Looking forward to your progress! Just checked the paper and it says the underlying backbone is still DETR. My guess would be that SAM3 uses more video frames during the training process and caused the dilution of sparse engineering-paper-like data.
1. The chat context is always provided, and that introduces a bit of uncertainty - when the chat history mentioned something the model is always inclined to connect with it.
2. When I tried to set each context to an empty string, the model doesn't show any evidence of remembering concepts. I told it 5 times that I love cats, and when asked about its favorite animal, its output remains "honeybee" and "octopus".
Looks pretty good but all CJK characters are displayed as questionmarks (???) and switching to a CJK native font does no help. So my user folder is now crowded with ???????? which makes it hard to navigate :(
For llama3 just ask him to install ollama and serve the model. Ollama has auto memory management and will free the model when not used, and whenever you make a call to the API (do let your friend know before you do this) ollama will reload the model back to memory again.
Not sure whether there are anything similar for SD though.
I wonder how would they calculate the metrics if the result is generated instead of retrieved? Is it likely that the LLM can generate exactly the same output as the desired result?
Totally makes sense - I'm already getting used to "ChatGPTing" instead of Googling these questions. I guess the future of these articles is that there are always fields that LLMs hallucinate at, fields that are not very common (explain the products of a small brand, pros &cons of their different models).
This is really cool! We are recently developing a project with heavy C++ and maybe a little Python scripts & wrappers and we are planning for a major refactor. Is it possible to adopt this with a C++ codebase?
Normally having a lot of choices is a good thing, but here we are facing a dozen of vector dbs with very similar features - to the root it's just some version of ANN implemented in C++/Rust/whatever, the "peak" means there's nothing new. People are flooding into this field not because there's something worth inventing, but more of fear to lag behind and miss the quick money. That's what I feel about vector DBs in Jan, 2024.
https://gist.github.com/Enoch2090/5026c417f86ff6ff4fbe30c22b...