startrail-org--pixelrag
wehub-resource-sync
143 行
7.5 KiB
Markdown
143 行
7.5 KiB
Markdown
# Compressed-image SFT — Final Results
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**Target:** maximize GPT-4.1 LLM-judge accuracy on `test_hn_with_answer.jsonl` (500 examples) for Qwen3-VL-4B under 2x / 3x / 5x / 9x pixel compression, **without inflating visual-token count at inference**.
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**Judge:** `gpt-4.1-2025-04-14`, SimpleQA A/B/C template (`sft/eval_baseline.py`).
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## Headline table
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| Compression | base (no SFT) | best specialized | best universal (one adapter) | specialized config |
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|---|---|---|---|---|
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| **0x** (original) | **0.958** | — | — | ceiling |
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| **2x** | 0.904 | **0.948** | 0.940 | r=128, 2ep, lr 2e-5 (LLM-only sufficient) |
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| **3x** | 0.826 | **0.894** | 0.892 | r=256, 1ep, lr 1e-5, LLM+ViT |
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| **5x** | 0.554 | **0.730** | 0.692 | r=128, 2ep, lr 3e-5, LLM+ViT |
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| **9x** | 0.180 | **0.378** | 0.302 | r=512, 2ep, lr 1e-5, LLM+ViT |
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Universal adapter is within **0.2–7.6 points** of each specialized adapter — at 3x essentially tied, at 9x still meaningfully behind.
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## Key breakthrough: unfreezing the ViT
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`lora_target: all` alone does **not** train the ViT in LlamaFactory — `freeze_vision_tower: true` is the default. Setting it to `false` unlocks LoRA on the ViT and gives big jumps at high compression:
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| Compression | LLM-only | LLM+ViT | Δ |
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|---|---|---|---|
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| 2x | 0.948 | 0.948 | 0 |
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| 3x | 0.882 | 0.894 | +1.2 |
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| 5x | 0.634 | 0.730 | **+9.6** |
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| 9x | 0.272 | 0.378 | **+8.2** |
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**Rule:** the harder the compression, the more the ViT needs adaptation. 2x base is already near-ceiling so ViT LoRA adds nothing; 9x has the most distribution shift from pretraining so ViT capacity helps most (+30% relative).
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## What worked
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1. **LoRA + ViT unfrozen** → primary mechanism for 5x/9x gains.
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2. **Bigger LoRA rank at high compression**: 9x went r=32→128→256→512 for +2.4, +1.2, +0.8, +0.8 (diminishing but monotonic).
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3. **Mixed-compression training** (universal adapter): single LoRA trained on 2x+3x+5x+9x samples simultaneously. With r=256 + ViT unfrozen, the gap to specialized is only 0.2–7.6 points.
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4. **Two epochs** at 2x/3x/5x; **one epoch** at 9x (overfits faster with bigger rank).
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## What did not work
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| Experiment | Outcome |
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|---|---|
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| **Full FT** (lr 1e-5) at 5x/9x | grad_norm 10+, output collapses (empty strings, `0000` artifacts). 9x fell to 0.176, below base 0.180. LoRA's implicit regularization wins. |
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| **Pre-upscaling 9x → original dim** before ViT | Gives big accuracy boost (EM 0.78) but inflates visual tokens ~9×, defeating the compression use case. **Rejected.** |
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| **LoRA dropout 0.1 at 9x** | No change in ceiling (~0.27). |
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| **More epochs (3+)** at 5x | 0.620 at 3ep < 0.634 at 2ep. Overfit. |
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| **Rank 256 at 5x** | 0.656 < 0.730 at r=128. Capacity saturated at r=128 for 5x. |
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| **Think / CoT training** (30k GPT-generated reasoning traces, `<think>reasoning</think>answer` targets) | 9x peak 0.286 < non-think 0.378. Traces generated blindly by GPT (no image) produce plausible-but-wrong reasoning at 9x where the image is unreadable. Model learns to fabricate confident reasoning that doesn't help the final answer. |
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## Compression-damage vs SFT-recovery
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```
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0x : ██████████████████████████████████████████ 0.958 (ceiling)
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2x : ███████████████████████████████████████▏ 0.904 → SFT 0.948 (−1.0 gap, 81% recovered)
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3x : ████████████████████████████████████ 0.826 → SFT 0.894 (−6.4 gap, 52% recovered)
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5x : ████████████████████████ 0.554 → SFT 0.730 (−22.8 gap, 44% recovered)
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9x : ███████▌ 0.180 → SFT 0.378 (−58.0 gap, 25% recovered)
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```
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SFT recovery at 2x/3x: 50-80%. At 5x/9x: 25-44% — residual gap is the physical compression limit the ViT can't undo.
