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amd/
Qwen3-Coder-Next-MXFP4

Name: Qwen3-Coder-Next-MXFP4
Availability: InStock

safetensors

qwen3_next

Qwen/Qwen3-Coder-Next

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Model Overview

Model Architecture: qwen3_next
- Input: Text
- Output: Text
Supported Hardware Microarchitecture: AMD MI350/MI355
ROCm: 7.1.0
Operating System(s): Linux
Inference Engine: vLLM
Model Optimizer: AMD-Quark (V0.11)
- moe
  - Weight quantization: OCP MXFP4, Static
  - Activation quantization: OCP MXFP4, Dynamic
- attn: linear_attn.out_proj, self_attn.o_proj
  - Weight quantization: OCP MXFP4, Static
  - Activation quantization: OCP MXFP4, Dynamic
Calibration Dataset: Pile

This model was built with Qwen3-Coder-Next model by applying AMD-Quark for MXFP4 quantization.

Model Quantization

The model was quantized from Qwen/Qwen3-Coder-Next using AMD-Quark. The weights and activations are quantized to MXFP4.

Quantization scripts:

Note that qwen3_next is not in the built-in model template list in Quark V0.11, it has to be registered before quantization.

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from datasets import load_dataset
from quark.torch import LLMTemplate, ModelQuantizer, export_safetensors
from quark.contrib.llm_eval import ppl_eval

# Register qwen3_next template
qwen3_next_template = LLMTemplate(
    model_type="qwen3_next",
    kv_layers_name=["*qkvz"],
    q_layer_name="*qkvz",
    exclude_layers_name=["lm_head", "*linear_attn.in_proj_ba", "*linear_attn.in_proj_qkvz","*mlp.gate", "*mlp.shared_expert_gate", "*self_attn.k_proj", "*self_attn.q_proj", "*self_attn.v_proj"],
)
LLMTemplate.register_template(qwen3_next_template)

# Configuration
ckpt_path = "Qwen/Qwen3-Coder-Next"
output_dir = "amd/Qwen3-Coder-Next-MXFP4"
quant_scheme = "mxfp4"
exclude_layers = ["lm_head", "*linear_attn.in_proj_ba", "*linear_attn.in_proj_qkvz","*mlp.gate", "*mlp.shared_expert_gate", "*self_attn.k_proj", "*self_attn.q_proj", "*self_attn.v_proj"]

# Load model
tokenizer = AutoTokenizer.from_pretrained(ckpt_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(ckpt_path, torch_dtype="auto", device_map="auto")
model.eval()

# Get quant config from template
template = LLMTemplate.get(model.config.model_type)
quant_config = template.get_config(scheme=quant_scheme, exclude_layers=exclude_layers)

# Quantize
quantizer = ModelQuantizer(quant_config)
model = quantizer.quantize_model(model)
model = quantizer.freeze(model)

# Export hf_format
export_safetensors(model, output_dir, custom_mode="quark")
tokenizer.save_pretrained(output_dir)

# Evaluate PPL (optional)
testdata = load_dataset("wikitext", "wikitext-2-raw-v1", split="test")
testenc = tokenizer("\n\n".join(testdata["text"]), return_tensors="pt")
ppl = ppl_eval(model, testenc, model.device)
print(f"Perplexity: {ppl.item()}")

Deployment

Use with vLLM

This model can be deployed efficiently using the vLLM backend.

Evaluation

The model was evaluated on GSM8K benchmarks.

Accuracy

Benchmark	Qwen3-Coder-Next	Qwen3-Coder-Next-MXFP4(this model)	Recovery
GSM8K (flexible-extract)	94.54	93.25	98.6%

Reproduction

The GSM8K results were obtained using the lm-evaluation-harness framework, based on the Docker image vllm/vllm-openai-rocm:v0.14.0.

Install the vLLM (commit ecb4f822091a64b5084b3a4aff326906487a363f) and lm-eval (Version: 0.4.10) in container first.

git clone https://github.com/vllm-project/vllm.git
cd vllm
python3 setup.py develop

pip install lm-eval

Launching server

MODEL=amd/Qwen3-Coder-Next-MXFP4
SAFETENSORS_FAST_GPU=1 \
VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 \
vllm serve $MODEL \
  --tensor-parallel-size 4 \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder \
  --trust-remote-code

Evaluating model in a new terminal

lm_eval \
  --model local-completions \
  --model_args "model=amd/Qwen3-Coder-Next-MXFP4,base_url=http://localhost:8000/v1/completions,num_concurrent=256,max_retries=10,max_gen_toks=2048,tokenized_requests=False,tokenizer_backend=None" \
  --tasks gsm8k \
  --num_fewshot 5 \
  --batch_size auto

amd/Qwen3-Coder-Next-MXFP4