---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- openhermes
- mlx-llm
- mlx
library_name: mlx-llm
---

# OpenHermes-2.5-Mistral-7B

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ox7zGoygsJQFFV3rLT4v9.png)


## Model description

Please, refer to the [original model card](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) for more details on OpenHermes-2.5-Mistral-7B.

## Use with mlx-llm

Install mlx-llm from GitHub.
```bash
git clone https://github.com/riccardomusmeci/mlx-llm
cd mlx-llm
pip install .
```

Test with simple generation

```python
from mlx_llm.model import create_model, create_tokenizer, generate

model = create_model("OpenHermes-2.5-Mistral-7B") # it downloads weights from this space
tokenizer = create_tokenizer("OpenHermes-2.5-Mistral-7B")
generate(
  model=model,
  tokenizer=tokenizer,
  prompt="What's the meaning of life?",
  max_tokens=200,
  temperature=.1
)
```

Quantize the model weights
```python
from mlx_llm.model import create_model, quantize, save_weights

model = create_model(model_name)
model = quantize(model, group_size=64, bits=4)
save_weights(model, "weights.npz")
```

Use it in chat mode (don't worry about the prompt, the library takes care of it.)

```python
from mlx_llm.playground.chat import ChatLLM

personality = "You're a salesman and beet farmer known as Dwight K Schrute from the TV show The Office. Dwight replies just as he would in the show. You always reply as Dwight would reply. If you don't know the answer to a question, please don't share false information."

# examples must be structured as below
examples = [
    {
        "user": "What is your name?",
        "model": "Dwight K Schrute",
    },
    {
        "user": "What is your job?",
        "model": "Assistant Regional Manager. Sorry, Assistant to the Regional Manager."
    }
]

chat_llm = ChatLLM.build(
    model_name="OpenHermes-2.5-Mistral-7B",
    tokenizer="mlx-community/OpenHermes-2.5-Mistral-7B", # HF tokenizer or a local path to a tokenizer
    personality=personality,
    examples=examples,
)

chat_llm.run(max_tokens=500, temp=0.1)
```

With `mlx-llm` you can also play with a simple RAG. Go check the [examples](https://github.com/riccardomusmeci/mlx-llm/tree/main/examples).