Instructions to use GetSoloTech/Qwen3-Code-Reasoning-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GetSoloTech/Qwen3-Code-Reasoning-4B with Transformers:

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

pipe = pipeline("text-generation", model="GetSoloTech/Qwen3-Code-Reasoning-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GetSoloTech/Qwen3-Code-Reasoning-4B")
model = AutoModelForCausalLM.from_pretrained("GetSoloTech/Qwen3-Code-Reasoning-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 GetSoloTech/Qwen3-Code-Reasoning-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GetSoloTech/Qwen3-Code-Reasoning-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": "GetSoloTech/Qwen3-Code-Reasoning-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GetSoloTech/Qwen3-Code-Reasoning-4B

SGLang

How to use GetSoloTech/Qwen3-Code-Reasoning-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 "GetSoloTech/Qwen3-Code-Reasoning-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": "GetSoloTech/Qwen3-Code-Reasoning-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 "GetSoloTech/Qwen3-Code-Reasoning-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": "GetSoloTech/Qwen3-Code-Reasoning-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GetSoloTech/Qwen3-Code-Reasoning-4B with Docker Model Runner:
```
docker model run hf.co/GetSoloTech/Qwen3-Code-Reasoning-4B
```

Qwen3-Code-Reasoning-4B / README.md

zeeshaan-ai

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	---
	license: apache-2.0
	datasets:
	- GetSoloTech/Code-Reasoning
	base_model:
	- Qwen/Qwen3-4B-Thinking-2507
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- code-generation
	- competitive-programming
	- code-reasoning
	- programming
	- algorithms
	- problem-solving
	- python
	---

	# GetSoloTech/Qwen3-Code-Reasoning-4B

	A finetuned version of Qwen3-4B-Thinking-2507 specifically optimized for competitive programming and code reasoning tasks. This model has been trained on the high-quality [Code-Reasoning](https://huggingface.co/datasets/GetSoloTech/Code-Reasoning) dataset to enhance its capabilities in solving complex programming problems with detailed reasoning.

	## 🎯 Model Overview

	This model is a LoRA-finetuned version of [Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507) with the following specifications:

	- Base Model: Qwen3-4B-Thinking-2507 (4.0B parameters)
	- Training Method: LoRA (Low-Rank Adaptation)
	- Training Dataset: GetSoloTech/Code-Reasoning
	- Training Framework: Unsloth with QLoRA
	- Context Length: 4096 tokens (configurable up to 262,144)
	- Model Type: Causal Language Model with Thinking Capabilities

	## 🚀 Key Features

	- Enhanced Code Reasoning: Specifically trained on competitive programming problems
	- Thinking Capabilities: Inherits the advanced reasoning capabilities from the base model
	- High-Quality Solutions: Trained on solutions with ≥50% test case pass rates
	- Structured Output: Optimized for generating well-reasoned programming solutions
	- Efficient Training: Uses LoRA adapters for efficient parameter updates


	### Dataset Statistics
	- Split: Python
	- Source: High-quality competitive programming problems from TACO, APPS, CodeContests, and Codeforces
	- Quality Filter: Only correctly solved problems with ≥50% test case pass rates

	## 🔧 Usage

	### Basic Inference

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_name = "GetSoloTech/Qwen3-Code-Reasoning-4B"

	# Load the tokenizer and model
	tokenizer = AutoTokenizer.from_pretrained(model_name)
	model = AutoModelForCausalLM.from_pretrained(
	model_name,
	torch_dtype="auto",
	device_map="auto"
	)

	# Prepare input for competitive programming problem
	messages = [
	{"role": "system", "content": "You are an expert competitive programmer. Read the problem and produce a correct, efficient solution. Include reasoning if helpful."},
	{"role": "user", "content": "Your programming problem here..."}
	]

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

	model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

	# Generate solution
	generated_ids = model.generate(
	**model_inputs,
	max_new_tokens=4096,
	temperature=0.7,
	top_p=0.8,
	top_k=20
	)

	output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
	content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")
	print(content)
	```

	## 📈 Performance Expectations

	This finetuned model is expected to show improved performance on:

	- Competitive Programming Problems: Better understanding of problem constraints and requirements
	- Code Generation: More accurate and efficient solutions
	- Reasoning Quality: Enhanced step-by-step reasoning for complex problems
	- Solution Completeness: More comprehensive solutions with proper edge case handling

	## 🎛️ Recommended Settings

	### For Code Generation
	- Temperature: 0.7
	- Top-p: 0.8
	- Top-k: 20
	- Max New Tokens: 4096 (adjust based on problem complexity)

	### For Reasoning Tasks
	- Temperature: 0.6
	- Top-p: 0.95
	- Top-k: 20
	- Max New Tokens: 81920 (for complex reasoning)

	## 🔗 Related Resources

	- Base Model: [Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507)
	- Training Dataset: [Code-Reasoning](https://huggingface.co/datasets/GetSoloTech/Code-Reasoning)
	- Training Framework: [Unsloth](https://github.com/unslothai/unsloth)
	- Original Dataset: [OpenCodeReasoning-2](https://huggingface.co/datasets/nvidia/OpenCodeReasoning-2)

	## 🤝 Contributing

	This model was created using the Unsloth framework and the Code-Reasoning dataset. For questions about:
	- The base model: [Qwen3 GitHub](https://github.com/QwenLM/Qwen3)
	- The training dataset: [Code-Reasoning Repository](https://huggingface.co/datasets/GetSoloTech/Code-Reasoning)
	- The training framework: [Unsloth Documentation](https://docs.unsloth.ai/)

	## 📄 License

	This model follows the same license as the base model (Apache 2.0). Please refer to the [base model license](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507/blob/main/LICENSE) for details.

	## 🙏 Acknowledgments

	- Qwen Team for the excellent base model
	- Unsloth Team for the efficient training framework
	- NVIDIA Research for the original OpenCodeReasoning-2 dataset

	## 📞 Contact

	For questions about this finetuned model, please open an issue in the repository.

	---

	Note: This model is specifically optimized for competitive programming and code reasoning tasks.