| | --- |
| | license: apache-2.0 |
| | datasets: |
| | - open-r1/codeforces-cots |
| | language: |
| | - en |
| | base_model: |
| | - Qwen/Qwen2.5-Coder-32B-Instruct |
| | pipeline_tag: text-generation |
| | --- |
| | |
| | # 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](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct) |
| |
|
| | ## Evaluation |
| |
|
| | We compare the performance of OlympicCoder models on two main benchmarks for competitive coding: |
| |
|
| | * **[IOI'2024:](https://github.com/huggingface/ioi)** 6 very challenging problems from the 2024 International Olympiad in Informatics. Models are allowed up to 50 submissions per problem. |
| | * **[LiveCodeBench:](https://livecodebench.github.io)** Python programming problems source from platforms like CodeForces and LeetCoder. We use the `v4_v5` subset of [`livecodebench/code_generation_lite`](https://huggingface.co/datasets/livecodebench/code_generation_lite), which corresponds to 268 problems. We use `lighteval` to evaluate models on LiveCodeBench using the sampling parameters described [here](https://github.com/huggingface/open-r1?tab=readme-ov-file#livecodebench). |
| |
|
| | > [!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 |
| |
|
| |  |
| |
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| |
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| |
|
| | ## Usage |
| | Here's how you can run the model using the `pipeline()` function from 🤗 Transformers: |
| |
|
| | ```python |
| | # 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'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. Check out our [blog post](https://huggingface.co/blog/open-r1/update-3#lesson-4-prefill-with-think-to-consistently-enable-long-cot) 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 |
| |
|
