Reinforcement Learning
Keras
PyTorch
JAX
Safetensors
English
advancedlisa
multimodal
vision
audio
multispectral
emotion-recognition
scene-understanding
object-detection
spatial-reasoning
conversational-ai
Instructions to use Qybera/LisaV3.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Qybera/LisaV3.0 with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://Qybera/LisaV3.0") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Qybera/LisaV3.0: direct link, hf CLI and curl.
- Browser
- Download file 765 MB
-
https://huggingface.co/Qybera/LisaV3.0/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Qybera/LisaV3.0/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Qybera/LisaV3.0/resolve/main/flax_model.msgpack
765 MB
- Xet hash:
- 464cfab1975c492be3adec689d272eaf818537949b83af21a768d1cdcca6ef23
- Size of remote file:
- 765 MB
- SHA256:
- e7df838568adf69c40187b160734384b5898f0388882e5890e38ea407e733ec6
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