HelixNet
| Property | Value |
|---|---|
| Base Model | Mistral-7B |
| License | Apache 2.0 |
| Architecture | Actor-Critic-Regenerator Framework |
| Training Data Size | Actor: 250K samples, Critic: 10K samples, Regenerator: 1K samples |
What is HelixNet?
HelixNet represents a groundbreaking approach to language model architecture, implementing a three-component system inspired by actor-critic frameworks in reinforcement learning. The model consists of three fine-tuned Mistral-7B LLMs working in concert: an actor for initial response generation, a critic for response evaluation, and a regenerator for response refinement.
Implementation Details
The system employs a sophisticated training methodology across three phases: The actor network was trained on 250K high-quality samples including Chain-of-Thought and Tree-of-Thought data, achieving impressive benchmark scores (MMLU: 63.10, HellaSWAG: 83.22). The critic was trained on 10K samples with GPT-4-generated critiques, while the regenerator was trained on 1K samples following LIMA's approach.
- Actor Network: Trained on diverse high-quality datasets including Open-Orca and SynthIA
- Critic Network: Specialized in providing intelligent critique for response improvement
- Regenerator Network: Focused on maintaining entropy while improving responses
Core Capabilities
- Enhanced response quality through multi-stage refinement
- Transferrable critic and regenerator components
- Tree of Thoughts and Chain of Thought reasoning
- Excellent benchmark performance across multiple metrics
Frequently Asked Questions
Q: What makes this model unique?
HelixNet's distinctive feature is its DNA-inspired triple-network architecture that allows for iterative improvement of responses through specialized components, each trained for a specific aspect of response generation and refinement.
Q: What are the recommended use cases?
The model excels in applications requiring detailed, well-reasoned responses with high accuracy. It's particularly suitable for tasks demanding sophisticated reasoning, explanation generation, and scenarios where response quality is critical.
