Instructions to use Irfanuruchi/Qwen3-4B-Computer-Science-Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Irfanuruchi/Qwen3-4B-Computer-Science-Models with Transformers:

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

pipe = pipeline("text-generation", model="Irfanuruchi/Qwen3-4B-Computer-Science-Models")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Irfanuruchi/Qwen3-4B-Computer-Science-Models", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Irfanuruchi/Qwen3-4B-Computer-Science-Models with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Irfanuruchi/Qwen3-4B-Computer-Science-Models"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Irfanuruchi/Qwen3-4B-Computer-Science-Models",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Irfanuruchi/Qwen3-4B-Computer-Science-Models

SGLang

How to use Irfanuruchi/Qwen3-4B-Computer-Science-Models 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 "Irfanuruchi/Qwen3-4B-Computer-Science-Models" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Irfanuruchi/Qwen3-4B-Computer-Science-Models",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Irfanuruchi/Qwen3-4B-Computer-Science-Models" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Irfanuruchi/Qwen3-4B-Computer-Science-Models",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Irfanuruchi/Qwen3-4B-Computer-Science-Models with Docker Model Runner:
```
docker model run hf.co/Irfanuruchi/Qwen3-4B-Computer-Science-Models
```

Qwen3-4B-Computer-Science OpenVINO INT4

This repository provides an OpenVINO INT4 deployment of Qwen3-4B-Computer-Science, optimized for efficient inference using Intel® OpenVINO™ and Optimum Intel.

The model is designed for software engineering and computer science workloads while significantly reducing storage requirements and memory usage through INT4 weight compression. It enables efficient deployment across OpenVINO-supported hardware while preserving the capabilities of the original model.

Model Information

Property	Value
Base Model	Irfanuruchi/Qwen3-4B-Computer-Science
Base Architecture	Qwen3
Parameters	~4 Billion
Deployment Format	OpenVINO IR
Weight Compression	INT4 Asymmetric
Compression Group Size	128
Runtime	Intel® OpenVINO™ Runtime
Library	Optimum Intel
Supported Hardware	OpenVINO-supported devices*
License	Apache License 2.0

* Supported execution devices depend on the installed OpenVINO Runtime, operating system, drivers, and available hardware. Depending on the platform, inference may be executed on supported CPUs, integrated GPUs, NPUs, or other OpenVINO-compatible accelerators.

Features

OpenVINO IR deployment format
INT4 asymmetric weight compression
Reduced storage footprint
Lower memory usage
Efficient inference on OpenVINO-supported hardware
Compatible with Optimum Intel and Hugging Face Transformers
Exported tokenizer and detokenizer
Chat template included
Ready for local deployment

Export Configuration

The model was exported using:

optimum-cli export openvino \
    --model Irfanuruchi/Qwen3-4B-Computer-Science \
    --task text-generation-with-past \
    --weight-format int4 \
    Qwen3-4B-Computer-Science-OpenVINO-INT4

Compression summary:

252 transformer layers compressed using INT4 asymmetric with group size 128
1 auxiliary layer stored using INT8 per-channel

Installation

pip install -U openvino optimum-intel transformers

Example Usage

from transformers import AutoTokenizer
from optimum.intel.openvino import OVModelForCausalLM

model_id = "Irfanuruchi/Qwen3-4B-Computer-Science-OpenVINO-INT4"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = OVModelForCausalLM.from_pretrained(
    model_id,
    device="CPU",
)

messages = [
    {
        "role": "system",
        "content": "You are a computer science assistant."
    },
    {
        "role": "user",
        "content": "Explain Floyd's cycle detection algorithm."
    },
]

prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)

inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    do_sample=False,
)

response = tokenizer.decode(
    outputs[0][inputs["input_ids"].shape[1]:],
    skip_special_tokens=True,
)

print(response)

Validation

The exported model was successfully validated using:

Intel® OpenVINO™ Runtime 2026.2.1
Local OpenVINO inference
Chat template support
Greedy decoding
Software engineering benchmark prompts

Validation confirmed successful generation of technically correct programming responses, including algorithm implementation and complexity analysis.

Intended Use

This model is intended for:

Software engineering assistance
Computer science education
Programming support
Code generation
Code review
Debugging
Algorithm design
Technical documentation
Technical question answering

Model Family

The Qwen3-4B-Computer-Science release family currently includes:

Model	Status
Transformers BF16	✅
GGUF (Multiple Quantization Variants)	✅
AWQ	✅
MLX 4-bit	✅
MLX 8-bit	✅
MLX BF16	✅
OpenVINO INT4	✅

Each release is maintained in its own repository with runtime-specific documentation, usage examples, integrity verification files, and configuration tailored to its target inference backend.

Credits

This release builds upon the work of several open-source projects and communities:

Qwen Team for the Qwen3 foundation model architecture.
Intel® OpenVINO™ Toolkit for the OpenVINO Runtime and deployment framework.
Hugging Face for the Transformers ecosystem and the Optimum Intel integration.
Optimum Intel for OpenVINO model export and runtime integration.

This repository provides an OpenVINO INT4 deployment of the original Qwen3-4B-Computer-Science model for efficient inference on OpenVINO-supported hardware.

Limitations

Like other large language models, this model may occasionally:

Generate incorrect or incomplete code
Hallucinate APIs or implementation details
Produce inefficient implementations
Misinterpret ambiguous instructions

INT4 weight compression may introduce minor quality differences compared to higher-precision variants.

All generated code should be reviewed and tested before use in production or safety-critical environments.

License

This repository is distributed under the Apache License 2.0.

This release is an OpenVINO INT4 conversion of the original Qwen3-4B-Computer-Science model and retains the licensing and attribution requirements applicable to the original project.

See the included LICENSE file for the complete license text.

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