---
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-Coder-Next/blob/main/LICENSE
base_model: Qwen/Qwen3-Coder-Next
---

# 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](https://docs.vllm.ai/en/latest/)
- **Model Optimizer:** [AMD-Quark](https://quark.docs.amd.com/latest/index.html) (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](https://huggingface.co/datasets/mit-han-lab/pile-val-backup)

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](https://quark.docs.amd.com/latest/index.html). 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](https://docs.vllm.ai/en/latest/) backend.

## Evaluation
The model was evaluated on GSM8K benchmarks. 

### Accuracy

<table>
  <tr>
   <td><strong>Benchmark</strong>
   </td>
   <td><strong>Qwen3-Coder-Next </strong>
   </td>
   <td><strong>Qwen3-Coder-Next-MXFP4(this model)</strong>
   </td>
   <td><strong>Recovery</strong>
   </td>
  </tr>
  <tr>
   <td>GSM8K (flexible-extract)
   </td>
   <td>94.54
   </td>
   <td>93.25
   </td>
   <td>98.6%
   </td>
  </tr>
</table>

### 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
```

# License
Modifications Copyright(c) 2025 Advanced Micro Devices, Inc. All rights reserved.