Welcome to your comprehensive guide on utilizing the Deepseek Coder 7B Base v1.5! This AI model is a powerhouse for generating code, and today, we’ll walk you through how to leverage its capabilities precisely.

1. Introduction to Deepseek-Coder-7B-Base-v1.5

Deepseek-Coder-7B-Base-v1.5 continues its impressive training from the Deepseek-LLM 7B model, utilizing a whopping 2 trillion tokens! It features a window size of 4K and is designed to excel in next token prediction. Whether you’re writing algorithms or debugging code, Deepseek Coder is equipped to assist.

To explore more about Deepseek, check the following:

• Home Page
• Chat with DeepSeek Coder
• Join our Discord
• WeChat (微信)

2. Evaluation Results

Deepseek Coder has shown remarkable performance, as illustrated in the evaluation image below:

3. How to Use Deepseek Coder

To get started with Deepseek Coder, follow these simple steps:

• Make sure you have the transformers library installed. If not, you can install it using pip install transformers.
• Use the following Python code snippet to interact with Deepseek Coder:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-7b-base-v1.5", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-7b-base-v1.5", trust_remote_code=True).cuda()

input_text = "#write a quick sort algorithm"
inputs = tokenizer(input_text, return_tensors="pt").cuda()
outputs = model.generate(**inputs, max_length=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Understanding the Code: An Analogy

Imagine you’re a chef in a massive kitchen (the GPU) with a helper (the model) who can quickly look up recipes (generate code). The ingredients (input_text) are provided to your helper, and they sift through the extensive cookbook (the trained model) to whip up a delicious dish (desired output). The role of the tokenizer is like your assistant, chopping the ingredients into manageable pieces before handing them over to the chef for cooking. Once cooked, you taste the result (print the output) to ensure it’s just right before serving it up!

4. License Information

Deepseek Coder is licensed under the MIT License, supporting commercial use. For details regarding the Model License, please refer to the LICENSE-MODEL.

5. Troubleshooting

If you encounter any issues while using Deepseek Coder, consider these troubleshooting tips:

• Ensure that you have a compatible GPU available and properly configured.
• Check that all necessary libraries are installed and updated, especially transformers.
• Verify your internet connection, as the model requires downloading components from online repositories.

For additional assistance, feel free to reach out to us or raise an issue on our GitHub page. 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.

6. Contact Us

If you have more questions about Deepseek Coder, feel free to reach us at service@deepseek.com.

Thanks for reading, and happy coding with Deepseek Coder!