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QwQ-Math-IO-500M-GGUF

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QwQ-Math-IO-500M-GGUF
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---
license: apache-2.0
language:
- en
base_model: prithivMLmods/QwQ-Math-IO-500M
pipeline_tag: text-generation
library_name: transformers
tags:
- qwq
- reasoning
- 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>

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## prithivMLmods/QwQ-Math-IO-500M - GGUF

This repo contains GGUF format model files for [prithivMLmods/QwQ-Math-IO-500M](https://huggingface.co/prithivMLmods/QwQ-Math-IO-500M).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4823](https://github.com/ggml-org/llama.cpp/commit/5bbe6a9fe9a8796a9389c85accec89dbc4d91e39).

## Our projects
<table border="1" cellspacing="0" cellpadding="10">
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  </tr>
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        display: inline-block;
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      ">🚀 Try it now! 🚀</a>
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    <th style="font-size: 25px;">Awesome MCP Servers</th>
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    <th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
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      ">👀 See what we built 👀</a>
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        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 |
| -------- | ---------- | --------- | ----------- |
| [QwQ-Math-IO-500M-Q2_K.gguf](https://huggingface.co/tensorblock/QwQ-Math-IO-500M-GGUF/blob/main/QwQ-Math-IO-500M-Q2_K.gguf) | Q2_K | 0.339 GB | smallest, significant quality loss - not recommended for most purposes |
| [QwQ-Math-IO-500M-Q3_K_S.gguf](https://huggingface.co/tensorblock/QwQ-Math-IO-500M-GGUF/blob/main/QwQ-Math-IO-500M-Q3_K_S.gguf) | Q3_K_S | 0.338 GB | very small, high quality loss |
| [QwQ-Math-IO-500M-Q3_K_M.gguf](https://huggingface.co/tensorblock/QwQ-Math-IO-500M-GGUF/blob/main/QwQ-Math-IO-500M-Q3_K_M.gguf) | Q3_K_M | 0.355 GB | very small, high quality loss |
| [QwQ-Math-IO-500M-Q3_K_L.gguf](https://huggingface.co/tensorblock/QwQ-Math-IO-500M-GGUF/blob/main/QwQ-Math-IO-500M-Q3_K_L.gguf) | Q3_K_L | 0.369 GB | small, substantial quality loss |
| [QwQ-Math-IO-500M-Q4_0.gguf](https://huggingface.co/tensorblock/QwQ-Math-IO-500M-GGUF/blob/main/QwQ-Math-IO-500M-Q4_0.gguf) | Q4_0 | 0.352 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [QwQ-Math-IO-500M-Q4_K_S.gguf](https://huggingface.co/tensorblock/QwQ-Math-IO-500M-GGUF/blob/main/QwQ-Math-IO-500M-Q4_K_S.gguf) | Q4_K_S | 0.385 GB | small, greater quality loss |
| [QwQ-Math-IO-500M-Q4_K_M.gguf](https://huggingface.co/tensorblock/QwQ-Math-IO-500M-GGUF/blob/main/QwQ-Math-IO-500M-Q4_K_M.gguf) | Q4_K_M | 0.398 GB | medium, balanced quality - recommended |
| [QwQ-Math-IO-500M-Q5_0.gguf](https://huggingface.co/tensorblock/QwQ-Math-IO-500M-GGUF/blob/main/QwQ-Math-IO-500M-Q5_0.gguf) | Q5_0 | 0.397 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [QwQ-Math-IO-500M-Q5_K_S.gguf](https://huggingface.co/tensorblock/QwQ-Math-IO-500M-GGUF/blob/main/QwQ-Math-IO-500M-Q5_K_S.gguf) | Q5_K_S | 0.413 GB | large, low quality loss - recommended |
| [QwQ-Math-IO-500M-Q5_K_M.gguf](https://huggingface.co/tensorblock/QwQ-Math-IO-500M-GGUF/blob/main/QwQ-Math-IO-500M-Q5_K_M.gguf) | Q5_K_M | 0.420 GB | large, very low quality loss - recommended |
| [QwQ-Math-IO-500M-Q6_K.gguf](https://huggingface.co/tensorblock/QwQ-Math-IO-500M-GGUF/blob/main/QwQ-Math-IO-500M-Q6_K.gguf) | Q6_K | 0.506 GB | very large, extremely low quality loss |
| [QwQ-Math-IO-500M-Q8_0.gguf](https://huggingface.co/tensorblock/QwQ-Math-IO-500M-GGUF/blob/main/QwQ-Math-IO-500M-Q8_0.gguf) | Q8_0 | 0.531 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/QwQ-Math-IO-500M-GGUF --include "QwQ-Math-IO-500M-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/QwQ-Math-IO-500M-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```