Model Card for OlympicCoder-7B

OlympicCoder-7B is a code model that achieves strong performance on competitive coding benchmarks such as LiveCodeBench and the 2024 International Olympiad in Informatics.

Repository: https://github.com/huggingface/open-r1
Blog post: https://huggingface.co/blog/open-r1/update-3

Model description

Model type: A 7B parameter model fine-tuned on a decontaminated version of the codeforces dataset.
Language(s) (NLP): Primarily English
License: apache-2.0
Finetuned from model: Qwen/Qwen2.5-Coder-7B-Instruct

Evaluation

We compare the performance of OlympicCoder models on two main benchmarks for competitive coding:

IOI'2024: 6 very challenging problems from the 2024 International Olympiad in Informatics. Models are allowed up to 50 submissions per problem.
LiveCodeBench: Python programming problems source from platforms like CodeForces and LeetCoder. We use the v4_v5 subset of livecodebench/code_generation_lite, which corresponds to 268 problems. We use lighteval to evaluate models on LiveCodeBench using the sampling parameters described here.

The OlympicCoder models were post-trained exclusively on C++ solutions generated by DeepSeek-R1. As a result the performance on LiveCodeBench should be considered to be partially out-of-domain, since this expects models to output solutions in Python.

IOI'24

LiveCodeBench

Usage

Here's how you can run the model using the pipeline() function from 🤗 Transformers:

# pip install transformers
# pip install accelerate

import torch
from transformers import pipeline

pipe = pipeline("text-generation", model="open-r1/OlympicCoder-7B", torch_dtype=torch.bfloat16, device_map="auto")

# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
    {"role": "user", "content": "Write a python program to calculate the 10th Fibonacci number"},
]
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(outputs[0]["generated_text"])
#<|im_start|>user
#Write a python program to calculate the 10th fibonacci number<|im_end|>
#<|im_start|>assistant
#<think>Okay, I need to write a Python program that calculates the 10th Fibonacci number. Hmm, the Fibonacci sequence starts with 0 and 1. Each subsequent number is the sum of the two preceding ones. So the sequence goes: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, and so on. ...

To ensure that the model consistently outputs a long chain-of-thought, we have edited the chat template to prefill the first assistant turn with a <think> token. As a result, the outputs from this model will not show the opening <think> token if you use the model's generate() method. To apply reinforcement learning with a format reward, either prepend the <think> token to the model's completions or amend the chat template to remove the prefill.

Training procedure

Training hyper-parameters

The following hyperparameters were used during training:

dataset: open-r1/codeforces-cots
learning_rate: 4.0e-5
train_batch_size: 2
seed: 42
packing: false
distributed_type: deepspeed-zero-3
num_devices: 8
gradient_accumulation_steps: 8
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine_with_min_lr
min_lr_rate: 0.1
lr_scheduler_warmup_ratio: 0.03
num_epochs: 10.0

@misc{penedo2025olympiccoder, title={OlympicCoder}, author={Guilherme Penedo and Anton Lozhkov and Hynek Kydlíček and Loubna Ben Allal and Edward Beeching and Agustín Piqueres Lajarín and Quentin Gallouédec and Nathan Habib and Lewis Tunstall and Leandro von Werra}, year={2025}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/open-r1/OlympicCoder-7B}} }