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| base_model: |
| - Qwen/Qwen3-4B |
| pipeline_tag: text-generation |
| library_name: transformers |
| license: apache-2.0 |
| --- |
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| # II-Search-4B |
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| A 4B parameter language model specialized in information seeking, multi-hop reasoning, and web-integrated search, achieving state-of-the-art performance among models of similar size. |
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| </aside> |
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| ## Model Description |
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| II-Search-4B is a 4B parameter language model based on Qwen3-4B, fine-tuned specifically for information seeking tasks and web-integrated reasoning. It excels at complex multi-hop information retrieval, fact verification, and comprehensive report generation. |
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| ### Key Features |
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| - Enhanced tool usage for web search and webpage visits |
| - Multi-hop reasoning capabilities with sophisticated planning |
| - Verified information retrieval with cross-checking |
| - Strong performance on factual QA benchmarks |
| - Comprehensive report generation for research queries |
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| ## Training Methodology |
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| Our training process consisted of three key phases: |
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| ### Phase 1: Tool Call Ability Stimulation |
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| We used a distillation approach from larger models (Qwen3-235B) to generate reasoning paths with function calling on multi-hop datasets. This established the base capabilities for tool use. |
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| ### Phase 2: Reasoning Improvement |
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| We addressed initial limitations by: |
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| - Creating synthetic problems requiring more reasoning turns, inspired by Random Walk algorithm |
| - Improving reasoning thought patterns for more efficient and cleaner reasoning paths |
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| ### Phase 3: Rejection Sampling & Report Generation |
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| We applied: |
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| - Filtering to keep only high-quality reasoning traces (correct answers with proper reasoning) |
| - STORM-inspired techniques to enhance comprehensive report generation |
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| ### Phase 4: Reinforcement Learning |
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| We trained the model using reinforcement learning |
| - Used dataset: [dgslibisey/MuSiQue](https://huggingface.co/datasets/dgslibisey/MuSiQue) |
| - Incorporated our in-house search database (containing Wiki data, Fineweb data, and ArXiv data) |
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| ## Performance |
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| | **Benchmark** | **Qwen3-4B** | **Jan-4B** | **WebSailor-3B** | **II-Search-4B** | |
| | --- | --- | --- | --- | --- | |
| | OpenAI/SimpleQA | 76.8 | 80.1 | 81.8 | 91.8 | |
| | Google/Frames | 30.7 | 24.8 | 34.0 | 67.5 | |
| | Seal_0 | 6.31 | 2.7 | 1.8 | 22.5 | |
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| ### Tool Usage Comparison |
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| **Simple QA (SerpDev)** |
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| | | **Qwen3-4B** | **Jan-4B** | **WebSailor-3B** | **II-Search-4B** | |
| | --- | --- | --- | --- | --- | |
| | # Search | 1.0 | 0.9 | 2.1 | 2.2 | |
| | # Visit | 0.1 | 1.9 | 6.4 | 3.5 | |
| | # Total Tools | 1.1 | 2.8 | 8.5 | 5.7 | |
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| All benchmark traces from models can be found at: https://huggingface.co/datasets/Intelligent-Internet/II-Search-Benchmark-Details |
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| ## Intended Use |
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| II-Search-4B is designed for: |
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| - Information seeking and factual question answering |
| - Research assistance and comprehensive report generation |
| - Fact verification and evidence-based reasoning |
| - Educational and research applications requiring factual accuracy |
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| ## Usage |
| To deploy and interact with the II-Search-4B model effectively, follow these options: |
| 1. Serve the model using vLLM or SGLang |
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| Use the following command to serve the model with vLLM (adjust parameters as needed for your hardware setup): |
| ```bash |
| vllm serve Intelligent-Internet/II-Search-4B --served-model-name II-Search-4B --tensor-parallel-size 8 --enable-reasoning --reasoning-parser deepseek_r1 --rope-scaling '{"rope_type":"yarn","factor":1.5,"original_max_position_embeddings":98304}' --max-model-len 131072 |
| ``` |
| This configuration enables distributed tensor parallelism across 8 GPUs, reasoning capabilities, custom RoPE scaling for extended context, and a maximum context length of 131,072 tokens. |
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| 2. Integrate web_search and web_visit tools |
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| Equip the served model with web_search and web_visit tools to enable internet-aware functionality. Alternatively, use a middleware like MCP for tool integration—see this example repository: https://github.com/hoanganhpham1006/mcp-server-template. |
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| ## Host on macOS with MLX for local use |
| As an alternative for Apple Silicon users, host the quantized [II-Search-4B-MLX](https://huggingface.co/Intelligent-Internet/II-Search-4B-MLX) version on your Mac. Then, interact with it via user-friendly interfaces like LM Studio or Ollama Desktop. |
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| ## Recommended Generation Parameters |
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| ```python |
| generate_cfg = { |
| 'top_k': 20, |
| 'top_p': 0.95, |
| 'temperature': 0.6, |
| 'repetition_penalty': 1.1, |
| 'max_tokens': 2048 |
| } |
| ``` |
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| - For a query that you need to find a short and accurate answer. Add the following phrase: "\n\nPlease reason step-by-step and put the final answer within \\\\boxed{}." |
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| ## Citation |
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|
| ``` |
| @misc{II-Search-4B, |
| author = {Intelligent Internet}, |
| title = {II-Search-4B: Information Seeking and Web-Integrated Reasoning LLM}, |
| year = {2025}, |
| publisher = {Hugging Face}, |
| journal = {Hugging Face Hub}, |
| howpublished = {\url{https://huggingface.co/II-Vietnam/II-Search-4B}}, |
| } |
| |
| ``` |
