
# 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)
```

{"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|>"}]}
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