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[PAPER LINK](https://arxiv.org/abs/2403.00522)\n\n\n## install\n`$ pip install vision-llama`\n\n\n## usage\n```python\n\nimport torch\nfrom vision_llama.main import VisionLlama\n\n# Forward Tensor\nx = torch.randn(1, 3, 224, 224)\n\n# Create an instance of the VisionLlamaBlock model with the specified parameters\nmodel = VisionLlama(\n    dim=768, depth=12, channels=3, heads=12, num_classes=1000\n)\n\n\n# Print the shape of the output tensor when x is passed through the model\nprint(model(x))\n\n```\n\n\n\n# License\nMIT\n\n## Citation\n```bibtex\n@misc{chu2024visionllama,\n    title={VisionLLaMA: A Unified LLaMA Interface for Vision Tasks}, \n    author={Xiangxiang Chu and Jianlin Su and Bo Zhang and Chunhua Shen},\n    year={2024},\n    eprint={2403.00522},\n    archivePrefix={arXiv},\n    primaryClass={cs.CV}\n}\n```\n\n## todo\n- [ ] Implement the AS2DRoPE rope, might just use axial rotary embeddings instead, my implementation is really bad\n- [x] Implement the GSA attention, i implemented it but's bad\n- [ ] Add imagenet training script with distributed","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkyegomez%2Fvisionllama","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkyegomez%2Fvisionllama","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkyegomez%2Fvisionllama/lists"}