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/vLLM
- Model Optimizer: AMD-Quark (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 using AMD-Quark. The weights are quantized to MXFP4 and activations are quantized to MXFP4.
Quantization scripts:
import os
from quark.torch import LLMTemplate, ModelQuantizer
# 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 framework.
Accuracy
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.