Overview
II-Search-4B is a 4 billion parameter language model developed by Intelligent-Internet, built upon the Qwen3-4B architecture. It is specifically fine-tuned for advanced information seeking, multi-hop reasoning, and web-integrated search tasks. The model demonstrates strong capabilities in complex information retrieval, fact verification, and generating comprehensive reports, positioning it as a leading model in its size class for these applications.
Key Capabilities
- Enhanced Tool Usage: Integrates web search and webpage visit tools for internet-aware functionality.
- Multi-hop Reasoning: Features sophisticated planning and improved reasoning thought patterns for complex queries.
- Verified Information Retrieval: Cross-checks information for factual accuracy.
- Comprehensive Report Generation: Excels at producing detailed reports for research queries.
- Strong Factual QA Performance: Achieves significant improvements on benchmarks like OpenAI/SimpleQA (91.8%) and Google/Frames (67.5%) compared to other 4B models.
Training Methodology Highlights
The training involved a multi-phase approach:
- Tool Call Ability Stimulation: Distillation from larger models (Qwen3-235B) to establish function calling on multi-hop datasets.
- Reasoning Improvement: Creation of synthetic problems and refinement of reasoning paths.
- Rejection Sampling & Report Generation: Filtering for high-quality reasoning traces and applying STORM-inspired techniques.
- Reinforcement Learning: Training with datasets like dgslibisey/MuSiQue and an in-house search database.
Intended Use Cases
- Information seeking and factual question answering.
- Research assistance and comprehensive report generation.
- Fact verification and evidence-based reasoning.
- Educational and research applications requiring high factual accuracy.