Reinforcement Learning
Keras
PyTorch
JAX
Safetensors
English
advancedlisa
multimodal
vision
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emotion-recognition
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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
| { | |
| "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 | |
| } |