We lifted 3 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| open-mmlab/multimodal-gpt | canonical | 0 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| build_dataset | Not yet run | open-mmlab/multimodal-gpt/mmgpt/datasets/builder.py code served (permissive licence) · get_code("298782a0d6335bbc") |
| create_model_and_transforms | Not yet run | open-mmlab/multimodal-gpt/mmgpt/models/builder.py code served (permissive licence) · get_code("313e8929699104dc") |
| create_toy_model_and_transforms | Not yet run | open-mmlab/multimodal-gpt/mmgpt/models/builder.py code served (permissive licence) · get_code("ed91d9967cc72cd4") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We present a vision and language model named MultiModal-GPT to conduct multi-round dialogue with humans. MultiModal-GPT can follow various instructions from humans, such as generating a detailed caption, counting the number of interested objects, and answering general questions from users. MultiModal-GPT is parameter-efficiently fine-tuned from OpenFlamingo, with Low-rank Adapter (LoRA) added both in the cross-attention part and the self-attention part of the language model. We first construct instruction templates with vision and language data for multi-modality instruction tuning to make the model understand and follow human instructions. We find the quality of training data is vital for the dialogue performance, where few data containing short answers can lead the model to respond shortly to any instructions. To further enhance the ability to chat with humans of the MultiModal-GPT, we utilize language-only instruction-following data to train the MultiModal-GPT jointly. The joint training of language-only and visual-language instructions with the \emph{same} instruction template effectively improves dialogue performance. Various demos show the ability of continuous dialogue of MultiModal-GPT with humans. Code, dataset, and demo are at https://github.com/open-mmlab/Multimodal-GPT
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2305.04790")
get_code_for_paper("2305.04790")
have("2305.04790")
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