snxtyle/chitrapathak-MarGuj

Chitrapathak MarGuj

LoRA for Marathi and Gujarati line OCR on krutrim-ai-labs/Chitrapathak-2.

The same adapter was trained in two steps. It was first trained on the Marathi real-10k set (chitrapathak-marathi-v7-lora), then continued for one epoch on 9,200 Gujarati gazette lines. The Gujarati run did not start a new LoRA. Base weights are not in this repo.

Training

	Marathi step	Gujarati step
Starting weights	Chitrapathak-2	this adapter, after the Marathi step
Lines	9,200 real Marathi lines	9,200 real Gujarati lines
Epochs	2	1
Batch	1, gradient accumulation 16	1, gradient accumulation 8
Learning rate	1e-4	1e-4
LoRA	r=32, alpha=64, language-model projections only	same adapter, continued

Prompt: Perform OCR on this image and transcribe all visible text exactly as it appears.

Gujarati frozen 100

Same 100 long lines (special_chars_expansion.jsonl) and the same Gujarati metric definitions. PARSeq runs are piecewise. Chitrapathak and IndicOCR runs are whole-line.

Metric	PARSeq base	PARSeq trained	Chitrapathak base	Chitrapathak MarGuj	IndicOCR base	IndicOCR MarGuj
CER ↓	31.57%	27.45%	80.35%	8.81%	1.76%	1.23%
Strict WER ↓	97.30%	99.44%	93.10%	32.25%	8.57%	7.36%
Punctuation F1 ↑	0.00%	36.66%	90.44%	97.30%	97.30%	98.60%
Matra ↑	84.01%	91.87%	0.00%	92.10%	98.53%	98.53%
Conjunct ↑	78.70%	89.51%	0.00%	64.20%	97.22%	97.53%
Numeral ↑	54.60%	82.18%	0.00%	92.53%	96.55%	95.98%
Character accuracy ↑	68.43%	72.55%	19.65%	91.19%	98.24%	98.77%
Exact match ↑	0/100	0/100	0/100	5/100	43/100	46/100

Gujarati 800-line holdout

Short gazette lines, at most 10 words. This is the split held out from Gujarati training. Chitrapathak MarGuj: CER 0.30%, strict WER 1.46%, exact match 747/800.

Use

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

base_id = "krutrim-ai-labs/Chitrapathak-2"
adapter_id = "snxtyle/chitrapathak-MarGuj"
device = "cuda" if torch.cuda.is_available() else "mps"
dtype = torch.bfloat16

model = AutoModelForImageTextToText.from_pretrained(
    base_id, dtype=dtype, attn_implementation="sdpa"
)
model = PeftModel.from_pretrained(model, adapter_id).to(device).eval()
processor = AutoProcessor.from_pretrained(base_id, max_pixels=300000, min_pixels=56 * 56)

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": [
        {"type": "image", "image": image},
        {"type": "text", "text": "Perform OCR on this image and transcribe all visible text exactly as it appears."},
    ]},
]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], padding=True, return_tensors="pt").to(device)
with torch.no_grad():
    out = model.generate(**inputs, max_new_tokens=512, do_sample=False)
gen = [o[len(i):] for i, o in zip(inputs.input_ids, out)]
print(processor.batch_decode(gen, skip_special_tokens=True)[0].strip())