---
license: apache-2.0
base_model:
- zai-org/GLM-OCR
pipeline_tag: image-text-to-text
tags:
- ocr
- synthos
- computer
- vizion
- llm
---

---
license: apache-2.0
language: en
base_model: zai-org/GLM-OCR
tags:
  - ocr
  - document-understanding
  - vision-language
  - glm-ocr
  - synthos
  - z.ai
pipeline_tag: image-to-text
library_name: transformers

# Cizi: Enhanced OCR Fine-Tuned on Z.AI GLM-OCR

**Cizi** is a specialized fine-tune of [Z.AI's GLM-OCR](https://huggingface.co/zai-org/GLM-OCR), optimized for [insert your specific niche, e.g., historical documents / medical forms / low-resource languages]. 

Built on top of GLM-OCR's lightweight 0.9B parameter GLM-V encoder-decoder architecture with Multi-Token Prediction (MTP), Synthos inherits state-of-the-art base OCR capabilities while being further aligned for [your specific use case] through targeted fine-tuning [[2]][[3]].

## 🌟 Key Features

- **Ultra-Lightweight:** Only ~0.9B parameters, enabling fast local inference on consumer hardware and edge devices [[1]].
- **Multi-Token Prediction:** Leverages MTP for faster decoding and improved contextual coherence in dense text regions [[2]].
- **[Your Niche] Specialization:** Fine-tuned on [X]K domain-specific samples to outperform base GLM-OCR on [specific benchmark/task].
- **Structured Markdown Output:** Faithfully preserves tables, headers, and reading order in complex layouts.
- **End-to-End Architecture:** No separate detection/recognition stages — single forward pass from pixels to structured text [[3]].


## 🚀 Quick Start

```python
from transformers import AutoModelForVision2Seq, AutoProcessor
import torch
from PIL import Image

model_id = "your-org/synthos"
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForVision2Seq.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

image = Image.open("document.png")
messages = [
    {"role": "user", "content": [
        {"type": "image"},
        {"type": "text", "text": "Transcribe this document faithfully in Markdown format."}
    ]}
]

inputs = processor.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)

output_ids = model.generate(inputs, max_new_tokens=2048, do_sample=False)
response = processor.batch_decode(output_ids, skip_special_tokens=True)[0]
print(response)