In the realm of programming, choosing the right tools can significantly enhance productivity. Today, we’re diving into DeepSeek Coder, a robust language model specifically crafted for code generation, offering a blend of flexibility and advanced features. Whether you’re a seasoned developer or just embarking on your coding journey, this guide will walk you through how to effectively use DeepSeek Coder.

1. Introduction to DeepSeek Coder

DeepSeek Coder stands out in the sea of programming tools. It consists of a variety of code language models, each meticulously trained on an impressive 2 trillion tokens, comprising 87% code and 13% natural language in both English and Chinese. This model empowers you with numerous size options, from 1B to 33B parameters, ensuring that there’s a fit for every project requirement. Below are some highlights:

• Massive Training Data: Trained from scratch on 2T tokens.
• Highly Flexible & Scalable: Offered in sizes from 1.3B to 33B, tailored to user needs.
• Superior Model Performance: Tops the charts among open-source code models.
• Advanced Code Completion Capabilities: Equipped with a window size of 16K for project-level code tasks.

2. Model Summary

The deepseek-coder-33b-instruct model, initialized from deepseek-coder-33b-base, boasts 33 billion parameters. It has been fine-tuned on a rich dataset comprising 2 billion tokens of instruction data.

• Home Page: DeepSeek
• Repository: deepseek-ai/deepseek-coder
• Chat With DeepSeek Coder: DeepSeek Coder

3. How to Use DeepSeek Coder

Using DeepSeek Coder is straightforward. Think of it as an intelligent assistant for coding. Imagine you’re asking a friend to help you refine your coding skills; that’s essentially what DeepSeek Coder does! Below is a sample code snippet illustrating how to generate a quick sort algorithm in Python:

python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-instruct", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()

messages = {
    'role': 'user',
    'content': 'write a quick sort algorithm in python'
}
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors='pt').to(model.device)

outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))

In this code, you’re essentially setting up a conversation with the DeepSeek Coder about writing a specific algorithm. This is akin to automatically delegating a task to an assistant who knows exactly how to get it done!

4. Troubleshooting

If you run into any issues while using DeepSeek Coder, here are some common troubleshooting tips:

• Model Not Loading: Ensure your <torch> and <transformers> libraries are up-to-date.
• Memory Errors: If having memory issues while running large models, consider using models with fewer parameters.
• Tokenization Issues: Double-check your input messages to ensure they are correctly formatted for the model.

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5. License Information

The DeepSeek Coder code repository is licensed under the MIT License, allowing for both personal and commercial use. For detailed information, you can refer to the LICENSE-MODEL.

6. Contact Information

If you have further questions, feel free to reach out via email at agi_code@deepseek.com.

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.