---
base_model:
- Qwen/Qwen3.5-397B-A17B-FP8
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, Image, Video
  - **Output:** Text
- **Supported Hardware Microarchitecture:** AMD MI300 MI350/MI355
- **ROCm**: 7.0.0
- **PyTorch**: 2.9.1
- **Transformers**: 5.3.0
- **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) (v0.12)
  - **Quantized layers:** Experts in language model only
  - **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:**
```
import os
from quark.torch import LLMTemplate, ModelQuantizer

# Register qwen3_5_moe template
qwen3_5_moe_template = LLMTemplate(
    model_type="qwen3_5_moe",
    kv_layers_name=["*k_proj", "*v_proj"],
    q_layer_name="*q_proj"
)
LLMTemplate.register_template(qwen3_5_moe_template)

# Configuration
ckpt_path = "Qwen/Qwen3.5-397B-A17B-FP8"
output_dir = "amd/Qwen3.5-397B-A17B-MXFP4"
quant_scheme = "mxfp4"
exclude_layers = ["lm_head", "model.visual.*", "mtp.*", "*mlp.gate", "*shared_expert_gate*", "*.linear_attn.*", "*.self_attn.*", "*.shared_expert.*"]

# Get quant config from template
template = LLMTemplate.get("qwen3_5_moe")
quant_config = template.get_config(scheme=quant_scheme, exclude_layers=exclude_layers)

# Quantize with File-to-file mode
quantizer = ModelQuantizer(quant_config)
quantizer.direct_quantize_checkpoint(
    pretrained_model_path=ckpt_path,
    save_path=output_dir,
)
```
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>94.54
   </td>
   <td>99.12%
   </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.

#### Evaluating model in a new terminal
```
lm_eval \
  --model vllm \
  --model_args pretrained=amd/Qwen3.5-397B-A17B-MXFP4,tensor_parallel_size=4,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.