Overview

SAM (Small Agentic Model) is a 7 billion parameter model developed by the SuperAGI Team, fine-tuned from Mistral 7B. Despite its smaller size, SAM-7B exhibits impressive reasoning abilities, surpassing models like GPT-3.5, Orca-2-7B, and Orca-2-13B on various reasoning benchmarks, including ARC-C and GSM8k. Notably, it achieves this performance even with a significantly smaller training dataset compared to some larger models.

Key Capabilities & Training

• Superior Reasoning: Outperforms several larger models on multi-hop reasoning tasks, as evidenced by benchmarks like GSM8k and ARC-C.
• Efficient Training: Fine-tuned on NVIDIA H100 GPUs for 4 hours over 1 epoch, using a dataset where all responses were generated by open-source models.
• Task Breakdown: Excels in breaking down complex tasks, making it suitable for agentic workflows.

Limitations & Use Cases

SAM is primarily designed for reasoning and task breakdown, not for general conversations or simple Q&A. It lacks moderation mechanisms and guardrails for toxicity or societal bias, making it unsuitable for production use without further development. The model's strength lies in demonstrating that high-quality, open-source generated data can induce better reasoning in smaller models.