Welcome to the fascinating world of DeepSeek, where advanced AI meets mathematical inquiry! In this guide, we’ll walk you through how to efficiently utilize the DeepSeek Math model for your mathematical queries. Get ready to dive into a world where complex calculations become a piece of cake!

1. Introduction to DeepSeek Math

The DeepSeek Math model is a powerful AI tool designed for engaging with mathematical problems through conversation. You can check out the comprehensive Introduction for more details.

2. How to Use DeepSeek Math

Now, let’s get practical! Here we will guide you on how to engage with the model using some simple examples.

Chat Completion

To test the functionality of DeepSeek Math, it’s recommended to use a chain-of-thought prompt. Here’s how you can do this:

• For English questions, format your input like this: “Please reason step by step, and put your final answer within boxed.”
• For Chinese questions, frame it as: “boxed”

Example Code

Here’s a sample code to initiate a chat with the DeepSeek Math model:

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)

Understanding the Code: An Analogy

Let’s think of this code as a recipe for baking a delicious cake. Each ingredient and step represents a crucial component needed to get your desired output:

• Importing Libraries: Think of this as gathering your baking tools and ingredients. Here, you’re gathering the necessary libraries like torch and transformers.
• Setting Up the Model: This is like preheating your oven and greasing your baking tray. You choose the model ‘deepseek-ai/deepseek-math-7b-instruct’ and load it into your environment.
• Creating Messages: Imagine mixing your ingredients – you’re preparing your query that you will feed to your model. The messages hold the questions you want answered.
• Generating Output: Finally, you put your cake in the oven! The model processes your message and generates the solution to your math question.

Special Notes

If you wish to avoid using the function apply_chat_template, you can also interact with DeepSeek Math using a simpler approach. Just format your messages with user and assistant roles, and remember that the tokenizer handles the special tokens under the hood.

3. License

This code repository is licensed under the MIT License, which means you can freely use it, including for commercial purposes, as outlined in the LICENSE-MODEL.

4. Troubleshooting

If you encounter any issues while using the DeepSeek Math model, here are some tips that might help:

• Ensure that all required libraries are properly installed and updated.
• Check the model name and input syntax carefully.
• For more complex queries, consider rephrasing for better clarity.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

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.