Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
Data Science
Towards AI·21d ago·5 min read
I Fused 3 Tiny Local LLMs on my Laptop and Matched the Reasoning of Anthropic Fable 5
TL;DR
High API costs can be avoided by fusing three small local language models (LLMs) on consumer hardware. Using active logit-level fusion, the author achieved reasoning capabilities comparable to Anthropic's Fable 5.
✦ Why It Matters
Engineers can implement active logit-level fusion to create cost-effective, private AI solutions on local hardware today.
Key Takeaways
- Experiment with fusing local models to reduce API costs.
- Implement active logit-level fusion for enhanced reasoning.
- Use PyTorch for efficient local model management.
- Maintain data privacy by processing everything on local hardware.
How It Works
Active logit-level fusion combines the outputs of multiple models at the logit level, allowing them to work in parallel. Each model contributes its strengths—such as coding, creativity, or logic—without merging their weights, preserving their unique capabilities.
This method intercepts the logits before sampling, enabling a weighted average of their outputs to be computed in real-time, akin to a jazz trio harmonizing together.