Instructions to use JuntaoTang/MLLMcheckpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JuntaoTang/MLLMcheckpoint with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("JuntaoTang/MLLMcheckpoint", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from JuntaoTang/MLLMcheckpoint: direct link, hf CLI and curl.
- Browser
- Download file 459 Bytes
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https://huggingface.co/JuntaoTang/MLLMcheckpoint/resolve/main/README.md
- Command line
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hf download hf://JuntaoTang/MLLMcheckpoint/README.md
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curl -L -o README.md https://huggingface.co/JuntaoTang/MLLMcheckpoint/resolve/main/README.md
459 Bytes
| library_name: transformers | |
| # MLLMcheckpoint | |
| Public checkpoints for evaluating 70 vision encoders with three language models: SmolLM2, Qwen3, and Qwen2.5. | |
| Each model is stored at `<llm>/<encoder>/` with two variants: | |
| - `base`: pretrained multimodal checkpoint | |
| - `instruct`: finetuned instruction-following checkpoint | |
| The repository contains 210 MLLMs and 2968 checkpoint files (1046023518806 bytes). See `manifest.tsv` for the complete inventory. | |