{
  "id": "demo-ml-pipeline/model-training-loop",
  "slug": "model-training-loop",
  "title": "Session: model-training-loop — 2026-02-02",
  "type": "source",
  "project": "demo-ml-pipeline",
  "date": "2026-02-02",
  "started": "2026-02-02T11:00:00+00:00",
  "ended": "2026-02-02T15:30:00+00:00",
  "model": "claude-opus-4-6",
  "cwd": "/Users/demo/code/demo-ml-pipeline",
  "git_branch": "feat/train-loop",
  "permission_mode": "default",
  "user_messages": "9",
  "tool_calls": "31",
  "tools_used": [
    "Read",
    "Write",
    "Edit",
    "Bash",
    "Grep",
    "WebFetch"
  ],
  "is_subagent": "false",
  "wikilinks_out": [
    "DistilBERT",
    "FineTuning",
    "HuggingFaceTrainer",
    "demo-ml-pipeline"
  ],
  "body_text": "Project: demo-ml-pipeline · Branch: feat/train-loop · Mode: default · Model: claude-opus-4-6\n\nSummary\n\nBuilt the training loop on top of transformers.Trainer. Fine-tuned distilbert-base-uncased on the shards from the previous session. Added W&B logging, early-stopping, and a checkpoint-on-best-val-f1 callback.\n\nConversation\n\nTurn 1 — User\n\nWrite the training loop. Fine-tune distilbert-base-uncased with the shards we prepped last session.\n\nTurn 1 — Assistant\n\nGoing with transformers.Trainer — it gives us logging, checkpointing, and mixed precision out of the box.\n\nKey choices:\n- learning_rate=2e-5 (the canonical BERT fine-tune LR)\n- per_device_train_batch_size=16\n- num_train_epochs=3 (data is small)\n- load_best_model_at_end=True with metric_for_best_model=\"f1\"\n- report_to=[\"wandb\"] — will pick up WANDB_PROJECT from env\n\nTools used in this turn:\n- Write: train.py\n\n \n\n- Bash: python train.py --config configs/dev.yaml — exit 0\n\nTurn 2 — User\n\nWhat F1 did we get?\n\nTurn 2 — Assistant\n\nBest checkpoint landed at 0.87 F1 on the val set at epoch 2. Loss was still trending down so we might squeeze more out of epoch 3+ with a bigger dataset, but for a dev smoke test this is fine.\n\nConnections\n\n- demo-ml-pipeline — project\n- DistilBERT — model\n- HuggingFaceTrainer — library\n- FineTuning — concept",
  "sha256": "edaaccecd29d781e",
  "source_url": "sessions/demo-ml-pipeline/model-training-loop.html"
}