PaliGemma by Google: Inference and Fine Tuning of Vision Language Model
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- Опубликовано: 14 май 2024
- In this video I'm diving deep into PaliGemma, a new vision language model by Google! PaliGemma can analyze images and text, making it super versatile for tasks like image captioning and question answering. I'll show you how to use this powerful tool and get the most out of it through fine-tuning.
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Notebook: github.com/AIAnytime/PaliGemm...
PaliGemma HF: huggingface.co/collections/go...
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Bro i love your channel, your videos are of high quality and so instructive.
And that hairstyle, clearly DOPE, i personnally think its the one :D
Thank you for your detailed explanation. Your classes are quite interesting and are building confidence to move further forward. I need some suggestions: I saw a medical chatbot using Llama 2 on a CPU machine, which was all open source. Similarly, I need to build an image-to-text multimodal model on a CPU using all open-source tools. Please provide your suggestions.
Hi thank you very much, is it the same kind of process for any vlm model on hugging face?
❤
Great vid!
also united are gonna bottle the FA cup xd.
🤞
@@AIAnytime i am actually just a jinx
We won 😅
Thank you for the tutorial. I have one question: How can we use our own fine-tuned model on inference time? Can you make a video on how to use our own fine-tuned PaliGemma model during inference or if you can suggest links to read. Thank you.
Exactly I have the same issue too, I cant use it and save_pretrained is not working
Please make a video on multimodal/visionLM with 'video data'. In place of the image it takes the video as input.
can Pali Gemma good for RAG?
Is the model also good for OCR tasks?
You need to fine tune it to achieve good results, it is a good basis for any visual understanding task
Sir can I use this in my local machine or in raspberry pi coz I want to make a robot via raspberry pi
If not can you please suggest me any alternative if not locally then via API (free)
> processor = PaliGemmaProcessor(model_id)
Give the following errors:
90 raise ValueError("You need to specify an `image_processor`.")
91 if tokenizer is None:
92 raise ValueError("You need to specify a `tokenizer`.")
93 if not hasattr(image_processor, "image_seq_length"):
94 raise ValueError("Image processor is missing an `image_seq_length` attribute.")
Should be PaliGemmaProcessor.from_pretrained(model_id)
i still have confusion on why targetting q, o, k, v, gate , up , down ....targetting all linear layer ? why all ?
Research shows that this is the closest to full fine-tuning in terms of performance
You put a lot of effort into this video, but your audio is terrible.
Will improve in future videos...
@@AIAnytime could use ai to improve it too