Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

"facebook/opt-125m" and other sub 1B parameter LM's? #11

Open
dcompgriff opened this issue Jan 16, 2025 · 2 comments
Open

"facebook/opt-125m" and other sub 1B parameter LM's? #11

dcompgriff opened this issue Jan 16, 2025 · 2 comments

Comments

@dcompgriff
Copy link

I see that the code has options for using small OPT LM's. Will you release weights for VLM's trained with these smaller LM models?

@zhangshaolei1998
Copy link
Collaborator

Thanks for your question.
The "small OPT LM" appears due to some settings in the original LLaVA code. We did not train the related LLaVA-Mini on smaller LMs.
If necessary, you can train an LLaVA-Mini based on smaller LMs, which will be more efficient.

@dcompgriff
Copy link
Author

dcompgriff commented Jan 21, 2025

Where are the training and fine-tuning scripts you use. I see scripts/llavamini/train.sh, but no script for fine-tuning that includes the "During instruction tuning, we combine 665K image instruction data from LLaVA (Liu et al., 2023b), 100K video instruction data from Video-ChatGPT" that the paper mentions. Do you just start with 'scripts/pretrain.sh' as your stage 1 with your "ICTNLP/llava-mini-llama-3-8b" as the model path? It's not clear because the --vision_tower parameters don't match for those two files.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants