Language-only QLoRA adapter (vision tower frozen) that turns Qwen3-VL-8B-Instruct into a single-call detector + transcriber for Ukrainian handwritten document pages — one VLM call returns a JSON list of {bbox, type, text} regions. Trained for the Kaggle Handwritten-to-Data competition on the RUKOPYS dataset.

Unmerged on purpose: serve it with vLLM --enable-lora on a shared Qwen/Qwen3-VL-8B-Instruct base — multiple adapter versions can run side-by-side on one base. The base weights are NOT in this repo; only the ~200 MB LoRA delta + adapter_config.json + processor (which are identical to the base under language-only LoRA).

Model details

Fine-tuned from: unsloth/Qwen3-VL-8B-Instruct-unsloth-bnb-4bit (serve/merge on Qwen/Qwen3-VL-8B-Instruct)
Method: QLoRA via Unsloth, language layers only (vision frozen)
LoRA: r=32, alpha=64, dropout=0
Task: document region detection + handwritten text recognition (HTR)
Language: Ukrainian (uk)
License: apache-2.0

Training

Epochs: 2 (eval/early-stop on)
Effective batch: 1 × 8 grad-accum = 8
LR: 0.0002 (cosine, 50 warmup), seed 3407
Image budget: ≤1024² px; max seq length: 4096
Optimizer: adamw_8bit; precision: 4-bit NF4 base + bf16 LoRA

Usage (vLLM `--enable-lora`)

bash

python -m vllm.entrypoints.openai.api_server \
  --model Qwen/Qwen3-VL-8B-Instruct \
  --enable-lora --lora-modules qwen3vl-rukopys-band-dense-v1=topor4ik/qwen3vl-rukopys-band-dense-v1 \
  --max-lora-rank 32 --max-model-len 16384

Then send chat-completions with model=<lora-module-name> and an image; the model returns {"regions": [{"bbox":[x1,y1,x2,y2], "type":"...", "text":"..."}]} with bbox in 0–1000 scale, top-to-bottom reading order.

topor4ik/qwen3vl-rukopys-band-dense-v1

LoRA adapter: Qwen3-VL-8B for Ukrainian handwriting (RUKOPYS)

Model details

Training

Usage (vLLM --enable-lora)

topor4ik/
qwen3vl-rukopys-band-dense-v1

Usage (vLLM `--enable-lora`)