---
language:
- en
library_name: transformers
license: apache-2.0
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
- TensorBlock
- GGUF
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
datasets:
- Open-Orca/OpenOrca
- OpenAssistant/oasst2
- HuggingFaceH4/ultrachat_200k
- meta-math/MetaMathQA
widget:
- messages:
  - role: user
    content: Why is drinking water so healthy?
pipeline_tag: text-generation
base_model: h2oai/h2o-danube-1.8b-sft
---

<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>
<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>
        </p>
    </div>
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## h2oai/h2o-danube-1.8b-sft - GGUF

This repo contains GGUF format model files for [h2oai/h2o-danube-1.8b-sft](https://huggingface.co/h2oai/h2o-danube-1.8b-sft).

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).

<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

```
<|system|>{system_prompt}</s><|prompt|>{prompt}</s><|answer|>
```

## Model file specification

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