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1. Chinchilla recipe smaller models pretrained on data increases inference speed
2. Instruction fine tuning training on a small high quality set of instructions unlocks the knowledge learned during foundation model training.
3. Latency constrained batching achieves high utilization under load during token generation
4. Containerized SLURM combines fast scheduling of SLURM with LLM containers
5. Mixed precision training uses matrix operations for training
There are so many low hanging fruits in LLM tuning, steering, and alignment. We are just getting started on this for enterprise and open source.
For this reason I disagree with Sam Altman that the age of bigger models is over.
We are still leaving orders of magnitude on the table, e.g. by not including optimizations like sparsity in these models.
References for inspiration: [1] - https://arxiv.org/abs/2203.15556
[2] - https://arxiv.org/abs/1910.10683
[3] - https://www.usenix.org/system/files/osdi22-yu.pdf
[4] - https://www.schedmd.com/
It's free, on small LLMs It's fast, taking 10-15 minutes It's like working with an unlimited prompt size, with 1000x+ more space than the largest prompts It's learning new information, not just trying to make sense of it given what it already learned (retrieval-augmented generation)
Layperson's blogpost: https://www.lamini.ai/blog/free-fast-and-furious-finetuning
