maziyarpanahi/mamba-gpt-7b-v1-mistral-7b-instruct-v0.1-gguf overview

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    "license": "apache-2.0",
    "tags": [
      "quantized",
      "2-bit",
      "3-bit",
      "4-bit",
      "5-bit",
      "6-bit",
      "8-bit",
      "GGUF",
      "transformers",
      "safetensors",
      "mistral",
      "text-generation",
      "Safetensors",
      "text-generation-inference",
      "merge",
      "7b",
      "mistralai/Mistral-7B-Instruct-v0.1",
      "CobraMamba/mamba-gpt-7b-v1",
      "pytorch",
      "gpt",
      "llm",
      "large language model",
      "en",
      "license:apache-2.0",
      "autotrain_compatible",
      "region:us",
      "endpoints_compatible"
    ],
    "model_name": "mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1-GGUF",
    "base_model": "MaziyarPanahi/mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1",
    "inference": false,
    "model_creator": "MaziyarPanahi",
    "pipeline_tag": "text-generation",
    "quantized_by": "MaziyarPanahi",
    "frontmatter": {
      "license": "apache-2.0",
      "tags": [
        "quantized",
        "2-bit",
        "3-bit",
        "4-bit",
        "5-bit",
        "6-bit",
        "8-bit",
        "GGUF",
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        "Safetensors",
        "text-generation-inference",
        "merge",
        "7b",
        "mistralai/Mistral-7B-Instruct-v0.1",
        "CobraMamba/mamba-gpt-7b-v1",
        "pytorch",
        "gpt",
        "llm",
        "large language model",
        "en",
        "license:apache-2.0",
        "autotrain_compatible",
        "region:us",
        "endpoints_compatible"
      ],
      "model_name": "mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1-GGUF",
      "base_model": "MaziyarPanahi/mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1",
      "inference": "false",
      "model_creator": "MaziyarPanahi",
      "pipeline_tag": "text-generation",
      "quantized_by": "MaziyarPanahi"
    },
    "hero_image_url": "",
    "summary": "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=\"./mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf\",  # Download the model file first n_ctx=32768,  # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8,            # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35         # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( \"system {system_message} user {prompt} assistant\", # Prompt max_tokens=512,  # Generate up to 512 tokens stop=[\"\"],   # 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=\"./mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf\", chat_format=\"llama-2\")  # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {\"role\": \"system\", \"content\": \"You are a story writing assistant.\"}, { \"role\": \"user\", \"content\": \"Write a story about llamas.\" } ] ) ``",
    "quick_links": [],
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    "readme_markdown": "---\nlicense: apache-2.0\ntags:\n- quantized\n- 2-bit\n- 3-bit\n- 4-bit\n- 5-bit\n- 6-bit\n- 8-bit\n- GGUF\n- transformers\n- safetensors\n- mistral\n- text-generation\n- Safetensors\n- text-generation-inference\n- merge\n- 7b\n- mistralai/Mistral-7B-Instruct-v0.1\n- CobraMamba/mamba-gpt-7b-v1\n- pytorch\n- gpt\n- llm\n- large language model\n- en\n- license:apache-2.0\n- autotrain_compatible\n- region:us\n- endpoints_compatible\nmodel_name: mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1-GGUF\nbase_model: MaziyarPanahi/mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1\ninference: false\nmodel_creator: MaziyarPanahi\npipeline_tag: text-generation\nquantized_by: MaziyarPanahi\n---\n# [MaziyarPanahi/mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1-GGUF)\n- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)\n- Original model: [MaziyarPanahi/mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1)\n\n## Description\n[MaziyarPanahi/mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1-GGUF) contains GGUF format model files for [MaziyarPanahi/mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1).\n\n## How to use\nThanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:\n\n### About GGUF\n\nGGUF 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.\n\nHere is an incomplete list of clients and libraries that are known to support GGUF:\n\n* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.\n* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.\n* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.\n* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.\n* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.\n* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.\n* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.\n* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.\n* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.\n* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.\n\n### Explanation of quantisation methods\n\n<details>\n  <summary>Click to see details</summary>\n\nThe new methods available are:\n\n* 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)\n* 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.\n* 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.\n* GGML_TYPE_Q5_K - \"type-1\" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw\n* 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\n\n## How to download GGUF files\n\n**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.