Wiki Screenshot Embedding LoRA Checkpoints

LoRA / DoRA adapter checkpoints for Qwen3-VL-Embedding-2B, fine-tuned on Wikipedia screenshot tiles for visual document retrieval.

All evals on mini-v8 (400 queries, 7426 tiles).

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Headline (Cross-reader / Cross-thinking comparison @ best ckpts)

Best per metric:

• R@1: dora_ls005/ckpt225 (0.7675)
• R@3: lora_vit/ckpt250 / ckpt300 (0.8925)
• Qwen3-VL no-think QA: dora_ls005/ckpt150 / lora_vit/ckpt200 tied (0.7875)
• Qwen3.5 no-think QA: dora_ls005/ckpt150 (0.7875)
• Qwen3.5 think QA (mt=8192): dora_ls005/ckpt150 (0.8525) ★ overall SOTA

dora_ls005/ckpt150 is the most well-rounded — wins all 3 QA metrics, only marginally behind on retrieval (R@1 -0.01, R@3 -0.01).

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Training Configs

dora_ls005 ★ NEW — best QA across all readers

v8r-style + DoRA + label smoothing 0.05 + tokens=4096

• Base model: Qwen/Qwen3-VL-Embedding-2B
• Data: natural_filtered_v2 (104k pairs, 2 hard negatives)
• LoRA: rank 32, alpha 32, DoRA enabled (use_dora: true), targets LLM (q/k/v/o_proj) + ViT (attn.qkv, attn.proj, mlp.linear_fc1/fc2)
• Loss: InfoNCE + label smoothing = 0.05
• LR: 7e-6, cosine schedule, warmup 20 (image) + 50 (text warmup)
• Batch size: 64 (single GPU; effective batch 64 — smaller than lora_vit's 256)
• Max steps: 350, save every 25
• Visual tokens: 4096
• Text warmup: 50 steps text-only before image training

Eval results (mini-v8, 400 queries / 7426 tiles)

Why thinking helps so much

The Qwen3.5-4B reader is a reasoning model. With enable_thinking=True and large max_tokens (≥8192) the answer quality jumps substantially:

max_tokens sensitivity for dora_ls005/ckpt150 + thinking:

• mt=2048 → 0.8175
• mt=4096 → 0.8325
• mt=8192 → 0.8525 ← sweet spot
• mt=16384 → 0.8500 (plateau)

→ must use max_tokens ≥ 8192 for thinking, otherwise reasoning gets truncated.

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lora_vit (v8_r_warmup50_lr7e6_lora_vit_350) — original Best

• Base model: Qwen/Qwen3-VL-Embedding-2B
• Data: natural_filtered_v2 (104k pairs, 2 hard negatives)
• LoRA: rank 32, alpha 32, target includes ViT layers (attn.qkv, attn.proj, mlp.linear_fc1/fc2)
• LR: 7e-6, cosine schedule, warmup 50 steps
• Batch size: 256 effective
• Max steps: 350
• Visual tokens: 4096

Eval Results

Best: ckpt200 — v8 QA = 0.793 (+8.5%), v6 QA = 0.750 (+10.5%)

hyper3 (v8_i_warmup50_lr7e6_hardswitch_350)

• Base model: Qwen/Qwen3-VL-Embedding-2B
• Data: natural_filtered_v2 (104k pairs, 2 hard negatives)
• LoRA: rank 32, alpha 32 (LLM layers only, no ViT)
• LR: 7e-6, cosine schedule, warmup 50 steps
• Batch size: 256 effective
• Max steps: 350 (hard switch)
• Visual tokens: 4096

Eval Results

Best: ckpt250 (v8 QA = 0.770, +6.3% over base)

Usage

from peft import PeftModel
from transformers import AutoModel

base = AutoModel.from_pretrained("Qwen/Qwen3-VL-Embedding-2B")

# Best overall (DoRA + LS=0.05): wins QA on every reader
model = PeftModel.from_pretrained(
    base,
    "Chrisyichuan/wiki-screenshot-embedding-lora",
    subfolder="dora_ls005/ckpt150",
)

DoRA is auto-detected from adapter_config.json (use_dora: true) — no extra code needed; PEFT loads lora_A, lora_B, and lora_magnitude_vector automatically.

Eval Benchmarks

• v6: 200 queries, 5291 tiles (hard-mini-v6)
• v8: 400 queries, 7426 tiles (hard-mini-v8, preferred benchmark)
• QA score pipeline: retrieval top-3 → VQA reader (Qwen3-VL-4B-Instruct or Qwen3.5-4B) → GPT-4.1 grader (correct/incorrect)
• For Qwen3.5 reader: enable_thinking=True + max_tokens=8192 recommended; enable_thinking=False + max_tokens=200 is the fast/cheap baseline.