Model Card for OlympicCoder-32B
OlympicCoder-32B is a code model that achieves very strong performance on competitive coding benchmarks such as LiveCodeBench andthe 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 32B 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-32B-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_v5subset oflivecodebench/code_generation_lite, which corresponds to 268 problems. We uselightevalto evaluate models on LiveCodeBench using the sampling parameters described here.
[!NOTE] 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-32B", 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. ...
[!IMPORTANT] 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'sgenerate()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. Check out our blog post for more details.
Training procedure
Training hyper-parameters
The following hyperparameters were used during training on 16 H100 nodes:
- dataset: open-r1/codeforces-cots_decontaminated
- learning_rate: 4.0e-5
- train_batch_size: 1
- seed: 42
- packing: false
- distributed_type: fsdp
- num_devices: 128
- gradient_accumulation_steps: 1
- 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-32B}} }