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## Full experiment matrix
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### 2x
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| run | r | ep | lr | ViT | LLM-judge peak |
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|---|---|---|---|---|---|
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| base | — | — | — | — | 0.904 |
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| v1 (LLM-only) | 128 | 2 | 2e-5 | frozen | **0.948** |
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| llmvit-v1 | 128 | 2 | 2e-5 | trained | 0.948 (tied) |
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### 3x
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| run | r | ep | lr | ViT | peak |
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|---|---|---|---|---|---|
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| base | — | — | — | — | 0.826 |
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| v1 | 32 | 1 | 1e-5 | frozen | 0.854 |
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| v2 | 128 | 2 | 2e-5 | frozen | 0.878 |
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| v3 | 256 | 2 | 2e-5 | frozen | 0.882 |
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| llmvit-v1 | 256 | 2 | 2e-5 | trained | 0.884 (then collapsed) |
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| llmvit-v2 | 256 | 1 | 1e-5 | trained | **0.894** |
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### 5x
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| run | r | ep | lr | ViT | peak |
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|---|---|---|---|---|---|
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| base | — | — | — | — | 0.554 |
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| v1 | 32 | 1 | 1e-5 | frozen | 0.598 |
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| v2 | 32 | 2 | 2e-5 | frozen | 0.628 |
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| v3 | 128 | 2 | 3e-5 | frozen | 0.634 |
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| v4 | 256 | 2 | 2e-5 | frozen | 0.620 |
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| v5 | 128 | 3 | 3e-5 | frozen | 0.620 |
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| fullft | — | 1 | 1e-5 | n/a | 0.562 (bad) |
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| llmvit-v1 | 128 | 2 | 3e-5 | trained | **0.730** |
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| llmvit-v2 | 256 | 2 | 2e-5 | trained | 0.656 |
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### 9x
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| run | r | ep | lr | ViT | peak |
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|---|---|---|---|---|---|
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| base | — | — | — | — | 0.180 |
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| v1 | 32 | 1 | 1e-5 | frozen | 0.228 |
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| v2 | 128 | 2 | 3e-5 | frozen | 0.252 |
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| v3 | 256 | 1 | 2e-5 | frozen | 0.264 |
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| v4 | 512 | 1 | 1.5e-5 | frozen | 0.272 |
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| v5 | 256 | 2 | 1e-5 dropout 0.1 | frozen | 0.266 |
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| fullft | — | 1 | 1e-5 | n/a | 0.176 (broken) |
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| llmvit-v1 | 256 | 1 | 2e-5 | trained | 0.354 |
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| llmvit-v2 | 512 | 2 | 1e-5 | trained | **0.378** |
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| think-v1 | 256 | 2 | 1e-5 | trained (+think) | 0.286 (worse) |
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### Universal (one adapter for all compressions)
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Trained on concatenated 2x+3x+5x+9x data (416k examples, 1 epoch).
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| run | r | lr | ViT | 2x | 3x | 5x | 9x |
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|---|---|---|---|---|---|---|---|
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| v1 | 128 | 2e-5 | frozen | 0.924 | 0.862 | 0.620 | 0.250 |
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| llmvit-v1 | 128 | 2e-5 | trained | 0.920 | 0.844 | 0.664 | 0.272 |
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| **llmvit-v2** | **256** | **1e-5** | **trained** | **0.940** | **0.892** | **0.692** | **0.302** |
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## Shipped artifacts (HuggingFace)
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Five LoRA adapters pushed to [Chrisyichuan](https://huggingface.co/Chrisyichuan):
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- `qwen3vl-4b-wiki-screenshot-2x-lora` — 0.948 at 2x
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- `qwen3vl-4b-wiki-screenshot-3x-lora` — 0.894 at 3x
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- `qwen3vl-4b-wiki-screenshot-5x-lora` — 0.730 at 5x
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- `qwen3vl-4b-wiki-screenshot-9x-lora` — 0.378 at 9x
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- `qwen3vl-4b-wiki-screenshot-universal-lora` — one LoRA, 0.940/0.892/0.692/0.302 across all four compressions
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## Practical recommendation
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For most deployments, ship the **universal adapter**: 1.28 GB LoRA, one merge at load time, handles any of 2x/3x/5x/9x with near-specialized accuracy (within 0.2–7.6 LLM-judge points). If you know you'll only ever serve a specific compression level, use the specialized adapter for that level.
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For 2x/3x the adapters are essentially production-ready (<10 point drop from uncompressed). 5x is usable when accuracy tolerance is moderate. 9x remains difficult — 0.378 is still far from the 0.958 ceiling, and this is dominated by the physical unreadability of 9×-compressed text rather than a capacity bottleneck we can solve with more training.
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## Files
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- Configs: `sft/train_qwen3vl_*.yaml`
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- Train logs: `logs/sft_train/sft_*.log`
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- Eval JSONs: `sft/eval_out/*.json`
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- Trace generator: `sft/generate_think_traces.py` (think failed but kept for reference)
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- Mixed-data builder: `sft/prepare_mixed_data.py`
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- Eval fanout: `sft/eval_fanout.sh`
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- HF push: `sft/push_to_hf.py`, `sft/push_universal_to_hf.py`
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- W&B project: https://wandb.ai/yichuan_wang-uc-berkeley-electrical-engineering-computer/qwen3vl-compressed-sft
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