Image-to-Text
Transformers
PyTorch
English
llava
text-generation
visual-question-answering
image-captioning
Instructions to use kaist-ai/volcano-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kaist-ai/volcano-7b 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="kaist-ai/volcano-7b")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCausalLM processor = AutoProcessor.from_pretrained("kaist-ai/volcano-7b") model = AutoModelForCausalLM.from_pretrained("kaist-ai/volcano-7b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from kaist-ai/volcano-7b: direct link, hf CLI and curl.
- Browser
- Download file 6.39 kB
-
https://huggingface.co/kaist-ai/volcano-7b/resolve/main/training_args.bin
- Command line
-
hf download hf://kaist-ai/volcano-7b/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/kaist-ai/volcano-7b/resolve/main/training_args.bin
6.39 kB
- Xet hash:
- c76a72a1ce8a6c0f6bfb5e951b6596daee69d203100e59076add59113db64e61
- Size of remote file:
- 6.39 kB
- SHA256:
- 343284e4996419c9487bf32ad2e45c30bcd0bee76c451bd74de3f27872290cb6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.