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Qwen/Qwen2.5-VL-7B-Instruct on the ohjoonhee/visual-cot-50k-poc dataset (Monet-SFT-125K Visual_CoT subset). Trains the model to emit <observation> and other Monet special tokens fluently before Stage 2's alignment objective layers in latent slots. Baseline reference for downstream Stage 2 / Pivot A runs.Qwen/Qwen2.5-VL-7B-Instruct(none)ohjoonhee/visual-cot-50k-poc (Monet-SFT-125K Visual_CoT subset, eval-200 excluded)<observation>, <abs_vis_token>, etc.) ARE
registered in the tokenizer and embedded so the model learns to produce
them, but the architectural latent-slot mechanism is unused at this stage.step-1500)1from transformers import AutoModelForVision2Seq, AutoProcessor
2m = AutoModelForVision2Seq.from_pretrained(
3 "ohjoonhee/vlatents-qwen25vl7b-stage3-upstream-baseline-v1", revision="step-1500", torch_dtype="bfloat16")
4p = AutoProcessor.from_pretrained("ohjoonhee/vlatents-qwen25vl7b-stage3-upstream-baseline-v1", revision="step-1500")