---
license: mit
library_name: transformers
datasets:
- AI-MO/NuminaMath-CoT
- KbsdJames/Omni-MATH
- RUC-AIBOX/STILL-3-Preview-RL-Data
- hendrycks/competition_math
language:
- en
base_model: agentica-org/DeepScaleR-1.5B-Preview
tags:
- TensorBlock
- GGUF
---

<div style="width: auto; margin-left: auto; margin-right: auto">
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</div>
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        <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>
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## agentica-org/DeepScaleR-1.5B-Preview - GGUF

This repo contains GGUF format model files for [agentica-org/DeepScaleR-1.5B-Preview](https://huggingface.co/agentica-org/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 b4658](https://github.com/ggerganov/llama.cpp/commit/855cd0734aca26c86cc23d94aefd34f934464ac9).

<div style="text-align: left; margin: 20px 0;">
    <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
        Run them on the TensorBlock client using your local machine ↗
    </a>
</div>

## Prompt template

```
<｜begin▁of▁sentence｜>{system_prompt}<｜User｜>{prompt}<｜Assistant｜>
```

## 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'
```