README.md

---
license: apache-2.0
library_name: mlx
language:
- en
- fr
- zh
- de
tags:
- programming
- code generation
- code
- codeqwen
- moe
- coding
- coder
- qwen2
- chat
- qwen
- qwen-coder
- Qwen3-Coder-30B-A3B-Instruct
- Qwen3-30B-A3B
- mixture of experts
- 128 experts
- 8 active experts
- 256k context
- qwen3
- finetune
- brainstorm 20x
- brainstorm
- optional thinking
- qwen3_moe
- mlx
base_model: DavidAU/Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M
pipeline_tag: text-generation
---

# Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M-q6-hi-mlx

This model [Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M-q6-hi-mlx](https://huggingface.co/Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M-q6-hi-mlx) was
converted to MLX format from [DavidAU/Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M](https://huggingface.co/DavidAU/Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M)
using mlx-lm version **0.26.1**.

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M-q6-hi-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)
```