Hi everyone. I'm one of the maintainers of this project. We're both excited and humbled to see it on Hacker News!
We created this handbook to make LLM inference concepts more accessible, especially for developers building real-world LLM applications. The goal is to pull together scattered knowledge into something clear, practical, and easy to build on.
We’re continuing to improve it, so feedback is very welcome!
Amazing work on this, beautifully put together and very useful!
aligundogdu•4h ago
It's a really beautiful project, and I’d like to ask something purely out of curiosity and with the best intentions. What’s the name of the design trend you used for your website? I really loved the website too.
holografix•1h ago
Very good reference thanks for collating this!
subset•1h ago
Ooh this looks really neat! I'd love to see more content in the future on Structured outputs/Guided generation and sampling. Another great reference on inference-time algorithms for sampling is here: https://rentry.co/samplers
qrios•48m ago
Thanks for putting this together! From now on I only need one link to point interested ones to learn.
Only one suggestion: On page "OpenAI-compatible API" it would be great to have also a simple example for the pure REST call instead of the need to import the OpenAI package.
sherlockxu•5h ago
We created this handbook to make LLM inference concepts more accessible, especially for developers building real-world LLM applications. The goal is to pull together scattered knowledge into something clear, practical, and easy to build on.
We’re continuing to improve it, so feedback is very welcome!
GitHub repo: https://github.com/bentoml/llm-inference-in-production
armcat•3h ago