LLMs
sari and
LLMs
I've learned so much from @hubermanlab, but podcasts make recall and discovery challenging.
Huberman AI uses the latest models from @openai to search every episode with Google-level accuracy and summarize the results.
https://t.co/1nctiAIuGO https://t.co/VpjnY2NcpM
riley tomasektwitter.comWeird GPT token for Reddit user davidjl123, “a keen member of the /r/counting subreddit. He’s posted incremented numbers there well over 163,000 times. Presumably that subreddit ended up in the training data used to create the tokenizer used by GPT-2, and since that particular username showed up hundreds of thousands of times it ended up getting
... See more“The fact that these things model language is probably one of the biggest discoveries in history. That you (LLM) can learn language by just predicting the next word … — that’s just shocking to me.”
- Mikhail Belkin, computer scientist at the University of California
NFX’s Generative Tech Open Source Market Map
docs.google.comThere’s a lot of hype around AI, and in particular, Large Language Models (LLMs). To be blunt, a lot of that hype is just some demo bullshit that would fall over the instant anyone tried to use it for a real task that their job depends on. The reality is far less glamorous: it’s hard to build a real product backed by an LLM .
Phillip Carter • All the Hard Stuff Nobody Talks About When Building Products With LLMs
Meta AI released LLaMA ... and they included a paper which described exactly what it was trained on. It was 5TB of data.
2/3 of it was from Common Crawl. It had content from GitHub, Wikipedia, ArXiv, StackExchange and something called “Books”.
What’s Books? 4.5% of the training data was books. Part of this was Project Gutenberg, which is public
Qwen-VL-Max - a Hugging Face Space by Qwen
huggingface.co