enclavelabs

enclave-scribe-devanagari

What it does

Given an image containing Devanagari text (Hindi, Marathi, Sanskrit, Nepali, Pali), returns the Unicode transcription. Best suited for word-level and short-line images. For full-page PDFs, use the EnclaveScribe agent pipeline which handles page rasterization and generation-config tuning.

Results

Evaluated on a 500-sample held-out slice of himalaya-ai/devanagari_ocr_dataset (never seen during training):

Table with columns: Metric, Base OLMoCR-2-7B, This adapter, Improvement
Metric	Base OLMoCR-2-7B	This adapter	Improvement
CER ↓	16.26 (1626%)	0.174 (17.4%)	~93×
WER ↓	22.64	0.468	48×
F1 ↑	0.013	0.534	41×
Latency ↓	1.75 s/sample	0.84 s/sample	2× faster

Base is unable to read Devanagari — it hallucinates verbose English descriptions instead of transcribing, which is why it's also slower (more tokens generated).

How to use

python
import torch
from transformers import AutoProcessor, AutoModelForImageTextToText
from peft import PeftModel
from PIL import Image

BASE = "allenai/olmOCR-2-7B-1025"
ADAPTER = "enclavelabs/enclave-scribe-devanagari"

processor = AutoProcessor.from_pretrained(BASE)
model = AutoModelForImageTextToText.from_pretrained(
    BASE, dtype=torch.bfloat16, device_map="auto"
)
model = PeftModel.from_pretrained(model, ADAPTER)
model.eval()

image = Image.open("hindi_word.png").convert("RGB")
messages = [{
    "role": "user",
    "content": [
        {"type": "image", "image": image},
        {"type": "text",  "text": "Transcribe the Devanagari text from this image:"},
    ],
}]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)

with torch.no_grad():
    out = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=False,
        repetition_penalty=1.1,   # prevents generation loops on long/dense inputs
    )

print(processor.batch_decode(
    out[:, inputs.input_ids.shape[1]:], skip_special_tokens=True
)[0].strip())

Important: use repetition_penalty=1.1 (or higher) in generation. Without it, the model can enter degenerate loops on long or ambiguous inputs. See "Limitations" below.

Training details

Base model: allenai/olmOCR-2-7B-1025 (Qwen2.5-VL-7B fine-tuned by Allen AI for English document OCR)
Method: LoRA (r=32, α=64, dropout=0)
Trainable parameters: ~95M of 8.4B (~1.1%)
Data: 28,824 word-level Devanagari samples from himalaya-ai/devanagari_ocr_dataset, covering Hindi, Marathi, Sanskrit, Nepali, Pali. Mix of printed and IIT-Indic-HW handwritten crops.
Hardware: 1× NVIDIA A10G 24GB (AWS g5.xlarge on-demand)
Runtime: 8.7 hours, 1 epoch, 901 steps
Precision: bf16 + Liger kernel + gradient checkpointing
Effective batch: 32 (per-device 1 × grad accum 32)
Optimizer: AdamW, cosine LR schedule, LR 1.5e-4, 27 warmup steps
Compute cost: ~$12

Limitations

Word-level training data: this adapter was trained on single-word Devanagari crops. It's strong on word/line-level transcription but weaker on full-page documents. For pages, use it via the EnclaveScribe agent which rasterizes pages and applies generation-config fixes.
Generation loops on dense inputs: without repetition_penalty ≥ 1.1 in generation config, the model can enter loops that emit <tool_call> tokens (a Qwen2.5-VL quirk that survives skip_special_tokens=True) until max_new_tokens is exhausted. Always pass repetition_penalty=1.1.
English regression: not measured against a held-out English benchmark. Qualitative testing on a bilingual Hindi/English gazette PDF suggests English is preserved, but this is not a formal claim.
Handwriting: training data includes IIT-Indic-HW handwritten crops but performance varies by writer style.
Not evaluated on: Tamil, Telugu, Kannada, Bengali, Gujarati, Punjabi, other Indic scripts. This adapter is Devanagari-family only.

Iter-4 plans

Iter-4 will add page-level Devanagari data (Nayana, IndicVisionBench) to close the page-level gap, and formally benchmark English regression. Follow github.com/Enclave-Labs-Inc/enclave-scribe for updates.

Citation

markdown
@software{enclavescribe_devanagari_2026,
  title  = {EnclaveScribe: Self-hostable Indic OCR — Devanagari adapter},
  author = {Enclave Labs},
  year   = {2026},
  url    = {https://huggingface.co/enclavelabs/enclave-scribe-devanagari},
}

License

MIT — same as the base model and the EnclaveScribe repo.

