diabolic6045/Sanskrit-Qwen2.5-VL-7B-Instruct-OCR

Sanskrit-Qwen2.5-VL-7B-Instruct-OCR

This model is a fine-tuned version of Qwen/Qwen2.5-VL-7B-Instruct on the diabolic6045/sanskrit-ocr-parallel-corpus-chat-template dataset. This data is converted from snskrt/Sanskrit_OCR_Parallel_Corpus by Sanskrit Datasets.

It achieves the following results on the evaluation set:

• —Loss: 0.2660
• —Memory/max Mem Active(gib): 20.79
• —Memory/max Mem Allocated(gib): 20.79
• —Memory/device Mem Reserved(gib): 21.46

Model description

This is a fine-tuned version of Qwen2.5-VL-7B-Instruct, specifically adapted for Sanskrit OCR (Optical Character Recognition) tasks. The model has been trained using LoRA (Low-Rank Adaptation) on a dataset of Sanskrit text images and their corresponding transcriptions.

Key Features:

• —Base Model: Qwen/Qwen2.5-VL-7B-Instruct (7 billion parameters)
• —Task: Sanskrit OCR - converting Sanskrit text images to machine-readable text
• —Training Method: LoRA fine-tuning with vision-language capabilities
• —Dataset: Sanskrit OCR Parallel Corpus with chat template formatting
• —Architecture: Vision-Language Model with multimodal understanding

Capabilities:

• —Read and transcribe Sanskrit text from images
• —Handle various Sanskrit scripts and fonts
• —Process both text and visual inputs simultaneously
• —Generate accurate Sanskrit text transcriptions

The model maintains the original Qwen2.5-VL's vision-language capabilities while being specialized for Sanskrit text recognition tasks.

Training and evaluation data

Training Dataset

The model was trained on the diabolic6045/sanskrit-ocr-parallel-corpus-chat-template dataset, which contains Sanskrit text images paired with their corresponding transcriptions. The dataset was converted from the original snskrt/Sanskrit_OCR_Parallel_Corpus and formatted with chat templates for vision-language training.

Evaluation Results

The model was evaluated on a test set of 314 Sanskrit text samples:

Key Insights:

• —The model successfully processes all test samples without failures
• —High character-level accuracy (86.38%) indicates good recognition of individual Sanskrit characters
• —Lower exact match accuracy (1.59%) suggests room for improvement in complete text transcription
• —Moderate token-level similarity (50.44%) shows reasonable semantic understanding

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 0.0002
• —trainbatchsize: 2
• —evalbatchsize: 2
• —seed: 42
• —distributed_type: multi-GPU
• —num_devices: 2
• —gradientaccumulationsteps: 4
• —totaltrainbatch_size: 16
• —totalevalbatch_size: 4
• —optimizer: Use OptimizerNames.ADAMWBNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
• —lrschedulertype: cosine
• —lrschedulerwarmup_steps: 110
• —training_steps: 1105

Training results

<br>

This model was trained using: <br> <img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/> <details><summary>See axolotl config</summary>

axolotl version: 0.12.2

base_model: Qwen/Qwen2.5-VL-7B-Instruct
processor_type: AutoProcessor

# these 3 lines are needed for now to handle vision chat templates w images
skip_prepare_dataset: true
remove_unused_columns: false
sample_packing: false

chat_template: qwen2_vl
datasets:
  - path: sanskrit_multimodal_train.json
    type: chat_template
    field_messages: messages
dataset_prepared_path: last_run_prepared
val_set_size: 0.01
output_dir: ./outputs/out-qwen2-5-vl

adapter: lora
lora_model_dir:

sequence_len: 2048
pad_to_sequence_len: false

lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'

wandb_project: Sanskrit-OCR
wandb_entity:
wandb_watch:
wandb_name: qwen2-5-vl-sanskrit-ocr
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

bf16: true
fp16:
tf32: true

gradient_checkpointing: true
logging_steps: 1
flash_attention: true
eager_attention:

warmup_ratio: 0.1
evals_per_epoch: 1
saves_per_epoch: 1
weight_decay: 0.0

# Automatically upload checkpoint and final model to HF
hub_model_id: diabolic6045/qwen2-5-vl-sanskrit-ocr-lora
# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

</details><br>

Framework versions

• —PEFT 0.17.0
• —Transformers 4.55.2
• —Pytorch 2.7.1+cu128
• —Datasets 4.0.0
• —Tokenizers 0.21.2