Welcome to the DeepSeek ecosystem! This guide will provide you with a user-friendly tutorial on how to utilize DeepSeekMath, an innovative tool for mathematical inquiries powered by AI. Let’s dive into the world of mathematical problem-solving!

1. What is DeepSeekMath?

DeepSeekMath is part of the DeepSeek family, designed to assist you in tackling mathematical problems using natural language processing. For more in-depth information, refer to the Introduction.

2. How to Use DeepSeekMath

Using the DeepSeekMath model is straightforward. Below, you will find examples of how you can engage the model for various types of queries:

Chat Completion

To get the best results, it’s recommended to use a chain-of-thought prompt. Here’s how you can structure your input:

• For English questions: “Please reason step by step, and put your final answer within boxed.”
• For Chinese questions: “boxed”

Sample Code

Here’s a practical example to get you started:

python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig

model_name = 'deepseek-ai/deepseek-math-7b-instruct'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map='auto')
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id

messages = [
    {'role': 'user', 'content': 'what is the integral of x^2 from 0 to 2? Please reason step by step, and put your final answer within boxed.'}
]

input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors='pt')
outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)

This snippet is akin to embarking on a journey through a new city. The import statements are like your travel gear, setting you up for adventure. The model_name serves as your destination, guiding the path you will take. Then you assemble your travel companions with tokenizer and model, each having a specific role to ensure you don’t get lost in translation. Finally, the print(result) is your souvenir—a tangible outcome from your exploration!

3. License Details

The DeepSeekMath repository operates under the MIT License. It is compatible with commercial use, making it a robust solution for various applications. Want to dive deeper? Check out the LICENSE-MODEL for further details.

4. Troubleshooting

If you encounter any issues while using DeepSeekMath, consider the following troubleshooting steps:

• Ensure that all the required libraries are correctly installed. Use pip install transformers torch to set them up.
• Check your device compatibility with torch_dtype=torch.bfloat16
• Verify your input structure corresponds to the expected format set in the examples.

If you need additional insights, updates, or wish to collaborate on AI development projects, stay connected with fxis.ai.

Feel free to raise an issue or contact us at service@deepseek.com if you still need help.

Conclusion

By leveraging the capabilities of DeepSeekMath, you can enhance your mathematical problem-solving experience significantly. Don’t hesitate to explore and experiment with the model to uncover its full potential.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.