Mungert/II-Search-4B-GGUF

II-Search-4B GGUF Models

Model Generation Details

This model was generated using llama.cpp at commit cd6983d5.

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Quantization Beyond the IMatrix

I've been experimenting with a new quantization approach that selectively elevates the precision of key layers beyond what the default IMatrix configuration provides.

In my testing, standard IMatrix quantization underperforms at lower bit depths, especially with Mixture of Experts (MoE) models. To address this, I'm using the --tensor-type option in llama.cpp to manually "bump" important layers to higher precision. You can see the implementation here:
👉 Layer bumping with llama.cpp

While this does increase model file size, it significantly improves precision for a given quantization level.

I'd love your feedback—have you tried this? How does it perform for you?

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Click here to get info on choosing the right GGUF model format

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II-Search-4B

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.

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

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/Intelligent-Internet/II-Search-Benchmark-Details

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}},
}

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🚀 If you find these models useful

Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks:

👉 Quantum Network Monitor

The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : Source Code Quantum Network Monitor. You will also find the code I use to quantize the models if you want to do it yourself GGUFModelBuilder

💬 How to test:
Choose an AI assistant type:

• TurboLLM (GPT-4.1-mini)
• HugLLM (Hugginface Open-source models)
• TestLLM (Experimental CPU-only)

What I’m Testing

I’m pushing the limits of small open-source models for AI network monitoring, specifically:

• Function calling against live network services
• How small can a model go while still handling:
  • Automated Nmap security scans
  • Quantum-readiness checks
  • Network Monitoring tasks

🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):

• ✅ Zero-configuration setup
• ⏳ 30s load time (slow inference but no API costs) . No token limited as the cost is low.
• 🔧 Help wanted! If you’re into edge-device AI, let’s collaborate!

Other Assistants

🟢 TurboLLM – Uses gpt-4.1-mini :

• **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
• Create custom cmd processors to run .net code on Quantum Network Monitor Agents
• Real-time network diagnostics and monitoring
• Security Audits
• Penetration testing (Nmap/Metasploit)

🔵 HugLLM – Latest Open-source models:

• 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.

💡 Example commands you could test:

1. "Give me info on my websites SSL certificate"
2. "Check if my server is using quantum safe encyption for communication"
3. "Run a comprehensive security audit on my server"
4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code on. This is a very flexible and powerful feature. Use with caution!

Final Word

I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is open source. Feel free to use whatever you find helpful.

If you appreciate the work, please consider buying me a coffee ☕. Your support helps cover service costs and allows me to raise token limits for everyone.

I'm also open to job opportunities or sponsorship.

Thank you! 😊