---
license: apache-2.0
language:
- en
- code
library_name: transformers
tags:
- causal-lm
- moe
- mixture-of-experts
- qwen
- distillation
- svd
- lora-merged
- code-generation
- mlx
- mlx-my-repo
- mlx
- mlx-my-repo
base_model: YOYO-AI/Qwen3-Coder-30B-A3B-Instruct-480B-Distill-V2-Fp32-mlx-fp16
---

# introvoyz041/Qwen3-Coder-30B-A3B-Instruct-480B-Distill-V2-Fp32-mlx-fp16-mlx-4Bit

The Model [introvoyz041/Qwen3-Coder-30B-A3B-Instruct-480B-Distill-V2-Fp32-mlx-fp16-mlx-4Bit](https://huggingface.co/introvoyz041/Qwen3-Coder-30B-A3B-Instruct-480B-Distill-V2-Fp32-mlx-fp16-mlx-4Bit) was converted to MLX format from [YOYO-AI/Qwen3-Coder-30B-A3B-Instruct-480B-Distill-V2-Fp32-mlx-fp16](https://huggingface.co/YOYO-AI/Qwen3-Coder-30B-A3B-Instruct-480B-Distill-V2-Fp32-mlx-fp16) using mlx-lm version **0.28.3**.

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("introvoyz041/Qwen3-Coder-30B-A3B-Instruct-480B-Distill-V2-Fp32-mlx-fp16-mlx-4Bit")

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)
```