---
language:
- fr
- it
- de
- es
- en
license: apache-2.0
tags:
- moe
- TensorBlock
- GGUF
base_model: cloudyu/Mixtral-8x7B-Instruct-v0.1-DPO
model-index:
- name: Mixtral-8x7B-Instruct-v0.1-DPO
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 69.8
      name: normalized accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=cloudyu/Mixtral-8x7B-Instruct-v0.1-DPO
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 87.83
      name: normalized accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=cloudyu/Mixtral-8x7B-Instruct-v0.1-DPO
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 71.05
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=cloudyu/Mixtral-8x7B-Instruct-v0.1-DPO
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 69.18
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=cloudyu/Mixtral-8x7B-Instruct-v0.1-DPO
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 81.37
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=cloudyu/Mixtral-8x7B-Instruct-v0.1-DPO
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 61.41
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=cloudyu/Mixtral-8x7B-Instruct-v0.1-DPO
      name: Open LLM Leaderboard
---

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## cloudyu/Mixtral-8x7B-Instruct-v0.1-DPO - GGUF

This repo contains GGUF format model files for [cloudyu/Mixtral-8x7B-Instruct-v0.1-DPO](https://huggingface.co./cloudyu/Mixtral-8x7B-Instruct-v0.1-DPO).

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

```
<s>[INST] {prompt} [/INST]
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Mixtral-8x7B-Instruct-v0.1-DPO-Q2_K.gguf](https://huggingface.co./tensorblock/Mixtral-8x7B-Instruct-v0.1-DPO-GGUF/blob/main/Mixtral-8x7B-Instruct-v0.1-DPO-Q2_K.gguf) | Q2_K | 17.311 GB | smallest, significant quality loss - not recommended for most purposes |
| [Mixtral-8x7B-Instruct-v0.1-DPO-Q3_K_S.gguf](https://huggingface.co./tensorblock/Mixtral-8x7B-Instruct-v0.1-DPO-GGUF/blob/main/Mixtral-8x7B-Instruct-v0.1-DPO-Q3_K_S.gguf) | Q3_K_S | 20.433 GB | very small, high quality loss |
| [Mixtral-8x7B-Instruct-v0.1-DPO-Q3_K_M.gguf](https://huggingface.co./tensorblock/Mixtral-8x7B-Instruct-v0.1-DPO-GGUF/blob/main/Mixtral-8x7B-Instruct-v0.1-DPO-Q3_K_M.gguf) | Q3_K_M | 22.546 GB | very small, high quality loss |
| [Mixtral-8x7B-Instruct-v0.1-DPO-Q3_K_L.gguf](https://huggingface.co./tensorblock/Mixtral-8x7B-Instruct-v0.1-DPO-GGUF/blob/main/Mixtral-8x7B-Instruct-v0.1-DPO-Q3_K_L.gguf) | Q3_K_L | 24.170 GB | small, substantial quality loss |
| [Mixtral-8x7B-Instruct-v0.1-DPO-Q4_0.gguf](https://huggingface.co./tensorblock/Mixtral-8x7B-Instruct-v0.1-DPO-GGUF/blob/main/Mixtral-8x7B-Instruct-v0.1-DPO-Q4_0.gguf) | Q4_0 | 26.444 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Mixtral-8x7B-Instruct-v0.1-DPO-Q4_K_S.gguf](https://huggingface.co./tensorblock/Mixtral-8x7B-Instruct-v0.1-DPO-GGUF/blob/main/Mixtral-8x7B-Instruct-v0.1-DPO-Q4_K_S.gguf) | Q4_K_S | 26.746 GB | small, greater quality loss |
| [Mixtral-8x7B-Instruct-v0.1-DPO-Q4_K_M.gguf](https://huggingface.co./tensorblock/Mixtral-8x7B-Instruct-v0.1-DPO-GGUF/blob/main/Mixtral-8x7B-Instruct-v0.1-DPO-Q4_K_M.gguf) | Q4_K_M | 28.448 GB | medium, balanced quality - recommended |
| [Mixtral-8x7B-Instruct-v0.1-DPO-Q5_0.gguf](https://huggingface.co./tensorblock/Mixtral-8x7B-Instruct-v0.1-DPO-GGUF/blob/main/Mixtral-8x7B-Instruct-v0.1-DPO-Q5_0.gguf) | Q5_0 | 32.231 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Mixtral-8x7B-Instruct-v0.1-DPO-Q5_K_S.gguf](https://huggingface.co./tensorblock/Mixtral-8x7B-Instruct-v0.1-DPO-GGUF/blob/main/Mixtral-8x7B-Instruct-v0.1-DPO-Q5_K_S.gguf) | Q5_K_S | 32.231 GB | large, low quality loss - recommended |
| [Mixtral-8x7B-Instruct-v0.1-DPO-Q5_K_M.gguf](https://huggingface.co./tensorblock/Mixtral-8x7B-Instruct-v0.1-DPO-GGUF/blob/main/Mixtral-8x7B-Instruct-v0.1-DPO-Q5_K_M.gguf) | Q5_K_M | 33.230 GB | large, very low quality loss - recommended |
| [Mixtral-8x7B-Instruct-v0.1-DPO-Q6_K.gguf](https://huggingface.co./tensorblock/Mixtral-8x7B-Instruct-v0.1-DPO-GGUF/blob/main/Mixtral-8x7B-Instruct-v0.1-DPO-Q6_K.gguf) | Q6_K | 38.381 GB | very large, extremely low quality loss |
| [Mixtral-8x7B-Instruct-v0.1-DPO-Q8_0.gguf](https://huggingface.co./tensorblock/Mixtral-8x7B-Instruct-v0.1-DPO-GGUF/blob/main/Mixtral-8x7B-Instruct-v0.1-DPO-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-8x7B-Instruct-v0.1-DPO-GGUF --include "Mixtral-8x7B-Instruct-v0.1-DPO-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-8x7B-Instruct-v0.1-DPO-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```