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NovaNet represents the pinnacle of Nova AI Labs' research in generative AI. Built on a foundation of transformer architecture and trained on diverse, high-quality datasets, NovaNet delivers exceptional performance across a wide range of language and reasoning tasks. Our model combines state-of-the-art techniques with novel architectural improvements to push the boundaries of what's possible with language models.

🔮 Advanced Model Multimodal capabilities with text, image, and code understanding

⚡ Optimized Speed 5x faster inference than comparable models

🧩 Integrated Ecosystem Works seamlessly with popular frameworks

🚀 Scalable From edge devices to cloud deployments

🔒 Privacy-Focused Options for secure, local inference

🧠 Model Variants
NovaNet Lite	1.3B parameters (optimized for edge devices)
NovaNet Standard	7B parameters (balanced performance)
NovaNet Pro	13B parameters (maximum capability)
NovaNet Enterprise	70B parameters (coming Q3 2025)
⚙️ Technical Details
Architecture	Enhanced Decoder-only Transformer
Context Length	16K tokens (32K in Enterprise variant)
Training Data	Proprietary Nova dataset + curated public data
Languages	English, Spanish, French, German, Japanese (more coming soon)
Quantization	4-bit, 8-bit, and FP16 options available
Last Updated	May 2025

BenchmarkNovaNet LiteNovaNet StandardNovaNet ProIndustry AverageMMLU72.4%78.9%83.7%75.2%HumanEval68.5%76.2%81.9%65.8%GSM8K71.3%79.8%84.2%70.5%HellaSwag83.7%87.5%91.3%82.1%TruthfulQA69.4%75.6%82.8%67.9%

🔧 Installation bash# Install via pip pip install novai-models transformers torch

Or using conda

conda install -c novaai novai-models transformers pytorch 🧠 Basic Usage pythonfrom transformers import AutoModelForCausalLM, AutoTokenizer

Load model and tokenizer

tokenizer = AutoTokenizer.from_pretrained("NovaAI-Labs/NovaNet-Standard") model = AutoModelForCausalLM.from_pretrained("NovaAI-Labs/NovaNet-Standard")

Generate text

input_text = "The future of artificial intelligence will" inputs = tokenizer(input_text, return_tensors="pt") outputs = model.generate(**inputs, max_length=100)

print(tokenizer.decode(outputs[0], skip_special_tokens=True)) 🐳 Docker Deployment bash# Pull the latest image docker pull novaai/novanet:latest

Run the container with API

docker run -p 8000:8000 novaai/novanet:latest

Comprehensive documentation is available at our official docs site:

Key Documentation Sections:

🚀 Getting Started	📘 API Reference	👨‍🏫 Tutorials
🔧 Advanced Usage	🧠 Model Details	💡 Examples

Fine-tuning for Custom Tasks pythonfrom novaai.trainer import NovaTrainer

trainer = NovaTrainer( model_name="NovaAI-Labs/NovaNet-Standard", output_dir="./my-fine-tuned-model", train_file="path/to/train.json", validation_file="path/to/validation.json", learning_rate=5e-5, num_train_epochs=3 )

trainer.train() trainer.save_model() Optimizing for Deployment pythonfrom novaai.optimization import NovaOptimizer

optimizer = NovaOptimizer( model_path="NovaAI-Labs/NovaNet-Standard", quantization="int8", pruning_level=0.3, target_device="gpu" )

optimizer.optimize() optimizer.save("./optimized-model") Inference with Special Capabilities pythonfrom novaai import NovaInference

inference = NovaInference( model="NovaAI-Labs/NovaNet-Pro", use_cache=True, max_batch_size=16 )

Special capabilities like continuous learning

result = inference.generate( "Explain quantum computing", use_knowledge_base=True, stream=True, safety_level="medium" )

NovaNet employs an enhanced transformer architecture with several key innovations: Attention Mechanism: Improved sparse attention patterns Position Encoding: Advanced relative positional embeddings Activation Functions: Custom non-linearities for better gradient flow Layer Structure: Novel residual connections with normalization Knowledge Integration: Specialized modules for factual recall

┌─────────────────────┐ │ Input Tokens │ └──────────┬──────────┘ ▼ ┌─────────────────────┐ │ Embeddings │ └──────────┬──────────┘ ▼ ┌─────────────────────┐ │ Transformer Blocks │ │ ┌─────────────────┐ │ │ │ Self-Attention │ │ │ └────────┬────────┘ │ │ ▼ │ │ ┌─────────────────┐ │ │ │ Feed Forward │ │ │ └────────┬────────┘ │ │ ▼ │ │ ┌─────────────────┐ │ │ │ Normalization │ │ │ └────────┬────────┘ │ └──────────┬──────────┘ ▼ ┌─────────────────────┐ │ Output Layer │ └─────────────────────┘

Version	Release Date	Key Features
1.0.0	May 2025	Initial public release with core capabilities
0.9.0	March 2025	Beta release with enhanced reasoning capabilities
0.8.0	January 2025	Alpha release with multilingual support
0.7.0	November 2024	Preview release with code generation features

Join our growing community of developers and researchers!

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NovaNet is released under the Apache 2.0 License.

Copyright 2025 Nova AI Labs

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

See the full LICENSE file for details.

Dineth Nethsara Founder & Lead Researcher @HexLordDev

Jane Smith ML Architecture Lead @janesmith

Alex Chen Data Science Director @alexchen

Maria Rodriguez Engineering Lead @mariarodriguez

...and over 30 amazing contributors from our open-source community!

Powering Tomorrow with AI — Nova AI Labs

Made with ❤️ in 2025