Model Overview
- Model Architecture: Qwen3_5MoeForConditionalGeneration
- Input: Text
- Output: Text
- Supported Hardware Microarchitecture: AMD MI300 MI350/MI355
- ROCm: 7.0.0
- PyTorch: 2.9.1
- Transformers: 5.3.0
- vLLM: 0.16.0rc2
- lm-evaluation-harness: 0.4.11
- Operating System(s): Linux
- Inference Engine: SGLang/vLLM
- Model Optimizer: AMD-Quark (v0.12)
- Weight quantization: OCP MXFP4, Static
- Activation quantization: OCP MXFP4, Dynamic
Model Quantization
The model was quantized from Qwen/Qwen3.5-35B-A3B-FP8 using AMD-Quark. 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/Qwen3.5-35B-A3B-FP8 \
--quant_scheme mxfp4 \
--file2file_quantization \
--exclude_layers $exclude_layers \
--output_dir amd/Qwen3.5-35B-A3B-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 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.
docker pull rocm/vllm-dev:nightly_main_20260211
Evaluating model in a new terminal
lm_eval \
--model vllm \
--model_args pretrained=amd/Qwen3.5-35B-A3B-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.