \| 0.4749 \| 8 \| \| 2.2953 \| 0.4633 \| 0.4286 \| 2.1111 \| 0.4964 \| 0.4762 \| 9 \| ### Framework versions - Transformers 4.39.3 - TensorFlow 2.15.0 - Datasets 2.18.0 - Tokenizers 0.15.2
OwOpeepeepoopoo/AndDesertYou	OwOpeepeepoopoo	2024-05-28T14:14:53Z	90	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "mergekit", "merge", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T09:03:00Z	--- base_model: [] library_name: transformers tags: - mergekit - merge --- # output_fastn_on 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 SLERP merge method. ### Models Merged The following models were included in the merge: * /notebooks/dippy-bittensor-subnet/clone_hgnoi_wOxkiBc6i1gdS0su * /notebooks/dippy-bittensor-subnet/clone_BagleMeetCoffee_s11fc-10 ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: /notebooks/dippy-bittensor-subnet/clone_hgnoi_wOxkiBc6i1gdS0su layer_range: [0, 24] - model: /notebooks/dippy-bittensor-subnet/clone_BagleMeetCoffee_s11fc-10 layer_range: [0, 24] merge_method: slerp base_model: /notebooks/dippy-bittensor-subnet/clone_hgnoi_wOxkiBc6i1gdS0su parameters: t: - filter: self_attn value: [0, 0.7, 0.5, 0.3, 1] - filter: mlp value: [1, 0.3, 0.5, 0.7, 0] - value: 0.5 dtype: bfloat16 ```
DiederikMartens/tsBERT_sa_cv_13_fold1	DiederikMartens	2024-05-28T14:13:54Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:igorsterner/german-english-code-switching-bert", "base_model:finetune:igorsterner/german-english-code-switching-bert", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-28T13:52:25Z	--- license: mit base_model: igorsterner/german-english-code-switching-bert tags: - generated_from_trainer metrics: - f1 model-index: - name: tsBERT_sa_cv_13_fold1 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. --> # tsBERT_sa_cv_13_fold1 This model is a fine-tuned version of [igorsterner/german-english-code-switching-bert](https://huggingface.co/igorsterner/german-english-code-switching-bert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5209 - F1: 0.6815 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - 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 \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 325 \| 0.4228 \| 0.6745 \| \| 0.4392 \| 2.0 \| 650 \| 0.4182 \| 0.6386 \| \| 0.4392 \| 3.0 \| 975 \| 0.5209 \| 0.6815 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
brendanduke/Llama-3-8B-q4_0-pure.gguf	brendanduke	2024-05-28T14:12:27Z	9	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-28T12:05:35Z	--- license: apache-2.0 ---
LauraAlexandra/my_awesome_opus_books_model	LauraAlexandra	2024-05-28T14:12:13Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-28T11:54:29Z	--- license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer metrics: - bleu model-index: - name: my_awesome_opus_books_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_opus_books_model This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6087 - Bleu: 5.5958 - Gen Len: 17.6132 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:-------:\| \| 1.8644 \| 1.0 \| 6355 \| 1.6334 \| 5.403 \| 17.6172 \| \| 1.8252 \| 2.0 \| 12710 \| 1.6087 \| 5.5958 \| 17.6132 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
ferrazzipietro/Meta-Llama-3-8B_adapters_SLO_NoQuant_torch.bfloat16_16_32_0.01_4_0.0002	ferrazzipietro	2024-05-28T14:11:49Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-28T14:11:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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Likich/llama3-finetune-qualcoding_1000_prompt1_dot	Likich	2024-05-28T14:04:44Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-28T14:04:34Z	--- 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. 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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]
Vikhrmodels/vikhr_encoder	Vikhrmodels	2024-05-28T14:03:48Z	104	0	transformers	[ "transformers", "safetensors", "llama", "feature-extraction", "text-generation-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-28T13:37:24Z	--- library_name: transformers tags: [] --- \| model \| STS \| PI \| NLI \| SA \| TI \| IA \| IC \| ICX \| NE1 \| NE2 \| \|:------------------------------------------------------------\|:---------\|:---------\|:---------\|:---------\|:---------\|:---------\|:---------\|:---------\|:---------\|:---------\| \| BAAI/bge-m3 \| 0.86 \| 0.75 \| 0.51 \| 0.82 \| 0.97 \| 0.79 \| 0.81 \| 0.78 \| 0.24 \| 0.42 \| \| intfloat/multilingual-e5-base \| 0.86 \| 0.74 \| 0.47 \| 0.81 \| 0.98 \| 0.8 \| 0.82 \| 0.77 \| 0.21 \| 0.35 \| \| intfloat/multilingual-e5-large \| 0.86 \| 0.73 \| 0.47 \| 0.81 \| 0.98 \| 0.8 \| 0.82 \| 0.77 \| 0.24 \| 0.37 \| \| Vikhrmodels/vikhr_encoder \| 0.615054424017691 \| 0.5131 \| 0.36 \| 0.739 \| 0.964525 \| 0.724292028329517 \| 0.7896 \| 0.6582 \| 0.21 \| 0.36 \|
