Model card for qwen3_vit_416m.qwen3_8_flash_next

A Qwen ViT image feature model extracted from Qwen3.8-Flash-Next. This is the classifier-ready wrapper with average pooling and affine-free LayerNorm over the encoder features.

NOTE: This checkpoint is a native timm remap of the original vision weights, with no additional training. It contains no language-model weights or trained image-classification head.

Model Notes

• Image inputs repeat one frame across the original temporal patch kernel. The temporal Conv3d weights are summed into a Conv2d for this image-only implementation.
• The backbone uses GELU-tanh MLPs, learned absolute positions and axial 2D RoPE. Absolute positions are interpolated for the input grid; RoPE is regenerated at each size.
• The timm transforms normalize RGB pixels using mean=(0.5, 0.5, 0.5) and std=(0.5, 0.5, 0.5). Rectangular inputs are supported. Each image dimension must be divisible by 16; any variant using the 2×2 merger requires divisibility by 32.
• forward_features() returns raw, unnormalized NHWC backbone features. The _enc variant returns spatially merged tokens from forward(); the classifier variant returns pooled image embeddings until a classification head is added.

Model Details

• Model Type: Image Feature Encoder
• Model Stats:
  • Params (M): 415.0
  • GMACs: 1280.1
  • Activations (M): 2993.6
  • Image size: 768 x 768
• Source revision: de4b8e4d43b917e7706784d8bb445c9af86a3540
• License source: https://huggingface.co/Qwen/Qwen3.8-Flash-Next/blob/de4b8e4d43b917e7706784d8bb445c9af86a3540/LICENSE
• Original: https://huggingface.co/Qwen/Qwen3.8-Flash-Next
• License: Qwen Community License 1.0
• Backbone width: 1152
• Papers:
  • Qwen3.8-Flash-Next: A New Architecture, Towards Ultimate Cost-Efficiency: https://qwen.ai/blog?id=qwen3.8-flash-next
  • PyTorch Image Models: https://github.com/huggingface/pytorch-image-models

Model Usage

Image Features

import torch
import timm
from PIL import Image

model = timm.create_model('hf-hub:timm/qwen3_vit_416m.qwen3_8_flash_next', pretrained=True).eval()
data_config = timm.data.resolve_model_data_config(model)
transform = timm.data.create_transform(**data_config, is_training=False)
image = Image.open('image.jpg').convert('RGB')
x = transform(image).unsqueeze(0)

with torch.inference_mode():
    output = model(x)  # (1, 1152): image embeddings
    features = model.forward_features(x)  # (1, 48, 48, 1152): raw backbone features (NHWC)

Intermediate Feature Maps

with torch.inference_mode():
    maps = model.forward_intermediates(
        x, indices=3, output_fmt='NCHW', intermediates_only=True,
    )
for feature_map in maps:
    print(feature_map.shape)  # (1, 1152, 48, 48)

Classification Fine-tuning

model = timm.create_model(
    'hf-hub:timm/qwen3_vit_416m.qwen3_8_flash_next', pretrained=True, num_classes=45,
)
logits = model(x)  # (1, 45)

The new linear head is randomly initialized and must be trained on your target dataset.

Citation

@misc{qwen3.8flashnext,
  title={{Qwen3.8-Flash-Next}: A New Architecture, Towards Ultimate Cost-Efficiency},
  author={{Qwen Team}},
  month={August},
  year={2026},
  url={https://qwen.ai/blog?id=qwen3.8-flash-next}
}

@misc{rw2019timm,
  author = {Ross Wightman},
  title = {PyTorch Image Models},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.4414861},
  howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}