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amd/Qwen3.5-397B-A17B-NVFP4
Qwen3.5-397B-A17B-NVFP4 is a image-text-to-text model from amd. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
- Model Architecture: Qwen35MoeForConditionalGeneration - Input: Text, Image, Video - Output: Text - Supported Hardware Microarchitecture: AMD MI300/MI350/MI355 (emulation) - ROCm: 7.2.2 - PyTorch: 2.10.0 - Transforme…
Downloads · 30 days
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From the Hugging Face model README
The model was quantized from Qwen/Qwen3.5-397B-A17B using AMD-Quark. The weights and activations are quantized to NVFP4.
Quantization scripts:
exclude_layers="lm_head model.visual.* mtp.* *mlp.gate *shared_expert* *shared_expert_gate* *linear_attn.* *self_attn.*"
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
export MODEL_DIR=Qwen/Qwen3.5-397B-A17B
export output_dir=amd/Qwen3.5-397B-A17B-NVFP4
python3 quantize_quark.py \
--model_dir $MODEL_DIR \
--quant_scheme nvfp4\
--num_calib_data 128 \
--multi_gpu balanced \
--exclude_layers $exclude_layers \
--model_export hf_format \
--output_dir $output_dir
This model can be deployed efficiently using the vLLM backend.
The model was evaluated on GSM8K benchmarks.
The GSM8K result was obtained using the lm-evaluation-harness framework, based on the Docker image rocm/vllm-dev:nightly_main_20260603.
Install the lm-eval (Version: 0.4.12) in container first.
pip install lm-eval[api]
export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
lm_eval --model vllm
--model_args pretrained=amd/Qwen3.5-397B-A17B-NVFP4,tensor_parallel_size=8,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
Modifications Copyright(c) 2026 Advanced Micro Devices, Inc. All rights reserved.