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DeepSeek-R1-Distill-Qwen-7B

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The result seems biased.

I'm also confused to get this much lower results compared to their reported, especially on AIME24...

We have tested the system prompt with temperature of 0.7.

You are a helpful and harmless assistant. You should think step-by-step.

Here are the evaluation results.

Models	AIME24	MATH500	GSM8K	GPQA-Diamond	ARC-Challenge	MMLU-Pro	MMLU	LiveCodeBench
deepseek-ai/DeepSeek-R1-Distill-Qwen-32B	46.67	88.20	-	57.58	-	-	-	-

More evaluation results can be found at https://huggingface.co/FuseAI/FuseO1-DeekSeekR1-QwQ-SkyT1-32B-Preview

The evaluation code is modified from SkyThought. In our evaluation, we set the temperature to 0.7 and the max_tokens to 32768. We provide the example to reproduce our results in evaluation.

The system prompt for evaluation is set to:

You are a helpful and harmless assistant. You should think step-by-step.

We are currently attempting to reproduce the results reported in the DeepSeek-R1 paper by experimenting with different system prompts. We will update our findings once we have acquired the original system prompt used in their study.

The updated evaluation results are presented here:

More evaluation results can be found at https://huggingface.co/FuseAI/FuseO1-DeekSeekR1-QwQ-SkyT1-32B-Preview

如何评价

Please kindly refer to the following link:
https://github.com/deepseek-ai/DeepSeek-R1#usage-recommendations

Покажи пример Telegram bot, python aiogram принимающий видео от юзера, обрабатывающий его и отправляющий обратно. База данных mongodb

Сделай

Can you confirm that this model can't answer the coding question which even standard qwen-7b-instruct answers?

Explain the bug in the following code:

from time import sleep
from multiprocessing.pool import ThreadPool
 
def task():
    sleep(1)
    return 'all done'

if __name__ == '__main__':
    with ThreadPool() as pool:
        result = pool.apply_async(task())
        value = result.get()
        print(value)

After long thinking it always answers that there is no bug. But the bug is in result = pool.apply_async(task). Almost all recent models of similar size answer it easily.