Code-Gemma-7b

• Developed by: UnityAI Projects
• Funded by [optional]: Predibase
• Shared by [optional]: Alex Scott (UnityAI Projects Founder)
• Model type: LLM
• Language(s) (NLP): English, Spanish
• License: Apache-2.0
• Finetuned from model [optional]: google/gemma-7b-it

Model Sources [optional]

Uses

The advent of large language models has significantly impacted various domains, including software development. We introduce Code-GEMMA-7B, a model fine-tuned from Google's GEMMA-7B Instruct, specifically tailored for coding simple applications. Leveraging the Code Alpaca dataset, Code-GEMMA-7B aims to streamline the development process, reduce coding errors, and enhance productivity for developers. We present the architecture, training methodology, and comprehensive evaluations demonstrating its efficacy in generating accurate, efficient, and contextually relevant code snippets across multiple programming languages.

Direct Use

Direct Use

The direct application of Code-GEMMA-7B without further fine-tuning or integration into a larger ecosystem offers a wide array of possibilities for developers, educators, and hobbyists alike. This section outlines how Code-GEMMA-7B can be utilized in its current state, emphasizing its strengths and the immediate benefits it brings to coding tasks and software development projects.

Code Generation and Assistance

Code-GEMMA-7B, even without additional customization, serves as a powerful tool for generating code snippets, functions, and even entire modules based on natural language descriptions. Users can input a description of the desired functionality in plain English, and the model will generate corresponding code in a variety of programming languages. This feature is particularly useful for:

• Rapid Prototyping: Developers can quickly generate code for testing new ideas or building prototypes, significantly speeding up the initial stages of development.
• Learning and Education: Beginners in programming can interact with Code-GEMMA-7B to understand how certain programming constructs work and to see examples of code that performs specific tasks.
• Code Suggestions: Experienced developers can use Code-GEMMA-7B to explore different ways to implement a function or to discover more efficient coding patterns.

Debugging and Code Optimization

Code-GEMMA-7B can analyze existing code to identify errors, suggest fixes, and recommend optimizations. This capability is invaluable for both new and seasoned developers, as it helps improve code quality and performance. Key applications include:

• Automated Debugging: By feeding the model with code snippets that contain errors, users can receive suggestions on how to fix these issues, reducing the time spent on debugging.
• Code Refactoring: Code-GEMMA-7B can suggest refactoring opportunities to make code more readable and maintainable, adhering to best practices in software development.

Documentation and Explanation

Another direct use of Code-GEMMA-7B is in generating documentation for code bases and explaining complex code snippets. This application is crucial for maintaining large code bases and for educational purposes, where understanding the logic behind code is as important as the code itself.

• Automatic Documentation: Generate comments and documentation for existing code, making it easier for others to understand and contribute to a project.
• Code Explanation: Input complex code snippets to receive a plain English explanation of what the code does, which is especially useful for learning or reviewing unfamiliar code.

Integration with Development Environments

While this section focuses on direct use without integration into larger systems, it's worth noting that Code-GEMMA-7B can be easily incorporated into popular Integrated Development Environments (IDEs) and code editors as a plugin or extension. This integration can streamline the workflow by providing real-time code generation, suggestions, and documentation directly within the development environment.

Out-of-Scope Use

Out-of-Scope Use

While Code-GEMMA-7B is a robust and versatile AI model designed to assist in a variety of coding-related tasks, it is important to delineate its limitations and potential areas of misuse. This section outlines scenarios that are considered out-of-scope for the intended use of Code-GEMMA-7B and highlights the types of use that the model is not optimized for or may be inappropriate.

Misuse and Malicious Use

• Security Exploits and Malware Creation: Code-GEMMA-7B should not be used to generate code for hacking, creating malware, or any other malicious activities. The model does not have the capability to discern the ethical implications of the code it generates, and it is the responsibility of the user to ensure that the model is used for ethical and legal purposes only.
• Plagiarism: Using Code-GEMMA-7B to generate code that is then passed off as the original work of a human without proper attribution is considered plagiarism and is unethical. Users should always provide appropriate credit for code generated by AI models.

Inappropriate or Ineffective Use Cases

• Large-Scale Software Development: While Code-GEMMA-7B is adept at generating code snippets and assisting with small-scale projects, it is not designed to build large, complex software systems. The model may not effectively manage the intricacies and interdependencies of large codebases.
• Real-Time Systems and Safety-Critical Applications: The model is not suitable for generating code for real-time systems or safety-critical applications (e.g., medical devices, automotive software) where errors can have severe consequences. Such systems require rigorous testing and validation that cannot be guaranteed by AI-generated code.
• Highly Specialized or Domain-Specific Coding: Code-GEMMA-7B may not perform well with highly specialized or domain-specific tasks that require extensive expert knowledge. The model's training on the Code Alpaca dataset may not encompass the depth of knowledge needed for such specialized coding.

Limitations in Understanding Context and Requirements

• Ambiguous or Vague Instructions: The model may struggle with generating appropriate code if provided with ambiguous or vague instructions. It relies on clear and specific input to produce accurate and relevant code.
• Understanding Business Logic and User Intent: Code-GEMMA-7B may not fully grasp complex business logic or the specific intent behind a user's request. It is not a substitute for human judgment and understanding when it comes to interpreting nuanced requirements.

Ethical and Legal Considerations

• Compliance with Regulations: Users must ensure that the use of Code-GEMMA-7B complies with all relevant laws, regulations, and industry standards. The model itself cannot assess legal compliance.
• Bias and Fairness: As with any AI model, there is a risk of bias in the generated code, which could stem from biases present in the training data. Users should be cautious and review the code for potential biases that could lead to unfair outcomes.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoModelWithHeads, AdapterType

# Load the model from your repository
model = AutoModelWithHeads.from_pretrained("shapermindai/code-gemma-7b")

# Add an adapter to the model
model.load_adapter("gemmadapter")

# Set the adapter type
model.set_active_adapters(AdapterType.text_task, "gemmadapter")

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: A10 24 GB x1
• Hours used: 10h 22m 21s
• Cloud Provider: Predibase
• Compute Region: US
• Carbon Emitted: [More Information Needed]

Experiments were conducted using Google Cloud Platform in region northamerica-northeast1, which has a carbon efficiency of 0.03kgCO^2/kWh. A cumulative of 10.5 hours of computation was performed on hardware of type A100 PCIe 40/80GB (TDP of 250W).

Total emissions are estimated to be 0.08 kgCO^2 of which 100 percents were directly offset by the cloud provider.

Estimations were conducted using the \href{https://mlco2.github.io/impact#compute}{MachineLearning Impact calculator} presented in \cite{lacoste2019quantifying}.

Model Card Authors

Perplexity AI, UnityAI Projects, Alex Scott

Model Card Contact

unityaidevs@proton.me