Instructions to use defford/GLM-OCR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use defford/GLM-OCR with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="defford/GLM-OCR")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("defford/GLM-OCR") model = AutoModelForMultimodalLM.from_pretrained("defford/GLM-OCR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 580 Bytes
e95db50 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | # Use a modern PyTorch image that already has CUDA 12+
FROM pytorch/pytorch:2.4.0-cuda12.1-cudnn9-devel
# Install the exact versions we need
RUN pip install --no-cache-dir \
transformers>=5.1.0 \
accelerate \
fastapi \
uvicorn \
python-multipart \
pillow
# Copy your model files into the container
COPY . /repository
WORKDIR /repository
# Expose the port Hugging Face expects
EXPOSE 80
# Start a tiny web server that talks to your handler.py
# We use a helper script to bridge the gap
CMD ["uvicorn", "handler:app", "--host", "0.0.0.0", "--port", "80"] |