Meta's Roadmap for Full Stack AI
summary
Llama drives 400M+ downloads and over 65,000 derivatives and Meta reports models trained on large token counts (the 4–5B class sees ~15T tokens). New releases include multimodal Llama 3 variants (11B and 90B) using adapter weights, 128k context from 3.1, and a 405B family used to generate synthetic training data from ~6B image‑text pairs plus ~600M high‑quality post‑train examples. Tooling: Llama Stack, a CLI/API for orchestration; TorchTune, execu‑torch, FP8/4‑bit quantization. Demo: a 1B, 4‑bit quantized model runs ~45 tokens/sec on Android and a 500MB safety model runs locally.
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chapters (7)
- 0:00Introduction and overview
- 3:55Llama adoption and growth
- 13:26Llama 3.2 vision models
- 18:19Introducing Llama Stack
- 22:10PyTorch components and libraries
- 27:26Demo of 1B model on mobile
- 30:45Q&A session
