README.md

---
library_name: peft
tags:
- unsloth
license: apache-2.0
datasets:
- iapp/thai_handwriting_dataset
- Thinnaphat/TH-HANDWRITTEN-CPE-OPH2025
- openthaigpt/thai-ocr-evaluation
base_model:
- deepseek-ai/DeepSeek-OCR
language:
- th
- en
pipeline_tag: image-text-to-text
---

**DeepSeek-OCR Thai ** is a fine-tuned version of [DeepSeek-OCR](https://huggingface.co/unsloth/DeepSeek-OCR) specifically optimized for recognizing Thai handwriting. This model leverages the power of the DeepSeek-OCR architecture and has been adapted using Low-Rank Adaptation (LoRA) for high-performance Thai OCR tasks, particularly focusing on handwritten text which often presents challenges for general-purpose OCR systems.

- **Base Model:** `unsloth/DeepSeek-OCR`
- **Language(s):** Thai, Eng
- **Task:** Optical Character Recognition (OCR) for Thai Language

## Inference
requirement libraries.
```
pip install -U addict transformers unsloth unsloth_zoo
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu126
```

```py
from unsloth import FastVisionModel
import torch
from transformers import AutoModel
import os
import builtins

# Fix for Unsloth/PEFT issue: "name 'VARIANT_KWARG_KEYS' is not defined"
builtins.VARIANT_KWARG_KEYS = ['alora_offsets']
os.environ["UNSLOTH_WARN_UNINITIALIZED"] = '0'

adapter = "sthaps/DeepSeek-ocr-Thai"
from huggingface_hub import snapshot_download
snapshot_download("unsloth/DeepSeek-OCR", local_dir = "deepseek_ocr")
model, tokenizer = FastVisionModel.from_pretrained(
    "./deepseek_ocr",
    load_in_4bit = False, # Use 4bit to reduce memory use. False for 16bit LoRA.
    auto_model = AutoModel,
    trust_remote_code = True,
    unsloth_force_compile = True,
    use_gradient_checkpointing = "unsloth", # True or "unsloth" for long context
)
model = PeftModel.from_pretrained(model, adapter)
model = model.eval().to(torch.bfloat16)

prompt = "<image>\nFree OCR"
image = "download.jpeg"
# Tiny: base_size = 512, image_size = 512, crop_mode = False
# Small: base_size = 640, image_size = 640, crop_mode = False
# Base: base_size = 1024, image_size = 1024, crop_mode = False
# Large: base_size = 1280, image_size = 1280, crop_mode = False

# Gundam: base_size = 1024, image_size = 640, crop_mode = True
res = model.infer(tokenizer, prompt=prompt, image_file=image, output_path = "output2", base_size = 1024, image_size = 640, crop_mode=True, save_results = True, test_compress = False)
print(res)
```

## Training Data
The model was fine-tuned on a comprehensive collection of Thai OCR datasets, including:
- **iapp/thai_handwriting_dataset**: A dataset focused on various styles of Thai handwriting.
- **Thinnaphat/TH-HANDWRITTEN-CPE-OPH2025**: Recent Thai handwritten data.
- **openthaigpt/thai-ocr-evaluation**: Standard Thai OCR evaluation data used to broaden the model's robustness.
- **[OCR image data for Thai documents](https://www.kaggle.com/datasets/appenlimited/ocr-image-data-for-thai-documents)**: Additional samples.

The training data was preprocessed to a conversation format with images saved locally and mapped to instruction-based prompts:
- **Instruction:** `<image>\nFree OCR. `

## Training Procedure
The model was trained using the `unsloth` library for memory-efficient and fast training.

- **Method:** PEFT (LoRA)
- **LoRA Config:**
  - Rank (R): 16
  - Alpha: 16
  - Target Modules: `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
- **Hyperparameters:**
  - Optimizer: `adamw_8bit`
  - Learning Rate: `2e-4`
  - Batch Size: `8`
  - Gradient Accumulation Steps: `2` (Effective Batch Size: 16)
  - Training Epochs: 1
  - LR Scheduler: `linear`
  - Warmup Steps: 50
  - Precision: `bf16` (if supported) or `fp16`