Snapshot repo. This is a frozen snapshot of the v5 recipe at checkpoint-16000. The main multik-3x-lora repo tracks the current best 3x adapter (currently identical to this one). Sister snapshot for the prior recipe (1:1 mixed) lives at Chrisyichuan/qwen3vl-4b-wiki-screenshot-multik-3x-v3-lora if archived.

LoRA adapter for Qwen/Qwen3-VL-4B-Instruct, fine-tuned as the reader in a retrieval-augmented Wikipedia-screenshot QA pipeline.

This adapter reads any number of screenshots from 1 to 6 at inference time, at a fixed 3x pixel compression per image. It exceeds the uncompressed no-SFT baseline at k≥2 and matches it at k=1 within 0.054.

Performance (GPT-4.1 LLM-judge on 500 test examples)

k (images per sample)	base@0x (no comp, no SFT)	v3 (1:1 mixed)	v5 (2:1 top3:vark) this	Δ vs v3	Δ vs base@0x
1	0.958	0.884	0.904	+0.020	-0.054
2	0.912	0.878	0.918	+0.040	+0.006
3	0.892	0.892	0.932	+0.040	+0.040
4	0.856	0.862	0.884	+0.022	+0.028
avg	0.905	0.879	0.910	+0.031	+0.005

Beats base@0x at k=2/3/4 even though images are 3× compressed (each dim scaled by 1/√3).
Beats prior fixed-k=3 specialist (v1) ceiling of 0.900 at k=3 by +0.032 — heavier oversample of the most common deployment k value works better than dedicating the entire model to k=3 alone.
Near k-invariant: 0.904 → 0.884 across k=1..4, only 0.020 spread (vs base@0x's 0.102 spread from 0.958 → 0.856).

Recipe v5 — 2× top3 oversample

Compared with v3 (1:1 mixed):

Field	v3 (prior)	v5 (this)
Datasets	`multimage_top3_train` + `multimage_vark_train` (1:1)	`multimage_top3_train` + `multimage_top3_train_dup` + `multimage_vark_train` (2:1 top3:vark)
Samples per epoch	208k	312k (+50%)
LoRA rank	256	256 (unchanged)
LoRA alpha	256	256 (unchanged)
LoRA targets	all linear layers (incl. ViT)	all linear layers (incl. ViT)
`freeze_vision_tower`	false	false
Optimizer	AdamW, cosine, peak 1e-5, warmup 3%	identical
Effective batch	32 (1 × 8 GPUs × 4 grad_accum)	identical
`cutoff_len`	8192	8192
Epochs	2 (final adapter, step 13004)	2 trained (full 19506 steps), but released checkpoint is step 16000 (~1.64 epochs) — peak eval exact-match before mild overfit
Hardware	8× H100 80GB, DeepSpeed ZeRO-2, bf16	identical
Wallclock	~7h	~9h

Why 2× top3 oversample?

In retrieval-augmented Wikipedia-screenshot QA, k=3 is the most common deployment shape (most retrievers return the top 3 docs). The variable-k (vark) split alone undertrained the model on this exact shape because its samples are uniform over k ∈ {1..6} (~17k per k). Adding a duplicate of the fixed-k=3 (top3) split shifts ~67% of training samples to k=3, which:

Lifts k=3 LLM-judge from 0.892 to 0.932 (+0.040) — even past the ceiling of v1, a model trained only on fixed-k=3 (which scored 0.900).
Surprisingly also lifts all other k (k=1: +0.020, k=2: +0.040, k=4: +0.022). Heavier exposure to a single, well-defined k pattern seems to teach a cleaner answering style that transfers across k values.
The dual-dataset structure of v3 (which already beat the vark-only v2 at 3x) was the right base; v5 just shifts the mixture ratio toward the deployment center.

