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:

Metric	Value
Total Samples	314
Successful Samples	314
Failed Samples	0
Success Rate	100.00%
Exact Match Accuracy	1.59%
Character-level Accuracy	86.38%
Token-level Jaccard Similarity	50.44%

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
train_batch_size: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 4
total_train_batch_size: 16
total_eval_batch_size: 4
optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 110
training_steps: 1105

Training results

Training Loss	Epoch	Step	Validation Loss	Mem Active(gib)	Mem Allocated(gib)	Mem Reserved(gib)
No log	0	0	3.3372	17.59	17.59	17.66
0.2428	1.0	369	0.3075	20.69	20.69	21.27
0.2057	2.0	738	0.2660	20.79	20.79	21.46

This model was trained using:

See axolotl config

axolotl version: 0.12.2

yaml

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

Framework versions

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

snskrt/qwen2-5-vl-sanskrit-ocr

qwen2-5-vl-sanskrit-ocr

Model description