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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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## Model Description
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.
### Key Features
- 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
## Training Methodology
Our training process consisted of three key phases:
### Phase 1: Tool Call Ability Stimulation
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.
### Phase 2: Reasoning Improvement
We addressed initial limitations by:
- Creating synthetic problems requiring more reasoning turns, inspired by Random Walk algorithm
- Improving reasoning thought patterns for more efficient and cleaner reasoning paths
### Phase 3: Rejection Sampling & Report Generation
We applied:
- Filtering to keep only high-quality reasoning traces (correct answers with proper reasoning)
- STORM-inspired techniques to enhance comprehensive report generation
### Phase 4: Reinforcement Learning
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)
## Performance
| **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 |
### Tool Usage Comparison
**Simple QA (SerpDev)**
| | **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 |
All benchmark traces from models can be found at: https://huggingface.co/datasets/II-Vietnam/Inspect-Search-Models-Benchmarking-Result
## Intended Use
II-Search-4B is designed for:
- 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
## Usage
To deploy and interact with the II-Search-4B model effectively, follow these options:
1. Serve the model using vLLM or SGLang
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.
2. Integrate web_search and web_visit tools
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.
## 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.
## Recommended Generation Parameters
```python
generate_cfg = {
'top_k': 20,
'top_p': 0.95,
'temperature': 0.6,
'repetition_penalty': 1.1,
'max_tokens': 2048
}
```
- 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{}."
## Citation
```
@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}},
}
``` |