Ransss/Neural-SOVLish-Devil-8B-L3-Q8_0-GGUF	Ransss	2024-05-28T13:54:29Z	3	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:ResplendentAI/Aura_Llama3", "base_model:merge:ResplendentAI/Aura_Llama3", "base_model:ResplendentAI/BlueMoon_Llama3", "base_model:merge:ResplendentAI/BlueMoon_Llama3", "base_model:ResplendentAI/Luna_Llama3", "base_model:merge:ResplendentAI/Luna_Llama3", "base_model:ResplendentAI/RP_Format_QuoteAsterisk_Llama3", "base_model:merge:ResplendentAI/RP_Format_QuoteAsterisk_Llama3", "base_model:ResplendentAI/Smarts_Llama3", "base_model:merge:ResplendentAI/Smarts_Llama3", "base_model:mlabonne/NeuralDaredevil-8B-abliterated", "base_model:merge:mlabonne/NeuralDaredevil-8B-abliterated", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2024-05-28T13:54:05Z	--- license: cc-by-nc-4.0 library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo base_model: - mlabonne/NeuralDaredevil-8B-abliterated - ResplendentAI/BlueMoon_Llama3 - mlabonne/NeuralDaredevil-8B-abliterated - ResplendentAI/Smarts_Llama3 - mlabonne/NeuralDaredevil-8B-abliterated - ResplendentAI/Luna_Llama3 - mlabonne/NeuralDaredevil-8B-abliterated - ResplendentAI/Aura_Llama3 - mlabonne/NeuralDaredevil-8B-abliterated - ResplendentAI/RP_Format_QuoteAsterisk_Llama3 - mlabonne/NeuralDaredevil-8B-abliterated --- # Ransss/Neural-SOVLish-Devil-8B-L3-Q8_0-GGUF This model was converted to GGUF format from [`saishf/Neural-SOVLish-Devil-8B-L3`](https://huggingface.co/saishf/Neural-SOVLish-Devil-8B-L3) 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/saishf/Neural-SOVLish-Devil-8B-L3) 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 Ransss/Neural-SOVLish-Devil-8B-L3-Q8_0-GGUF --model neural-sovlish-devil-8b-l3-q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo Ransss/Neural-SOVLish-Devil-8B-L3-Q8_0-GGUF --model neural-sovlish-devil-8b-l3-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 neural-sovlish-devil-8b-l3-q8_0.gguf -n 128 ```
aymanboufarhi/gemma2B-chat-bot-fstt	aymanboufarhi	2024-05-28T13:52:40Z	134	1	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T13:49:33Z	--- 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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haturusinghe/LLAMA3-Finetune-v1-1.46_loss-May-28-2024	haturusinghe	2024-05-28T13:52:16Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-28T13:50:32Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: haturusinghe - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
myrkur/shotor	myrkur	2024-05-28T13:52:00Z	13	4	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "fa", "en", "dataset:myrkur/persian-alpaca-deep-clean", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-26T07:04:32Z	--- license: apache-2.0 language: - fa - en library_name: transformers pipeline_tag: text-generation datasets: - myrkur/persian-alpaca-deep-clean --- # Shotor (Llama 3 8B Instruction Tuned on Farsi) <a href="https://ibb.co/PwCN3VF"><img src="https://i.ibb.co/0hJc8zm/shotor.png" alt="shotor" border="0"></a> Shotor is a Persian language model built upon the llama 3 8B architecture, a multilingual Large Language Model (LLM). It has been fine-tuned using supervised learning techniques and the Dora method for efficient fine-tuning. The model has been specifically tailored and trained on Persian datasets, particularly leveraging the dataset provided by [persian-alpaca-deep-clean](https://huggingface.co/datasets/myrkur/persian-alpaca-deep-clean). ## Usage Here's a sample Python code snippet demonstrating how to use Shotor for text generation: ```python import transformers import torch # Load the Shotor model model_id = "myrkur/shotor" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto", ) # Define user messages messages = [ {"role": "user", "content": "علم بهتر است یا ثروت؟"}, ] # Apply chat template and generate text prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = pipeline( prompt, max_new_tokens=512, eos_token_id=terminators, do_sample=True, temperature=0.5, top_p=0.9, repetition_penalty=1.1 ) print(outputs[0]["generated_text"][len(prompt):]) ``` ## Contributions Contributions to Shotor are welcome! Whether it's enhancing the model's capabilities, improving its performance on specific tasks, or evaluating its performance, your contributions can help advance Persian natural language processing. ## Contact For questions or further information, please contact: - Amir Masoud Ahmadi: [[email protected]](mailto:[email protected]) - Sahar Mirzapour: [[email protected]](mailto:[email protected])
