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lgtk
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Model Details
Model Provider
lgtk
Model Tree
Qwen/Qwen2.5-VL-3B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| Base model | Qwen/Qwen2.5-VL-3B-Instruct |
| Fine-tuning method | QLoRA — LoRA rank 32, alpha 64, 4-bit NF4 quantization |
| LoRA target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Trainable parameters | 74M (1.94% of 3.8B total) |
| Training data | MoDeTrans (1,635 real) + SynthMoDe (4,086 synthetic) = 5,721 total |
| Training epochs | 2 |
| Training time | ~9.75 hours on RTX 5060 (8.5 GB VRAM) |
| Test CER | 0.328 (on 204 held-out MoDeTrans examples; real images only) |
| Zero-shot baseline CER | 0.930 |
A 65% reduction in character error rate vs zero-shot. CER 0.328 means roughly 33% of characters require expert correction — designed for human-in-the-loop use.
| Quality tier | CER range | Count |
|---|---|---|
| Perfect | 0.0 | 3 |
| Excellent | < 0.10 | 5 |
| Good | 0.10–0.20 | 38 |
| Fair | 0.20–0.40 | 109 |
| Poor | 0.40–0.70 | 44 |
| Very poor | 0.70–1.0 | 3 |
The majority of examples fall in the "fair" tier — usable as a first draft for expert review.
| Model | Test CER | Poor + worse |
|---|---|---|
Real data only (lgtk/qwen25vl-3b-modi-lora) | 0.332 | 59 examples |
| Real + synthetic (this adapter) | 0.328 | 47 examples |
Synthetic data gave a small but consistent improvement, mainly reducing poor-tier examples. Vowel-length confusion was unchanged (font rendering does not capture that ambiguity).
Input: photograph or scan of a handwritten Modi-script Marathi document Output: Devanagari transliteration of that text
This is transliteration, not translation — the language (Marathi) does not change, only the script. Modi was the administrative script of Maharashtra from roughly the 13th to the mid-20th century; most surviving documents are from the Shivakalin (17th c.), Peshwekalin (18th–early 19th c.), and Anglakalin (1818–1952) eras.
Install dependencies:
bash
pip install transformers peft bitsandbytes accelerate pillow torchpython
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor, BitsAndBytesConfigfrom peft import PeftModelfrom PIL import Imageimport torch MODEL_ID = "Qwen/Qwen2.5-VL-3B-Instruct"ADAPTER_ID = "lgtk/qwen25vl-3b-modi-synth-lora" # ← this repo bnb = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16,)base = Qwen2_5_VLForConditionalGeneration.from_pretrained( MODEL_ID, quantization_config=bnb, device_map="auto")model = PeftModel.from_pretrained(base, ADAPTER_ID)model.eval()processor = AutoProcessor.from_pretrained(MODEL_ID, max_pixels=512 * 28 * 28) PROMPT = ( "This image contains handwritten text in Modi script, a historical cursive " "script used to write the Marathi language. " "Transliterate the text in this image into Devanagari script. " "Output only the Devanagari text, with no explanation.") image = Image.open("your_modi_image.jpg").convert("RGB")messages = [{ "role": "user", "content": [ {{"type": "image", "image": image}}, {{"type": "text", "text": PROMPT}}, ],}]text_in = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = processor(text=[text_in], images=[image], return_tensors="pt").to(model.device) with torch.no_grad(): out = model.generate(**inputs, max_new_tokens=256, do_sample=False) result = processor.batch_decode( out[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)[0].strip()print(result)Developed and tested on a desktop with an NVIDIA RTX 5060 GPU (8.5 GB VRAM) running WSL2 on Windows. At 4-bit NF4 quantization, the adapter uses approximately 3–4 GB of VRAM during inference. A 5060 or better is recommended; the pipeline should also fit on any GPU with ≥6 GB VRAM.
historyHulk/MoDeTrans on HuggingFacehistoryHulk/SynthMoDe on HuggingFace| Hallucination loops | > 1.0 | 2 |