Visual Question Answering
Transformers
Safetensors
PyTorch
English
tinyllava
text-generation
vision-language
custom_code
Eval Results (legacy)
Instructions to use keeeeenw/MicroLlava-Qwen3-0.6B-base-siglip2-so400m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use keeeeenw/MicroLlava-Qwen3-0.6B-base-siglip2-so400m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="keeeeenw/MicroLlava-Qwen3-0.6B-base-siglip2-so400m", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("keeeeenw/MicroLlava-Qwen3-0.6B-base-siglip2-so400m", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5c5390f250e36dc82b9f43662dd6c41fc02e57f249894da79a8eeafc5c6dfe1b
- Size of remote file:
- 6.61 kB
- SHA256:
- 1d5e808eea1e6b30b303dfcc8d900854ba6f14b6fa5ca34cdf8cd8dd6858225c
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