Instructions to use GetSoloTech/Gemma3-Code-Reasoning-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GetSoloTech/Gemma3-Code-Reasoning-4B with Transformers:

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

pipe = pipeline("text-generation", model="GetSoloTech/Gemma3-Code-Reasoning-4B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("GetSoloTech/Gemma3-Code-Reasoning-4B")
model = AutoModelForImageTextToText.from_pretrained("GetSoloTech/Gemma3-Code-Reasoning-4B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use GetSoloTech/Gemma3-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/Gemma3-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/Gemma3-Code-Reasoning-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GetSoloTech/Gemma3-Code-Reasoning-4B

SGLang

How to use GetSoloTech/Gemma3-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/Gemma3-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/Gemma3-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/Gemma3-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/Gemma3-Code-Reasoning-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GetSoloTech/Gemma3-Code-Reasoning-4B with Docker Model Runner:
```
docker model run hf.co/GetSoloTech/Gemma3-Code-Reasoning-4B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

GetSoloTech/Gemma3-Code-Reasoning-4B

A finetuned version of google/gemma-3-4b-it specifically optimized for competitive programming and code reasoning tasks. This model has been trained on the high-quality 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 gemma-3-4b-it with the following specifications:

Base Model: gemma-3-4b-it (4.0B parameters)
Training Method: LoRA (Low-Rank Adaptation)
Training Dataset: GetSoloTech/Code-Reasoning
Training Framework: Unsloth with QLoRA
Context Length: 4096 tokens
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 ≥85% test case pass rates

🔧 Usage

Basic Inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "GetSoloTech/Gemma3-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=1.0,
    top_p=0.95,
    top_k=64
)

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