Seungjun @dev_seungjun
| prev Google Summer of Code 23 @ TensorFlow, WWDC21 Scholar github.com/seungjun-green Joined December 2019-
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Seungjun retweeted
Introducing EmbeddingGemma🎉 🔥With only 308M params, this is the top open model under 500M 🌏Trained on 100+ languages 🪆Flexible embeddings (768 to 128 dims) with Matryoshka 🤗Works with your favorite open tools 🤏Runs with as little as 200MB developers.googleblog.com/en/introducing…
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AI generated videos are disgusting. Maybe my expression could sound a little excessive, but I want to experience content that real human put his/her own effort into it. Maybe in future I would pay money not to see any of ai generated content
. @OpenAI please stop instruction fine-tuning GPTs to do sugar coating and to ask a question at the end of the response 🙏🙏🙏
apple's software is getting worse as they continue to add new non-essential features.
Seungjun retweeted
Some fun things people may have missed from Gemma 3 270M: 1. Out of 270M params, 170M are embedding params and 100M are transformers blocks. Bert from 2018 was larger 🤯 2. The vocabulary is quite large (262144 tokens). This makes Gemma 3 270M very good model to be hyper…
they made diagrams "without thinking" lol
they made diagrams "without thinking" lol
LLaMA-ADAPTER paper summary <Wrote my own version of summary in a short&concise way> To summarize the main idea of the paper, it is the following: “input the adaption prompts along with image tokens to the top L layers of the LLaMA, but control their influence using a…
I predict that Grok4 gonna be the winner
I suspect that OpenAI is intentionally making gpts to use em-dash during instruction fine-tuning so that in future, they can know whether the scrapped text is GPT generated or not.
I suspect that OpenAI is intentionally making gpts to use em-dash during instruction fine-tuning so that in future, they can know whether the scrapped text is GPT generated or not.
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