~/talk

Keynote: Pre-training and Fine-tuning of Code Generation Models

slides:none on file

A 15B code model called StarCoder trains on permissive‑license GitHub plus issues, commits and notebooks with deduplication, decontamination and PII removal and uses Megatron‑LM, multi‑query attention and fill‑in‑the‑middle; it reaches parity or better than Codex on Python and supports 80+ languages, fine tunes with PEFT/LoRA instead of full retraining, and deploys via Text Generation Inference and Kubernetes endpoints serving about 12,000 users after training with 15B parameters on 512 GPUs for 24 days with an 8000-token context.

AI-written summary · about

  1. 0:00Code generation models intro
  2. 2:07Hugging Face’s open code models
  3. 3:11Open code model principles
  4. 4:36StarCoder training data
  5. 7:43StarCoder architecture and training
  6. 8:46Fine-tuning with PEFT
  7. 9:28BigCode ecosystem
  8. 9:49VS Code extensions
  9. 10:09Text Generation Inference
  10. 12:15Kubernetes for Hugging Face
  11. 13:42Model development behind scenes
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