Pre Trained Model Zoo With 100 Checkpoints Across Architectures And Datasets

via “progressive checkpoint-based model training with intermediate evaluation”

1.1B model pre-trained on 3T tokens for edge use.

Unique: Releases 7 intermediate checkpoints with tracked performance metrics (commonsense reasoning scores) enabling empirical scaling law analysis without requiring full retraining, combined with optimized distributed training achieving 24k tokens/sec/GPU throughput (56% model FLOPS utilization) — higher than Pythia-1.1B's equivalent throughput

vs others: More transparent scaling trajectory than Llama 2 (which released only final model), and faster training efficiency than Pythia-1.1B (3,456 vs 4,830 GPU hours for 300B tokens) due to optimized batch size and learning rate schedule