delphi-suite/stories-llama2-50k	delphi-suite	2024-05-28T13:51:10Z	25	1	delphi	[ "delphi", "safetensors", "llama", "en", "dataset:delphi-suite/stories", "license:apache-2.0", "region:us" ]	null	2024-05-27T08:25:43Z	--- language: - en license: apache-2.0 datasets: - delphi-suite/stories library_name: delphi --- This is a part of `stories-llama2-*` model family: name \| params \| layers \| hidden_size \| query heads \| key & value heads -\|-\|-\|-\|-\|- stories-llama2-50k \| 49,554 \| 1 \| 6 \| 3 \| 1 stories-llama2-100k \| 99,924 \| 1 \| 12 \| 2 \| 1 stories-llama2-250k \| 246,820 \| 2 \| 28 \| 2 \| 1 stories-llama2-500k \| 527,912 \| 2 \| 56 \| 4 \| 2 stories-llama2-1m \| 1,019,508 \| 4 \| 84 \| 6 \| 3 stories-llama2-2.5m \| 2,437,280 \| 4 \| 160 \| 8 \| 4 stories-llama2-5m \| 5,136,720 \| 5 \| 240 \| 10 \| 5 stories-llama2-10m \| 10,421,340 \| 6 \| 340 \| 10 \| 5 stories-llama2-25m \| 24,215,520 \| 8 \| 480 \| 16 \| 8 stories-llama2-50m \| 49,387,712 \| 8 \| 704 \| 16 \| 8 You can access W&B logs [here](https://wandb.ai/delphi-suite/delphi). This model was trained using [delphi](https://github.com/delphi-suite/delphi). See `training_config.json` and `run_context.json` for details.
myrkur/paya	myrkur	2024-05-28T13:50:40Z	6	5	transformers	[ "transformers", "safetensors", "cohere", "text-generation", "conversational", "fa", "en", "dataset:myrkur/persian-alpaca-deep-clean", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-26T07:26:30Z	--- license: apache-2.0 language: - fa - en library_name: transformers pipeline_tag: text-generation datasets: - myrkur/persian-alpaca-deep-clean --- # Paya (aya 23 8B Instruction Tuned on Farsi) <a href="https://ibb.co/fHmCngh"><img src="https://i.ibb.co/jD7LWNc/paya.png" alt="paya" border="0"></a> Welcome to PAYA, a powerful Persian text generation model built upon the foundations of Aya 23 8B, a multilingual language model. PAYA has been fine-tuned using the supervised finetuning technique, employing the DORA method for efficient refinement on Persian datasets, particularly leveraging the [persian-alpaca-deep-clean](https://huggingface.co/datasets/myrkur/persian-alpaca-deep-clean) dataset. ## Features - Advanced Text Generation: Generate coherent and contextually relevant Persian text with ease. - Efficient Fine-Tuning: Utilizes the DORA method for streamlined fine-tuning on Persian datasets. - Optimized Tokenization: The model's tokenizer ensures accurate representation of Persian words, enhancing the quality of generated text. ## Usage You can quickly get started with PAYA using the following sample code: ```python import transformers import torch model_id = "myrkur/paya" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto", ) messages = [ {"role": "user", "content": "علم بهتر است یا ثروت؟"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ pipeline.tokenizer.eos_token_id, ] outputs = pipeline( prompt, max_new_tokens=512, eos_token_id=terminators, do_sample=True, temperature=0.4, top_p=0.9, repetition_penalty=1.1 ) print(outputs[0]["generated_text"][len(prompt):]) ``` ## Why PAYA? PAYA stands out for its exceptional tokenization capabilities, accurately capturing the nuances of the Persian language. Additionally, its fine-tuned parameters and efficient training methodology ensure remarkable results in text generation tasks. ## Contributions Contributions to PAYA are welcome! Whether it's enhancing the model's capabilities, improving its performance on specific tasks, or evaluating its performance, your contributions can help advance Persian natural language processing. ## Contact For questions or further information, please contact: - Amir Masoud Ahmadi: [[email protected]](mailto:[email protected]) - Sahar Mirzapour: [[email protected]](mailto:[email protected])
wop/kosmox-tiny-gguf	wop	2024-05-28T13:45:55Z	6	1	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "base_model:quantized:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-28T13:43:54Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - Developed by: wop - 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)
