transformers safetensors mistral text-generation gpt llm large language model h2o-llmstudio mlx conversational en dataset:HuggingFaceH4/ultrafeedback_binarized dataset:Intel/orca_dpo_pairs dataset:argilla/distilabel-math-preference-dpo dataset:Open-Orca/OpenOrca dataset:OpenAssistant/oasst2 dataset:HuggingFaceH4/ultrachat_200k dataset:meta-math/MetaMathQA license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
- mlx
datasets:
- HuggingFaceH4/ultrafeedback_binarized
- Intel/orca_dpo_pairs
- argilla/distilabel-math-preference-dpo
- Open-Orca/OpenOrca
- OpenAssistant/oasst2
- HuggingFaceH4/ultrachat_200k
- meta-math/MetaMathQA
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
widget:
- text: <|prompt|>Why is drinking water so healthy?</s><|answer|>
---

# mlx-community/h2o-danube-1.8b-chat-4bit-mlx
This model was converted to MLX format from [`h2oai/h2o-danube-1.8b-chat`]().
Refer to the [original model card](https://huggingface.co/h2oai/h2o-danube-1.8b-chat) for more details on the model.
## Use with mlx

```bash
pip install mlx-lm
```

```python
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/h2o-danube-1.8b-chat-4bit-mlx")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
