Qwen3-Coder-Next-MLX-5bit

Qwen3-Coder-Next - MLX 5-bit

5-bit weight-quantized MLX version of Qwen/Qwen3-Coder-Next, Qwen's 80B-A3B agentic coding MoE (512 experts, 10 active; hybrid Gated DeltaNet + Gated Attention; 256k context). Only ~3B parameters are active per token, so it runs far faster than its 80B total suggests. Converted with mlx_lm — the canonical MLX runtime for qwen3_next — and smoke-verified (chat + code probes) on Apple Silicon with this exact payload before publishing. See PROVENANCE.md.

Approximate model size: ~55 GB

Model Specifications

Quickstart

from mlx_lm import load, generate

model, tokenizer = load("majentik/Qwen3-Coder-Next-MLX-5bit")

prompt = tokenizer.apply_chat_template(
    [{"role": "user", "content": "Write a Python function that merges two sorted lists."}],
    add_generation_prompt=True, tokenize=False,
)
print(generate(model, tokenizer, prompt=prompt, max_tokens=512))

Or from the command line:

mlx_lm.generate --model majentik/Qwen3-Coder-Next-MLX-5bit --prompt "Refactor this function ..."

Variants in this family

Smoke verification covers load + short-form generation quality gates only; it is not a benchmark. For maximum fidelity use the largest variant that fits your unified memory (leave ~20% headroom for KV cache).