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

Libraries

How to use Intelligent-Internet/II-Search-4B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Intelligent-Internet/II-Search-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Intelligent-Internet/II-Search-4B")
model = AutoModelForCausalLM.from_pretrained("Intelligent-Internet/II-Search-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Intelligent-Internet/II-Search-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Intelligent-Internet/II-Search-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Intelligent-Internet/II-Search-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Intelligent-Internet/II-Search-4B

SGLang

How to use Intelligent-Internet/II-Search-4B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Intelligent-Internet/II-Search-4B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Intelligent-Internet/II-Search-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Intelligent-Internet/II-Search-4B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Intelligent-Internet/II-Search-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

hoanganhpham commited on Aug 5, 2025

Commit

a6fb2e8

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1 Parent(s): cfa4338

Update README.md

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@@ -46,8 +46,8 @@ We applied:
 ### Phase 4: Reinforcement Learning
 We trained the model using reinforcement learning
-- UsedDataset: MuSiQue (19k samples)
-- Incorporated our in-house search database (containing Wiki data, Fineweb data, and arXiv data)
 ## Performance
@@ -81,6 +81,7 @@ II-Search-4B is designed for:
 ## 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
@@ -88,6 +89,7 @@ vllm serve Intelligent-Internet/II-Search-4B --served-model-name II-Search-4B --
 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

 ### 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
 ## 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