How to Run OlympicCoder-32B

About 1 min

How to Run OlympicCoder-32B

In this article, we will explore how to run the OlympicCoder-32B model and delve into its features and usage. OlympicCoder-32B is a 3.2 billion parameter model developed by open-r1, specializing in competitive programming tasks, such as solving algorithmic problems and code generation. It has demonstrated excellent performance in LiveCodeBench and the International Olympiad in Informatics programming benchmark tests.

1. Model Overview

OlympicCoder-32B is derived from the QwQ-32B model and has been fine-tuned using a cleaned Codeforces dataset. It supports English input and is available under the Apache-2.0 license. This model is inspired by Qwen/Qwen2.5-Coder-32B-Instruct, but its focus is on preparing for competitive programming contests and algorithm practice.

2. How to Run OlympicCoder-32B

To run this model, you need to install the Transformers and Accelerate libraries. Here are the basic steps to run the model:

2.1. Install Required Libraries

pip install transformers
pip install accelerate

2.2. Create a Pipeline and Generate Code

First, import the necessary libraries and create a generation pipeline using the OlympicCoder-32B model. Here is a specific example:

import torch
from transformers import pipeline

# Create a pipeline to use the OlympicCoder-32B model
pipe = pipeline(
    "text-generation", 
    model="open-r1/OlympicCoder-32B", 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Define the programming problem to address
messages = [
    {"role": "user", "content": "Write a Python program to calculate the 10th Fibonacci number"}
]

# Map and generate code
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=8000, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)

# Print the generated code
print(outputs["generated_text"])

Conclusion

OlympicCoder-32B is a powerful tool, especially in competitive programming and code generation. By leveraging its strong multi-step problem-solving capabilities and generation quality, we can create high-quality technical documentation and articles. Properly optimizing SEO elements will further enhance the content's ranking in search engines.