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---
license: apache-2.0
base_model: Kquant03/MistralTrix8x9B
tags:
- TensorBlock
- GGUF
---
<div style="width: auto; margin-left: auto; margin-right: auto">
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## Kquant03/MistralTrix8x9B - GGUF
This repo contains GGUF format model files for [Kquant03/MistralTrix8x9B](https://huggingface.co/Kquant03/MistralTrix8x9B).
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).
## Our projects
<table border="1" cellspacing="0" cellpadding="10">
<tr>
<th colspan="2" style="font-size: 25px;">Forge</th>
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<th colspan="2">
<img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>
</th>
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<th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>
</tr>
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<a href="https://github.com/TensorBlock/forge" target="_blank" style="
display: inline-block;
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">π Try it now! π</a>
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<th style="font-size: 25px;">Awesome MCP Servers</th>
<th style="font-size: 25px;">TensorBlock Studio</th>
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<th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th>
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<th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
<th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
</tr>
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<a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
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color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">π See what we built π</a>
</th>
<th>
<a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">π See what we built π</a>
</th>
</tr>
</table>
## Prompt template
```
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [MistralTrix8x9B-Q2_K.gguf](https://huggingface.co/tensorblock/MistralTrix8x9B-GGUF/blob/main/MistralTrix8x9B-Q2_K.gguf) | Q2_K | 21.601 GB | smallest, significant quality loss - not recommended for most purposes |
| [MistralTrix8x9B-Q3_K_S.gguf](https://huggingface.co/tensorblock/MistralTrix8x9B-GGUF/blob/main/MistralTrix8x9B-Q3_K_S.gguf) | Q3_K_S | 25.500 GB | very small, high quality loss |
| [MistralTrix8x9B-Q3_K_M.gguf](https://huggingface.co/tensorblock/MistralTrix8x9B-GGUF/blob/main/MistralTrix8x9B-Q3_K_M.gguf) | Q3_K_M | 28.113 GB | very small, high quality loss |
| [MistralTrix8x9B-Q3_K_L.gguf](https://huggingface.co/tensorblock/MistralTrix8x9B-GGUF/blob/main/MistralTrix8x9B-Q3_K_L.gguf) | Q3_K_L | 30.171 GB | small, substantial quality loss |
| [MistralTrix8x9B-Q4_0.gguf](https://huggingface.co/tensorblock/MistralTrix8x9B-GGUF/blob/main/MistralTrix8x9B-Q4_0.gguf) | Q4_0 | 33.009 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [MistralTrix8x9B-Q4_K_S.gguf](https://huggingface.co/tensorblock/MistralTrix8x9B-GGUF/blob/main/MistralTrix8x9B-Q4_K_S.gguf) | Q4_K_S | 33.386 GB | small, greater quality loss |
| [MistralTrix8x9B-Q4_K_M.gguf](https://huggingface.co/tensorblock/MistralTrix8x9B-GGUF/blob/main/MistralTrix8x9B-Q4_K_M.gguf) | Q4_K_M | 35.515 GB | medium, balanced quality - recommended |
| [MistralTrix8x9B-Q5_0.gguf](https://huggingface.co/tensorblock/MistralTrix8x9B-GGUF/blob/main/MistralTrix8x9B-Q5_0.gguf) | Q5_0 | 40.240 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [MistralTrix8x9B-Q5_K_S.gguf](https://huggingface.co/tensorblock/MistralTrix8x9B-GGUF/blob/main/MistralTrix8x9B-Q5_K_S.gguf) | Q5_K_S | 40.240 GB | large, low quality loss - recommended |
| [MistralTrix8x9B-Q5_K_M.gguf](https://huggingface.co/tensorblock/MistralTrix8x9B-GGUF/blob/main/MistralTrix8x9B-Q5_K_M.gguf) | Q5_K_M | 41.487 GB | large, very low quality loss - recommended |
| [MistralTrix8x9B-Q6_K.gguf](https://huggingface.co/tensorblock/MistralTrix8x9B-GGUF/blob/main/MistralTrix8x9B-Q6_K.gguf) | Q6_K | 47.922 GB | very large, extremely low quality loss |
| [MistralTrix8x9B-Q8_0](https://huggingface.co/tensorblock/MistralTrix8x9B-GGUF/blob/main/MistralTrix8x9B-Q8_0) | Q8_0 | 4.228 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/MistralTrix8x9B-GGUF --include "MistralTrix8x9B-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/MistralTrix8x9B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
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