kishlay9890/chandra-gptq-4bit

Chandra GPTQ 4-bit

Chandra-GPTQ-4bit is a 4-bit GPTQ-quantized version of datalab-to/chandra, optimized for efficient inference with vLLM on modern GPUs such as A100.

This model significantly reduces VRAM usage while maintaining strong OCR and document understanding performance.

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🔎 Model Overview

• Base Model: datalab-to/chandra
• Quantization: GPTQ 4-bit
• Precision: W4A16
• Group Size: 128
• Symmetric Quantization: Yes
• Format: GPTQ
• Primary Use: OCR / Image-to-Text / Document Understanding

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✨ Capabilities

Chandra is a multimodal OCR model capable of:

• Converting documents to Markdown, HTML, or JSON
• Preserving detailed layout information
• Extracting tables, forms, and structured data
• Handling handwriting
• Reconstructing checkboxes and form elements
• Extracting images and captions
• Supporting 40+ languages
• Understanding complex layouts (math, tables, diagrams)

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🚀 Usage with vLLM (Recommended)

Install vLLM:

pip install vllm

Start OpenAI-compatible API server:

python -m vllm.entrypoints.openai.api_server \
  --host 0.0.0.0 \
  --port $PORT \
  --model kishlay9890/chandra-gptq-4bit \
  --max-model-len 8192 \
  --tensor-parallel-size 1 \
  --gpu-memory-utilization $GPU_UTIL \
  --max-num-batched-tokens 8192 \
  --max-num-seqs 8 \
  --seed 1234

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💾 Hardware Requirements

Recommended:

• NVIDIA A100 (40GB or higher)
• CUDA 12+
• 16–24GB VRAM minimum for inference

Compared to FP16 models, this 4-bit version significantly lowers memory requirements.

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📜 License

Please refer to the original base model (datalab-to/chandra) for license details.

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🙌 Acknowledgements

• Base model: datalab-to/chandra
• Quantization: GPTQ

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