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

# mlx-community/nvidia-Llama-3.1-Nemotron-70B-Reward-HF-AQ41

The Model [mlx-community/nvidia-Llama-3.1-Nemotron-70B-Reward-HF-AQ41](https://huggingface.co/mlx-community/nvidia-Llama-3.1-Nemotron-70B-Reward-HF-AQ41) 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 **0.18.2**.

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/nvidia-Llama-3.1-Nemotron-70B-Reward-HF-AQ41")

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)
```