Instructions to use gabriellarson/II-Search-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gabriellarson/II-Search-4B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="gabriellarson/II-Search-4B-GGUF",
	filename="II-Search-4B-F16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use gabriellarson/II-Search-4B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf gabriellarson/II-Search-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf gabriellarson/II-Search-4B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf gabriellarson/II-Search-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf gabriellarson/II-Search-4B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf gabriellarson/II-Search-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf gabriellarson/II-Search-4B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf gabriellarson/II-Search-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf gabriellarson/II-Search-4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/gabriellarson/II-Search-4B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use gabriellarson/II-Search-4B-GGUF with Ollama:
```
ollama run hf.co/gabriellarson/II-Search-4B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use gabriellarson/II-Search-4B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for gabriellarson/II-Search-4B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for gabriellarson/II-Search-4B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for gabriellarson/II-Search-4B-GGUF to start chatting

Pi new

How to use gabriellarson/II-Search-4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf gabriellarson/II-Search-4B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "gabriellarson/II-Search-4B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use gabriellarson/II-Search-4B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf gabriellarson/II-Search-4B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default gabriellarson/II-Search-4B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use gabriellarson/II-Search-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/gabriellarson/II-Search-4B-GGUF:Q4_K_M
```

Lemonade

How to use gabriellarson/II-Search-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull gabriellarson/II-Search-4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.II-Search-4B-GGUF-Q4_K_M

List all available models

lemonade list

II-Search-4B

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/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:

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}},
}

Downloads last month: 101

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

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