---
license: apache-2.0
language:
- ko
- en
tags:
- h2o-danube2
- korean
- sLLM
- llm
- TensorBlock
- GGUF
datasets:
- uonlp/CulturaX
base_model: jjhsnail0822/danube-ko-1.8b-base
---

<div style="width: auto; margin-left: auto; margin-right: auto">
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</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
    <div style="display: flex; flex-direction: column; align-items: flex-start;">
        <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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## jjhsnail0822/danube-ko-1.8b-base - GGUF

This repo contains GGUF format model files for [jjhsnail0822/danube-ko-1.8b-base](https://huggingface.co./jjhsnail0822/danube-ko-1.8b-base).

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).

<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

```

```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [danube-ko-1.8b-base-Q2_K.gguf](https://huggingface.co./tensorblock/danube-ko-1.8b-base-GGUF/blob/main/danube-ko-1.8b-base-Q2_K.gguf) | Q2_K | 0.684 GB | smallest, significant quality loss - not recommended for most purposes |
| [danube-ko-1.8b-base-Q3_K_S.gguf](https://huggingface.co./tensorblock/danube-ko-1.8b-base-GGUF/blob/main/danube-ko-1.8b-base-Q3_K_S.gguf) | Q3_K_S | 0.788 GB | very small, high quality loss |
| [danube-ko-1.8b-base-Q3_K_M.gguf](https://huggingface.co./tensorblock/danube-ko-1.8b-base-GGUF/blob/main/danube-ko-1.8b-base-Q3_K_M.gguf) | Q3_K_M | 0.867 GB | very small, high quality loss |
| [danube-ko-1.8b-base-Q3_K_L.gguf](https://huggingface.co./tensorblock/danube-ko-1.8b-base-GGUF/blob/main/danube-ko-1.8b-base-Q3_K_L.gguf) | Q3_K_L | 0.937 GB | small, substantial quality loss |
| [danube-ko-1.8b-base-Q4_0.gguf](https://huggingface.co./tensorblock/danube-ko-1.8b-base-GGUF/blob/main/danube-ko-1.8b-base-Q4_0.gguf) | Q4_0 | 1.007 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [danube-ko-1.8b-base-Q4_K_S.gguf](https://huggingface.co./tensorblock/danube-ko-1.8b-base-GGUF/blob/main/danube-ko-1.8b-base-Q4_K_S.gguf) | Q4_K_S | 1.014 GB | small, greater quality loss |
| [danube-ko-1.8b-base-Q4_K_M.gguf](https://huggingface.co./tensorblock/danube-ko-1.8b-base-GGUF/blob/main/danube-ko-1.8b-base-Q4_K_M.gguf) | Q4_K_M | 1.062 GB | medium, balanced quality - recommended |
| [danube-ko-1.8b-base-Q5_0.gguf](https://huggingface.co./tensorblock/danube-ko-1.8b-base-GGUF/blob/main/danube-ko-1.8b-base-Q5_0.gguf) | Q5_0 | 1.213 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [danube-ko-1.8b-base-Q5_K_S.gguf](https://huggingface.co./tensorblock/danube-ko-1.8b-base-GGUF/blob/main/danube-ko-1.8b-base-Q5_K_S.gguf) | Q5_K_S | 1.213 GB | large, low quality loss - recommended |
| [danube-ko-1.8b-base-Q5_K_M.gguf](https://huggingface.co./tensorblock/danube-ko-1.8b-base-GGUF/blob/main/danube-ko-1.8b-base-Q5_K_M.gguf) | Q5_K_M | 1.241 GB | large, very low quality loss - recommended |
| [danube-ko-1.8b-base-Q6_K.gguf](https://huggingface.co./tensorblock/danube-ko-1.8b-base-GGUF/blob/main/danube-ko-1.8b-base-Q6_K.gguf) | Q6_K | 1.432 GB | very large, extremely low quality loss |
| [danube-ko-1.8b-base-Q8_0.gguf](https://huggingface.co./tensorblock/danube-ko-1.8b-base-GGUF/blob/main/danube-ko-1.8b-base-Q8_0.gguf) | Q8_0 | 1.854 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/danube-ko-1.8b-base-GGUF --include "danube-ko-1.8b-base-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/danube-ko-1.8b-base-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```