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

Libraries

How to use prithivMLmods/II-Search-CIR-4B-GGUF with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/II-Search-CIR-4B-GGUF", dtype="auto")

llama-cpp-python

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/II-Search-CIR-4B-GGUF",
	filename="II-Search-CIR-4B-GGUF.BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use prithivMLmods/II-Search-CIR-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 prithivMLmods/II-Search-CIR-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/II-Search-CIR-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 prithivMLmods/II-Search-CIR-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/II-Search-CIR-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 prithivMLmods/II-Search-CIR-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/II-Search-CIR-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 prithivMLmods/II-Search-CIR-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/II-Search-CIR-4B-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use prithivMLmods/II-Search-CIR-4B-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

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

SGLang

How to use prithivMLmods/II-Search-CIR-4B-GGUF 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 "prithivMLmods/II-Search-CIR-4B-GGUF" \
    --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": "prithivMLmods/II-Search-CIR-4B-GGUF",
		"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 "prithivMLmods/II-Search-CIR-4B-GGUF" \
        --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": "prithivMLmods/II-Search-CIR-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use prithivMLmods/II-Search-CIR-4B-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/II-Search-CIR-4B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use prithivMLmods/II-Search-CIR-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 prithivMLmods/II-Search-CIR-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 prithivMLmods/II-Search-CIR-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 prithivMLmods/II-Search-CIR-4B-GGUF to start chatting

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

II-Search-CIR-4B-GGUF

II-Search-CIR-4B is a 4-billion-parameter language model built on Qwen3-4B and enhanced with Code-Integrated Reasoning (CIR), enabling it not only to call external tools (such as web search and web visit) through code blocks during inference, but also to programmatically process, filter, and reason over results within those code blocks; optimized through supervised fine-tuning and reinforcement learning on challenging reasoning datasets, the model achieves state-of-the-art or leading results on major factual QA and information-seeking benchmarks (like OpenAI/SimpleQA, Google/Frames, and Seal_0), and it can be efficiently deployed using vLLM or SGLang with up to 128k-token contexts (with YaRN RoPE scaling), supporting advanced research, educational, and web-integrated applications, with datasets, code samples, and evaluation results provided in the official Hugging Face repository.

Model Files

File Name	Size	Quant Type
II-Search-4B-GGUF.BF16.gguf	8.05 GB	BF16
II-Search-4B-GGUF.F16.gguf	8.05 GB	F16
II-Search-4B-GGUF.F32.gguf	16.1 GB	F32
II-Search-4B-GGUF.Q2_K.gguf	1.67 GB	Q2_K
II-Search-4B-GGUF.Q3_K_L.gguf	2.24 GB	Q3_K_L
II-Search-4B-GGUF.Q3_K_M.gguf	2.08 GB	Q3_K_M
II-Search-4B-GGUF.Q3_K_S.gguf	1.89 GB	Q3_K_S
II-Search-4B-GGUF.Q4_K_M.gguf	2.5 GB	Q4_K_M
II-Search-4B-GGUF.Q4_K_S.gguf	2.38 GB	Q4_K_S
II-Search-4B-GGUF.Q5_K_M.gguf	2.89 GB	Q5_K_M
II-Search-4B-GGUF.Q5_K_S.gguf	2.82 GB	Q5_K_S
II-Search-4B-GGUF.Q6_K.gguf	3.31 GB	Q6_K
II-Search-4B-GGUF.Q8_0.gguf	4.28 GB	Q8_0

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

Downloads last month: 68

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

32-bit

Model tree for prithivMLmods/II-Search-CIR-4B-GGUF

Base model

Intelligent-Internet/II-Search-CIR-4B

Quantized

(7)

this model