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Llama-3.2-Rabbit-Ko-1B-Instruct-GGUF

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Llama-3.2-Rabbit-Ko-1B-Instruct-GGUF
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---
license: llama3.2
datasets:
- CarrotAI/ko-instruction-dataset
- CarrotAI/Magpie-Ko-Pro-AIR
language:
- ko
- en
base_model: CarrotAI/Llama-3.2-Rabbit-Ko-1B-Instruct
pipeline_tag: text-generation
tags:
- TensorBlock
- GGUF
---

<div style="width: auto; margin-left: auto; margin-right: auto">
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## CarrotAI/Llama-3.2-Rabbit-Ko-1B-Instruct - GGUF

This repo contains GGUF format model files for [CarrotAI/Llama-3.2-Rabbit-Ko-1B-Instruct](https://huggingface.co/CarrotAI/Llama-3.2-Rabbit-Ko-1B-Instruct).

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 colspan="2" style="font-size: 25px;">Forge</th>
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      <img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>
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    <th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>
  </tr>
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    <th colspan="2">
      <a href="https://github.com/TensorBlock/forge" target="_blank" style="
        display: inline-block;
        padding: 8px 16px;
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        font-weight: bold;
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      ">🚀 Try it now! 🚀</a>
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  <tr>
    <th style="font-size: 25px;">Awesome MCP Servers</th>
    <th style="font-size: 25px;">TensorBlock Studio</th>
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    <th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th>
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    <th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
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      <a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
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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

```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Llama-3.2-Rabbit-Ko-1B-Instruct-Q2_K.gguf](https://huggingface.co/tensorblock/Llama-3.2-Rabbit-Ko-1B-Instruct-GGUF/blob/main/Llama-3.2-Rabbit-Ko-1B-Instruct-Q2_K.gguf) | Q2_K | 0.581 GB | smallest, significant quality loss - not recommended for most purposes |
| [Llama-3.2-Rabbit-Ko-1B-Instruct-Q3_K_S.gguf](https://huggingface.co/tensorblock/Llama-3.2-Rabbit-Ko-1B-Instruct-GGUF/blob/main/Llama-3.2-Rabbit-Ko-1B-Instruct-Q3_K_S.gguf) | Q3_K_S | 0.642 GB | very small, high quality loss |
| [Llama-3.2-Rabbit-Ko-1B-Instruct-Q3_K_M.gguf](https://huggingface.co/tensorblock/Llama-3.2-Rabbit-Ko-1B-Instruct-GGUF/blob/main/Llama-3.2-Rabbit-Ko-1B-Instruct-Q3_K_M.gguf) | Q3_K_M | 0.691 GB | very small, high quality loss |
| [Llama-3.2-Rabbit-Ko-1B-Instruct-Q3_K_L.gguf](https://huggingface.co/tensorblock/Llama-3.2-Rabbit-Ko-1B-Instruct-GGUF/blob/main/Llama-3.2-Rabbit-Ko-1B-Instruct-Q3_K_L.gguf) | Q3_K_L | 0.733 GB | small, substantial quality loss |
| [Llama-3.2-Rabbit-Ko-1B-Instruct-Q4_0.gguf](https://huggingface.co/tensorblock/Llama-3.2-Rabbit-Ko-1B-Instruct-GGUF/blob/main/Llama-3.2-Rabbit-Ko-1B-Instruct-Q4_0.gguf) | Q4_0 | 0.771 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Llama-3.2-Rabbit-Ko-1B-Instruct-Q4_K_S.gguf](https://huggingface.co/tensorblock/Llama-3.2-Rabbit-Ko-1B-Instruct-GGUF/blob/main/Llama-3.2-Rabbit-Ko-1B-Instruct-Q4_K_S.gguf) | Q4_K_S | 0.776 GB | small, greater quality loss |
| [Llama-3.2-Rabbit-Ko-1B-Instruct-Q4_K_M.gguf](https://huggingface.co/tensorblock/Llama-3.2-Rabbit-Ko-1B-Instruct-GGUF/blob/main/Llama-3.2-Rabbit-Ko-1B-Instruct-Q4_K_M.gguf) | Q4_K_M | 0.808 GB | medium, balanced quality - recommended |
| [Llama-3.2-Rabbit-Ko-1B-Instruct-Q5_0.gguf](https://huggingface.co/tensorblock/Llama-3.2-Rabbit-Ko-1B-Instruct-GGUF/blob/main/Llama-3.2-Rabbit-Ko-1B-Instruct-Q5_0.gguf) | Q5_0 | 0.893 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Llama-3.2-Rabbit-Ko-1B-Instruct-Q5_K_S.gguf](https://huggingface.co/tensorblock/Llama-3.2-Rabbit-Ko-1B-Instruct-GGUF/blob/main/Llama-3.2-Rabbit-Ko-1B-Instruct-Q5_K_S.gguf) | Q5_K_S | 0.893 GB | large, low quality loss - recommended |
| [Llama-3.2-Rabbit-Ko-1B-Instruct-Q5_K_M.gguf](https://huggingface.co/tensorblock/Llama-3.2-Rabbit-Ko-1B-Instruct-GGUF/blob/main/Llama-3.2-Rabbit-Ko-1B-Instruct-Q5_K_M.gguf) | Q5_K_M | 0.912 GB | large, very low quality loss - recommended |
| [Llama-3.2-Rabbit-Ko-1B-Instruct-Q6_K.gguf](https://huggingface.co/tensorblock/Llama-3.2-Rabbit-Ko-1B-Instruct-GGUF/blob/main/Llama-3.2-Rabbit-Ko-1B-Instruct-Q6_K.gguf) | Q6_K | 1.022 GB | very large, extremely low quality loss |
| [Llama-3.2-Rabbit-Ko-1B-Instruct-Q8_0.gguf](https://huggingface.co/tensorblock/Llama-3.2-Rabbit-Ko-1B-Instruct-GGUF/blob/main/Llama-3.2-Rabbit-Ko-1B-Instruct-Q8_0.gguf) | Q8_0 | 1.321 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/Llama-3.2-Rabbit-Ko-1B-Instruct-GGUF --include "Llama-3.2-Rabbit-Ko-1B-Instruct-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/Llama-3.2-Rabbit-Ko-1B-Instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```