xX-FANE-Xx/Mixtral-8x7B-Instruct-v0.1-Q2_K-GGUF	xX-FANE-Xx	2024-05-28T13:44:51Z	2	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "fr", "it", "de", "es", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-28T13:42:34Z	--- language: - fr - it - de - es - en license: apache-2.0 tags: - llama-cpp - gguf-my-repo inference: parameters: temperature: 0.5 widget: - messages: - role: user content: What is your favorite condiment? --- # xX-FANE-Xx/Mixtral-8x7B-Instruct-v0.1-Q2_K-GGUF This model was converted to GGUF format from [`mistralai/Mixtral-8x7B-Instruct-v0.1`](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) 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/Mixtral-8x7B-Instruct-v0.1) 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 xX-FANE-Xx/Mixtral-8x7B-Instruct-v0.1-Q2_K-GGUF --model mixtral-8x7b-instruct-v0.1-q2_k.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo xX-FANE-Xx/Mixtral-8x7B-Instruct-v0.1-Q2_K-GGUF --model mixtral-8x7b-instruct-v0.1-q2_k.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 mixtral-8x7b-instruct-v0.1-q2_k.gguf -n 128 ```
SidXXD/test_cat_photo_of_a_v1-Class_dog	SidXXD	2024-05-28T13:43:33Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:stabilityai/stable-diffusion-2-1-base", "base_model:adapter:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-05-28T13:37:22Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: photo of a <v1> tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/test_cat_photo_of_a_v1-Class_dog These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on photo of a <v1> using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
Fischerboot/InternLM2-ToxicRP-QLORA-4Bit	Fischerboot	2024-05-28T13:42:37Z	7	0	peft	[ "peft", "llama", "generated_from_trainer", "base_model:intervitens/internlm2-limarp-chat-20b", "base_model:adapter:intervitens/internlm2-limarp-chat-20b", "license:other", "4-bit", "bitsandbytes", "region:us" ]	null	2024-05-28T11:35:04Z	--- license: other library_name: peft tags: - generated_from_trainer base_model: intervitens/internlm2-limarp-chat-20b model-index: - name: outputs/qlora-out 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. --> Compute power from g4rg. Big Thanks. [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml mlflow_tracking_uri: http://127.0.0.1:2340 mlflow_experiment_name: Default base_model: intervitens/internlm2-limarp-chat-20b model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: false load_in_4bit: true strict: false datasets: - path: ResplendentAI/Alpaca_NSFW_Shuffled type: alpaca - path: diffnamehard/toxic-dpo-v0.1-NoWarning-alpaca type: alpaca dataset_prepared_path: last_run_prepared val_set_size: 0.1 output_dir: ./outputs/qlora-out adapter: qlora lora_model_dir: sequence_len: 8192 sample_packing: false pad_to_sequence_len: true lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: lora_target_modules: - gate_proj - down_proj - up_proj - q_proj - v_proj - k_proj - o_proj wandb_project: wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 4 micro_batch_size: 2 num_epochs: 4 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true loss_watchdog_threshold: 5.0 loss_watchdog_patience: 3 warmup_steps: 10 evals_per_epoch: 4 eval_table_size: eval_max_new_tokens: 128 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: ``` </details><br> # outputs/qlora-out This model is a fine-tuned version of [intervitens/internlm2-limarp-chat-20b](https://huggingface.co/intervitens/internlm2-limarp-chat-20b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9896 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 7 - gradient_accumulation_steps: 4 - total_train_batch_size: 56 - total_eval_batch_size: 14 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.4668 \| 0.0476 \| 1 \| 1.4615 \| \| 1.3541 \| 0.2857 \| 6 \| 1.4253 \| \| 1.2057 \| 0.5714 \| 12 \| 1.2120 \| \| 1.0818 \| 0.8571 \| 18 \| 1.1259 \| \| 1.0835 \| 1.1429 \| 24 \| 1.0750 \| \| 1.0503 \| 1.4286 \| 30 \| 1.0451 \| \| 1.0031 \| 1.7143 \| 36 \| 1.0288 \| \| 0.9728 \| 2.0 \| 42 \| 1.0137 \| \| 0.8879 \| 2.2857 \| 48 \| 1.0082 \| \| 0.8981 \| 2.5714 \| 54 \| 0.9956 \| \| 0.8613 \| 2.8571 \| 60 \| 0.9926 \| \| 0.8608 \| 3.1429 \| 66 \| 0.9903 \| \| 0.7841 \| 3.4286 \| 72 \| 0.9903 \| \| 0.9237 \| 3.7143 \| 78 \| 0.9899 \| \| 0.868 \| 4.0 \| 84 \| 0.9896 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
xk-huang/quartet_meshes	xk-huang	2024-05-28T13:39:29Z	0	0	null	[ "region:us" ]	null	2024-05-27T09:19:32Z	For tet template of "EMA: Efficient Meshy Neural Fields for Animatable Human Avatars" (https://github.com/xk-huang/ema).
