Qwen3-Coder-REAP-25B-A3B-qx65x-hi-mlx

This version of the Deckard(qx) formula uses embeddings at 6 bit, along with the head and select attention paths, leaving the rest at 5 bit.

The model is quantized with group size 32(hi).

It is aimed as a mid-range quant with a quality approaching q8, that would run comfortably on a smaller Mac.

This is an update from the model: Qwen3-Coder-REAP-25B-A3B-qx64-hi-mlx that uses the base and embeddings at 4 bit.

Metrics coming soon.

-G

This model Qwen3-Coder-REAP-25B-A3B-qx65x-hi-mlx was converted to MLX format from cerebras/Qwen3-Coder-REAP-25B-A3B using mlx-lm version 0.28.3.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Qwen3-Coder-REAP-25B-A3B-qx65x-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)