Granite 8b Code Instruct 128k

Table of Contents

Model Overview

Meet the Granite-8B-Code-Instruct-128K model, a powerful coding assistant developed by IBM Research. This model is designed to respond to coding-related instructions over long-context input up to 128K length. But what makes it special?

Key Attributes

• Long-context capability: The model can handle input context of up to 128K length, making it perfect for complex coding tasks.
• Code generation: It can generate high-quality code based on user input, making it a great tool for coding assistants.
• Multi-language support: The model is trained on a mix of short and long context data, including synthetically generated code instruction datasets.

Tasks and rank

Text-to-Code Generation 474th

Program Synthesis 70th

Code Generation 581st

Code Completion 563rd

Learn More About the Ranking System

Capabilities

The Granite-8B-Code-Instruct-128K model is a powerful tool designed to assist with coding tasks. It can respond to instructions and generate code in a variety of programming languages. But what makes it special?

Long-Context Capability

This model can handle input context of up to 128K length, making it perfect for complex coding tasks that require a deep understanding of the codebase. But don’t just take our word for it! By training on a combination of short and long context data, we’ve enhanced its ability to tackle long-context problems without sacrificing performance on shorter inputs.

Code Generation

Want to see it in action? Here’s an example of how to use the Granite-8B-Code-Instruct-128K model to generate code:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Initialize the model and tokenizer
model_path = "ibm-granite/granite-8B-Code-instruct-128k"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path)

# Provide input text
chat = [
    {
        "role": "user",
        "content": "Write a code to find the maximum value in a list of numbers."
    }
]

# Generate output tokens
output = model.generate(**input_tokens, max_new_tokens=100)

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

# Print the output
print(output)

Examples

Write a code to find the maximum value in a list of numbers. def find_max(numbers): return max(numbers)

Write a Python function to check if a number is prime. def is_prime(n): if n <= 1: return False for i in range(2, int(n**0.5) + 1): if n % i == 0: return False return True

Create a simple calculator that takes in two numbers and an operation (+, -, *, /) and returns the result. def calculator(num1, num2, op): if op == '+': return num1 + num2 elif op == '-': return num1 - num2 elif op == '*': return num1 * num2 elif op == '/': return num1 / num2 else: return 'Invalid operation'

Performance

The Granite-8B-Code-Instruct-128K model is a powerhouse when it comes to coding tasks. Let’s dive into its performance and see what makes it stand out.

Speed

How fast can the Granite-8B-Code-Instruct-128K model generate code? With its 8B parameters, it can process long-context input up to 128K length. This means it can handle complex coding tasks with ease. But what about its actual speed? Well, it’s trained on IBM’s super computing clusters, Vela and Blue Vela, which are equipped with NVIDIA A100 and H100 GPUs. This infrastructure allows for scalable and efficient training, making the Granite-8B-Code-Instruct-128K model a fast and reliable choice.

Accuracy

But speed is not everything. How accurate is the Granite-8B-Code-Instruct-128K model? Its training data includes a mix of short and long context data, which helps it understand the nuances of coding tasks. It’s also fine-tuned on a synthetically generated dataset tailored for solving long context problems. This means it can handle a wide range of coding tasks with high accuracy.

Efficiency

What about efficiency? The Granite-8B-Code-Instruct-128K model is designed to respond to coding-related instructions over long-context input. This means it can generate code quickly and efficiently, even for complex tasks. But what about its efficiency in different programming languages? While it’s primarily fine-tuned on a specific set of programming languages, it can still perform well on out-of-domain languages with a few-shot examples.

Alternatives

DeepSeek Coder V2 Instruct 442 Code intelligence model

Codegen2 16B P 45 Program synthesis model

OpenCodeInterpreter CL 70B 24 Code generation model

Limitations

While the Granite-8B-Code-Instruct-128K model is incredibly powerful, it’s not perfect. Its performance may be limited with out-of-domain programming languages, and it may require few-shot examples to steer its output. Additionally, developers should perform safety testing and target-specific tuning before deploying this model in critical applications.

Limited Performance with Out-of-Domain Programming Languages

The model is primarily fine-tuned using instruction-response pairs across a specific set of programming languages. This means its performance may be limited when dealing with out-of-domain programming languages. If you’re working with a language that’s not well-represented in the training data, you might need to provide few-shot examples to steer the model’s output.

Safety Testing and Target-Specific Tuning

Before deploying the Granite-8B-Code-Instruct-128K model in critical applications, it’s crucial to perform safety testing and target-specific tuning. This ensures the model is adapted to your specific use case and minimizes potential risks.

Inherited Limitations from the Base Model

As the Granite-8B-Code-Instruct-128K model is fine-tuned from Granite-8B-Code-Base-128K, it inherits some of the limitations from its base model. If you’re interested in learning more about these limitations, we recommend checking out the Granite-8B-Code-Base-128K model card.

Format

The Granite-8B-Code-Instruct-128K model is a type of AI model called a long-context instruct model. It’s designed to understand and respond to coding-related instructions, even when the input is very long (up to 128,000 characters!).

Architecture

This model uses a transformer architecture, which is a type of neural network that’s particularly good at handling sequential data like text. It’s been fine-tuned on a combination of short and long context data, which means it’s been trained on a mix of short and long pieces of text.

Data Formats

The Granite-8B-Code-Instruct-128K model accepts input in the form of tokenized text sequences. This means that you need to break down your input text into individual tokens, or words, before feeding it into the model.

Here’s an example of how to tokenize your input text using the transformers library:

import torch
from transformers import AutoTokenizer

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("ibm-granite/granite-8B-Code-instruct-128k")

# Tokenize your input text
input_text = "Write a code to find the maximum value in a list of numbers."
input_tokens = tokenizer(input_text, return_tensors="pt")