README.md

---
license: apache-2.0
library_name: mlx
tags:
- language
- granite-4.1
- mlx
base_model: ibm-granite/granite-4.1-8b
pipeline_tag: text-generation
---

# granite-4.1-8b-mxfp8-mlx

Brainwaves
```brainwaves
         arc   arc/e boolq hswag obkqa piqa  wino
mxfp8    0.486,0.666,0.875,0.636,0.450,0.766,0.631
```

# Other models in this size

gemma-4-E4B-it
```brainwaves
         arc   arc/e boolq hswag obkqa piqa  wino
bf16     0.490,0.674,0.793,0.612,0.416,0.756,0.669
mxfp8    0.480,0.656,0.797,0.608,0.400,0.755,0.665
mxfp4    0.455,0.607,0.851,0.585,0.402,0.744,0.651
```

Qwen3.5-9B
```brainwaves
mxfp8    0.417,0.458,0.623,0.634,0.338,0.737,0.639
mxfp4    0.419,0.472,0.622,0.634,0.352,0.739,0.644
q8-hi    0.413,0.455,0.622,0.642,0.346,0.746,0.654
q8       0.418,0.455,0.622,0.643,0.342,0.748,0.659
```

This model [granite-4.1-8b-mxfp8-mlx](https://huggingface.co/granite-4.1-8b-mxfp8-mlx) was
converted to MLX format from [ibm-granite/granite-4.1-8b](https://huggingface.co/ibm-granite/granite-4.1-8b)
using mlx-lm version **0.31.3**.

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("granite-4.1-8b-mxfp8-mlx")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True, return_dict=False,
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)
```