---
license: apache-2.0
tags:
- moe
- merge
- mergekit
- TensorBlock
- GGUF
base_model: shadowml/Mixolar-4x7b
model-index:
- name: Mixolar-4x7b
  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: 71.08
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/Mixolar-4x7b
      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: 88.44
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/Mixolar-4x7b
      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: 66.29
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/Mixolar-4x7b
      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: 71.81
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/Mixolar-4x7b
      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: 83.58
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/Mixolar-4x7b
      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: 63.91
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/Mixolar-4x7b
      name: Open LLM Leaderboard
---

<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
    <div style="display: flex; flex-direction: column; align-items: flex-start;">
        <p style="margin-top: 0.5em; margin-bottom: 0em;">
            Feedback and support: TensorBlock's  <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
        </p>
    </div>
</div>

## shadowml/Mixolar-4x7b - GGUF

This repo contains GGUF format model files for [shadowml/Mixolar-4x7b](https://huggingface.co/shadowml/Mixolar-4x7b).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).

## Prompt template

```
### System:
{system_prompt}

### User:
{prompt}

### Assistant:
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Mixolar-4x7b-Q2_K.gguf](https://huggingface.co/tensorblock/Mixolar-4x7b-GGUF/tree/main/Mixolar-4x7b-Q2_K.gguf) | Q2_K | 12.283 GB | smallest, significant quality loss - not recommended for most purposes |
| [Mixolar-4x7b-Q3_K_S.gguf](https://huggingface.co/tensorblock/Mixolar-4x7b-GGUF/tree/main/Mixolar-4x7b-Q3_K_S.gguf) | Q3_K_S | 14.499 GB | very small, high quality loss |
| [Mixolar-4x7b-Q3_K_M.gguf](https://huggingface.co/tensorblock/Mixolar-4x7b-GGUF/tree/main/Mixolar-4x7b-Q3_K_M.gguf) | Q3_K_M | 16.101 GB | very small, high quality loss |
| [Mixolar-4x7b-Q3_K_L.gguf](https://huggingface.co/tensorblock/Mixolar-4x7b-GGUF/tree/main/Mixolar-4x7b-Q3_K_L.gguf) | Q3_K_L | 17.447 GB | small, substantial quality loss |
| [Mixolar-4x7b-Q4_0.gguf](https://huggingface.co/tensorblock/Mixolar-4x7b-GGUF/tree/main/Mixolar-4x7b-Q4_0.gguf) | Q4_0 | 18.947 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Mixolar-4x7b-Q4_K_S.gguf](https://huggingface.co/tensorblock/Mixolar-4x7b-GGUF/tree/main/Mixolar-4x7b-Q4_K_S.gguf) | Q4_K_S | 19.113 GB | small, greater quality loss |
| [Mixolar-4x7b-Q4_K_M.gguf](https://huggingface.co/tensorblock/Mixolar-4x7b-GGUF/tree/main/Mixolar-4x7b-Q4_K_M.gguf) | Q4_K_M | 20.325 GB | medium, balanced quality - recommended |
| [Mixolar-4x7b-Q5_0.gguf](https://huggingface.co/tensorblock/Mixolar-4x7b-GGUF/tree/main/Mixolar-4x7b-Q5_0.gguf) | Q5_0 | 23.134 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Mixolar-4x7b-Q5_K_S.gguf](https://huggingface.co/tensorblock/Mixolar-4x7b-GGUF/tree/main/Mixolar-4x7b-Q5_K_S.gguf) | Q5_K_S | 23.134 GB | large, low quality loss - recommended |
| [Mixolar-4x7b-Q5_K_M.gguf](https://huggingface.co/tensorblock/Mixolar-4x7b-GGUF/tree/main/Mixolar-4x7b-Q5_K_M.gguf) | Q5_K_M | 23.844 GB | large, very low quality loss - recommended |
| [Mixolar-4x7b-Q6_K.gguf](https://huggingface.co/tensorblock/Mixolar-4x7b-GGUF/tree/main/Mixolar-4x7b-Q6_K.gguf) | Q6_K | 27.583 GB | very large, extremely low quality loss |
| [Mixolar-4x7b-Q8_0.gguf](https://huggingface.co/tensorblock/Mixolar-4x7b-GGUF/tree/main/Mixolar-4x7b-Q8_0.gguf) | Q8_0 | 35.725 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/Mixolar-4x7b-GGUF --include "Mixolar-4x7b-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/Mixolar-4x7b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```