metadata

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 was converted to MLX format from DavidAU/Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M using mlx-lm version 0.26.1.

Use with mlx

pip install mlx-lm

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)