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
• 8 active experts
• 256k context
• qwen3
• finetune
• brainstorm 20x
• brainstorm
• thinking
• reasoning
• uncensored
• abliterated
• qwen3_moe
• mlx base_model: DavidAU/Qwen3-42B-A3B-2507-Thinking-Abliterated-uncensored-TOTAL-RECALL-v2-Medium-MASTER-CODER pipeline_tag: text-generation

Qwen3-42B-A3B-2507-Thinking-Abliterated-uncensored-TOTAL-RECALL-v2-Medium-MASTER-CODER-qx4-mlx

This model Qwen3-42B-A3B-2507-Thinking-Abliterated-uncensored-TOTAL-RECALL-v2-Medium-MASTER-CODER-qx4-mlx was converted to MLX format from DavidAU/Qwen3-42B-A3B-2507-Thinking-Abliterated-uncensored-TOTAL-RECALL-v2-Medium-MASTER-CODER using mlx-lm version 0.26.3.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Qwen3-42B-A3B-2507-Thinking-Abliterated-uncensored-TOTAL-RECALL-v2-Medium-MASTER-CODER-qx4-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)