Visual Question Answering
Transformers
Safetensors
PyTorch
English
tinyllava
text-generation
vision-language
custom_code
Eval Results (legacy)
Instructions to use keeeeenw/MicroLlava with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use keeeeenw/MicroLlava with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="keeeeenw/MicroLlava", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("keeeeenw/MicroLlava", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 773d5306e09a21e3621e4018f1da403aec4a6af2815328f16631c292bfdbadb3
- Size of remote file:
- 5.31 kB
- SHA256:
- 3bc89336d39be48ff46d93564b5e69623905c077e01b176d40305e68b0ab42d6
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