---
base_model:
- Qwen/Qwen3.5-397B-A17B
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3.5-397B-A17B/blob/main/LICENSE
---

# Model Overview

- **Model Architecture:** Qwen3_5MoeForConditionalGeneration
  - **Input:** Text
  - **Output:** Text
- **Supported Hardware Microarchitecture:** AMD MI300 MI350/MI355
- **ROCm**: 7.0
- **PyTorch**: 2.8.0
- **Transformers**: 5.2.0
- **Operating System(s):** Linux
- **Inference Engine:** [SGLang](https://docs.sglang.ai/)/[vLLM](https://docs.vllm.ai/en/latest/)
- **Model Optimizer:** [AMD-Quark](https://quark.docs.amd.com/latest/index.html) (v0.11)
  - **Weight quantization:** OCP MXFP4, Static
  - **Activation quantization:** OCP MXFP4, Dynamic


# Model Quantization

The model was quantized from [Qwen/Qwen3.5-397B-A17B-FP8](https://huggingface.co/Qwen/Qwen3.5-397B-A17B-FP8) using [AMD-Quark](https://quark.docs.amd.com/latest/index.html). The weights are quantized to MXFP4 and activations are quantized to MXFP4.


**Quantization scripts:**
```
cd Quark/examples/torch/language_modeling/llm_ptq/
export exclude_layers="lm_head  model.visual.*  mtp.*  *mlp.gate *shared_expert_gate* *.linear_attn.*  *.self_attn.*  *.shared_expert.*"
python3 quantize_quark.py --model_dir Qwen/Qwen3.5-397B-A17B-FP8 \
                          --quant_scheme  mxfp4 \
                          --file2file_quantization \
                          --exclude_layers $exclude_layers \
                          --output_dir amd/Qwen3.5-397B-A17B-MXFP4
```
For further details or issues, please refer to the AMD-Quark documentation or contact the respective developers.

# Evaluation
The model was evaluated on gsm8k benchmarks using the [vllm](https://github.com/vllm-project/vllm/tree/v0.13.0) framework.

### Accuracy

<table>
  <tr>
   <td><strong>Benchmark</strong>
   </td>
   <td><strong>Qwen/Qwen3.5-397B-A17B-FP8 </strong>
   </td>
   <td><strong>amd/Qwen3.5-397B-A17B-MXFP4(this model)</strong>
   </td>
   <td><strong>Recovery</strong>
   </td>
  </tr>
  <tr>
   <td>gsm8k (flexible-extract) 
   </td>
   <td>95.38
   </td>
   <td>94.24
   </td>
   <td>98.80%
   </td>
  </tr>
</table>


### Reproduction

The GSM8K results were obtained using the vLLM framework, based on the Docker image `rocm/vllm-dev:nightly_main_20260211`, and vLLM is installed inside the container with fixes applied for model support.

#### Evaluating model in a new terminal
```
lm_eval \
  --model vllm \
  --model_args pretrained=$MODEL,tensor_parallel_size=1,max_model_len=262144,gpu_memory_utilization=0.90,max_gen_toks=2048,trust_remote_code=True,reasoning_parser=qwen3 \
  --tasks gsm8k  --num_fewshot 5 \
  --batch_size auto
```


# License
Modifications Copyright(c) 2026 Advanced Micro Devices, Inc. All rights reserved.