Model Card for DeepThink-T1-Tuned

Model Details

DeepThink-T1-Tuned is a Small Language Model (SLM) with 2.273 billion parameters, developed through a rigorous knowledge distillation process from the larger DeepThink-T1-Base model.

• Developed by: Pure AI Develop Team
• Model type: Small Language Model (SLM)
• Language(s): English (primarily)
• License: Apache 2.0
• Resources: DeepThink Development Plan

Model Description

DeepThink-T1-Tuned is designed to address the growing need for efficient and deployable AI solutions, particularly in environments with limited computational resources.

Core Design Principles:

• Efficiency: Optimized for lower computational requirements, faster inference, and reduced energy consumption
• Deployment Flexibility: Suitable for on-device (edge) deployment
• Customizability: Easily fine-tunable for specialized tasks and domain-specific applications

Intended Uses

• Edge AI applications: Powering intelligent features on smartphones, IoT devices, and embedded systems
• Resource-constrained environments: Deploying AI functionalities with limited hardware or connectivity
• Domain-specific tasks: Fine-tuning for specialized applications
• Research and development: Base model for efficient AI research

Limitations

• Generalization: Limited capacity compared to larger LLMs
• Nuance and Complexity: May struggle with highly nuanced tasks
• Bias Risks: May reflect biases present in training data

Ethical Considerations

Value Alignment Framework includes:

• Bias mitigation in training data and outputs
• Transparency and explainability
• Privacy through on-device processing
• Reduced environmental impact

Security

GuardianNet Security Features:

• Real-time monitoring of model behavior
• Adversarial attack detection
• Content safety filtering
• Secure deployment framework
• Threat intelligence integration

Training Data

Trained using diverse dataset with knowledge distillation from DeepThink-T1-Base model. Detailed dataset composition will be provided in future updates.

Technical Specifications

Parameter	Specification
Parameters	2.273 Billion
Architecture	HAILI with Transformer
Training Framework	PyTorch, TensorFlow
Security Infrastructure	GuardianNet AI Security Cloud

Evaluation Results

Performance metrics to be added

Environmental Impact

Carbon footprint estimates to be added ```