GT-REX

GT-REX: Production OCR Model

<p align="center"> <strong>GothiTech Recognition and Extraction eXpert</strong> </p> <p align="center"> <a href="https://huggingface.co/gothitech/GT-REX"><img src="https://img.shields.io/badge/Model-GT--REX-blue" alt="Model"></a> <a href="#"><img src="https://img.shields.io/badge/License-MIT-green.svg" alt="License: MIT"></a> <a href="#"><img src="https://img.shields.io/badge/vLLM-Supported-orange" alt="vLLM"></a> <a href="#"><img src="https://img.shields.io/badge/Params-~3B-red" alt="Parameters"></a> </p>

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GT-REX is a state-of-the-art production-grade OCR model developed by GothiTech for enterprise document understanding, text extraction, and intelligent document processing. Built on a Vision-Language Model (VLM) architecture, it delivers high-accuracy text extraction from complex documents including invoices, contracts, forms, handwritten notes, and dense tables.

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Table of Contents

• GT-REX Variants
• Key Features
• Model Details
• Quick Start
• Installation
• Usage Examples
• Use Cases
• Performance Benchmarks
• Prompt Engineering Guide
• API Integration
• Troubleshooting
• Hardware Recommendations
• License
• Citation

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GT-REX Variants

GT-REX ships with three optimized configurations tailored to different performance and accuracy requirements. All variants share the same underlying model weights — they differ only in inference settings.

How to Choose a Variant

• Nano: You need maximum throughput and documents are simple (receipts, IDs, labels).
• Pro: General-purpose. Best balance for invoices, contracts, forms, and reports.
• Ultra: Documents have fine print, dense tables, medical records, or legal footnotes.

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GT-Rex-Nano

Speed-optimized for high-volume batch processing

Best for: Thumbnails, previews, high-throughput pipelines (100+ docs/min), mobile uploads, receipt scanning.

from vllm import LLM

llm = LLM(
    model="gothitech/GT-REX",
    trust_remote_code=True,
    max_model_len=2048,
    gpu_memory_utilization=0.6,
    max_num_seqs=256,
    limit_mm_per_prompt={"image": 1},
)

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GT-Rex-Pro (Default)

Balanced quality and speed for standard enterprise documents

Best for: Contracts, forms, invoices, reports, government documents, insurance claims.

from vllm import LLM

llm = LLM(
    model="gothitech/GT-REX",
    trust_remote_code=True,
    max_model_len=4096,
    gpu_memory_utilization=0.75,
    max_num_seqs=128,
    limit_mm_per_prompt={"image": 1},
)

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GT-Rex-Ultra

Maximum quality with adaptive processing for complex documents

Best for: Legal documents, fine print, dense tables, medical records, engineering drawings, academic papers, multi-column layouts.

from vllm import LLM

llm = LLM(
    model="gothitech/GT-REX",
    trust_remote_code=True,
    max_model_len=8192,
    gpu_memory_utilization=0.85,
    max_num_seqs=64,
    limit_mm_per_prompt={"image": 1},
)

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Key Features

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Model Details

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Quick Start

Get running in under 5 minutes:

from vllm import LLM, SamplingParams
from PIL import Image

# 1. Load model (Pro variant - default)
llm = LLM(
    model="gothitech/GT-REX",
    trust_remote_code=True,
    max_model_len=4096,
    gpu_memory_utilization=0.75,
    max_num_seqs=128,
    limit_mm_per_prompt={"image": 1},
)

# 2. Prepare input
image = Image.open("document.png")
prompt = "Extract all text from this document."

# 3. Run inference
sampling_params = SamplingParams(
    temperature=0.0,
    max_tokens=4096,
)

outputs = llm.generate(
    [{
        "prompt": prompt,
        "multi_modal_data": {"image": image},
    }],
    sampling_params=sampling_params,
)

# 4. Get results
result = outputs[0].outputs[0].text
print(result)

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Installation

Prerequisites

• Python 3.9+
• CUDA 11.8+ (GPU required)
• 8 GB+ VRAM (Pro variant), 4 GB+ (Nano), 12 GB+ (Ultra)

Install Dependencies

pip install vllm pillow torch transformers

Verify Installation

from vllm import LLM
print("vLLM installed successfully!")

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Usage Examples

Basic Text Extraction

prompt = "Extract all text from this document image."