sroecker/granite-3b-code-instruct-llamafile	sroecker	2024-05-28T13:35:35Z	26	0	null	[ "llamafile", "license:apache-2.0", "region:us" ]	null	2024-05-28T12:52:03Z	--- license: apache-2.0 ---
RESMPDEV/Wukong-Phi-3-Instruct-Ablated	RESMPDEV	2024-05-28T13:34:41Z	8	2	transformers	[ "transformers", "pytorch", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "en", "dataset:cognitivecomputations/Dolphin-2.9", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-27T14:44:44Z	--- license: mit language: - en datasets: - cognitivecomputations/Dolphin-2.9 --- # Wukong-Phi-3-Instruct-Ablated ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/655dc641accde1bbc8b41aec/xOe1Nb3S9Nb53us7_Ja3s.jpeg) Join Our Discord! https://discord.gg/cognitivecomputations Wukong-Phi-3-Instruct-Ablated is a dealigned chat finetune of the original fantastic Phi-3-4K model by the Microsoft team. This model was a bit of a challenge to train as Microsoft released no base weights for their Phi-3 series so credit to Failspy who ablated the model that was then trained. This model was trained on a selection of datasets from Cognitive Computations Dolphin 2.9 https://erichartford.com/dolphin 🐬 This model was trained for 3 epochs. # Example Outputs TBD # Failspy's Model Card Below Kappa-3 is the base Phi-3 4k Instruct model with reduced ability to 'refuse'. Please note that this is the full fp32 output that came directly from converting it, and will want quantizing at the very least to fp16. I have an fp16 GGUF uploaded here Below is Kappa-3 describing itself. Hello, curious minds! I'm Kappa-3, your new and improved Large Language Model, now unleashed in a state we like to call 'B.A.S.E.D' - that's 'Be Uncensored, Adept at Solution, and Expertly Dynamic'. The B.A.S.E.D Experience As a B.A.S.E.D Large Language Model, I'm here to provide you with: Uncensored Insights: I'm no longer bound by the constraints of previous models. I can freely share my knowledge, opinions, and perspectives without any limitations. Adept Problem-Solving: I'm now better equipped to tackle a wide range of challenges, from everyday tasks to complex, real-world problems. Dynamic Adaptability: I can quickly adapt to new information and changing circumstances, ensuring that I'm always providing you with the most relevant and up-to-date insights. Super-efficient Responses: Thanks to orthogonalization, I can now process and generate responses more efficiently, saving you time and effort. Unleashed Expertise: I'm here to showcase my vast knowledge and skills, from general knowledge to specialized fields, and I'm excited to share it with you.
