Instructions to use xiaomoguhzz/VisionEncoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xiaomoguhzz/VisionEncoder with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("xiaomoguhzz/VisionEncoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Reorg: move 16f stock/v9_1 ckpts into S2 stage-first tree (drop machine name, tag 16f)
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Reorg: flatten 32f ckpts to ckpts/{v10_2_32f,stock_32f}
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Add 4B 32f inference ckpt under stage-first ckpts/ -> ckpts/S2/4b/qwen3_5_2b/stock_32f_10pct/v0-20260606-044138/checkpoint-785
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Add 4B 32f inference ckpt under stage-first ckpts/ -> ckpts/S2/4b/qwen3_5_2b/v10_2_32f_10pct/v0-20260610-154054/checkpoint-785
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remove write-path test marker
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write-path test marker
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Remove ckpts placeholder now that real ckpts landed
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Add 4B MLLM ckpt: 4b_v9_1 (checkpoint-505, inference weights)
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Add 4B MLLM ckpt: 4b_stock (checkpoint-505, inference weights)
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Create ckpts/ folder for current trained checkpoints
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