---
license: mpl-2.0
library_name: transformers
tags:
- gemma-3
- synthetic-data
- textbooks
- distillation
- utility
- summarization
- lightning
- conversational
- mlx
- mlx-my-repo
base_model: TitleOS/Spark-270M-FP16
datasets:
- TitleOS/Spark-Lightning-Synthetic-Textbooks
language:
- en
pipeline_tag: text-generation
---

# bradyclarke/Spark-270M-FP16-mlx-6Bit

The Model [bradyclarke/Spark-270M-FP16-mlx-6Bit](https://huggingface.co/bradyclarke/Spark-270M-FP16-mlx-6Bit) was converted to MLX format from [TitleOS/Spark-270M-FP16](https://huggingface.co/TitleOS/Spark-270M-FP16) using mlx-lm version **0.29.1**.

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("bradyclarke/Spark-270M-FP16-mlx-6Bit")

prompt="hello"

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

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