mistralai/Mixtral-8x7B-Instruct-v0.1 - GGUF
This repo contains GGUF format model files for mistralai/Mixtral-8x7B-Instruct-v0.1.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.
Our projects
| Forge | |
|---|---|
| An OpenAI-compatible multi-provider routing layer. | |
| 🚀 Try it now! 🚀 | |
| Awesome MCP Servers | TensorBlock Studio |
| A comprehensive collection of Model Context Protocol (MCP) servers. | A lightweight, open, and extensible multi-LLM interaction studio. |
| 👀 See what we built 👀 | 👀 See what we built 👀 |
<s> [INST] {system_prompt}
{prompt} [/INST]
Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| Mixtral-8x7B-Instruct-v0.1-Q2_K.gguf | Q2_K | 17.311 GB | smallest, significant quality loss - not recommended for most purposes |
| Mixtral-8x7B-Instruct-v0.1-Q3_K_S.gguf | Q3_K_S | 20.433 GB | very small, high quality loss |
| Mixtral-8x7B-Instruct-v0.1-Q3_K_M.gguf | Q3_K_M | 22.546 GB | very small, high quality loss |
| Mixtral-8x7B-Instruct-v0.1-Q3_K_L.gguf | Q3_K_L | 24.170 GB | small, substantial quality loss |
| Mixtral-8x7B-Instruct-v0.1-Q4_0.gguf | Q4_0 | 26.444 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| Mixtral-8x7B-Instruct-v0.1-Q4_K_S.gguf | Q4_K_S | 26.746 GB | small, greater quality loss |
| Mixtral-8x7B-Instruct-v0.1-Q4_K_M.gguf | Q4_K_M | 28.448 GB | medium, balanced quality - recommended |
| Mixtral-8x7B-Instruct-v0.1-Q5_0.gguf | Q5_0 | 32.231 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| Mixtral-8x7B-Instruct-v0.1-Q5_K_S.gguf | Q5_K_S | 32.231 GB | large, low quality loss - recommended |
| Mixtral-8x7B-Instruct-v0.1-Q5_K_M.gguf | Q5_K_M | 33.230 GB | large, very low quality loss - recommended |
| Mixtral-8x7B-Instruct-v0.1-Q6_K.gguf | Q6_K | 38.381 GB | very large, extremely low quality loss |
| Mixtral-8x7B-Instruct-v0.1-Q8_0.gguf | Q8_0 | 49.626 GB | very large, extremely low quality loss - not recommended |
Downloading instruction
Command line
Firstly, install Huggingface Client
pip install -U "huggingface_hub[cli]"
Then, downoad the individual model file the a local directory
huggingface-cli download tensorblock/Mixtral-8x7B-Instruct-v0.1-GGUF --include "Mixtral-8x7B-Instruct-v0.1-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:
huggingface-cli download tensorblock/Mixtral-8x7B-Instruct-v0.1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'