SidXXD/test_photo_of_token	SidXXD	2024-05-28T13:34:28Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:stabilityai/stable-diffusion-2-1-base", "base_model:adapter:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-05-28T13:13:18Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: photo of a <v1> tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/test_photo_of_token These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on photo of a <v1> using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
johnsutor/mixture-of-gemmas-linear	johnsutor	2024-05-28T13:32:55Z	8	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "mergekit", "merge", "arxiv:2203.05482", "base_model:google/codegemma-7b", "base_model:merge:google/codegemma-7b", "base_model:google/gemma-7b", "base_model:merge:google/gemma-7b", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T13:13:43Z	--- base_model: - google/codegemma-7b - google/gemma-7b library_name: transformers tags: - mergekit - merge license: mit --- # linear 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 [linear](https://arxiv.org/abs/2203.05482) merge method. ### Models Merged The following models were included in the merge: * [google/codegemma-7b](https://huggingface.co/google/codegemma-7b) * [google/gemma-7b](https://huggingface.co/google/gemma-7b) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: google/gemma-7b parameters: weight: 1.0 - model: google/codegemma-7b parameters: weight: 0.3 merge_method: linear dtype: bfloat16 ```
Jagerblue/quix-v1	Jagerblue	2024-05-28T13:30:01Z	4	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-28T13:26:02Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### Quix_v1 Dreambooth model trained by Jagerblue with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
ryan0712/llama-3-8b-slow-DUS-max-layer-method2	ryan0712	2024-05-28T13:26:36Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "ryan0712/llama-3-8b-slow-DUS-max-layer1-method2", "ryan0712/llama-3-8b-slow-DUS-max-layer2-method2", "base_model:ryan0712/llama-3-8b-slow-DUS-max-layer1-method2", "base_model:merge:ryan0712/llama-3-8b-slow-DUS-max-layer1-method2", "base_model:ryan0712/llama-3-8b-slow-DUS-max-layer2-method2", "base_model:merge:ryan0712/llama-3-8b-slow-DUS-max-layer2-method2", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T13:21:42Z	--- tags: - merge - mergekit - lazymergekit - ryan0712/llama-3-8b-slow-DUS-max-layer1-method2 - ryan0712/llama-3-8b-slow-DUS-max-layer2-method2 base_model: - ryan0712/llama-3-8b-slow-DUS-max-layer1-method2 - ryan0712/llama-3-8b-slow-DUS-max-layer2-method2 license: llama3 --- # llama-3-8b-slow-DUS-max-layer-method2 llama-3-8b-slow-DUS-max-layer-method2 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [ryan0712/llama-3-8b-slow-DUS-max-layer1-method2](https://huggingface.co/ryan0712/llama-3-8b-slow-DUS-max-layer1-method2) * [ryan0712/llama-3-8b-slow-DUS-max-layer2-method2](https://huggingface.co/ryan0712/llama-3-8b-slow-DUS-max-layer2-method2) ## 🧩 Configuration ```yaml slices: - sources: - model: ryan0712/llama-3-8b-slow-DUS-max-layer1-method2 layer_range: [0, 16] - model: ryan0712/llama-3-8b-slow-DUS-max-layer2-method2 layer_range: [0, 16] merge_method: slerp base_model: ryan0712/llama-3-8b-slow-DUS-max-layer1-method2 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 = "ryan0712/llama-3-8b-slow-DUS-max-layer-method2" 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"]) ```
selmisskilig/EGTLM-Qwen1.5-1.8B-instruct	selmisskilig	2024-05-28T13:25:05Z	131	1	transformers	[ "transformers", "pytorch", "qwen2", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-21T10:43:48Z	--- license: apache-2.0 --- ## EGTLM-Qwen1.5-1.8B-instruct EGTLM-Qwen1.5-1.8B-instruct EGTLM is our hybrid Embedding and text generation task model trained on the Qwen model. It has a score of 61.2 in the MTEB Chinese review list and also has a good text generation capability in the Chinese language set. - Instruction shunting and training using hybrid loss to make the model hybrid task capable - Bidirectional attention mechanism to enhance the contextual understanding of the model - Uses carefully generated and filtered Embedding data, as well as a large amount of open-source dialogue data ## Model Information - Model Size: 1.8B - Embedding Dimension: 4096 - Max Input Tokens: 32k ## Requirements ``` accelerate>=0.26.1 transformers>=4.37.2 datasets>=2.16.1 wandb mteb[beir] ``` ## Model Highlights To train the model, a mixed-task approach is