---
title: OpenHermes-2.5-Mistral-7B
canonical_url: "https://www.modelscope.cn/models/mlx-community/OpenHermes-2.5-Mistral-7B"
md_url: "https://www.modelscope.cn/models/mlx-community/OpenHermes-2.5-Mistral-7B.md"
repository: mlx-community/OpenHermes-2.5-Mistral-7B
last_updated: 2024-12-20
license: apache-2.0
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - mistral
architectures:
  - MistralForCausalLM
library_name:
  - mlx
  - pytorch
frameworks:
  - pytorch
language:
  - en
inference_backends:
  - "deploy_task text/emb"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 1003
stars: 0
tags:
  - openhermes
  - mlx-llm
  - mlx
---

# OpenHermes-2.5-Mistral-7B

> OpenHermes-2.5-Mistral-7B - mlx-community 在 ModelScope 开源的模型。OpenHermes-2.5-Mistral-7B

mlx-community/OpenHermes-2.5-Mistral-7B 是 ModelScope 魔搭社区上的text-generation模型，采用 apache-2.0 许可，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: mlx-community/OpenHermes-2.5-Mistral-7B
- **License**: apache-2.0
- **Tasks**: text-generation
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Tags**: openhermes, mlx-llm, mlx
- **Downloads**: 1003
- **Stars**: 0
- **Last updated**: 2024-12-20

Source: https://www.modelscope.cn/models/mlx-community/OpenHermes-2.5-Mistral-7B

---

# 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).