README.md

---
license: mit
language:
- en
base_model: numind/NuExtract-tiny
new_version: numind/NuExtract-v1.5
tags:
- TensorBlock
- GGUF
---

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## numind/NuExtract-tiny - GGUF

This repo contains GGUF format model files for [numind/NuExtract-tiny](https://huggingface.co/numind/NuExtract-tiny).

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).


## 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>
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    <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;
      text-decoration: none;
      border-radius: 6px;
      font-weight: bold;
      font-family: sans-serif;
    ">👀 See what we built 👀</a>
  </th>
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    <a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
      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 |
| -------- | ---------- | --------- | ----------- |
| [NuExtract-tiny-Q2_K.gguf](https://huggingface.co/tensorblock/NuExtract-tiny-GGUF/blob/main/NuExtract-tiny-Q2_K.gguf) | Q2_K | 0.230 GB | smallest, significant quality loss - not recommended for most purposes |
| [NuExtract-tiny-Q3_K_S.gguf](https://huggingface.co/tensorblock/NuExtract-tiny-GGUF/blob/main/NuExtract-tiny-Q3_K_S.gguf) | Q3_K_S | 0.248 GB | very small, high quality loss |
| [NuExtract-tiny-Q3_K_M.gguf](https://huggingface.co/tensorblock/NuExtract-tiny-GGUF/blob/main/NuExtract-tiny-Q3_K_M.gguf) | Q3_K_M | 0.264 GB | very small, high quality loss |
| [NuExtract-tiny-Q3_K_L.gguf](https://huggingface.co/tensorblock/NuExtract-tiny-GGUF/blob/main/NuExtract-tiny-Q3_K_L.gguf) | Q3_K_L | 0.277 GB | small, substantial quality loss |
| [NuExtract-tiny-Q4_0.gguf](https://huggingface.co/tensorblock/NuExtract-tiny-GGUF/blob/main/NuExtract-tiny-Q4_0.gguf) | Q4_0 | 0.286 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [NuExtract-tiny-Q4_K_S.gguf](https://huggingface.co/tensorblock/NuExtract-tiny-GGUF/blob/main/NuExtract-tiny-Q4_K_S.gguf) | Q4_K_S | 0.288 GB | small, greater quality loss |
| [NuExtract-tiny-Q4_K_M.gguf](https://huggingface.co/tensorblock/NuExtract-tiny-GGUF/blob/main/NuExtract-tiny-Q4_K_M.gguf) | Q4_K_M | 0.298 GB | medium, balanced quality - recommended |
| [NuExtract-tiny-Q5_0.gguf](https://huggingface.co/tensorblock/NuExtract-tiny-GGUF/blob/main/NuExtract-tiny-Q5_0.gguf) | Q5_0 | 0.322 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [NuExtract-tiny-Q5_K_S.gguf](https://huggingface.co/tensorblock/NuExtract-tiny-GGUF/blob/main/NuExtract-tiny-Q5_K_S.gguf) | Q5_K_S | 0.322 GB | large, low quality loss - recommended |
| [NuExtract-tiny-Q5_K_M.gguf](https://huggingface.co/tensorblock/NuExtract-tiny-GGUF/blob/main/NuExtract-tiny-Q5_K_M.gguf) | Q5_K_M | 0.328 GB | large, very low quality loss - recommended |
| [NuExtract-tiny-Q6_K.gguf](https://huggingface.co/tensorblock/NuExtract-tiny-GGUF/blob/main/NuExtract-tiny-Q6_K.gguf) | Q6_K | 0.360 GB | very large, extremely low quality loss |
| [NuExtract-tiny-Q8_0.gguf](https://huggingface.co/tensorblock/NuExtract-tiny-GGUF/blob/main/NuExtract-tiny-Q8_0.gguf) | Q8_0 | 0.465 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/NuExtract-tiny-GGUF --include "NuExtract-tiny-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/NuExtract-tiny-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```