README.md

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# mlx-community/dbrx-instruct-4bit
This model was converted to MLX format from [`databricks/dbrx-instruct`]() using mlx-lm version [b80adbc
 ](https://github.com/ml-explore/mlx-examples/commit/b80adbcc3ee5b00ad43432faede408b983f152c2) after DBRX support was added by [Awni Hannun](https://github.com/awni).
 
Refer to the [original model card](https://huggingface.co/databricks/dbrx-instruct) for more details on the model.

## Conversion

Conversion was done with:
```bash
python -m mlx_lm.convert --hf-path databricks/dbrx-instruct -q --upload-repo mlx-community/dbrx-instruct-4bit
```

## Use with mlx

Make you you first upgrade mlx-lm and mlx to the latest.

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

python -m mlx_lm.generate --model mlx-community/dbrx-instruct-4bit --prompt "Hello" --trust-remote-code --use-default-chat-template --max-tokens 500
```

Remember, this is an Instruct model, so you will need to use the instruct prompt template by appending `--use-default-chat-template`

## Example:

```bash
python -m mlx_lm.generate --model dbrx-instruct-4bit --prompt "What's the difference between PCA vs UMAP vs t-SNE?" --trust-remote-code --use-default-chat-template  --max-tokens 1000
```

Output:


![image/png](https://cdn-uploads.huggingface.co/production/uploads/630f2745982455e61cc5fb1d/AW-tHeKBOAVEfqP1zNH0j.png)

On my Macbook Pro M2 with 96GB of Unified Memory, DBRX Instruct in 4-bit for the above prompt it eats 70.2GB of RAM.


if the mlx-lm package was updated it can also be installed from pip:

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

To use it from Python you can do the following:

```python
from mlx_lm import load, generate

model, tokenizer = load(
   "mlx-community/dbrx-instruct-4bit",
   tokenizer_config={"trust_remote_code": True}
)

chat = [
   {"role": "user", "content": "What's the difference between PCA vs UMAP vs t-SNE?"},
   # We need to add the Assistant role as well, otherwise mlx_lm will error on generation.
   {"role": "assistant", "content": "The "},
]

prompt = tokenizer.apply_chat_template(chat, add_generation_prompt=True, tokenize=False)

response = generate(model, tokenizer, prompt=prompt, verbose=True, temp=0.6, max_tokens=1500)
```

Converted and uploaded by [eek](https://huggingface.co/eek)