Instructions to use svjack/vit-gpt-diffusion-zh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use svjack/vit-gpt-diffusion-zh 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="svjack/vit-gpt-diffusion-zh")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("svjack/vit-gpt-diffusion-zh") model = AutoModelForMultimodalLM.from_pretrained("svjack/vit-gpt-diffusion-zh", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from svjack/vit-gpt-diffusion-zh: direct link, hf CLI and curl.
- Browser
- Download file 3.63 kB
-
https://huggingface.co/svjack/vit-gpt-diffusion-zh/resolve/main/training_args.bin
- Command line
-
hf download hf://svjack/vit-gpt-diffusion-zh/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/svjack/vit-gpt-diffusion-zh/resolve/main/training_args.bin
3.63 kB
- Xet hash:
- c98b6d7baa112f33cb35282bf5a1ba7892ca0d75914fcbe71761b845c5796371
- Size of remote file:
- 3.63 kB
- SHA256:
- 415fa6a5b93328a56170dca0646dada1a4c0809e484b308598914a5bbfcb3bc0
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