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--- |
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license: apache-2.0 |
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datasets: |
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- open-r1/codeforces-cots |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen2.5-Coder-32B-Instruct |
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pipeline_tag: text-generation |
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--- |
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# Model Card for OlympicCoder-32B |
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OlympicCoder-32B is a code model that achieves very strong performance on competitive coding benchmarks such as LiveCodeBench andthe 2024 International Olympiad in Informatics. |
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* Repository: https://github.com/huggingface/open-r1 |
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* Blog post: https://huggingface.co/blog/open-r1/update-3 |
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## Model description |
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- **Model type:** A 32B parameter model fine-tuned on a decontaminated version of the codeforces dataset. |
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- **Language(s) (NLP):** Primarily English |
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- **License:** apache-2.0 |
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- **Finetuned from model:** [Qwen/Qwen2.5-Coder-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct) |
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## Evaluation |
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We compare the performance of OlympicCoder models on two main benchmarks for competitive coding: |
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* **[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. |
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* **[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). |
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> [!NOTE] |
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> 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. |
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### IOI'24 |
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### LiveCodeBench |
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## Usage |
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Here's how you can run the model using the `pipeline()` function from 🤗 Transformers: |
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```python |
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# pip install transformers |
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# pip install accelerate |
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import torch |
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from transformers import pipeline |
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pipe = pipeline("text-generation", model="open-r1/OlympicCoder-32B", torch_dtype=torch.bfloat16, device_map="auto") |
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# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating |
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messages = [ |
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{"role": "user", "content": "Write a python program to calculate the 10th Fibonacci number"}, |
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] |
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prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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outputs = pipe(prompt, max_new_tokens=8000, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) |
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print(outputs[0]["generated_text"]) |
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#<|im_start|>user |
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#Write a python program to calculate the 10th fibonacci number<|im_end|> |
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#<|im_start|>assistant |
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#<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. ... |
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``` |
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> [!IMPORTANT] |
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> 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. |
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## Training procedure |
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### Training hyper-parameters |
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The following hyperparameters were used during training on 16 H100 nodes: |
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- dataset: open-r1/codeforces-cots_decontaminated |
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- learning_rate: 4.0e-5 |
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- train_batch_size: 1 |
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- seed: 42 |
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- packing: false |
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- distributed_type: fsdp |
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- num_devices: 128 |
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- gradient_accumulation_steps: 1 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine_with_min_lr |
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- min_lr_rate: 0.1 |
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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 10.0 |
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