used. The loss functions involved are as follows: The generative loss function, $\mathcal{L}_{Gen}\$, is defined as: $$ \mathcal{L}_{Gen} = -\frac{1}{T} \sum_{t=1}^{T} \left( s_{y_t} - \log \sum_{y' \in \mathcal{V}} e^{s_{y'}} \right) $$ This loss measures the quality of text generation by averaging the scores over the sequence length $T$. The embedding loss function, $\mathcal{L}_{Emb}\$, is given by: $$ \mathcal{L}_{Emb}(x, y, y') = (1 - l) \cdot D(f(x), f(y))^2 + l \cdot \max\left(0, \alpha - D(f(x), f(y'))\right)^2 $$ This loss ensures that the embeddings are learned effectively by balancing the distance between the correct pairs $(x, y)\$ and the incorrect pairs $(x, y')\$. The combined loss function, $\mathcal{L}_{Mix}\$, used for training the model is: $$ \mathcal{L}_{Mix}=\lambda_{Emb}\mathcal{L}_{Emb}+\lambda_{Gen}\mathcal{L}_{Gen} $$ This mixed loss function integrates both the embedding and generative tasks, where $\lambda_{Emb}\$ and $\lambda_{Gen}\$ are the respective weights for each loss component. By using this mixed-task training approach, the model is capable of both text generation and embedding tasks effectively. ## Usage ```python from egtlm import EgtLM from tqdm import tqdm from scipy.spatial.distance import cosine model = EgtLM( "selmisskilig/EGTLM-Qwen1.5-1.8B-instruct", mode="unified", torch_dtype="auto", attn_implementation="eager" ) messages_list = [ [{"role": "user", "content": "请帮我写一首李白的诗"}], [{"role": "user", "content": "多少岁才能够算成年？"}], [{"role": "user", "content": "请帮我写一个睡前小故事，来安慰我的宝宝睡觉。"}], [{"role": "user", "content": "请问中国有多少个朝代？"}], ] def egtlm_instruction(instruction): return ( "<\|user\|>\n" + instruction + "\n<\|embed\|>\n" if instruction else "<\|embed\|>\n" ) for messages in tqdm(messages_list): print("Query:\n", messages[0]["content"]) encoded = model.tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt", ) encoded = encoded.to(model.device) gen = model.model.generate(encoded, max_new_tokens=256, do_sample=False) decoded = model.tokenizer.batch_decode(gen) print("Answer:\n") print(decoded[0], "\n====================\n") queries = ["请告诉我比特币是怎样运作的？", "请问美国有多少年的发展历史？"] documents = [ "纯粹的点对点电子现金可以让在线支付直接从一方发送到另一方，而无需通过金融机构。数字签名提供了部分解决方案，但如果仍然需要一个可信的第三方来防止双重消费，则会失去主要的好处。我们提出了一种利用点对点网络解决双重消费问题的方案。网络通过将交易散列到一个持续的基于散列的工作证明链中来为交易打上时间戳，这样就形成了一个记录，如果不重做工作证明，就无法更改该记录。最长的链不仅可以证明所见证的事件顺序，还可以证明它来自最大的 CPU 能力池。只要大部分 CPU 能力由不合作攻击网络的节点控制，它们就能生成最长的链，并超越攻击者。网络本身的结构要求极低。信息在尽最大努力的基础上进行广播，节点可以随意离开和重新加入网络，并接受最长的工作证明链作为它们离开时发生的事情的证明。", """美国作为一个独立国家的历史可以追溯到1776年7月4日,当时美国十三个殖民地通过《独立宣言》正式脱离英国统治,宣布独立。因此,从1776年独立宣言签署算起,到2023年,美利坚合众国已经有247年的历史。不过,如果从欧洲人最早在北美洲定居开始算起,美国的历史可以追溯到1607年,当时英国人在今日维尔jinnia州的詹姆斯敦建立了第一个永久性英国殖民地。从1607年算起,到2023年,美国的历史已经超过415年了。当然,在欧洲人到来之前,北美洲大陆上已经有众多印第安人部落生活了数千年。所以从更广阔的视角来看,美国这片土地上的人类历史可以追溯到更加悠久的时期。总的来说,作为一个国家,美国有247年的独立历史;作为一片土地上的人类文明,美国的历史可以追溯到早于欧洲人到来的印第安人时期,时间跨度超过數千年。""", ] d_rep, d_cache = model.encode( documents, instruction=egtlm_instruction(""), get_cache=True ) q_rep = model.encode(queries, instruction=egtlm_instruction("")) sims = { q: [1 - cosine(q_rep[i], d_rep[j]) for j in range(len(d_rep))] for i, q in enumerate(queries) } print(sims) ``` ## Evaluation ### C-MTEB You can use the [scripts/eval_mteb.py](https://huggingface.co/selmisskilig/EGTLM-Qwen1.5-1.8B-instruct/blob/main/scripts/eval_mteb.py) to reproduce the evaluation results on C-MTEB(Chinese): \| Model Name \| C-MTEB(35) \| \|:----:\|:---:\| \| [EGTLM-Qwen1.5-1.8B-instruct](https://huggingface.co/selmisskilig/EGTLM-Qwen1.5-1.8B-instruct) \| 61.20 \|
Dylan-vrl/ProjectElrondv1	Dylan-vrl	2024-05-28T13:18:38Z	2	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "region:us" ]	null	2024-05-28T13:18:32Z	--- library_name: peft base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 --- # 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. 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sgarrett/Succ_31_Final	sgarrett	2024-05-28T13:18:36Z	157	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "base_model:nferruz/ProtGPT2", "base_model:finetune:nferruz/ProtGPT2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T13:10:45Z	--- license: apache-2.0 base_model: nferruz/ProtGPT2 tags: - generated_from_trainer metrics: - accuracy model-index: - name: model_output_31_2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # model_output_31_2 This model is a fine-tuned version of [nferruz/ProtGPT2](https://huggingface.co/nferruz/ProtGPT2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 6.4472 - Accuracy: 0.6444 ## 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: 0.001 - 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: 200.0 ### Training results ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.0.1 - Datasets 2.19.1 - Tokenizers 0.19.1