Why ckpt-16000 (not final)?

step	epoch	eval_loss	eval_em	LLM-judge avg (k=1..4)
14400	1.48	0.163	0.766	(not benchmarked)
16000	1.64	0.164	0.772	0.910 ← released
17600	1.80	0.165	0.769	(not benchmarked)
19200	1.97	0.165	0.769	(not benchmarked)
19506 (final)	2.00	0.166	0.768	0.901 (this trails ckpt-16000 at k=1/2/3, ties at k=4)

Train loss in epoch 2 was 0.04-0.06 (vs epoch 1's 0.10-0.20) — the model continued to fit the training distribution well, but the gains stopped transferring to held-out test around step 16000. Pull a mid-epoch-2 checkpoint at peak eval em.

Data — variable-k retrieval-augmented multi-image (with 2× top3 weighting)

Built from the Chrisyichuan/screenshot-training-natural-filtered-v2 QA dataset (~104k train examples):

For each query, retrieve top-6 screenshots from a Qwen3-VL-2B embedding index (dora-ls005 checkpoint) over 28M Wikipedia tiles.
Two splits per epoch (each composed independently):
- top3 (×2): each sample is fixed k=3 — the gold + top 2 non-gold hits, gold position randomized. Listed twice in dataset: to oversample 2×.
- vark (×1): each sample uniformly samples k ∈ {1..6}, gold always included, position randomized.
Apply 3x compression to all images (each dim scaled by 1/sqrt(3) via PIL LANCZOS).
Train the reader with <image>×k \n {query} → gold answer.

Effective per-epoch distribution:

k=3: 2 × 104k (top3 oversample) + ~17k (vark k=3 share) = ~225k samples (72%)
k=1, 2, 4, 5, 6 each: ~17k = ~5.4% each (28% combined)

Total per-epoch samples: 312k (1.5× v3's 208k). Train wallclock scales accordingly.

Gold-retrieval rate at top-6 across splits: ~75%. When gold is missing from retrieval, gold is still always included by construction in both splits.

Usage

python

from transformers import AutoProcessor, Qwen3VLForConditionalGeneration
from peft import PeftModel
import torch

base = "Qwen/Qwen3-VL-4B-Instruct"
adapter = "Chrisyichuan/qwen3vl-4b-wiki-screenshot-multik-3x-v5-lora"  # or "Chrisyichuan/qwen3vl-4b-wiki-screenshot-multik-3x-lora" for the same weights via main repo

model = Qwen3VLForConditionalGeneration.from_pretrained(base, torch_dtype=torch.bfloat16).cuda()
model = PeftModel.from_pretrained(model, adapter).merge_and_unload()
processor = AutoProcessor.from_pretrained(base)

# k can be any integer in 1..6. Images must already be 3x-compressed (each dim × 1/sqrt(3)).
messages = [{"role": "user", "content": [
    {"type": "image", "image": img_1},
    # ... up to img_6 ...
    {"type": "text",  "text": your_question},
]}]
# ... standard Qwen3-VL inference

Notes / limitations

Pixel budget is fixed at 3x. If your deployment can afford less compression, use the 2x sibling; if more, use the 4x sibling.
Training always included gold in the image set. If your retriever misses the gold at inference, this adapter has not seen that distribution — expect degradation on those queries.
For k ∈ {1, 2, 3, 4} this adapter was evaluated with GPT-4.1 LLM-judge on a 500-example test set. k=5/6 were trained on but not explicitly benchmarked.
The 2× top3 oversample is asymmetric on purpose. Whether the same recipe generalizes to 2x compression: under test (see the 2x sibling card after the 2x_v4 run completes). At 4x compression the same recipe is unlikely to help — mixed-data already regressed at 4x (prior finding) due to insufficient pixel budget to absorb additional training signal.
Sister adapters at other compression levels: Chrisyichuan/qwen3vl-4b-wiki-screenshot-multik-2x-lora, Chrisyichuan/qwen3vl-4b-wiki-screenshot-multik-4x-lora. Latest 3x: Chrisyichuan/qwen3vl-4b-wiki-screenshot-multik-3x-lora.