microsoft

/

llava-med-v1.5-mistral-7b

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Hi - please check our repo (https://github.com/microsoft/LLaVA-Med?tab=readme-ov-file#contents) for the use of LLaVA-Med v1.5.

Hi, thanks for you response. I want to finetune your base-model on my own data. Let me know if it is possible to do so in near future? Thank you

Hi, did you solve the problem? I met the same problem that Transformers does not recognize this architecture. I download the model files and run them offline.

No. It's not possible. We switched to original llava model.

Hi, thanks for you response. I want to finetune your base-model on my own data. Let me know if it is possible to do so in near future? Thank you

Hello. I want to fine tune the Llava-med on my own dataset. Is it possible, did you find a solution?

I can load it successfully, steps are as following:

1. clone repository from https://github.com/microsoft/LLaVA-Med and create virtual environment
2. download parameters from this repository
3. use the following code to load model:

from llava.model.builder import load_pretrained_model
tokenizer, model, image_processor, context_len = load_pretrained_model(
        model_path='<path_to_downloaded_repository(this)>',
        model_base=None,
        model_name='llava-med-v1.5-mistral-7b'
 )

Then I can use this model like any other models in Hugging Face transformers library.

Thanks for the update @mizukiQ

Also, wanted to ask - what's the max resolution of image that can be used? ViTL14 supports 224 x 224.
And what are various strategies to handle CT/ MR images.

I am also willing to join on discord or zoom to catchup and exchange-notes with other builders in the space.

Best,
Shash

I can load it successfully, steps are as following:

1. clone repository from https://github.com/microsoft/LLaVA-Med and create virtual environment
2. download parameters from this repository
3. use the following code to load model:

from llava.model.builder import load_pretrained_model
tokenizer, model, image_processor, context_len = load_pretrained_model(
        model_path='<path_to_downloaded_repository(this)>',
        model_base=None,
        model_name='llava-med-v1.5-mistral-7b'
 )

Then I can use this model like any other models in Hugging Face transformers library.

thank you!
I also want to ask how should I prepare my dataset. I have images and captions. How should I convert them for fine tuning on lava med?
Is there any tutorial ?

Thanks for the update @mizukiQ

Also, wanted to ask - what's the max resolution of image that can be used? ViTL14 supports 224 x 224.
And what are various strategies to handle CT/ MR images.

I am also willing to join on discord or zoom to catchup and exchange-notes with other builders in the space.

Best,
Shash

I am not the official researcher of llava-med, here's some configuration from their code:
llava-med utilizes CLIPImageProcessor to handle image, and its crop_size is (336, 336), and it will split it into patches with shape (24, 24), where each patch is 14 x 14.
llava-med handles the images in different modalities (CT or MR) in the same way.

thank you!
I also want to ask how should I prepare my dataset. I have images and captions. How should I convert them for fine tuning on lava med?
Is there any tutorial ?

I publish some data loading code here (the code is from my reproduction of llava-med, it may be not good enough)
You can replace SlakeDataset class with your own dataset class (since image caption and vqa have the similar form I + Q -> T), just keep two interfaces the same. and finetune the model.

I actually want to convert my image-text pair dataset to QA dataset like in the paper.
So I need to get question and answers from my texts first.
maybe after that I can use the exact code without changing in the repo
do you have any recommendation for that "meta-llama/Llama-3-8b-chat-hf" and "meta-llama/Meta-Llama-3-8B-Instruct" models seems suitable for this task