this is the most comprehensive and in-depth blog to understand vLLM. must read if you are into inference and ML systems and also helpful for beginners who want to contribute to vLLM. thank you aleksa!!
this is the most comprehensive and in-depth blog to understand vLLM. must read if you are into inference and ML systems and also helpful for beginners who want to contribute to vLLM. thank you aleksa!! https://t.co/jHXaJ620Kr
DeepMind built a simple RAG technique that:
- reduces hallucinations by 40%
- improves answer relevancy by 50%
Let's understand how to use it in RAG systems (with code):
along with the gpt-oss launch 🎉 we shared some quality resources i'm surprised aren't getting more attention:
- playground: gpt-oss [dot] com (!)
- harmony: how we tokenize chat, CoT, tool calls
- raw CoT
- fine-tuning
- ollama
...and so many cookbooks. links to all below 👇
for those struggling with deep learning math I resurface this tutorial I made last year
in 27min I show my process by working through QHAdam optimizer which is this alphabet soup of letters
become a self-taught ml engineer
i published this blog yesterday, a year ago -- 8.9k reads to date.
i'm pretty excited that my alma mater invited me to give a talk on this tomorrow
(link to blog in comments)
The very first task I usually give new pretraining people is to run a tiny transformer, profile it, and understand it deeply.
I wrote up a small tutorial covering this exact workflow. I talk about how to measure GPU perf, how to estimate tensor core speedup, etc.
Take a look:
I learnt a lot from O'Reilly books, this is surreal
I'm writing a book with amazing people @micuelll@andimarafioti@orr_zohar about VLMs with @huggingface 📖💗
Early Access (first two chapters in raw) are available to everyone, we'd love to have your feedback!
If I could only give people one tool for LLM Evals, it would be error analysis. Nothing else comes close
This is what Look At Your Data ™️ means
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