Structured JSON Extraction

prompt = '''Extract the following fields from this invoice as JSON:
{
    "invoice_number": "",
    "date": "",
    "vendor_name": "",
    "total_amount": "",
    "line_items": [
        {"description": "", "quantity": "", "unit_price": "", "amount": ""}
    ]
}'''

Table Extraction (Markdown Format)

prompt = "Extract all tables from this document in Markdown table format."

Key-Value Pair Extraction

prompt = '''Extract all key-value pairs from this form.
Return as:
Key: Value
Key: Value'''

Handwritten Text Transcription

prompt = "Transcribe all handwritten text from this image accurately."

Multi-Document Batch Processing

from PIL import Image
from vllm import LLM, SamplingParams

llm = LLM(
    model="gothitech/GT-REX",
    trust_remote_code=True,
    max_model_len=4096,
    gpu_memory_utilization=0.75,
    max_num_seqs=128,
    limit_mm_per_prompt={"image": 1},
)

# Prepare batch
image_paths = ["doc1.png", "doc2.png", "doc3.png"]
prompts = []
for path in image_paths:
    img = Image.open(path)
    prompts.append({
        "prompt": "Extract all text from this document.",
        "multi_modal_data": {"image": img},
    })

# Run batch inference
sampling_params = SamplingParams(temperature=0.0, max_tokens=4096)
outputs = llm.generate(prompts, sampling_params=sampling_params)

# Collect results
for i, output in enumerate(outputs):
    print(f"--- Document {i + 1} ---")
    print(output.outputs[0].text)
    print()

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Use Cases

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Performance Benchmarks

Throughput by Variant (NVIDIA A100 80GB)

Accuracy by Document Type (Pro Variant)

Note: Benchmark numbers are approximate and may vary based on document quality, content complexity, and hardware configuration.

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Prompt Engineering Guide

Get the best results from GT-REX with these prompt strategies:

Tips for Best Results

Do:

• Be specific about what to extract ("Extract the invoice number and total amount")
• Specify output format ("Return as JSON", "Return as Markdown table")
• Provide schema for structured extraction (show the expected JSON keys)
• Use clear instructions ("Transcribe exactly as written, preserving spelling errors")

Don't:

• Use vague prompts ("What is this?")
• Ask for analysis or summarization (GT-REX is optimized for extraction)
• Include unrelated context in the prompt

Example Prompts

# Simple extraction
"Extract all text from this document."

# Targeted extraction
"Extract only the table on this page as a Markdown table."

# Schema-driven extraction
"Extract data matching this schema: {name: str, date: str, amount: float}"

# Preservation mode
"Transcribe this document exactly as written, preserving original formatting."

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API Integration

FastAPI Server Example

from fastapi import FastAPI, UploadFile
from PIL import Image
from vllm import LLM, SamplingParams
import io

app = FastAPI()

llm = LLM(
    model="gothitech/GT-REX",
    trust_remote_code=True,
    max_model_len=4096,
    gpu_memory_utilization=0.75,
    max_num_seqs=128,
    limit_mm_per_prompt={"image": 1},
)

sampling_params = SamplingParams(temperature=0.0, max_tokens=4096)


@app.post("/extract")
async def extract_text(file: UploadFile, prompt: str = "Extract all text."):
    image_bytes = await file.read()
    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")

    outputs = llm.generate(
        [{
            "prompt": prompt,
            "multi_modal_data": {"image": image},
        }],
        sampling_params=sampling_params,
    )

    return {"text": outputs[0].outputs[0].text}

cURL Example

curl -X POST "http://localhost:8000/extract" \
  -F "file=@invoice.png" \
  -F "prompt=Extract all text from this invoice as JSON."

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Troubleshooting

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Hardware Recommendations

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License

This model is released under the MIT License. You are free to use, modify, and distribute it for both commercial and non-commercial purposes.

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Citation

If you use GT-REX in your work, please cite:

@misc{gtrex-2026,
  title   = {GT-REX: Production-Grade OCR with Vision-Language Models},
  author  = {Hathaliya, Jenis},
  year    = {2026},
  month   = {February},
  url     = {https://huggingface.co/gothitech/GT-REX},
  note    = {GothiTech Recognition and Extraction eXpert}
}

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Contact and Support

• Developer: Jenis Hathaliya
• Organization: GothiTech
• HuggingFace: gothitech

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<p align="center"> Built by <strong>GothiTech</strong> </p> <p align="center"> <em>Last updated: February 2026</em><br> <em>GT-REX | Variants: Nano | Pro | Ultra</em> </p>