Qwen3-Coder-Next-int4-mixed-AutoRound

Qwen3-Coder-Next INT4 Mixed-Bits (AutoRound)

Model Details

This is a mixed-bits INT4 quantized version of Qwen/Qwen3-Coder-Next (80B MoE, 14B active parameters) generated using Intel AutoRound.

Quantization Strategy (Intel MoE Recipe)

• Group size: 128
• Symmetric: Yes
• Tuning: iters=50, GPU-accelerated with SignRound optimization

Model Size

• Original BF16: ~160GB
• Quantized: ~41GB

Hardware Requirements

Important: This mixed-bits quantization requires GPUs with SM 9.0+ (Ada Lovelace/Hopper) for optimal kernel support. RTX 3090 (SM 8.6) may experience kernel compatibility issues due to the 8-bit non-expert layers requiring ConchLinearKernel.

• Minimum VRAM: ~48GB (2x RTX 4090 recommended)
• Tensor Parallel: TP=2 (16 attention heads divisible by 2)

For RTX 3090 users, consider using uniform 4-bit quantization instead.

How To Use

vLLM (Recommended)

Requires vLLM >= 0.15.0 with Qwen3-Next support:

from vllm import LLM, SamplingParams

model = LLM(
    model="raydelossantos/Qwen3-Coder-Next-int4-mixed-AutoRound",
    tensor_parallel_size=2,
    trust_remote_code=True,
    gpu_memory_utilization=0.9,
)

prompts = ["Write a Python function to calculate fibonacci numbers"]
sampling_params = SamplingParams(temperature=0.7, top_p=0.95, max_tokens=2048)
outputs = model.generate(prompts, sampling_params)

for output in outputs:
    print(output.outputs[0].text)

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "raydelossantos/Qwen3-Coder-Next-int4-mixed-AutoRound"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype="auto",
    trust_remote_code=True,
)

prompt = "Write a Python function to calculate fibonacci numbers"
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

inputs = tokenizer([text], return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Quantization Code

This model was quantized using the following approach:

from auto_round import AutoRound

model_name = "Qwen/Qwen3-Coder-Next"

# Build layer config for mixed-bits (Intel recipe)
layer_config = {}
for i in range(48):  # 48 layers
    prefix = f"model.layers.{i}"
    
    # Attention layers -> 8-bit
    if i in [3, 7, 11, 15, 19, 23, 27, 31, 35, 39, 43, 47]:  # self_attn layers
        for proj in ["q_proj", "k_proj", "v_proj", "o_proj"]:
            layer_config[f"{prefix}.self_attn.{proj}"] = {"bits": 8}
    else:  # linear_attn layers
        for proj in ["in_proj_qkvz", "in_proj_ba", "out_proj"]:
            layer_config[f"{prefix}.linear_attn.{proj}"] = {"bits": 8}
    
    # MLP gate -> 8-bit
    layer_config[f"{prefix}.mlp.gate"] = {"bits": 8}
    
    # shared_expert_gate -> 16-bit (skipped)
    layer_config[f"{prefix}.mlp.shared_expert_gate"] = {"bits": 16}

autoround = AutoRound(
    model_name,
    bits=4,  # Default for experts
    group_size=128,
    sym=True,
    iters=50,
    lr=5e-3,
    layer_config=layer_config,
    device_map="0,1,2",
    low_gpu_mem_usage=True,
)

autoround.quantize_and_save(format="auto_round", output_dir="./output")

Acknowledgments

• Base Model: Qwen/Qwen3-Coder-Next by Qwen Team
• Quantization: Intel AutoRound
• Reference: Intel/Qwen3-Next-80B-A3B-Thinking-int4-mixed-AutoRound

Citation

@article{cheng2023optimize,
  title={Optimize weight rounding via signed gradient descent for the quantization of llms},
  author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi},
  journal={arXiv preprint arXiv:2309.05516},
  year={2023}
}

License

Apache 2.0 (follows base model license)