---
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
- Qwen3-30B-A3B-Thinking-2507
- Qwen3-30B-A3B
- mixture of experts
- 128 experts
- 10 active experts
- 256k context
- qwen3
- finetune
- brainstorm 40x
- brainstorm
- thinking
- reasoning
- qwen3_moe
- mlx
base_model: DavidAU/Qwen3-53B-A3B-2507-THINKING-TOTAL-RECALL-v2-MASTER-CODER
pipeline_tag: text-generation
---

# Qwen3-53B-A3B-2507-THINKING-TOTAL-RECALL-v2-MASTER-CODER-1m-dwq5-mlx

Set only as much context as you need.

If your app takes 15k context to draft, set it to 32k, and the model will focus on the task and the available timeline.

Increase the context as you move to production-ready code.

If you start with an 1M context it will plan accordingly :) 

This model is a me/now personality, sharp wording, sometimes sassy.

This model [Qwen3-53B-A3B-2507-THINKING-TOTAL-RECALL-v2-MASTER-CODER-1m-dwq5-mlx](https://huggingface.co/Qwen3-53B-A3B-2507-THINKING-TOTAL-RECALL-v2-MASTER-CODER-1m-dwq5-mlx) was
converted to MLX format from [DavidAU/Qwen3-53B-A3B-2507-THINKING-TOTAL-RECALL-v2-MASTER-CODER](https://huggingface.co/DavidAU/Qwen3-53B-A3B-2507-THINKING-TOTAL-RECALL-v2-MASTER-CODER)
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("Qwen3-53B-A3B-2507-THINKING-TOTAL-RECALL-v2-MASTER-CODER-1m-dwq5-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)
```