---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- dare
- super mario merge
- pytorch
- mixtral
- merge
- TensorBlock
- GGUF
base_model: martyn/mixtral-megamerge-dare-8x7b-v2
---

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## martyn/mixtral-megamerge-dare-8x7b-v2 - GGUF

This repo contains GGUF format model files for [martyn/mixtral-megamerge-dare-8x7b-v2](https://huggingface.co/martyn/mixtral-megamerge-dare-8x7b-v2).

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).

<div style="text-align: left; margin: 20px 0;">
    <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
        Run them on the TensorBlock client using your local machine ↗
    </a>
</div>

## Prompt template

```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [mixtral-megamerge-dare-8x7b-v2-Q2_K.gguf](https://huggingface.co/tensorblock/mixtral-megamerge-dare-8x7b-v2-GGUF/blob/main/mixtral-megamerge-dare-8x7b-v2-Q2_K.gguf) | Q2_K | 17.311 GB | smallest, significant quality loss - not recommended for most purposes |
| [mixtral-megamerge-dare-8x7b-v2-Q3_K_S.gguf](https://huggingface.co/tensorblock/mixtral-megamerge-dare-8x7b-v2-GGUF/blob/main/mixtral-megamerge-dare-8x7b-v2-Q3_K_S.gguf) | Q3_K_S | 20.433 GB | very small, high quality loss |
| [mixtral-megamerge-dare-8x7b-v2-Q3_K_M.gguf](https://huggingface.co/tensorblock/mixtral-megamerge-dare-8x7b-v2-GGUF/blob/main/mixtral-megamerge-dare-8x7b-v2-Q3_K_M.gguf) | Q3_K_M | 22.546 GB | very small, high quality loss |
| [mixtral-megamerge-dare-8x7b-v2-Q3_K_L.gguf](https://huggingface.co/tensorblock/mixtral-megamerge-dare-8x7b-v2-GGUF/blob/main/mixtral-megamerge-dare-8x7b-v2-Q3_K_L.gguf) | Q3_K_L | 24.170 GB | small, substantial quality loss |
| [mixtral-megamerge-dare-8x7b-v2-Q4_0.gguf](https://huggingface.co/tensorblock/mixtral-megamerge-dare-8x7b-v2-GGUF/blob/main/mixtral-megamerge-dare-8x7b-v2-Q4_0.gguf) | Q4_0 | 26.444 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [mixtral-megamerge-dare-8x7b-v2-Q4_K_S.gguf](https://huggingface.co/tensorblock/mixtral-megamerge-dare-8x7b-v2-GGUF/blob/main/mixtral-megamerge-dare-8x7b-v2-Q4_K_S.gguf) | Q4_K_S | 26.746 GB | small, greater quality loss |
| [mixtral-megamerge-dare-8x7b-v2-Q4_K_M.gguf](https://huggingface.co/tensorblock/mixtral-megamerge-dare-8x7b-v2-GGUF/blob/main/mixtral-megamerge-dare-8x7b-v2-Q4_K_M.gguf) | Q4_K_M | 28.448 GB | medium, balanced quality - recommended |
| [mixtral-megamerge-dare-8x7b-v2-Q5_0.gguf](https://huggingface.co/tensorblock/mixtral-megamerge-dare-8x7b-v2-GGUF/blob/main/mixtral-megamerge-dare-8x7b-v2-Q5_0.gguf) | Q5_0 | 32.231 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [mixtral-megamerge-dare-8x7b-v2-Q5_K_S.gguf](https://huggingface.co/tensorblock/mixtral-megamerge-dare-8x7b-v2-GGUF/blob/main/mixtral-megamerge-dare-8x7b-v2-Q5_K_S.gguf) | Q5_K_S | 32.231 GB | large, low quality loss - recommended |
| [mixtral-megamerge-dare-8x7b-v2-Q5_K_M.gguf](https://huggingface.co/tensorblock/mixtral-megamerge-dare-8x7b-v2-GGUF/blob/main/mixtral-megamerge-dare-8x7b-v2-Q5_K_M.gguf) | Q5_K_M | 33.230 GB | large, very low quality loss - recommended |
| [mixtral-megamerge-dare-8x7b-v2-Q6_K.gguf](https://huggingface.co/tensorblock/mixtral-megamerge-dare-8x7b-v2-GGUF/blob/main/mixtral-megamerge-dare-8x7b-v2-Q6_K.gguf) | Q6_K | 38.381 GB | very large, extremely low quality loss |
| [mixtral-megamerge-dare-8x7b-v2-Q8_0.gguf](https://huggingface.co/tensorblock/mixtral-megamerge-dare-8x7b-v2-GGUF/blob/main/mixtral-megamerge-dare-8x7b-v2-Q8_0.gguf) | Q8_0 | 49.626 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/mixtral-megamerge-dare-8x7b-v2-GGUF --include "mixtral-megamerge-dare-8x7b-v2-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/mixtral-megamerge-dare-8x7b-v2-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```