Granite 20b Code Base R1.1

Table of Contents

Model Overview

Meet the Granite-20B-Code-Base-r1.1, a powerful AI designed for code generation, explanation, and fixing tasks. Developed by IBM Research, this model is an updated version of its predecessor, trained on a massive dataset of 3 trillion tokens from 116 programming languages.

Tasks and rank

Text-to-Code Generation 208th

Code Generation 251st

Code Completion 240th

Learn More About the Ranking System

Capabilities

This model is a powerhouse when it comes to handling various code-related tasks. But how does it do all this? Let’s take a closer look.

Training Data

The model was trained on a massive dataset of 3 trillion tokens from 116 programming languages. This means it has a deep understanding of programming languages and syntax. But that’s not all - it was also trained on 1 trillion tokens of high-quality data from code and natural language domains. This helps the model reason and follow instructions.

Unique Features

The Granite-20B-Code-Base-r1.1 has several unique features that set it apart from other models. For example, it was trained using a two-phase training strategy, which helps it learn from a large amount of data. It also uses a carefully designed mixture of high-quality data from code and natural language domains to improve its ability to reason and follow instructions.

Alternatives

DeepSeek Coder V2 Instruct 442 Code intelligence model

OpenCodeInterpreter CL 70B 24 Code generation model

Comparison to Other Models

So how does Granite-20B-Code-Base-r1.1 compare to other models in terms of performance? ==Other models== may have similar capabilities, but Granite-20B-Code-Base-r1.1 has been specifically designed for code-related tasks, making it a more efficient and accurate choice.

Model	Speed	Accuracy	Efficiency
Granite-20B-Code-Base-r1.1	High	High	High
==Other Models==	Medium	Medium	Medium

Performance

This model is designed to process large amounts of code data quickly and efficiently. With its two-phase training strategy, it can handle tasks such as code generation, code explanation, and code fixing with ease. But just how fast is it?

Imagine you’re a developer working on a project, and you need to generate a piece of code to complete a task. With Granite-20B-Code-Base-r1.1, you can get the code you need in a matter of seconds. This is because the model is trained on a massive dataset of 3 trillion tokens from 116 programming languages, allowing it to understand the syntax and structure of code quickly.

Example Use Case

Here’s an example of how to use the Granite-20B-Code-Base-r1.1:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda"  # or "cpu"
model_path = "ibm-granite/granite-20b-code-base-r1.1"
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)
for i in output: print(i)

Examples

Explain this JavaScript function: function calculateArea(length, width) { return length * width; } This function calculates the area of a rectangle by multiplying its length and width.

Fix the bug in this Python code: def greet(name): print('Hello, ' + name + '!') greet('Alice') def greet(name): print(f'Hello, {name}!') greet('Alice')

Generate a Java method to check if a number is even. public boolean isEven(int num) { return num % 2 == 0; }

Limitations

Granite-20B-Code-Base-r1.1 is a powerful tool for code generation and other tasks, but it’s not perfect. Let’s explore some of its limitations.

Lack of Safety Alignment

This model hasn’t undergone safety alignment, which means it may produce problematic outputs. This is a risk to be aware of, especially when using the model for crucial decisions or impactful information.

Hallucination in Generation

Smaller models like Granite-20B-Code-Base-r1.1 might be more susceptible to hallucination in generation scenarios. This means they might copy source code verbatim from the training dataset, rather than generating original code. This is an active area of research, and more work is needed to understand and mitigate this issue.

Malicious Utilization

As with all Large Language Models, there’s a risk of malicious utilization. We urge the community to use Granite-20B-Code-Base-r1.1 with ethical intentions and in a responsible way.

Dependence on Training Data

The model’s performance is only as good as the data it was trained on. If the training data contains biases or inaccuracies, the model may learn and replicate these flaws.

Limited Domain Knowledge

While Granite-20B-Code-Base-r1.1 is trained on a large amount of code data, it may not have the same level of domain-specific knowledge as a human expert. This can lead to limitations in its ability to generate high-quality code for specific tasks or domains.

Uncertainty in Complex Scenarios

In complex or nuanced scenarios, the model’s outputs may not always be accurate or coherent. This is especially true when dealing with tasks that require a deep understanding of context, nuance, or subtlety.

By understanding these limitations, you can use Granite-20B-Code-Base-r1.1 more effectively and responsibly.