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DeepScaleR-1.5B-Preview-GGUF
File size: 6,803 Bytes 
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---
base_model: unsloth/DeepScaleR-1.5B-Preview
language:
- en
library_name: transformers
license: mit
tags:
- deepseek
- unsloth
- transformers
- qwen
- 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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## unsloth/DeepScaleR-1.5B-Preview - GGUF

This repo contains GGUF format model files for [unsloth/DeepScaleR-1.5B-Preview](https://huggingface.co/unsloth/DeepScaleR-1.5B-Preview).

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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    <th colspan="2" style="font-size: 25px;">Forge</th>
  </tr>
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    <th colspan="2">
      <img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>
    </th>
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  <tr>
    <th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>
  </tr>
  <tr>
    <th colspan="2">
      <a href="https://github.com/TensorBlock/forge" target="_blank" style="
        display: inline-block;
        padding: 8px 16px;
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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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  </tr>
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      <a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
        display: inline-block;
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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

```

```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [DeepScaleR-1.5B-Preview-Q2_K.gguf](https://huggingface.co/tensorblock/DeepScaleR-1.5B-Preview-GGUF/blob/main/DeepScaleR-1.5B-Preview-Q2_K.gguf) | Q2_K | 0.753 GB | smallest, significant quality loss - not recommended for most purposes |
| [DeepScaleR-1.5B-Preview-Q3_K_S.gguf](https://huggingface.co/tensorblock/DeepScaleR-1.5B-Preview-GGUF/blob/main/DeepScaleR-1.5B-Preview-Q3_K_S.gguf) | Q3_K_S | 0.861 GB | very small, high quality loss |
| [DeepScaleR-1.5B-Preview-Q3_K_M.gguf](https://huggingface.co/tensorblock/DeepScaleR-1.5B-Preview-GGUF/blob/main/DeepScaleR-1.5B-Preview-Q3_K_M.gguf) | Q3_K_M | 0.924 GB | very small, high quality loss |
| [DeepScaleR-1.5B-Preview-Q3_K_L.gguf](https://huggingface.co/tensorblock/DeepScaleR-1.5B-Preview-GGUF/blob/main/DeepScaleR-1.5B-Preview-Q3_K_L.gguf) | Q3_K_L | 0.980 GB | small, substantial quality loss |
| [DeepScaleR-1.5B-Preview-Q4_0.gguf](https://huggingface.co/tensorblock/DeepScaleR-1.5B-Preview-GGUF/blob/main/DeepScaleR-1.5B-Preview-Q4_0.gguf) | Q4_0 | 1.066 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [DeepScaleR-1.5B-Preview-Q4_K_S.gguf](https://huggingface.co/tensorblock/DeepScaleR-1.5B-Preview-GGUF/blob/main/DeepScaleR-1.5B-Preview-Q4_K_S.gguf) | Q4_K_S | 1.072 GB | small, greater quality loss |
| [DeepScaleR-1.5B-Preview-Q4_K_M.gguf](https://huggingface.co/tensorblock/DeepScaleR-1.5B-Preview-GGUF/blob/main/DeepScaleR-1.5B-Preview-Q4_K_M.gguf) | Q4_K_M | 1.117 GB | medium, balanced quality - recommended |
| [DeepScaleR-1.5B-Preview-Q5_0.gguf](https://huggingface.co/tensorblock/DeepScaleR-1.5B-Preview-GGUF/blob/main/DeepScaleR-1.5B-Preview-Q5_0.gguf) | Q5_0 | 1.259 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [DeepScaleR-1.5B-Preview-Q5_K_S.gguf](https://huggingface.co/tensorblock/DeepScaleR-1.5B-Preview-GGUF/blob/main/DeepScaleR-1.5B-Preview-Q5_K_S.gguf) | Q5_K_S | 1.259 GB | large, low quality loss - recommended |
| [DeepScaleR-1.5B-Preview-Q5_K_M.gguf](https://huggingface.co/tensorblock/DeepScaleR-1.5B-Preview-GGUF/blob/main/DeepScaleR-1.5B-Preview-Q5_K_M.gguf) | Q5_K_M | 1.285 GB | large, very low quality loss - recommended |
| [DeepScaleR-1.5B-Preview-Q6_K.gguf](https://huggingface.co/tensorblock/DeepScaleR-1.5B-Preview-GGUF/blob/main/DeepScaleR-1.5B-Preview-Q6_K.gguf) | Q6_K | 1.464 GB | very large, extremely low quality loss |
| [DeepScaleR-1.5B-Preview-Q8_0.gguf](https://huggingface.co/tensorblock/DeepScaleR-1.5B-Preview-GGUF/blob/main/DeepScaleR-1.5B-Preview-Q8_0.gguf) | Q8_0 | 1.895 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/DeepScaleR-1.5B-Preview-GGUF --include "DeepScaleR-1.5B-Preview-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/DeepScaleR-1.5B-Preview-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```