---
title: Dolphin-Mistral-24B-Venice-Edition-mlx-8Bit
canonical_url: "https://www.modelscope.cn/models/mlx-community/Dolphin-Mistral-24B-Venice-Edition-mlx-8Bit"
md_url: "https://www.modelscope.cn/models/mlx-community/Dolphin-Mistral-24B-Venice-Edition-mlx-8Bit.md"
repository: mlx-community/Dolphin-Mistral-24B-Venice-Edition-mlx-8Bit
last_updated: 2025-06-19
license: apache-2.0
model_type:
  - mistral
architectures:
  - MistralForCausalLM
base_model:
  - cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition
base_model_relation: quantized
parameters: 6.6B
tensor_type:
  - F16
  - U32
library_name:
  - mlx
  - safetensors
  - pytorch
frameworks:
  - pytorch
inference_backends:
  - "deploy_task text/emb"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 220
stars: 0
tags:
  - mlx
  - uncensored
  - dolphin
  - venice
---

# Dolphin-Mistral-24B-Venice-Edition-mlx-8Bit

> Dolphin-Mistral-24B-Venice-Edition-mlx-8Bit - mlx-community 在 ModelScope 开源的模型。mlx-community/Dolphin-Mistral-24B-Venice-Edition-mlx-8Bit

mlx-community/Dolphin-Mistral-24B-Venice-Edition-mlx-8Bit 是 ModelScope 魔搭社区上的 6.6B 参数机器学习模型，采用 apache-2.0 许可，基于 cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition 构建，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: mlx-community/Dolphin-Mistral-24B-Venice-Edition-mlx-8Bit
- **License**: apache-2.0
- **Parameters**: 6.6B
- **Base model**: cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Tags**: mlx, uncensored, dolphin, venice
- **Downloads**: 220
- **Stars**: 0
- **Last updated**: 2025-06-19

Source: https://www.modelscope.cn/models/mlx-community/Dolphin-Mistral-24B-Venice-Edition-mlx-8Bit

---

# mlx-community/Dolphin-Mistral-24B-Venice-Edition-mlx-8Bit

MLX 8bit quant of [cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition](https://huggingface.co/cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition) released **2025-06-12**.

*"This is an updated version based on feedback we received on v1"*, see [discussion](https://huggingface.co/cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition/discussions/1) at original repo.

The Model [mlx-community/Dolphin-Mistral-24B-Venice-Edition-mlx-8Bit](https://huggingface.co/mlx-community/Dolphin-Mistral-24B-Venice-Edition-mlx-8Bit) was converted to MLX format from [cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition](https://huggingface.co/cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition) using mlx-lm version **0.22.3**.

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/Dolphin-Mistral-24B-Venice-Edition-mlx-8Bit")

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)
```