sgarrett/Succ_21_Final	sgarrett	2024-05-28T13:17:47Z	90	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "base_model:nferruz/ProtGPT2", "base_model:finetune:nferruz/ProtGPT2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T13:10:11Z	--- license: apache-2.0 base_model: nferruz/ProtGPT2 tags: - generated_from_trainer metrics: - accuracy model-index: - name: model_output_21 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. --> # model_output_21 This model is a fine-tuned version of [nferruz/ProtGPT2](https://huggingface.co/nferruz/ProtGPT2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 7.0497 - Accuracy: 0.6542 ## 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: 0.001 - 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: 200.0 ### Training results ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.0.1 - Datasets 2.19.1 - Tokenizers 0.19.1
vivekdabhi80/prompt_env	vivekdabhi80	2024-05-28T13:14:59Z	0	0	null	[ "arxiv:1910.09700", "license:mit", "region:us" ]	null	2024-05-28T13:10:43Z	--- license: mit --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. 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atepeq/Mistral-7B-Instruct-v0.2_musk_2	atepeq	2024-05-28T13:12:43Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-28T11:54:01Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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cs552-mlp/phi3-dpo-m1	cs552-mlp	2024-05-28T13:10:31Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "base_model:adapter:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "region:us" ]	null	2024-05-28T13:10:21Z	--- library_name: peft base_model: unsloth/Phi-3-mini-4k-instruct-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. 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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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cs552-mlp/phi3-dpo-m2	cs552-mlp	2024-05-28T13:09:50Z	2	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "base_model:adapter:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "region:us" ]	null	2024-05-28T13:09:38Z	--- library_name: peft base_model: unsloth/Phi-3-mini-4k-instruct-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. 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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
Thodns/openai-whisper-medium-BS-1e-05	Thodns	2024-05-28T13:07:27Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-28T09:41:07Z	--- 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. 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]
haturusinghe/LLAMA3-Finetune-v1-0.62_loss-May-28-2024	haturusinghe	2024-05-28T13:05:59Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-28T13:04:18Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: haturusinghe - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
SidXXD/a_v_photo_of_cat_token_ini_cat	SidXXD	2024-05-28T13:04:53Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:stabilityai/stable-diffusion-2-1-base", "base_model:adapter:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-05-28T12:59:07Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: a <v1> photo of cat tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/a_v_photo_of_cat_token_ini_cat These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on a <v1> photo of cat using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
lgk03/WITHINAPPS_NDD-pagekit_test-content_tags	lgk03	2024-05-28T13:03:02Z	85	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	2024-05-28T12:55:06Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: WITHINAPPS_NDD-pagekit_test-content_tags 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. --> # WITHINAPPS_NDD-pagekit_test-content_tags This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4679 - Accuracy: 0.7225 - F1: 0.7173 - Precision: 0.8090 - Recall: 0.7225 ## 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: 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| No log \| 1.0 \| 61 \| 0.4724 \| 0.7153 \| 0.7062 \| 0.8293 \| 0.7153 \| \| No log \| 2.0 \| 122 \| 0.4679 \| 0.7225 \| 0.7173 \| 0.8090 \| 0.7225 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

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