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Source:https://github.com/SoraKumo001/next-streaming

rawsh 4 daysReload

Batched reward model inference and Best-of-N sampling

rawsh 37 daysReload

You can actually get solid performance with pretrained chat models: https://raw.sh/posts/chess_puzzles

On lichess puzzles gpt4o with the compiled prompt is around 70%, I think the 270M transformer is around 95%

rawsh 72 daysReload

Teaching LLMs to solve chess puzzles with DSPy and Finetuning

rawsh 87 daysReload

Bit confused what the value add is over a framework like DSPy. This still requires you to create an eval dataset with ground truth, basically the only hard part of using DSPy. Easily getting the optimized prompt and having some metrics out of the box is not worth nearly $1k/mo IMO

Side note: I’ve had a lot of luck combining automatic prompt optimization with finetuning. There is definitely some synergy https://raw.sh/posts/chess_puzzles

rawsh 91 daysReload

Teaching chat models to solve chess puzzles

rawsh 87 daysReload

Side note: I’ve had a lot of luck combining automatic prompt optimization with finetuning. There is definitely some synergy https://raw.sh/posts/chess_puzzles

rawsh 37 daysReload

You can actually get solid performance with pretrained chat models: https://raw.sh/posts/chess_puzzles

On lichess puzzles gpt4o with the compiled prompt is around 70%, I think the 270M transformer is around 95%