---
language:
- en
license: apache-2.0
base_model: Felladrin/TinyMistral-248M-Chat-v2
datasets:
- HuggingFaceH4/ultrachat_200k
- Felladrin/ChatML-ultrachat_200k
- Open-Orca/OpenOrca
- Felladrin/ChatML-OpenOrca
- hkust-nlp/deita-10k-v0
- Felladrin/ChatML-deita-10k-v0
- LDJnr/Capybara
- Felladrin/ChatML-Capybara
- databricks/databricks-dolly-15k
- Felladrin/ChatML-databricks-dolly-15k
- euclaise/reddit-instruct-curated
- Felladrin/ChatML-reddit-instruct-curated
- CohereForAI/aya_dataset
- Felladrin/ChatML-aya_dataset
- HuggingFaceH4/ultrafeedback_binarized
pipeline_tag: text-generation
widget:
- messages:
  - role: system
    content: You are a highly knowledgeable and friendly assistant. Your goal is to
      understand and respond to user inquiries with clarity. Your interactions are
      always respectful, helpful, and focused on delivering the most accurate information
      to the user.
  - role: user
    content: Hey! Got a question for you!
  - role: assistant
    content: Sure! What's it?
  - role: user
    content: What are some potential applications for quantum computing?
- messages:
  - role: user
    content: Heya!
  - role: assistant
    content: Hi! How may I help you?
  - role: user
    content: I'm interested in developing a career in software engineering. What would
      you recommend me to do?
- messages:
  - role: user
    content: Morning!
  - role: assistant
    content: Good morning! How can I help you today?
  - role: user
    content: Could you give me some tips for becoming a healthier person?
- messages:
  - role: system
    content: You are a very creative assistant. User will give you a task, which you
      should complete with all your knowledge.
  - role: user
    content: Hello! Can you please elaborate a background story of an RPG game about
      wizards and dragons in a sci-fi world?
tags:
- TensorBlock
- GGUF
---

<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>

## Felladrin/TinyMistral-248M-Chat-v2 - GGUF

This repo contains GGUF format model files for [Felladrin/TinyMistral-248M-Chat-v2](https://huggingface.co/Felladrin/TinyMistral-248M-Chat-v2).

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>
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    <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;
      color: white;
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      font-weight: bold;
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    ">👀 See what we built 👀</a>
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      display: inline-block;
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      color: white;
      text-decoration: none;
      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-Chat-v2-Q2_K.gguf](https://huggingface.co/tensorblock/Felladrin_TinyMistral-248M-Chat-v2-GGUF/blob/main/TinyMistral-248M-Chat-v2-Q2_K.gguf) | Q2_K | 0.105 GB | smallest, significant quality loss - not recommended for most purposes |
| [TinyMistral-248M-Chat-v2-Q3_K_S.gguf](https://huggingface.co/tensorblock/Felladrin_TinyMistral-248M-Chat-v2-GGUF/blob/main/TinyMistral-248M-Chat-v2-Q3_K_S.gguf) | Q3_K_S | 0.120 GB | very small, high quality loss |
| [TinyMistral-248M-Chat-v2-Q3_K_M.gguf](https://huggingface.co/tensorblock/Felladrin_TinyMistral-248M-Chat-v2-GGUF/blob/main/TinyMistral-248M-Chat-v2-Q3_K_M.gguf) | Q3_K_M | 0.129 GB | very small, high quality loss |
| [TinyMistral-248M-Chat-v2-Q3_K_L.gguf](https://huggingface.co/tensorblock/Felladrin_TinyMistral-248M-Chat-v2-GGUF/blob/main/TinyMistral-248M-Chat-v2-Q3_K_L.gguf) | Q3_K_L | 0.137 GB | small, substantial quality loss |
| [TinyMistral-248M-Chat-v2-Q4_0.gguf](https://huggingface.co/tensorblock/Felladrin_TinyMistral-248M-Chat-v2-GGUF/blob/main/TinyMistral-248M-Chat-v2-Q4_0.gguf) | Q4_0 | 0.149 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [TinyMistral-248M-Chat-v2-Q4_K_S.gguf](https://huggingface.co/tensorblock/Felladrin_TinyMistral-248M-Chat-v2-GGUF/blob/main/TinyMistral-248M-Chat-v2-Q4_K_S.gguf) | Q4_K_S | 0.149 GB | small, greater quality loss |
| [TinyMistral-248M-Chat-v2-Q4_K_M.gguf](https://huggingface.co/tensorblock/Felladrin_TinyMistral-248M-Chat-v2-GGUF/blob/main/TinyMistral-248M-Chat-v2-Q4_K_M.gguf) | Q4_K_M | 0.156 GB | medium, balanced quality - recommended |
| [TinyMistral-248M-Chat-v2-Q5_0.gguf](https://huggingface.co/tensorblock/Felladrin_TinyMistral-248M-Chat-v2-GGUF/blob/main/TinyMistral-248M-Chat-v2-Q5_0.gguf) | Q5_0 | 0.176 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [TinyMistral-248M-Chat-v2-Q5_K_S.gguf](https://huggingface.co/tensorblock/Felladrin_TinyMistral-248M-Chat-v2-GGUF/blob/main/TinyMistral-248M-Chat-v2-Q5_K_S.gguf) | Q5_K_S | 0.176 GB | large, low quality loss - recommended |
| [TinyMistral-248M-Chat-v2-Q5_K_M.gguf](https://huggingface.co/tensorblock/Felladrin_TinyMistral-248M-Chat-v2-GGUF/blob/main/TinyMistral-248M-Chat-v2-Q5_K_M.gguf) | Q5_K_M | 0.179 GB | large, very low quality loss - recommended |
| [TinyMistral-248M-Chat-v2-Q6_K.gguf](https://huggingface.co/tensorblock/Felladrin_TinyMistral-248M-Chat-v2-GGUF/blob/main/TinyMistral-248M-Chat-v2-Q6_K.gguf) | Q6_K | 0.204 GB | very large, extremely low quality loss |
| [TinyMistral-248M-Chat-v2-Q8_0.gguf](https://huggingface.co/tensorblock/Felladrin_TinyMistral-248M-Chat-v2-GGUF/blob/main/TinyMistral-248M-Chat-v2-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/Felladrin_TinyMistral-248M-Chat-v2-GGUF --include "TinyMistral-248M-Chat-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/Felladrin_TinyMistral-248M-Chat-v2-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```