---
license: llama3.1
language:
- en
inference: false
fine-tuning: false
tags:
- nvidia
- llama3.1
- reward model
- mlx
- mlx-my-repo
datasets:
- nvidia/HelpSteer2
base_model: nvidia/Llama-3.1-Nemotron-70B-Reward-HF
---

# cnfusion/Llama-3.1-Nemotron-70B-Reward-HF-Q2-mlx

The Model [cnfusion/Llama-3.1-Nemotron-70B-Reward-HF-Q2-mlx](https://huggingface.co/cnfusion/Llama-3.1-Nemotron-70B-Reward-HF-Q2-mlx) was converted to MLX format from [nvidia/Llama-3.1-Nemotron-70B-Reward-HF](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Reward-HF) using mlx-lm version **0.19.2**.

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("cnfusion/Llama-3.1-Nemotron-70B-Reward-HF-Q2-mlx")

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)
```