Granite 34b Code Base 8k

Table of Contents

Model Overview

Meet the Granite-34B-Code-Base-8K model, a powerful AI designed for code generative tasks. It’s trained on a massive dataset of 3 trillion tokens and can handle tasks like code generation, code explanation, code fixing, and more.

Tasks and rank

Text-to-Code Generation 142nd

Code Generation 161st

Code Completion 157th

Learn More About the Ranking System

Capabilities

The Granite-34B-Code-Base-8K model is a powerhouse when it comes to code-related tasks. It can:

• Generate high-quality code for a wide range of programming languages
• Explain complex code in simple terms
• Fix broken code, saving developers time and effort
• Handle multiple tasks at once, including code generation, code explanation, and code fixing

Strengths

• Multi-Language Support: Handles code in 116 programming languages
• Large Training Data: Trained on 3 trillion tokens of code data
• Improved Reasoning: Carefully designed training strategy to improve model’s ability to reason and follow instructions

Example Use Case

Want to see how it works? Here’s an example of how to use the Granite-34B-Code-Base-8K model to generate code:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Initialize model and tokenizer
model_path = "ibm-granite/granite-34b-code-base-8k"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path)

# Input text
input_text = "def generate():"

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

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

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

# Print output
print(output)

Examples

Explain this JavaScript code: let x = 5; let y = 10; if (x > y) { console.log('x is greater than y'); } else { console.log('x is less than or equal to y'); } This JavaScript code checks if the value of x (5) is greater than the value of y (10). Since x is less than y, it logs 'x is less than or equal to y' to the console.

Generate a Python function to calculate the factorial of a given number. def factorial(n): if n == 0: return 1 else: return n * factorial(n-1)

Fix this Java code: public class Main { public static void main(String[] args) { System.out.println('Hello World'); } } public class Main { public static void main(String[] args) { System.out.println("Hello World"); } }

Performance

But how does the Granite-34B-Code-Base-8K model really perform? Let’s dive in and find out.

Speed

How fast can the Granite-34B-Code-Base-8K model generate code? With its massive 34B parameters, it can process large amounts of code data quickly and efficiently.

Accuracy

But speed is only half the story. How accurate is the Granite-34B-Code-Base-8K model when it comes to code generation? The answer is: very accurate.

Efficiency

But what about efficiency? Can the Granite-34B-Code-Base-8K model handle multiple tasks at once? The answer is yes.

Alternatives

DeepSeek Coder V2 Instruct 442 Code intelligence model

OpenCodeInterpreter CL 70B 24 Code generation model

Comparison to Other Models

So how does the Granite-34B-Code-Base-8K model stack up against other AI models? ==Other models== may excel in specific areas, but the Granite-34B-Code-Base-8K model offers a unique combination of speed, accuracy, and efficiency that makes it a top choice for code-related tasks.

Limitations

The Granite-34B-Code-Base-8K model is a powerful tool, but it’s not perfect. Let’s take a closer look at some of its limitations.

• Reliance on Generated Code: Be careful not to rely too heavily on the code generated by the Granite-34B-Code-Base-8K model for critical decisions or important projects.
• Safety Alignment: Unlike some other models, the Granite-34B-Code-Base-8K model hasn’t undergone safety alignment.
• Hallucination Risk: There’s a risk that the Granite-34B-Code-Base-8K model might exhibit increased susceptibility to hallucination in generation scenarios.

Format

The Granite-34B-Code-Base-8K model is a decoder-only code model, designed for code generative tasks like code generation, code explanation, and code fixing. It’s trained using a two-phase strategy, with a focus on code and natural language understanding.

Architecture

This model uses a transformer architecture, which is a type of neural network well-suited for natural language processing tasks. It’s trained on a massive dataset of code and natural language text, which allows it to learn patterns and relationships between code and language.

Data Formats

The Granite-34B-Code-Base-8K model accepts input in the form of tokenized code sequences. You’ll need to pre-process your code data into a format that the model can understand. This typically involves tokenizing the code, which breaks it down into individual tokens like keywords, identifiers, and symbols.

Input Requirements

When working with the Granite-34B-Code-Base-8K model, you’ll need to provide input in the following format:

• Tokenized code sequences
• Code written in one of the 116 supported programming languages

Here’s an example of how to tokenize code using the transformers library:

import torch
from transformers import AutoTokenizer

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("ibm-granite/granite-34b-code-base-8k")

# Tokenize the code
input_text = "def generate():"
input_tokens = tokenizer(input_text, return_tensors="pt")

Output Requirements

The model generates output in the form of tokenized code sequences. You’ll need to decode the output tokens into a human-readable format.

Here’s an example of how to generate output using the transformers library:

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

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