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:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Qybera/LisaV3.0") - Notebooks
- Google Colab
- Kaggle
File size: 499 Bytes
f6ee8c1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | {
"do_resize": true,
"size": {
"height": 224,
"width": 224
},
"do_normalize": true,
"image_mean": [
0.485,
0.456,
0.406
],
"image_std": [
0.229,
0.224,
0.225
],
"do_convert_rgb": true,
"feature_extractor_type": "LISAFeatureExtractor",
"do_normalize_audio": true,
"audio_sample_rate": 16000,
"n_mels": 80,
"hop_length": 512,
"max_duration": 60,
"return_attention_mask": true,
"return_token_type_ids": false
} |