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

1---2license: llama3.23datasets:4- CarrotAI/ko-instruction-dataset5- CarrotAI/Magpie-Ko-Pro-AIR6language:7- ko8- en9base_model: CarrotAI/Llama-3.2-Rabbit-Ko-1B-Instruct10pipeline_tag: text-generation11tags:12- TensorBlock13- GGUF14---15 16<div style="width: auto; margin-left: auto; margin-right: auto">17<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">18</div>19 20[![Website](https://img.shields.io/badge/Website-tensorblock.co-blue?logo=google-chrome&logoColor=white)](https://tensorblock.co)21[![Twitter](https://img.shields.io/twitter/follow/tensorblock_aoi?style=social)](https://twitter.com/tensorblock_aoi)22[![Discord](https://img.shields.io/badge/Discord-Join%20Us-5865F2?logo=discord&logoColor=white)](https://discord.gg/Ej5NmeHFf2)23[![GitHub](https://img.shields.io/badge/GitHub-TensorBlock-black?logo=github&logoColor=white)](https://github.com/TensorBlock)24[![Telegram](https://img.shields.io/badge/Telegram-Group-blue?logo=telegram)](https://t.me/TensorBlock)25 26 27## CarrotAI/Llama-3.2-Rabbit-Ko-1B-Instruct - GGUF28 29This 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).30 31The 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).32 33## Our projects34<table border="1" cellspacing="0" cellpadding="10">35  <tr>36    <th colspan="2" style="font-size: 25px;">Forge</th>37  </tr>38  <tr>39    <th colspan="2">40      <img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>41    </th>42  </tr>43  <tr>44    <th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>45  </tr>46  <tr>47    <th colspan="2">48      <a href="https://github.com/TensorBlock/forge" target="_blank" style="49        display: inline-block;50        padding: 8px 16px;51        background-color: #FF7F50;52        color: white;53        text-decoration: none;54        border-radius: 6px;55        font-weight: bold;56        font-family: sans-serif;57      ">🚀 Try it now! 🚀</a>58    </th>59  </tr>60 61  <tr>62    <th style="font-size: 25px;">Awesome MCP Servers</th>63    <th style="font-size: 25px;">TensorBlock Studio</th>64  </tr>65  <tr>66    <th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th>67    <th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th>68  </tr>69  <tr>70    <th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>71    <th>A lightweight, open, and extensible multi-LLM interaction studio.</th>72  </tr>73  <tr>74    <th>75      <a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="76        display: inline-block;77        padding: 8px 16px;78        background-color: #FF7F50;79        color: white;80        text-decoration: none;81        border-radius: 6px;82        font-weight: bold;83        font-family: sans-serif;84      ">👀 See what we built 👀</a>85    </th>86    <th>87      <a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="88        display: inline-block;89        padding: 8px 16px;90        background-color: #FF7F50;91        color: white;92        text-decoration: none;93        border-radius: 6px;94        font-weight: bold;95        font-family: sans-serif;96      ">👀 See what we built 👀</a>97    </th>98  </tr>99</table>100## Prompt template101 102```103<|begin_of_text|><|start_header_id|>system<|end_header_id|>104 105{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>106 107{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>108```109 110## Model file specification111 112| Filename | Quant type | File Size | Description |113| -------- | ---------- | --------- | ----------- |114| [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 |115| [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 |116| [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 |117| [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 |118| [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 |119| [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 |120| [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 |121| [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 |122| [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 |123| [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 |124| [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 |125| [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 |126 127 128## Downloading instruction129 130### Command line131 132Firstly, install Huggingface Client133 134```shell135pip install -U "huggingface_hub[cli]"136```137 138Then, downoad the individual model file the a local directory139 140```shell141huggingface-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_DIR142```143 144If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:145 146```shell147huggingface-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'148```149