alt Hacker NewsJetBrains' local single-line autocomplete model is 0.1B (w/ 1536-token context, ~170 lines of code): https://blog.jetbrains.com/blog/2024/04/04/full-line-code-co...
For context, GPT-2-small is 0.124B params (w/ 1024-token context).
Replies
smaddox • 01/21/2025
You can train that size of a model on ~1 billion tokens in ~3 minutes on a rented 8xH100 80GB node (~$9/hr on Lambda Labs, RunPod io, etc.) using the NanoGPT speed run repo: https://github.com/KellerJordan/modded-nanogpt
For that short of a run, you'll spend more time waiting for the node to come up, downloading the dataset, and compiling the model, though.
WithinReason • 01/21/2025
That size is on the edge of something you can train at home
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I wonder how big that model is in RAM/disk. I use LLMs for FFMPEG all the time, and I was thinking about training a model on just the FFMPEG CLI arguments. If it was small enough, it could be a package for FFMPEG. e.g. `ffmpeg llm "Convert this MP4 into the latest royalty-free codecs in an MKV."`