nanochat-ar / reports /base-model-training.md
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Base model training

timestamp: 2026-07-01 09:43:00

  • run: dummy
  • device_type:
  • fp8: False
  • fp8_recipe: tensorwise
  • depth: 12
  • aspect_ratio: 64
  • head_dim: 128
  • max_seq_len: 1024
  • window_pattern: L
  • num_iterations: 50
  • target_flops: -1.0000
  • target_param_data_ratio: 12
  • device_batch_size: 4
  • total_batch_size: 65,536
  • embedding_lr: 0.3000
  • unembedding_lr: 0.0080
  • weight_decay: 0.2800
  • matrix_lr: 0.0200
  • scalar_lr: 0.5000
  • warmup_steps: 40
  • warmdown_ratio: 0.6500
  • final_lr_frac: 0.0500
  • resume_from_step: -1
  • eval_every: 25
  • eval_tokens: 131,072
  • core_metric_every: -1
  • core_metric_max_per_task: 500
  • sample_every: -1
  • save_every: -1
  • model_tag: ar-d12-eager
  • Number of parameters: 286,261,730
  • Number of FLOPs per token: 7.738517e+08
  • Calculated number of iterations: 50
  • Number of training tokens: 3,276,800
  • Tokens : Scaling params ratio: 0.0298
  • DDP world size: 1
  • warmup_steps: 40
  • warmdown_ratio: 0.6500
  • final_lr_frac: 0.0500
  • Minimum validation bpb: 1.2144
  • Final validation bpb: 1.2144
  • CORE metric estimate: None
  • MFU %: 11.43%
  • Total training flops: 2.535757e+15
  • Total training time: 0.97m
  • Peak memory usage: 6444.42MiB