II-Search-4B-GGUF

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
• Incorporated our in-house search database (containing Wiki data, Fineweb data, and ArXiv data)

Performance

Tool Usage Comparison

Simple QA (SerpDev)

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):

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 version on your Mac. Then, interact with it via user-friendly interfaces like LM Studio or Ollama Desktop.

Recommended Generation Parameters

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}},
}