Instructions to use FoundationVision/unitok_mllm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FoundationVision/unitok_mllm with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("FoundationVision/unitok_mllm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Improve model card for UniTok with metadata and usage
Verified signatureThis PR significantly enhances the model card for UniTok, a unified tokenizer for visual generation and understanding. It includes: - The appropriate `pipeline_tag: any-to-any`, accurately reflecting its versatile multimodal capabilities for both generation and understanding. - The `library_name: transformers`, indicating compatibility with the Hugging Face Transformers library for automated usage snippets. - Updated `license` to `mit`, as explicitly stated in the project's GitHub repository. - Links to the paper ([UniTok: A Unified Tokenizer for Visual Generation and Understanding](https://huggingface.co/papers/2502.20321)), the official project page (`https://foundationvision.github.io/UniTok/`), and the GitHub repository (`https://github.com/foundationvision/unitok`). - Comprehensive information from the GitHub README, including abstract, news, performance metrics, model weights, installation instructions, and sample usage code for inference (both CLI and Python snippets …15e6e76
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initial commit
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