---
language:
- en
license: apache-2.0
tags:
- Mistral
- instruct
- finetune
- chatml
- DPO
- RLHF
- gpt4
- synthetic data
- distillation
- function calling
- json mode
- mlx
base_model: NousResearch/Hermes-2-Pro-Mistral-7B
datasets:
- teknium/OpenHermes-2.5
widget:
- example_title: Hermes 2 Pro
  messages:
  - role: system
    content: You are a sentient, superintelligent artificial general intelligence,
      here to teach and assist me.
  - role: user
    content: Write a short story about Goku discovering kirby has teamed up with Majin
      Buu to destroy the world.
model-index:
- name: Hermes-2-Pro-Mistral-7B
  results: []
---

# mlx-community/Hermes-2-Pro-Mistral-7B-3bit

The Model [mlx-community/Hermes-2-Pro-Mistral-7B-3bit](https://huggingface.co/mlx-community/Hermes-2-Pro-Mistral-7B-3bit) was
converted to MLX format from [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)
using mlx-lm version **0.20.4**.

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/Hermes-2-Pro-Mistral-7B-3bit")

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)
```