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
Commit History
-
Update README.md
Verified signatured9f5843 -
Update README.md
Verified signatured27b7ef -
Update README.md
Verified signature49ed32b -
Update README.md
Verified signature62d50ad -
Upload preprocessor_config.json
Verified signatureea2a75c -
Upload README.md
Verified signaturead1e449
-
Upload 6 files
Verified signatureb2675ac
-
Update README.md
Verified signature4706da8
-
Update README.md
Verified signature4ae84e2 -
update of the config.json vocab size to 10000
Verified signaturefd7d789 -
Update config.json
Verified signature27ef4ad -
added a msgpack to the model weight use
Verified signature53549c0
-
Upload pytorch_model.bin
Verified signature0919635 -
Upload 2 files
Verified signature06fa45f -
Upload model.safetensors
Verified signaturee49cfcc -
Update README.md
Verified signature3f87b69 -
Update README.md
Verified signatureb973306 -
new baseline model of LisaV3
Verified signaturef6ee8c1 -
initial commit
Verified signaturef1b2331