Litap007/shivalipi-vlm
image-to-text model by Litap007
From the publisher
Model card & documentation
Source previewRead the publisher’s intended use, setup instructions, evaluations and limitations. The original model card is the source of truth.
Shivalipi-VLM is a specialized Vision-Encoder-Decoder model fine-tuned to transcribe historical handwritten Modi Lipi (मोडी लिपी) manuscript lines directly into standard Devanagari Marathi (देवनागरी मराठी). Developed and maintained by Rohit Manikrao Patil as the official default model backbone for the open-source shivalipi Python library. The model was trained on a stratified blend of synthetic and authentic Maratha-era historical manuscripts: The recommended way to use this model is via the shivalipi Python package: The model is evaluated using Character Error Rate…
Read the full model card ↗ · Preview checked 2026-11-06T08:16:33.195Z
Inside the original model card — Document outline
- Shivalipi-VLM (शिवलिपी) 📜
- Model Overview
- Training Data
- Quick Inference with the shivalipi Library
- Usage with Hugging Face Transformers
- Evaluation & Metrics
- Author & Maintainer
- License
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Documentation belongs to its respective authors. Reported project/model license: apache-2.0. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
Model overview
Litap007/shivalipi-vlm is a image-to-text model repository published by Litap007 on Hugging Face. The source reports the transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- image-to-text
- Library
- transformers
- Recent downloads (30 days)
- 0
- Cumulative likes
- 0
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 61,596,672
- Architecture
- VisionEncoderDecoderModel
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2026-11-06T06:20:29.000Z
- Last modified
- 2026-11-06T06:34:45.000Z
Compatibility and lineage
Reported languages: mr
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-11-06T06:53:49.736Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.
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