Instructions to use kkatiz/THAI-BLIP-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kkatiz/THAI-BLIP-2 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="kkatiz/THAI-BLIP-2")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("kkatiz/THAI-BLIP-2") model = AutoModelForMultimodalLM.from_pretrained("kkatiz/THAI-BLIP-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2c44a7ca73bb811bbd1a15c37b5a7c43fcbdb3a829f53497406247a73389cd46
- Size of remote file:
- 4.73 GB
- SHA256:
- 55df5e6684e8a31c7732e3a90b15440277aeb1210c09f9e41c62f3c27c2eef35
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.