Comments (1)
Hi, thanks for your interest! An efficient way could be training your framework just in two steps like MM-CoT: (i) rationale generation; (ii) answer inference; no matter the backbone modules are.
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Related Issues (20)
- How are the vision features generated here ? How to view detr.npy and clip.npy images HOT 1
- typo in utils.prompt line 104 and 106 HOT 1
- Implementation Mm-cot HOT 1
- Question: PC requirements
- How to train
- Question about two stages training? HOT 1
- I can't find main_central.py. HOT 1
- ImportError: cannot import name 'Conv2dSame' from 'timm.models.layers' (unknown location) HOT 5
- [17:28:39] [Model]: Loading declare-lab/flan-alpaca-large... HOT 3
- Where is Gold Rationale from? HOT 1
- "blip2_vicuna_instruct" can't find lead to nonetype HOT 1
- Request for Release of Multimodal-CoT Large 738M Model HOT 3
- While running ‵extract_caption.py`, raise many garbled text. So will you put the models in `https://huggingface.co/Salesforce/instructblip-vicuna-7b/tree/main` the `llm` folder? HOT 1
- ValueError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length. Perhaps your features (`image_ids` in this case) have excessive nesting (inputs type `list` where type `int` is expected). HOT 1
- OverflowError: out of range integral type conversion attempted HOT 3
- Where is the main_central.py
- Can not train on GPU.
- Question on fine-tuning time HOT 1
- OverflowError: can't convert negative int to unsigned HOT 1
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