PRIME-RL

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Eurus-2-7B-PRIME

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PRIME 119

Hello! This is our code for inference using vllm for aime dataset:

from vllm import LLM, SamplingParams
model_path = <your model path>
def generate_sample_batch(question_list):
    llm = LLM(
        model=model_path,    # the model path
        trust_remote_code=True,
        tensor_parallel_size=torch.cuda.device_count(),
        gpu_memory_utilization=0.80,
    )
    sampling_params = SamplingParams(max_tokens=4096,
                                     temperature=0,
                                     stop=["\n###\nProblem: ", "<|eot_id|>"], )
    outputs = llm.generate(question_list, sampling_params, use_tqdm=True)
    completions = [output.outputs[0].text for output in outputs]
    return completions

def make_conv_hf(question, tokenizer):
    system_prompt = "\nWhen tackling complex reasoning tasks, you have access to the following actions. Use them as needed to progress through your thought process.\n\n[ASSESS]\n\n[ADVANCE]\n\n[VERIFY]\n\n[SIMPLIFY]\n\n[SYNTHESIZE]\n\n[PIVOT]\n\n[OUTPUT]\n\nYou should strictly follow the format below:\n\n[ACTION NAME]\n\n# Your action step 1\n\n# Your action step 2\n\n# Your action step 3\n\n...\n\nNext action: [NEXT ACTION NAME]\n"
    content = question + "\n\nPresent the answer in LaTex format: \\boxed{Your answer}"
    msg = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": content}
    ]
    chat = tokenizer.apply_chat_template(msg, tokenize=False, add_generation_prompt=True)
    return chat

tokenizer = AutoTokenizer.from_pretrained(model_path)
all_problems = []    #all_problems should be a list like [questions,question2,...]
completions = generate_sample_batch(
            [make_conv_hf(problem_data, tokenizer) for problem_data in all_problems])   

We use 2×80G A800 GPUs.

Hello, there is a little supplement, we test scripts from Eurus (https://github.com/OpenBMB/Eurus)

Hope it helps.

Still can't reproduce with the above script. I am evaluating on https://huggingface.co/datasets/AI-MO/aimo-validation-aime which contains also AIME24. It solves only 11 of 90 problems which means only 12% . Did you evaluated with the model weights uploaded here? Maybe something wrong during model upload?

Hi, we just upload the eval script to the GitHub repository (https://github.com/PRIME-RL/PRIME) and will merge soon. We also test the model and the results are as follows:
{
"2024_AIME_I_Problems": {
"total": 15,
"success": 5
},
"2023_AIME_I_Problems": {
"total": 15,
"success": 1
},
"2023_AIME_II_Problems": {
"total": 15,
"success": 2
},
"2022_AIME_I_Problems": {
"total": 15,
"success": 2
},
"2024_AIME_II_Problems": {
"total": 15,
"success": 3
},
"2022_AIME_II_Problems": {
"total": 15,
"success": 1
}
}

AIME ALL-total: 90, success: 14, rate: 0.15555555555555556

AIME2024-total: 30, success: 8, rate: 0.26666666666666666

Still can't reproduce with the above script. I am evaluating on https://huggingface.co/datasets/AI-MO/aimo-validation-aime which contains also AIME24. It solves only 11 of 90 problems which means only 12% . Did you evaluated with the model weights uploaded here? Maybe something wrong during model upload?

So what does it mean? AIME 24 is easier than AIME 22/23 or the model is overfitting on AIME 24?