SAM-1-Base: Specialized for E-commerce AI

SAM-1-Base (Shopping Agent Model) is a 7.62 billion parameter language model developed by SnapCart AI, built upon the Qwen2.5-7B-Instruct architecture. This model has been fine-tuned using LoRA adaptation, which is fully merged into the base weights, making it ready for direct inference. It features a substantial context length of 32,768 tokens and uses the ChatML format.

Key Capabilities & Performance

SAM-1-Base is specifically designed for 8 core shopping assistant capabilities, demonstrating strong performance on the proprietary SAM-Bench benchmark, where it scored 90.55/100. Its strengths include:

• Query Understanding: Achieves 98.37% in parsing shopping queries.
• Attribute Extraction: Scores 97.57% in extracting structured product attributes.
• Product Comparison: Reaches 94.88% in comparing products and selecting the best option.
• Purchase Decision: Scores 94.11% in making purchase recommendations.
• Review Synthesis: Achieves 92.45% in summarizing reviews with sentiment analysis.

While excelling in many areas, the model's performance in Product Recommendation (77.76%) and Personalization (77.59%) indicates areas for future improvement in preference modeling.

Intended Use Cases

• Research and Evaluation: Ideal for studying and comparing shopping assistant models.
• Prototyping: Suitable for developing e-commerce AI features.
• Academic Study: Useful for research into domain-specific LLM fine-tuning.

Limitations

It's important to note that SAM-1-Base was evaluated on synthetic data, is English-only, and lacks multimodal capabilities. It also inherits the biases and knowledge cutoff of its base model, Qwen2.5-7B-Instruct.