---
title: OpenHermes-2.5-Mistral-7B-4bit-mlx
canonical_url: "https://www.modelscope.cn/models/mlx-community/OpenHermes-2.5-Mistral-7B-4bit-mlx"
md_url: "https://www.modelscope.cn/models/mlx-community/OpenHermes-2.5-Mistral-7B-4bit-mlx.md"
repository: mlx-community/OpenHermes-2.5-Mistral-7B-4bit-mlx
last_updated: 2024-12-19
license: apache-2.0
model_type:
  - mistral
architectures:
  - MistralForCausalLM
parameters: 1.2B
tensor_type:
  - U32
  - F16
library_name:
  - mlx
  - safetensors
  - 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: 1008
stars: 0
tags:
  - mistral
  - instruct
  - finetune
  - chatml
  - gpt4
  - "synthetic data"
  - distillation
  - mlx
---

# OpenHermes-2.5-Mistral-7B-4bit-mlx

> OpenHermes-2.5-Mistral-7B-4bit-mlx - mlx-community 在 ModelScope 开源的模型。mlx-community/OpenHermes-2.5-Mistral-7B-4bit-mlx This model was converted to MLX format from teknium/OpenHermes-2.5-Mistral-7B. Refer to the original model card for more details on the…

mlx-community/OpenHermes-2.5-Mistral-7B-4bit-mlx 是 ModelScope 魔搭社区上的 1.2B 参数机器学习模型，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-4bit-mlx
- **License**: apache-2.0
- **Parameters**: 1.2B
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Tags**: mistral, instruct, finetune, chatml, gpt4, synthetic data, distillation, mlx
- **Downloads**: 1008
- **Stars**: 0
- **Last updated**: 2024-12-19

Source: https://www.modelscope.cn/models/mlx-community/OpenHermes-2.5-Mistral-7B-4bit-mlx

---

# mlx-community/OpenHermes-2.5-Mistral-7B-4bit-mlx
This model was converted to MLX format from [`teknium/OpenHermes-2.5-Mistral-7B`]().
Refer to the [original model card](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) for more details on the model.
## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/OpenHermes-2.5-Mistral-7B-4bit-mlx")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```