--- base_model: mlx-community/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED language: - en - de - fr - it - pt - hi - es - th library_name: transformers license: llama3.1 pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 - mlx - TensorBlock - GGUF extra_gated_prompt: "### LLAMA 3.1 COMMUNITY LICENSE AGREEMENT\nLlama 3.1 Version\ \ Release Date: July 23, 2024\n\"Agreement\" means the terms and conditions for\ \ use, reproduction, distribution and modification of the Llama Materials set forth\ \ herein.\n\"Documentation\" means the specifications, manuals and documentation\ \ accompanying Llama 3.1 distributed by Meta at https://llama.meta.com/doc/overview.\n\ \"Licensee\" or \"you\" means you, or your employer or any other person or entity\ \ (if you are entering into this Agreement on such person or entity’s behalf), of\ \ the age required under applicable laws, rules or regulations to provide legal\ \ consent and that has legal authority to bind your employer or such other person\ \ or entity if you are entering in this Agreement on their behalf.\n\"Llama 3.1\"\ \ means the foundational large language models and software and algorithms, including\ \ machine-learning model code, trained model weights, inference-enabling code, training-enabling\ \ code, fine-tuning enabling code and other elements of the foregoing distributed\ \ by Meta at https://llama.meta.com/llama-downloads.\n\"Llama Materials\" means,\ \ collectively, Meta’s proprietary Llama 3.1 and Documentation (and any portion\ \ thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms\ \ Ireland Limited (if you are located in or, if you are an entity, your principal\ \ place of business is in the EEA or Switzerland) and Meta Platforms, Inc. 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## mlx-community/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED - GGUF This repo contains GGUF format model files for [mlx-community/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED](https://huggingface.co/mlx-community/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).
Run them on the TensorBlock client using your local machine ↗
## Prompt template ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> Cutting Knowledge Date: December 2023 Today Date: 26 Jul 2024 {system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|> {prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ## Model file specification | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-Q2_K.gguf](https://huggingface.co/tensorblock/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-GGUF/blob/main/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-Q2_K.gguf) | Q2_K | 26.375 GB | smallest, significant quality loss - not recommended for most purposes | | [Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-Q3_K_S.gguf](https://huggingface.co/tensorblock/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-GGUF/blob/main/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-Q3_K_S.gguf) | Q3_K_S | 30.912 GB | very small, high quality loss | | [Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-Q3_K_M.gguf](https://huggingface.co/tensorblock/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-GGUF/blob/main/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-Q3_K_M.gguf) | Q3_K_M | 34.267 GB | very small, high quality loss | | [Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-Q3_K_L.gguf](https://huggingface.co/tensorblock/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-GGUF/blob/main/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-Q3_K_L.gguf) | Q3_K_L | 37.141 GB | small, substantial quality loss | | [Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-Q4_0.gguf](https://huggingface.co/tensorblock/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-GGUF/blob/main/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-Q4_0.gguf) | Q4_0 | 39.970 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-Q4_K_S.gguf](https://huggingface.co/tensorblock/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-GGUF/blob/main/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-Q4_K_S.gguf) | Q4_K_S | 40.347 GB | small, greater quality loss | | [Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-Q4_K_M.gguf](https://huggingface.co/tensorblock/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-GGUF/blob/main/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-Q4_K_M.gguf) | Q4_K_M | 42.520 GB | medium, balanced quality - recommended | | [Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-Q5_0.gguf](https://huggingface.co/tensorblock/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-GGUF/blob/main/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-Q5_0.gguf) | Q5_0 | 48.657 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-Q5_K_S.gguf](https://huggingface.co/tensorblock/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-GGUF/blob/main/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-Q5_K_S.gguf) | Q5_K_S | 48.657 GB | large, low quality loss - recommended | | [Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-Q5_K_M.gguf](https://huggingface.co/tensorblock/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-GGUF/blob/main/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-Q5_K_M.gguf) | Q5_K_M | 49.950 GB | large, very low quality loss - recommended | | [Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-Q8_0](https://huggingface.co/tensorblock/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-GGUF/blob/main/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-Q8_0) | Q6_K | 74.975 GB | very large, extremely low quality loss | | [Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-Q6_K](https://huggingface.co/tensorblock/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-GGUF/blob/main/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-Q6_K) | Q8_0 | 57.888 GB | very large, extremely low quality loss - not recommended | ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-GGUF --include "Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try: ```shell huggingface-cli download tensorblock/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' ```