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of [Z.AI's GLM-OCR](https://huggingface.co/zai-org/GLM-OCR), optimized for [insert your specific niche, e.g., historical documents / medical forms / low-resource languages].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