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
```

Irfanuruchi commited on 7 days ago

Commit

e0393ca

verified ·

1 Parent(s): af7da8a

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@@ -1,3 +1,58 @@
 ---
-license: apache-2.0
 ---

+# Qwen3-4B-Computer-Science Models
+Official model releases for the **Qwen3-4B-Computer-Science** project.
+This collection contains the available model formats maintained by the project for different inference runtimes and hardware platforms.
 ---
+## Available Formats
+| Format | Status | Primary Runtime |
+|---------|:------:|-----------------|
+| BF16 | ✅ | Transformers |
+| GGUF | ✅ | llama.cpp, Ollama, LM Studio |
+| AWQ | ✅ | Compressed-Tensors compatible runtimes |
 ---
+## Planned Formats
+| Format | Status |
+|---------|:------:|
+| MLX | ⏳ Planned |
+| OpenVINO | ⏳ Planned |
+Additional formats may be added in future releases.
+---
+## Included Models
+- **Qwen3-4B-Computer-Science**
+  - Original BF16 release for Transformers and fine-tuning.
+- **Qwen3-4B-Computer-Science-GGUF**
+  - Quantized GGUF releases for llama.cpp-compatible inference.
+- **Qwen3-4B-Computer-Science-AWQ**
+  - Activation-aware Weight Quantization (AWQ) release using the Compressed-Tensors format.
+---
+## Purpose
+The goal of this collection is to provide officially maintained releases of the same model architecture in multiple deployment formats while preserving consistent model behavior across supported inference backends.
+Each repository contains its own documentation, integrity verification files, and runtime-specific usage instructions.
+---
+## Future Releases
+Planned additions include:
+- MLX for Apple Silicon
+- OpenVINO for Intel CPUs and accelerators
+Future formats will be added to this collection as they become available.