Granite 20b Code Base 8k

Table of Contents

Model Overview

The Granite-20B-Code-Base-8K model is a powerful tool for code generative tasks. But what makes it so special? Let’s dive in and explore its capabilities.

Tasks and rank

Text-to-Code Generation 216th

Code Generation 260th

Code Completion 248th

Learn More About the Ranking System

Capabilities

Meet the Granite-20B-Code-Base-8K model, a powerful decoder-only code model designed to handle a wide range of code generative tasks. Its primary tasks include:

• Code generation
• Code explanation
• Code fixing
• Generating unit tests
• Generating documentation
• Addressing technical debt issues
• Vulnerability detection
• Code translation

But that’s not all! This model is trained on a massive dataset of 3 trillion tokens from 116 programming languages, making it a versatile tool for developers.

How it Works

The model uses a two-phase training strategy to learn from its vast dataset. In the first phase, it’s trained on a large corpus of code data to understand programming languages and syntax. In the second phase, it’s fine-tuned on a mixture of high-quality code and natural language data to improve its reasoning and instruction-following abilities.

Strengths

So, what sets the Granite-20B-Code-Base-8K model apart from others? Here are a few key strengths:

• Comprehensive understanding of programming languages: With training data from 116 programming languages, this model has a broad knowledge base to draw from.
• Improved reasoning and instruction-following: The model’s two-phase training strategy helps it to better understand and follow instructions.
• High-quality code generation: The model is capable of generating high-quality code that’s similar to what a human developer would write.

Example Use Case

Want to see the Granite-20B-Code-Base-8K model in action? Here’s an example of how to use it to generate code:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_path = "ibm-granite/granite-20b-code-base-8k"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path)

# Define the input text
input_text = "def generate():"

# Tokenize the input text
input_tokens = tokenizer(input_text, return_tensors="pt")

# Generate output tokens
output = model.generate(**input_tokens)

# Decode the output tokens into text
output = tokenizer.batch_decode(output)

# Print the generated code
print(output)

Examples

Explain the purpose of the 'finally' block in a Python try-except statement. The 'finally' block is used to execute a block of code regardless of whether an exception was thrown or not. It is typically used for cleanup operations, such as closing files or releasing system resources.

Generate a JavaScript function to calculate the area of a rectangle. function calculateArea(length, width) { return length * width; }

Fix the syntax error in the following Python code: 'print('Hello World'' print('Hello World')

Performance

The Granite-20B-Code-Base-8K model showcases remarkable performance in various tasks. Let’s dive into its speed, accuracy, and efficiency.

Speed

The model’s training process was accelerated using two of IBM’s supercomputing clusters, Vela and Blue Vela, equipped with NVIDIA A100 and H100 GPUs. This infrastructure enabled the model to be trained over thousands of GPUs, making it possible to process large amounts of data quickly.

Accuracy

The Granite-20B-Code-Base-8K model has been trained on a massive dataset of 3 trillion tokens from 116 programming languages, ensuring a comprehensive understanding of programming languages and syntax. This training data, combined with a carefully designed mixture of high-quality data from code and natural language domains, has improved the model’s ability to reason and follow instructions.

Efficiency

The model’s efficiency can be seen in its ability to handle various code-related tasks, such as:

• Code generation
• Code explanation
• Code fixing
• Generating unit tests
• Generating documentation
• Addressing technical debt issues
• Vulnerability detection
• Code translation

Alternatives

DeepSeek Coder V2 Instruct 442 Code intelligence model

OpenCodeInterpreter CL 70B 24 Code generation model

Limitations

The Granite-20B-Code-Base-8K model is a powerful tool for code generation, but it’s not perfect. Here are some of its limitations:

• Reliance on Generated Code: Don’t rely solely on the Granite-20B-Code-Base-8K model for critical code generation tasks.
• Safety Alignment: The Granite-20B-Code-Base-8K model hasn’t undergone safety alignment, which may result in problematic outputs.
• Hallucination Risk: Smaller models may be more prone to hallucination in generation scenarios.
• Malicious Utilization: Use the Granite-20B-Code-Base-8K model with ethical intentions and in a responsible way.
• Data Quality: Generated code may not meet your standards due to data quality issues.

Format

The Granite-20B-Code-Base-8K model is a decoder-only code model, designed for code generative tasks like code generation, code explanation, and code fixing. It uses a two-phase training strategy and is trained on a massive amount of code data from 116 programming languages.

Architecture

The model is a transformer-based architecture, which is great for handling sequential data like code. It’s trained on a huge dataset, which allows it to understand the syntax and structure of programming languages.

Data Formats

The Granite-20B-Code-Base-8K model supports a wide range of programming languages, including popular ones like Python, Java, and C++. It can handle code in various formats, such as:

• Code snippets
• Functions
• Classes
• Modules

Input Requirements

To use the model, you’ll need to preprocess your input code by tokenizing it. You can use a library like transformers to do this. Here’s an example:

import torch
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("ibm-granite/granite-20b-code-base-8k")
input_text = "def generate():"
input_tokens = tokenizer(input_text, return_tensors="pt")

Output Requirements

The model generates output in the form of tokenized text. You’ll need to decode this output to get the final generated code. Here’s an example:

output = model.generate(**input_tokens)
output = tokenizer.batch_decode(output)
print(output)

Special Requirements

Keep in mind that the Granite-20B-Code-Base-8K model is a large language model, and its output may not always be perfect. It’s essential to review and test the generated code to ensure it meets your requirements.

Also, be aware of the potential risks and limitations associated with using large language models, such as the possibility of generating problematic outputs or relying too heavily on the model for critical decisions.