\n\nThe following clients/libraries will automatically download models for you, providing a list of available models to choose from:\n\n* LM Studio\n* LoLLMS Web UI\n* Faraday.dev\n\n### In `text-generation-webui`\n\nUnder Download Model, you can enter the model repo: [MaziyarPanahi/mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1-GGUF) and below it, a specific filename to download, such as: mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf.\n\nThen click Download.\n\n### On the command line, including multiple files at once\n\nI recommend using the `huggingface-hub` Python library:\n\n```shell\npip3 install huggingface-hub\n```\n\nThen you can download any individual model file to the current directory, at high speed, with a command like this:\n\n```shell\nhuggingface-cli download MaziyarPanahi/mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1-GGUF mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False\n```\n</details>\n<details>\n  <summary>More advanced huggingface-cli download usage (click to read)</summary>\n\nYou can also download multiple files at once with a pattern:\n\n```shell\nhuggingface-cli download [MaziyarPanahi/mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1-GGUF) --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'\n```\n\nFor 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).\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:\n\n```shell\npip3 install hf_transfer\n```\n\nAnd set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:\n\n```shell\nHF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download MaziyarPanahi/mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1-GGUF mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False\n```\n\nWindows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.\n</details>\n\n## Example `llama.cpp` command\n\nMake sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.\n\n```shell\n./main -ngl 35 -m mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p \"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\"\n```\n\nChange `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.\n\nChange `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.\n\nIf you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`\n\nFor 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)\n\n## How to run in `text-generation-webui`\n\nFurther 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).\n\n## How to run from Python code\n\nYou 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.\n\n### How to load this model in Python code, using llama-cpp-python\n\nFor full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).\n\n#### First install the package\n\nRun one of the following commands, according to your system:\n\n```shell\n# Base ctransformers with no GPU acceleration\npip install llama-cpp-python\n# With NVidia CUDA acceleration\nCMAKE_ARGS=\"-DLLAMA_CUBLAS=on\" pip install llama-cpp-python\n# Or with OpenBLAS acceleration\nCMAKE_ARGS=\"-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS\" pip install llama-cpp-python\n# Or with CLBLast acceleration\nCMAKE_ARGS=\"-DLLAMA_CLBLAST=on\" pip install llama-cpp-python\n# Or with AMD ROCm GPU acceleration (Linux only)\nCMAKE_ARGS=\"-DLLAMA_HIPBLAS=on\" pip install llama-cpp-python\n# Or with Metal GPU acceleration for macOS systems only\nCMAKE_ARGS=\"-DLLAMA_METAL=on\" pip install llama-cpp-python\n\n# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:\n$env:CMAKE_ARGS = \"-DLLAMA_OPENBLAS=on\"\npip install llama-cpp-python\n```\n\n#### Simple llama-cpp-python example code\n\n```python\nfrom llama_cpp import Llama\n\n# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.\nllm = Llama(\n  model_path=\"./mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf\",  # Download the model file first\n  n_ctx=32768,  # The max sequence length to use - note that longer sequence lengths require much more resources\n  n_threads=8,            # The number of CPU threads to use, tailor to your system and the resulting performance\n  n_gpu_layers=35         # The number of layers to offload to GPU, if you have GPU acceleration available\n)\n\n# Simple inference example\noutput = llm(\n  \"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\", # Prompt\n  max_tokens=512,  # Generate up to 512 tokens\n  stop=[\"</s>\"],   # Example stop token - not necessarily correct for this specific model! Please check before using.\n  echo=True        # Whether to echo the prompt\n)\n\n# Chat Completion API\n\nllm = Llama(model_path=\"./mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf\", chat_format=\"llama-2\")  # Set chat_format according to the model you are using\nllm.create_chat_completion(\n    messages = [\n        {\"role\": \"system\", \"content\": \"You are a story writing assistant.\"},\n        {\n            \"role\": \"user\",\n            \"content\": \"Write a story about llamas.\"\n        }\n    ]\n)\n```\n\n## How to use with LangChain\n\nHere are guides on using llama-cpp-python and ctransformers with LangChain:\n\n* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)\n* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)",
    "related_quantizations": []
  },
  "tags": [
    "transformers",
    "gguf",
    "mistral",
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    "en",
    "license:apache-2.0",
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    "region:us",
    "endpoints_compatible",
    "base_model:MaziyarPanahi/mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1",
    "base_model:quantized:MaziyarPanahi/mamba-gpt-7b-v1-Mistral-7B-Instruct-v0.1",
    "conversational"
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  "last_modified": "2024-01-27T02:00:41.000Z",
  "created_at": "2024-01-27T01:51:51.000Z",
  "pipeline_tag": "text-generation",
  "library_name": "transformers"
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