AIGCodeGeek-DS-6.7B is an intriguing addition to the realm of Code-LLM families, offering competitive performance on various benchmarks. In this article, we will explore how to utilize this model effectively while addressing some common troubleshooting issues you may encounter. Let’s dive in!

Model Overview

AIGCodeGeek-DS-6.7B is developed by Leon Li and licensed under DeepSeek. Fine-tuned from the deepseek-coder-6.7b-base, it blends high-quality open-source samples and private datasets for robust performance.

Requirements

To get started with the AIGCodeGeek-DS-6.7B model, you’ll need to ensure that your environment meets the necessary requirements. You can set this up either with the same requirements as the DeepSeek-Coder-6.7B or install the following packages:

• torch=2.0
• tokenizers=0.14.0
• transformers=4.35.0
• accelerate
• sympy=1.12
• pebble
• timeout-decorator
• attrdict

Quick Start Guide

To initiate the AIGCodeGeek-DS-6.7B model, you will be using the following Python code:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aigcode/AIGCodeGeek-DS-6.7B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("aigcode/AIGCodeGeek-DS-6.7B", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()

messages = [
    {"role": "user", "content": "write a merge 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))

Understanding the Code: An Analogy

Think of using AIGCodeGeek-DS-6.7B like assembling a gourmet meal with various ingredients:

• Importing Ingredients: Just as you gather your vegetables and spices, the code starts by importing necessary libraries like torch and transformers.
• Preparing Your Dish: The tokenizer and model are akin to prepping your kitchen. You fetch them using the from_pretrained method, establishing a base for your culinary masterpiece.
• Cooking Process: Creating the messages is like deciding on your recipe. You specify the task, just like you’d decide on the flavors for your dish.
• Cooking the Meal: The model generates the response similarly to how you cook. You monitor the cooking variables like temperature and time using parameters such as max_new_tokens and do_sample.
• Tasting and Serving: Finally, printing the output is like plating your dish, ready to serve it for the world to enjoy!

Troubleshooting Tips

• If you encounter errors during the model loading process, ensure that all package dependencies are installed correctly. Use pip install package_name to install any missing packages.
• Should there be issues with CUDA, confirm that your GPU is compatible and that the CUDA drivers are up to date.
• For further clarifications and assistance, feel free to connect with peers or help forums. For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

AIGCodeGeek-DS-6.7B offers an exciting venture into the world of code generation. With a straightforward setup and execution method, you will be navigating through coding challenges and solutions in no time. 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.

Acknowledgements

Special thanks to the open-source community for the resources that have shaped this model, including contributions from DeepSeekCoder, WizardCoder, Magicoder, and many others.