---
language:
- en
license: apache-2.0
datasets:
- Locutusque/TM-DATA-V2
- LLM360/TxT360
- mlfoundations/dclm-baseline-1.0
- Skylion007/openwebtext
- JeanKaddour/minipile
- eminorhan/gutenberg_en
tags:
- TensorBlock
- GGUF
base_model: M4-ai/TinyMistral-248M-v3
model-index:
- name: TinyMistral-248M-v3
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 16.39
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=M4-ai/TinyMistral-248M-v3
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 1.78
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=M4-ai/TinyMistral-248M-v3
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 0.0
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=M4-ai/TinyMistral-248M-v3
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 0.0
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=M4-ai/TinyMistral-248M-v3
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 5.15
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=M4-ai/TinyMistral-248M-v3
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 1.47
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=M4-ai/TinyMistral-248M-v3
      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;">
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        </p>
    </div>
</div>

## M4-ai/TinyMistral-248M-v3 - GGUF

This repo contains GGUF format model files for [M4-ai/TinyMistral-248M-v3](https://huggingface.co/M4-ai/TinyMistral-248M-v3).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5165](https://github.com/ggml-org/llama.cpp/commit/1d735c0b4fa0551c51c2f4ac888dd9a01f447985).

## Our projects
<table border="1" cellspacing="0" cellpadding="10">
<tr>
  <th style="font-size: 25px;">Awesome MCP Servers</th>
  <th style="font-size: 25px;">TensorBlock Studio</th>
</tr>
  <tr>
    <th><img src="https://imgur.com/2Xov7B7.jpeg" alt="Project A" width="450"/></th>
    <th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Project B" width="450"/></th>
  </tr>
  <tr>
    <th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
    <th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
  </tr>
<tr>
  <th>
    <a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
      display: inline-block;
      padding: 8px 16px;
      background-color: #FF7F50;
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    ">👀 See what we built 👀</a>
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      display: inline-block;
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      border-radius: 6px;
      font-weight: bold;
      font-family: sans-serif;
    ">👀 See what we built 👀</a>
  </th>
</tr>
</table>

## 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 |
| -------- | ---------- | --------- | ----------- |
| [TinyMistral-248M-v3-Q2_K.gguf](https://huggingface.co/tensorblock/M4-ai_TinyMistral-248M-v3-GGUF/blob/main/TinyMistral-248M-v3-Q2_K.gguf) | Q2_K | 0.105 GB | smallest, significant quality loss - not recommended for most purposes |
| [TinyMistral-248M-v3-Q3_K_S.gguf](https://huggingface.co/tensorblock/M4-ai_TinyMistral-248M-v3-GGUF/blob/main/TinyMistral-248M-v3-Q3_K_S.gguf) | Q3_K_S | 0.120 GB | very small, high quality loss |
| [TinyMistral-248M-v3-Q3_K_M.gguf](https://huggingface.co/tensorblock/M4-ai_TinyMistral-248M-v3-GGUF/blob/main/TinyMistral-248M-v3-Q3_K_M.gguf) | Q3_K_M | 0.129 GB | very small, high quality loss |
| [TinyMistral-248M-v3-Q3_K_L.gguf](https://huggingface.co/tensorblock/M4-ai_TinyMistral-248M-v3-GGUF/blob/main/TinyMistral-248M-v3-Q3_K_L.gguf) | Q3_K_L | 0.137 GB | small, substantial quality loss |
| [TinyMistral-248M-v3-Q4_0.gguf](https://huggingface.co/tensorblock/M4-ai_TinyMistral-248M-v3-GGUF/blob/main/TinyMistral-248M-v3-Q4_0.gguf) | Q4_0 | 0.149 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [TinyMistral-248M-v3-Q4_K_S.gguf](https://huggingface.co/tensorblock/M4-ai_TinyMistral-248M-v3-GGUF/blob/main/TinyMistral-248M-v3-Q4_K_S.gguf) | Q4_K_S | 0.149 GB | small, greater quality loss |
| [TinyMistral-248M-v3-Q4_K_M.gguf](https://huggingface.co/tensorblock/M4-ai_TinyMistral-248M-v3-GGUF/blob/main/TinyMistral-248M-v3-Q4_K_M.gguf) | Q4_K_M | 0.156 GB | medium, balanced quality - recommended |
| [TinyMistral-248M-v3-Q5_0.gguf](https://huggingface.co/tensorblock/M4-ai_TinyMistral-248M-v3-GGUF/blob/main/TinyMistral-248M-v3-Q5_0.gguf) | Q5_0 | 0.176 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [TinyMistral-248M-v3-Q5_K_S.gguf](https://huggingface.co/tensorblock/M4-ai_TinyMistral-248M-v3-GGUF/blob/main/TinyMistral-248M-v3-Q5_K_S.gguf) | Q5_K_S | 0.176 GB | large, low quality loss - recommended |
| [TinyMistral-248M-v3-Q5_K_M.gguf](https://huggingface.co/tensorblock/M4-ai_TinyMistral-248M-v3-GGUF/blob/main/TinyMistral-248M-v3-Q5_K_M.gguf) | Q5_K_M | 0.179 GB | large, very low quality loss - recommended |
| [TinyMistral-248M-v3-Q6_K.gguf](https://huggingface.co/tensorblock/M4-ai_TinyMistral-248M-v3-GGUF/blob/main/TinyMistral-248M-v3-Q6_K.gguf) | Q6_K | 0.204 GB | very large, extremely low quality loss |
| [TinyMistral-248M-v3-Q8_0.gguf](https://huggingface.co/tensorblock/M4-ai_TinyMistral-248M-v3-GGUF/blob/main/TinyMistral-248M-v3-Q8_0.gguf) | Q8_0 | 0.264 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/M4-ai_TinyMistral-248M-v3-GGUF --include "TinyMistral-248M-v3-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/M4-ai_TinyMistral-248M-v3-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```