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2·2 months agoWow , yeah I found a demo here: https://huggingface.co/spaces/Qwen/Qwen2.5
A whole host of LLM models seems to be released. Thanks for the tip!
I’ll see if I can turn them into something useful 👍
That’s good to know. I’ll try them out. Thanks.
Hmm. I mean the FLUX model looks good
, so there must maybe be some magic with the T5 ?
I have no clue, so any insights are welcome.
T5 Huggingface: https://huggingface.co/docs/transformers/model_doc/t5
T5 paper : https://arxiv.org/pdf/1910.10683
Any suggestions on what LLM i ought to use instead of T5?
New stuff
Paper: https://arxiv.org/abs/2303.03032
Takes only a few seconds to calculate.
Most similiar suffix tokens : "vfx "
most similiar prefix tokens : “imperi-”
I count casualty_rate = number_shot / (number_shot + number_subdued)
Which in this case is 22/64 = 34% casualty rate for civilians
and 98/131 = 75% casualty rate for police
So its 64-131 between work done by bystanders vs. work done by police?
And casualty rate is actually lower for bystanders doing the work (with their guns) than the police?
Simple and cool.
Florence 2 image captioning sounds interesting to use.
Do people know of any other image-to-text models (apart from CLIP) ?