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:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("JuntaoTang/MLLMcheckpoint", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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 checkpointinstruct: finetuned instruction-following checkpoint
The repository contains 210 MLLMs and 2968 checkpoint files (1046023518806 bytes). See manifest.tsv for the complete inventory.
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