Available on FriendliAI

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Model Details

Model Provider

enclavelabs

Model Tree

Base

allenai/olmOCR-2-7B-1025

Adapter

this model

Input Modalities

TextImage

Output Modalities

Text

Supported Functionality

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What it does

Results

Evaluated on a 500-sample held-out slice of himalaya-ai/devanagari_ocr_dataset (never seen during training):

Table with columns: Metric, Base OLMoCR-2-7B, This adapter, Improvement
Metric	Base OLMoCR-2-7B	This adapter	Improvement
CER ↓	16.26 (1626%)	0.174 (17.4%)	~93×
WER ↓	22.64	0.468	48×
F1 ↑	0.013	0.534	41×
Latency ↓	1.75 s/sample	0.84 s/sample	2× faster

Base is unable to read Devanagari — it hallucinates verbose English descriptions instead of transcribing, which is why it's also slower (more tokens generated).

How to use

python
import torch
from transformers import AutoProcessor, AutoModelForImageTextToText
from peft import PeftModel
from PIL import Image

BASE = "allenai/olmOCR-2-7B-1025"
ADAPTER = "enclavelabs/enclave-scribe-devanagari"

processor = AutoProcessor.from_pretrained(BASE)
model = AutoModelForImageTextToText.from_pretrained(
    BASE, dtype=torch.bfloat16, device_map="auto"
)
model = PeftModel.from_pretrained(model, ADAPTER)
model.eval()

image = Image.open("hindi_word.png").convert("RGB")
messages = [{
    "role": "user",
    "content": [
        {"type": "image", "image": image},
        {"type": "text",  "text": "Transcribe the Devanagari text from this image:"},
    ],
}]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)

with torch.no_grad():
    out = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=False,
        repetition_penalty=1.1,   # prevents generation loops on long/dense inputs
    )

print(processor.batch_decode(
    out[:, inputs.input_ids.shape[1]:], skip_special_tokens=True
)[0].strip())

Important: use repetition_penalty=1.1 (or higher) in generation. Without it, the model can enter degenerate loops on long or ambiguous inputs. See "Limitations" below.

Training details

Base model: allenai/olmOCR-2-7B-1025 (Qwen2.5-VL-7B fine-tuned by Allen AI for English document OCR)
Method: LoRA (r=32, α=64, dropout=0)
Trainable parameters: ~95M of 8.4B (~1.1%)
Data: 28,824 word-level Devanagari samples from himalaya-ai/devanagari_ocr_dataset, covering Hindi, Marathi, Sanskrit, Nepali, Pali. Mix of printed and IIT-Indic-HW handwritten crops.
Hardware: 1× NVIDIA A10G 24GB (AWS g5.xlarge on-demand)
Runtime: 8.7 hours, 1 epoch, 901 steps
Precision: bf16 + Liger kernel + gradient checkpointing
Effective batch: 32 (per-device 1 × grad accum 32)
Optimizer: AdamW, cosine LR schedule, LR 1.5e-4, 27 warmup steps
Compute cost: ~$12

Limitations

Word-level training data: this adapter was trained on single-word Devanagari crops. It's strong on word/line-level transcription but weaker on full-page documents. For pages, use it via the EnclaveScribe agent which rasterizes pages and applies generation-config fixes.
Generation loops on dense inputs: without repetition_penalty ≥ 1.1 in generation config, the model can enter loops that emit <tool_call> tokens (a Qwen2.5-VL quirk that survives skip_special_tokens=True) until max_new_tokens is exhausted. Always pass repetition_penalty=1.1.
English regression: not measured against a held-out English benchmark. Qualitative testing on a bilingual Hindi/English gazette PDF suggests English is preserved, but this is not a formal claim.
Handwriting: training data includes IIT-Indic-HW handwritten crops but performance varies by writer style.
Not evaluated on: Tamil, Telugu, Kannada, Bengali, Gujarati, Punjabi, other Indic scripts. This adapter is Devanagari-family only.

Iter-4 plans

Citation

markdown
@software{enclavescribe_devanagari_2026,
  title  = {EnclaveScribe: Self-hostable Indic OCR — Devanagari adapter},
  author = {Enclave Labs},
  year   = {2026},
  url    = {https://huggingface.co/enclavelabs/enclave-scribe-devanagari},
}

License

MIT — same as the base model and the EnclaveScribe repo.

enclave-scribe-devanagari

README

What it does

Results

How to use

Training details

Limitations

Iter-4 plans

Citation

License

Explore FriendliAI today

README

What it does

Results

How to use

Training details

Limitations

Iter-4 plans

Citation

License