COMMANDS QUICKSTART (repo/)

This file provides copy/paste command combinations for the main entrypoints:
- VLM_finetune/run_ablation_sequential_ft.py
- VLM_inference/run_ft_eval_ablation.py
- VLM_inference/test_base_models/run_ft_eval_update.py

Notes
- Replace <USER> in paths with your username.
- These scripts assume your datasets/adapters follow the folder conventions described in their headers.
- All commands are meant to be run from /users/<USER>/repo unless you prefer absolute paths.


1) Sequential fine-tuning launcher
Script: VLM_finetune/run_ablation_sequential_ft.py

A. Dry-run: list all planned runs (no training launched)
  python VLM_finetune/run_ablation_sequential_ft.py --dry_run

B. Train a single task at a single percentage
  python VLM_finetune/run_ablation_sequential_ft.py --task a1 --perc 100 -- 

C. Train multiple tasks and multiple dataset percentages
  python VLM_finetune/run_ablation_sequential_ft.py --tasks a1,b1,c1 --percs 25,50,75,100 -- 

D. Overwrite existing outputs (dangerous: deletes output folders)
  python VLM_finetune/run_ablation_sequential_ft.py --task a1 --perc 25 --overwrite -- 

E. Change splits root / output root / HF cache location
  python VLM_finetune/run_ablation_sequential_ft.py \
    --splits_root /users/<USER>/ablation_study_dataset/ablation_splits \
    --output_root /mydata/<USER>/ablation_study_outputs/finetune_lora \
    --hf_home /mydata/<USER>/VLM_cache \
    --tasks a1,b1 --percs 50,100 \
    -- 

F. Forward extra FT.py arguments (everything after "--" is passed through)
  python VLM_finetune/run_ablation_sequential_ft.py --task a1 --perc 25 -- \
    --num_train_epochs 1 \
    --learning_rate 2e-5 \
    --per_device_train_batch_size 1 \
    --gradient_accumulation_steps 8 \
    


2) Ablation evaluation runner (evaluate many adapters)
Script: VLM_inference/run_ft_eval_ablation.py

A. Evaluate ALL discovered (task, perc) splits under splits-root
  python VLM_inference/run_ft_eval_ablation.py \
    --splits-root /users/<USER>/ablation_study_dataset_c1/ablation_splits \
    --adapter-root /mydata/<USER>/ablation_study_outputs/finetune_lora \
    --cache-dir /mydata/<USER>/VLM_cache

B. Evaluate a subset of tasks and percs
  python VLM_inference/run_ft_eval_ablation.py --tasks a1,b1 --percs 25,50,100

C. Evaluate only a few samples per split (fast smoke test)
  python VLM_inference/run_ft_eval_ablation.py --tasks a1 --percs 100 --num-samples 20 

D. Skip missing adapters (useful while training is still running)
  python VLM_inference/run_ft_eval_ablation.py --skip-missing-adapter

E. Evaluate BASE model only (ignore adapters)
  python VLM_inference/run_ft_eval_ablation.py --tasks a1 --percs 100 --no-adapter

F. Evaluate exactly one adapter path (ignore task/perc discovery)
  python VLM_inference/run_ft_eval_ablation.py \
    --adapter /mydata/<USER>/ablation_study_outputs/finetune_lora/task_a1/perc_100

G. Choice-task robustness options (avoid whitespace-only outputs)
  python VLM_inference/run_ft_eval_ablation.py --tasks a1 --percs 100 \
    --retry-empty-choice 2 \
    --force-choice-letter-fallback

H. Box-task generation settings (sampling / beams)
  python VLM_inference/run_ft_eval_ablation.py --tasks b2 --percs 100 \
    --max-new-tokens-boxes 4096 \
    --boxes-num-beams 3

I. Dry-run (show planned runs but do not execute inference)
  python VLM_inference/run_ft_eval_ablation.py --dry-run


3) Single-run evaluator (one task, optional circuit, optional adapter)
Script: VLM_inference/test_base_models/run_ft_eval_update.py

A. Evaluate one task across ALL circuits (default sampling logic)
  python VLM_inference/test_base_models/run_ft_eval_update.py --task a1 --num-samples 200

B. Evaluate one task for one circuit
  python VLM_inference/test_base_models/run_ft_eval_update.py --task b2 --circuit a --num-samples 200

C. Evaluate an ablation task (a1/b1/c1) using its ablation split JSON
  python VLM_inference/test_base_models/run_ft_eval_update.py --task a1 --num-samples -1

D. Evaluate with a LoRA adapter
  python VLM_inference/test_base_models/run_ft_eval_update.py \
    --task a1 --num-samples 200 \
    --adapter /mydata/<USER>/ablation_study_outputs/finetune_lora/task_a1/perc_100

E. Evaluate a different base model (no adapter)
  python VLM_inference/test_base_models/run_ft_eval_update.py \
    --task a1 --num-samples 50 \
    --model meta-llama/Llama-3.2-11B-Vision

F. Set cache dir for model/tokenizer downloads
  python VLM_inference/test_base_models/run_ft_eval_update.py --task a1 --cache-dir /mydata/<USER>/VLM_cache

G. Debug Hugging Face auth without running full inference
  python VLM_inference/test_base_models/run_ft_eval_update.py \
    --task a1 --num-samples 5 --dry-run-hf \
    --model meta-llama/Llama-3.2-11B-Vision \
    --hf-token-file /users/<USER>/.secrets/hf_token.env

H. Force single GPU (avoid some device_map='auto' issues)
  python VLM_inference/test_base_models/run_ft_eval_update.py --task a1 --force-single-gpu

I. Change output directory and run tag
  python VLM_inference/test_base_models/run_ft_eval_update.py \
    --task a1 --num-samples 200 \
    --out-dir /users/<USER>/VLM/inference_eval/outputs \
    --run-tag my_experiment_001
