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amd/Qwen3.5-397B-A17B-MXFP4

sourceHugging Faceapache-2.0updated 2mo agoView on Hugging Face

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Model Card

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

<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