Training

Pinned sources The Llama 3 Herd of Models (arXiv 2407.21783v3), whose results are all for Llama 3.1 · for Llama 3, its model card (meta-llama/llama3 @ a0940f9) and Meta’s post of April 2024 · no training code is published · How to read

  1. Start of training
  2. Llama 3, April 2024, predecessor2 stages
  3. Whether Llama 3.1 continues these checkpoints: not stated
  4. Llama 3 pre-training15T+ tokens at 8K15T8Knot statednot stated
  5. Llama 3 post-trainingSFT, RS, PPO, DPOnot statednot statedSFTPPODPOnot stated
  6. Llama 3 (April 2024)
  7. Initial pre-trainingat 4K, then 8K15.6T8KNTPall
  8. Off this path: domain experts (code, multilingual)2 stages
  9. Branched from the pre-training run; the point is not named
  10. Code expert1T tokens, mostly code1T16KNTPnot stated
  11. Multilingual expert90% multilingual tokensnot statednot statedNTPnot stated
  12. Long-context pre-trainingsix stages, 8K to 128K800B128KNTPallcontext to 128K
  13. Annealinglearning rate to 040M128KNTPall
  14. Pre-trained model
  15. × 6 roundsRejection sampling and SFTthen DPO, each roundnot stated128KSFTRS and synthetic datanot stated
  16. DPOafter SFT, each roundnot statedshortDPO+ NLL 0.2not stated
  17. Llama 3.1 405B Instruct
  • tokens, log scale
  • derived: computed from stated numbers
  • context length
  • all parameters train
  • a part trains
  • all but a part trains
  • NTPdashed: concluded, no source names it
  • not stated by the sources
  • the architecture changes
  • checkpoint
  • checkpoint, weights published
  • a join the sources leave unexplained
  • data from a branch into a stage
  • stages repeated together

How Llama 3.1 405B was made: the stages on its path, top to bottom, and what changes from one to the next.

Of the 10 stages (5 on the path, 5 off it), the sources give or let us compute tokens for 5, and state or let us conclude the context length for 7, the objective for 9 and what is trained for 3; what is concluded is drawn dashed. The rest is hatched: nothing is filled in.

Select a stage for its numbers, its reason and the model as that stage trains it.