umar igan

umarigan

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updated a dataset about 8 hours ago

umarigan/deepseek-r1-reasoning-prompts

posted an update about 8 hours ago

** Extracting Reasoning Prompts with DeepSeek-R1: A Step Towards Better AI Reasoning ** Hi everyone! 👋 I’m excited to share a small but impactful project I’ve been working on, where I extracted **reasoning prompts** using the **DeepSeek-R1 model**. Reasoning prompts are a powerful way to understand how AI models arrive at their answers, and they can be used to train smaller, more efficient models to generate reasoning. Let me walk you through the process and explain why this is important. --- #### **The Code: Extracting Reasoning Prompts** Here’s the code I used to extract reasoning prompts from the `openaccess-ai-collective/oo-gpt4-filtered` dataset: ```python from tqdm import tqdm import time reasoning_data = [] for example in tqdm(ds, desc="answering"): try: response = client.chat.completions.create( model='deepseek-reasoner', # Using DeepSeek-R1 for reasoning messages=[ {"role": "system", "content": example['system_prompt']}, {"role": "user", "content": example['question']}, ], stream=False, max_tokens=4096, temperature=0.7, ) answer = response.choices[0].message.content reasoning = response.choices[0].message.reasoning_content reasonng_example = { "id": example['id'], "question": example['question'], 'answer': answer, 'reasoning': reasoning, } reasoning_data.append(reasonng_example) except Exception as e: print(f"Error translating example: {e}") time.sleep(3) # Wait for 3 seconds before continuing continue # Skip the current example and move to the next one ``` data: https://huggingface.co./datasets/umarigan/deepseek-r1-reasoning-prompts

published a dataset about 8 hours ago

umarigan/deepseek-r1-reasoning-prompts

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updated a dataset about 8 hours ago

umarigan/deepseek-r1-reasoning-prompts

Updated about 8 hours ago • 2

posted an update about 8 hours ago

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from tqdm import tqdm
import time

reasoning_data = []

for example in tqdm(ds, desc="answering"):
    try:
        response = client.chat.completions.create(
            model='deepseek-reasoner',  # Using DeepSeek-R1 for reasoning
            messages=[
                {"role": "system", "content": example['system_prompt']},
                {"role": "user", "content": example['question']},
            ],
            stream=False,
            max_tokens=4096,
            temperature=0.7,
        )
        
        answer = response.choices[0].message.content
        reasoning = response.choices[0].message.reasoning_content

        reasonng_example = {
            "id": example['id'],
            "question": example['question'],
            'answer': answer,
            'reasoning': reasoning,
        }

        reasoning_data.append(reasonng_example)
    except Exception as e:
        print(f"Error translating example: {e}")
        time.sleep(3)  # Wait for 3 seconds before continuing
        continue  # Skip the current example and move to the next one