{ "data": { "dev": "/content/jevlet_data/mixture_v6/dev.jsonl", "train": "/content/jevlet_data/mixture_v6/train.jsonl", "vault": "/content/jevlet_data/mixture_v6/vault/vault.jsonl" }, "device": "auto", "evaluation": { "batch_size": 64, "benchmark_repeats": 5, "max_examples": 6000, "permutation_examples": 64 }, "model": { "attention_topology": "block_bidir", "backbone": "BAAI/bge-small-en-v1.5", "decision_head": "pointer", "family": "pretrained", "gradient_checkpointing": false, "max_option_tokens": 40, "max_packed_len": 1024, "max_question_tokens": 64, "max_state_tokens": 224, "option_pool": "mean" }, "notes": "Same 1.6M-example budget as pretrained_daily_v6.json (100k x 16) at effective batch 64: 25k steps, learning rates scaled by sqrt(4), warmup and snapshots at the same example counts. jevlet.colab fits batch_size x gradient_accumulation to the GPU and keeps their product at effective_batch.", "provenance": { "base_config": "configs/pretrained_daily_v6_colab.json", "created": 1790338776.0874045, "git_commit": "9d62c46989e965e0d823491e670dc01ed12a345e" }, "seed": 1337, "sessions": [ { "gpu": { "batch_size": 64, "bf16": true, "memory_gb": 79.3, "name": "NVIDIA A100-SXM4-80GB", "workers": 8 }, "resume": false, "time": 1790338782.2884605 }, { "gpu": { "batch_size": 64, "bf16": true, "memory_gb": 79.3, "name": "NVIDIA A100-SXM4-80GB", "workers": 8 }, "resume": true, "time": 1790352643.7766407 } ], "training": { "amp_dtype": "bf16", "backbone_learning_rate": 0.0001, "batch_size": 64, "effective_batch": 64, "gradient_accumulation": 1, "keep_steps": [ 2500, 6250, 12500 ], "learning_rate": 0.002, "log_every_steps": 50, "loss": "ce_brier", "lr_schedule": "cosine", "max_grad_norm": 1.0, "max_steps": 25000, "min_lr_ratio": 0.05, "mirror_dir": "/content/drive/MyDrive/Jevlet/runs/v6", "num_workers": 8, "resume_from": "/content/runs/v6/resume.pt", "save_every_steps": 500, "warmup_steps": 500, "weight_decay": 0.01 } }