张宗平
357e2e60bd
- src/tsmm/train/stage2.py: loads stage1 ckpt (optional), freeze_llm + enable_lora(r=16) on q/v_proj, masked LM loss on sft.jsonl, bs=4/grad_accum=8/ ctx=1024/BF16/gradient checkpointing, ckpt every 2000 steps (encoder+projector state + peft LoRA adapter via save_pretrained). - scripts/train_stage2.sh: wrapper with design defaults. - Smoke verified: --max_steps 4, LoRA injected, finite loss, peak 3.29 GB (≤11GB budget), ckpt+adapter saved.
24 lines
574 B
Bash
Executable File
24 lines
574 B
Bash
Executable File
#!/usr/bin/env bash
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# Stage ② SFT training (T4.1). Loads stage ① ckpt, enables LoRA(r=16) on q/v_proj.
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set -euo pipefail
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DATA="${1:-data/sft.jsonl}"
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MAX_STEPS="${2:-10000}"
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STAGE1_CKPT="${3:-checkpoints/stage1/stage1_final.pt}"
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CKPT_DIR="${4:-checkpoints/stage2}"
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cd "$(dirname "$0")/.."
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.venv/bin/python -m tsmm.train.stage2 \
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--data "$DATA" \
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--stage1_ckpt "$STAGE1_CKPT" \
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--ckpt_dir "$CKPT_DIR" \
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--max_steps "$MAX_STEPS" \
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--bs 4 \
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--grad_accum 8 \
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--ctx 1024 \
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--lr 1e-4 \
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--lora_r 16 \
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--log_every 20 \
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--ckpt_every 2000 \
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--tensorboard
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