Instructions to use vedaco/Tera.v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use vedaco/Tera.v3 with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://vedaco/Tera.v3") - Notebooks
- Google Colab
- Kaggle
Tera.V3: Sovereign Intelligence
Tera.V3 is a high-efficiency Multimodal "Dense-Elite" architecture designed for private, sovereign deployment. (Proprietary / Non-Open Source)
Key Features
- Multimodal: Integrated
TeraVisionEncoderfor simultaneous image and text processing. - Sovereign Efficiency: Designed to outperform massive models through architectural precision.
- Stable Core: Utilizes
LogSquaredReLUandTokenShiftfor deep sequential modeling.
Model Details
- Optimizer: LionSovereign
- Architecture: Multimodal Transformer-style
- Status: Pre-trained on synthetic multimodal data (5,000 steps)
Usage
Weights are stored in .weights.h5 format for Keras 3 / TensorFlow compatibility.
- Downloads last month
- -
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support