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---
language:
- en
library_name: transformers
license: apache-2.0
tags:
- gpt
- llm
- large language model
- TensorBlock
- GGUF
base_model: h2oai/h2o-danube-1.8b-base
---

<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>

[![Website](https://img.shields.io/badge/Website-tensorblock.co-blue?logo=google-chrome&logoColor=white)](https://tensorblock.co)
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## h2oai/h2o-danube-1.8b-base - GGUF

This repo contains GGUF format model files for [h2oai/h2o-danube-1.8b-base](https://huggingface.co/h2oai/h2o-danube-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 b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).

## Our projects
<table border="1" cellspacing="0" cellpadding="10">
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  </tr>
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    <th colspan="2">
      <a href="https://github.com/TensorBlock/forge" target="_blank" style="
        display: inline-block;
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      ">πŸš€ Try it now! πŸš€</a>
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    <th style="font-size: 25px;">Awesome MCP Servers</th>
    <th style="font-size: 25px;">TensorBlock Studio</th>
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  <tr>
    <th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th>
    <th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th>
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  <tr>
    <th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
    <th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
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      <a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
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      ">πŸ‘€ See what we built πŸ‘€</a>
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      ">πŸ‘€ See what we built πŸ‘€</a>
    </th>
  </tr>
</table>
## Prompt template

```

```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [h2o-danube-1.8b-base-Q2_K.gguf](https://huggingface.co/tensorblock/h2o-danube-1.8b-base-GGUF/blob/main/h2o-danube-1.8b-base-Q2_K.gguf) | Q2_K | 0.711 GB | smallest, significant quality loss - not recommended for most purposes |
| [h2o-danube-1.8b-base-Q3_K_S.gguf](https://huggingface.co/tensorblock/h2o-danube-1.8b-base-GGUF/blob/main/h2o-danube-1.8b-base-Q3_K_S.gguf) | Q3_K_S | 0.820 GB | very small, high quality loss |
| [h2o-danube-1.8b-base-Q3_K_M.gguf](https://huggingface.co/tensorblock/h2o-danube-1.8b-base-GGUF/blob/main/h2o-danube-1.8b-base-Q3_K_M.gguf) | Q3_K_M | 0.905 GB | very small, high quality loss |
| [h2o-danube-1.8b-base-Q3_K_L.gguf](https://huggingface.co/tensorblock/h2o-danube-1.8b-base-GGUF/blob/main/h2o-danube-1.8b-base-Q3_K_L.gguf) | Q3_K_L | 0.980 GB | small, substantial quality loss |
| [h2o-danube-1.8b-base-Q4_0.gguf](https://huggingface.co/tensorblock/h2o-danube-1.8b-base-GGUF/blob/main/h2o-danube-1.8b-base-Q4_0.gguf) | Q4_0 | 1.052 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [h2o-danube-1.8b-base-Q4_K_S.gguf](https://huggingface.co/tensorblock/h2o-danube-1.8b-base-GGUF/blob/main/h2o-danube-1.8b-base-Q4_K_S.gguf) | Q4_K_S | 1.060 GB | small, greater quality loss |
| [h2o-danube-1.8b-base-Q4_K_M.gguf](https://huggingface.co/tensorblock/h2o-danube-1.8b-base-GGUF/blob/main/h2o-danube-1.8b-base-Q4_K_M.gguf) | Q4_K_M | 1.112 GB | medium, balanced quality - recommended |
| [h2o-danube-1.8b-base-Q5_0.gguf](https://huggingface.co/tensorblock/h2o-danube-1.8b-base-GGUF/blob/main/h2o-danube-1.8b-base-Q5_0.gguf) | Q5_0 | 1.271 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [h2o-danube-1.8b-base-Q5_K_S.gguf](https://huggingface.co/tensorblock/h2o-danube-1.8b-base-GGUF/blob/main/h2o-danube-1.8b-base-Q5_K_S.gguf) | Q5_K_S | 1.271 GB | large, low quality loss - recommended |
| [h2o-danube-1.8b-base-Q5_K_M.gguf](https://huggingface.co/tensorblock/h2o-danube-1.8b-base-GGUF/blob/main/h2o-danube-1.8b-base-Q5_K_M.gguf) | Q5_K_M | 1.302 GB | large, very low quality loss - recommended |
| [h2o-danube-1.8b-base-Q6_K.gguf](https://huggingface.co/tensorblock/h2o-danube-1.8b-base-GGUF/blob/main/h2o-danube-1.8b-base-Q6_K.gguf) | Q6_K | 1.503 GB | very large, extremely low quality loss |
| [h2o-danube-1.8b-base-Q8_0.gguf](https://huggingface.co/tensorblock/h2o-danube-1.8b-base-GGUF/blob/main/h2o-danube-1.8b-base-Q8_0.gguf) | Q8_0 | 1.947 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/h2o-danube-1.8b-base-GGUF --include "h2o-danube-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/h2o-danube-1.8b-base-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```