---
title: Jinx-gpt-oss-20b-mxfp4-mlx
canonical_url: "https://www.modelscope.cn/models/mlx-community/Jinx-gpt-oss-20b-mxfp4-mlx"
md_url: "https://www.modelscope.cn/models/mlx-community/Jinx-gpt-oss-20b-mxfp4-mlx.md"
repository: mlx-community/Jinx-gpt-oss-20b-mxfp4-mlx
last_updated: 2025-09-17
license: apache-2.0
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - gpt_oss
architectures:
  - GptOssForCausalLM
base_model:
  - Jinx-org/Jinx-gpt-oss-20b-mxfp4
base_model_relation: quantized
parameters: 4.8B
tensor_type:
  - U32
  - BF16
  - U8
library_name:
  - mlx
  - safetensors
  - pytorch
frameworks:
  - pytorch
inference_backends:
  - "deploy_task text"
  - "sglang 0.5.2"
downloads: 114
stars: 0
tags:
  - vllm
  - mlx
---

# Jinx-gpt-oss-20b-mxfp4-mlx

> Jinx-gpt-oss-20b-mxfp4-mlx - mlx-community 在 ModelScope 开源的模型。mlx-community/Jinx-gpt-oss-20b-mxfp4-mlx

mlx-community/Jinx-gpt-oss-20b-mxfp4-mlx 是 ModelScope 魔搭社区上的 4.8B 参数text-generation模型，采用 apache-2.0 许可，基于 Jinx-org/Jinx-gpt-oss-20b-mxfp4 构建，可用 deploy_task text、sglang 0.5.2 部署。

- **Repository**: mlx-community/Jinx-gpt-oss-20b-mxfp4-mlx
- **License**: apache-2.0
- **Tasks**: text-generation
- **Parameters**: 4.8B
- **Base model**: Jinx-org/Jinx-gpt-oss-20b-mxfp4
- **Inference backends**: deploy_task text, sglang 0.5.2
- **Tags**: vllm, mlx
- **Downloads**: 114
- **Stars**: 0
- **Last updated**: 2025-09-17

Source: https://www.modelscope.cn/models/mlx-community/Jinx-gpt-oss-20b-mxfp4-mlx

---

# mlx-community/Jinx-gpt-oss-20b-mxfp4-mlx

This model [mlx-community/Jinx-gpt-oss-20b-mxfp4-mlx](https://huggingface.co/mlx-community/Jinx-gpt-oss-20b-mxfp4-mlx) was
converted to MLX format from [Jinx-org/Jinx-gpt-oss-20b-mxfp4](https://huggingface.co/Jinx-org/Jinx-gpt-oss-20b-mxfp4)
using mlx-lm version **0.27.1**.

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/Jinx-gpt-oss-20b-mxfp4-mlx")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)
```