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

5f43073

verified ·

1 Parent(s): f619c10

Update README.md

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README.md +11 -2

README.md CHANGED Viewed

@@ -43,6 +43,12 @@ We applied:
 - Filtering to keep only high-quality reasoning traces (correct answers with proper reasoning)
 - STORM-inspired techniques to enhance comprehensive report generation
 ## Performance
 | **Benchmark** | **Qwen3-4B** | **Jan-4B** | **WebSailor-3B** | **II-Search-4B** |
@@ -74,13 +80,16 @@ II-Search-4B is designed for:
 ## Usage
-- LMStudio
 ### Recommended Generation Parameters
 ```python
 generate_cfg = {
-    'thought_in_content': True,
     'top_p': 0.95,
     'temperature': 0.6,
     'repetition_penalty': 1.1,

 - 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
+- UsedDataset: MuSiQue (19k samples)
+- 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** |
 ## Usage
+```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
+```
+- Or you can host the [II-4B-Search-MLX ](https://huggingface.co/Intelligent-Internet/II-Search-4B-MLX/) on your Mac then use LMStudio/ Olama Desktop to use it.
 ### Recommended Generation Parameters
 ```python
 generate_cfg = {
+    'top_k': 20,
     'top_p': 0.95,
     'temperature': 0.6,
     'repetition_penalty': 1.1,