Granite 8b Code Base 4k

Table of Contents

Model Overview

Meet the Granite-8B-Code-Base-4K model, a game-changer in the world of code generation and intelligence. Developed by IBM Research, this decoder-only code model is designed to tackle a wide range of tasks, including code generation, explanation, fixing, and more.

Tasks and rank

Text-to-Code Generation 495th

Code Generation 605th

Code Completion 587th

Learn More About the Ranking System

Capabilities

The Granite-8B-Code-Base-4K model is a powerful tool designed to help developers with various code-related tasks. This decoder-only code model is trained on a massive dataset of 4 trillion tokens from 116 programming languages, making it a versatile solution for:

• Code generation: Create new code from scratch or complete partially written code.
• Code explanation: Understand and explain existing code, making it easier to maintain and debug.
• Code fixing: Identify and fix errors in code, saving time and reducing frustration.
• Unit test generation: Automatically generate unit tests for your code, ensuring it’s thoroughly tested.
• Documentation generation: Create high-quality documentation for your code, making it easier for others to understand.
• Technical debt reduction: Help reduce technical debt by identifying and addressing issues in your codebase.
• Vulnerability detection: Identify potential security vulnerabilities in your code, ensuring it’s secure.

How it Works

The Granite-8B-Code-Base-4K model uses a two-phase training strategy to learn from a massive dataset of code and natural language text. This approach enables it to understand the nuances of programming languages and generate high-quality code.

Training Data

The model is trained on a massive dataset of:

• 4 trillion tokens from 116 programming languages
• 500 billion tokens from a mixture of high-quality code and natural language data

This diverse dataset enables the model to learn from a wide range of programming languages and generate code that’s accurate and effective.

Example Use Case

Here’s an example of how to use the Granite-8B-Code-Base-4K model to generate code:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda"  # or "cpu"
model_path = "ibm-granite/granite-8b-code-base-4k"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
model.eval()

input_text = "def generate():"
input_tokens = tokenizer(input_text, return_tensors="pt")
for i in input_tokens: input_tokens[i] = input_tokens[i].to(device)
output = model.generate(**input_tokens)
output = tokenizer.batch_decode(output)
print(output)

Examples

Explain the following JavaScript code: function greet(name) { console.log('Hello, ' + name); } This JavaScript function, named greet, takes one argument, name. When called, it logs a personalized greeting message to the console, using the provided name.

Fix the syntax error in this Python code: def calculate_area(width, hieght): return width * hieght def calculate_area(width, height): return width * height

Generate a unit test for the following Python function: def add(a, b): return a + b import unittest def test_add_function(self): self.assertEqual(add(1, 2), 3) self.assertEqual(add(-1, 1), 0) self.assertEqual(add(-1, -1), -2)

Performance

The Granite-8B-Code-Base-4K model demonstrates impressive performance in various code-related tasks. Here are some examples:

• Speed: The model can generate high-quality code snippets in a matter of seconds.
• Accuracy: The model can generate accurate and functional code in a wide range of programming languages.
• Efficiency: The model is designed to be efficient in its use of resources, making it accessible to developers with different computational resources.

Limitations

While the Granite-8B-Code-Base-4K model is a powerful tool, it’s not perfect. Here are some limitations to consider:

• Reliance on Training Data: The model is only as good as the data it was trained on.
• Lack of Safety Alignment: The model hasn’t undergone safety alignment, which means it may produce problematic outputs.
• Hallucination Risk: Smaller models like Granite-8B-Code-Base-4K might be more prone to hallucination.

Alternatives

DeepSeek Coder V2 Instruct 442 Code intelligence model

OpenCodeInterpreter CL 70B 24 Code generation model

Ethical Considerations

The Granite-8B-Code-Base-4K model can be used for malicious purposes. It’s crucial to use the model responsibly and with ethical intentions. Always verify and test generated code thoroughly, and use it as a tool, not a replacement for human judgment.