qwen3_vit.qwen3_vl

A Qwen ViT image feature model extracted from Qwen3-VL-4B-Instruct. 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.
• Qwen3-VL DeepStack projectors are omitted. Intermediate backbone features are available through forward_intermediates() or features_only=True.

Model Details

• Model Type: Image Feature Encoder
• Model Stats:
  • Params (M): 305.5
  • GMACs: 959.1
  • Activations (M): 2607.0
  • Image size: 768 x 768
• Source revision: ebb281ec70b05090aa6165b016eac8ec08e71b17
• License source: https://raw.githubusercontent.com/QwenLM/Qwen3-VL/96588727e44c78b25ba03ea03b8e12f7e64fd0da/LICENSE
• Original: https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct
• License: Apache 2.0
• Backbone width: 1024
• Papers:
  • Qwen3-VL Technical Report: https://arxiv.org/abs/2511.21631
  • 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_306m.qwen3_vl_4b', 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, 1024): image embeddings
    features = model.forward_features(x)  # (1, 48, 48, 1024): 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, 1024, 48, 48)

Classification Fine-tuning

model = timm.create_model(
    'hf-hub:timm/qwen3_vit_306m.qwen3_vl_4b', 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

@article{Qwen3-VL,
  title={Qwen3-VL Technical Report},
  author={Bai, Shuai and others},
  journal={arXiv preprint arXiv:2511.21631},
  year={2025}
}

@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}}
}