Welcome to the realm of AI-driven coding assistance with DeepSeek Coder! This powerful AI model is here to revolutionize the way you approach coding by providing efficient code completion and generation capabilities. In this blog, we will explore how to leverage DeepSeek Coder, troubleshoot potential issues, and ensure you have a smooth coding experience.

1. Introduction to DeepSeek Coder

DeepSeek Coder is a suite of code language models meticulously trained from scratch on 2 trillion tokens, with a composition of 87% code and 13% natural language in both English and Chinese. This ensures versatility in handling various coding tasks. With model sizes ranging from 1B to an impressive 33B, DeepSeek Coder is designed to meet diverse needs in programming language support.

• Massive Training Data: Trained from scratch on 2 trillion tokens.
• Highly Flexible & Scalable: Offers models from 1.3B to 33B, tailored to your specific requirements.
• Superior Performance: Outperforms many publicly available models on renowned benchmarks.
• Advanced Code Completion: Includes a window size of 16K to facilitate project-level code completion.

2. Model Summary

The specific model we’ll be focusing on is deepseek-coder-6.7b-instruct, a 6.7 billion parameter model fine-tuned for specific tasks, initialized from deepseek-coder-6.7b-base.

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

3. How to Use DeepSeek Coder

Let’s dive into how you can start using DeepSeek Coder effectively. Below is an example of how to run the chat model inference.

python
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the tokenizer and model
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()

# Prepare your message
messages = [{'role': 'user', 'content': 'write a quick sort algorithm in python'}]

# Process input
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors='pt').to(model.device)

# Generate output
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)

# Display output
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))

Think of DeepSeek Coder as a master architect. Just like an architect drafts a blueprint to create a magnificent building, you provide a prompt to DeepSeek Coder, and it constructs the corresponding coding structure. By inputting your desired coding function or logic, it understands your needs and delivers the meticulously crafted code—thanks to its wealth of knowledge derived from years of training.

4. License Information

DeepSeek Coder operates under the MIT License, ensuring its flexibility for both non-commercial and commercial use. You can review the LICENSE-MODEL for more detailed information.

5. Troubleshooting Tips

Sometimes, things don’t go according to plan. Here are some troubleshooting recommendations:

• Model Not Loading: Ensure you have a stable internet connection and that the model path is correct.
• Out of Memory Errors: Opt for a smaller model size to conserve resources especially when running on limited hardware.
• Unexpected Output: Double-check your input prompt for clarity and completeness. The model performs optimally with detailed instructions.

If you encounter issues or need assistance, feel free to contact us at agi_code@deepseek.com.

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.