Add native timm Qwen vision weights, model card, and original license

LICENSE

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README.md

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+---
+tags:
+- image-feature-extraction
+- timm
+- transformers
+pipeline_tag: image-feature-extraction
+library_name: timm
+base_model: Qwen/Qwen3-VL-30B-A3B-Instruct
+license: apache-2.0
+---
+# Model card for qwen3_vit_416m.qwen3_vl_30b_a3b
+
+A Qwen ViT image feature model extracted from [Qwen3-VL-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-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): 415.0
+  - GMACs: 1280.1
+  - Activations (M): 2993.6
+  - Image size: 768 x 768
+- **Source revision:** [9c4b90e1e4ba969fd3b5378b57d966d725f1b86c](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct/tree/9c4b90e1e4ba969fd3b5378b57d966d725f1b86c)
+- **License source:** https://raw.githubusercontent.com/QwenLM/Qwen3-VL/96588727e44c78b25ba03ea03b8e12f7e64fd0da/LICENSE
+- **Original:** https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct
+- **License:** [Apache 2.0](LICENSE)
+- **Backbone width:** 1152
+- **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
+
+```python
+import torch
+import timm
+from PIL import Image
+
+model = timm.create_model('hf-hub:timm/qwen3_vit_416m.qwen3_vl_30b_a3b', 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
+
+```python
+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
+
+```python
+model = timm.create_model(
+    'hf-hub:timm/qwen3_vit_416m.qwen3_vl_30b_a3b', 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
+```bibtex
+@article{Qwen3-VL,
+  title={Qwen3-VL Technical Report},
+  author={Bai, Shuai and others},
+  journal={arXiv preprint arXiv:2511.21631},
+  year={2025}
+}
+```
+```bibtex
+@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}}
+}
+```

config.json

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+{
+  "architecture": "qwen3_vit_416m",
+  "num_classes": 0,
+  "num_features": 1152,
+  "global_pool": "avg",
+  "model_args": {
+    "global_pool": "avg",
+    "encoder_pool": ""
+  },
+  "pretrained_cfg": {
+    "tag": "qwen3_vl_30b_a3b",
+    "custom_load": false,
+    "input_size": [
+      3,
+      768,
+      768
+    ],
+    "fixed_input_size": false,
+    "interpolation": "bicubic",
+    "crop_pct": 1.0,
+    "crop_mode": "squash",
+    "mean": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "std": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "num_classes": 0,
+    "pool_size": null,
+    "first_conv": "encoder.patch_embed.proj",
+    "classifier": "head",
+    "license": "apache-2.0",
+    "origin_url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct"
+  }
+}

conversion.json

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+{
+  "source_repo": "Qwen/Qwen3-VL-30B-A3B-Instruct",
+  "source_revision": "9c4b90e1e4ba969fd3b5378b57d966d725f1b86c",
+  "source_filename": "model-00013-of-00013.safetensors",
+  "license_source": "https://raw.githubusercontent.com/QwenLM/Qwen3-VL/96588727e44c78b25ba03ea03b8e12f7e64fd0da/LICENSE",
+  "license_sha256": "c71d239df91726fc519c6eb72d318ec65820627232b2f796219e87dcf35d0ab4",
+  "timm_version": "1.0.29",
+  "torch_version": "2.9.1+cu130"
+}

model.safetensors

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