Granite 8b Code Instruct GGUF

Table of Contents

Model Overview

Meet the Granite 8B Code Instruct model, developed by IBM Research. This model is a fine-tuned version of the ==Granite 8B Code Base== model, designed to enhance its instruction-following capabilities, including logical reasoning and problem-solving skills.

What can it do?

• Follow complex instructions
• Reason logically
• Solve problems

Tasks and rank

Text-to-Code Generation 377th

Code Search 98th

Code Generation 451st

Code Completion 437th

Code Comment Generation 77th

Learn More About the Ranking System

Capabilities

This model is a powerful tool that can help with a variety of tasks, including:

• Logical Reasoning: Fine-tuned to enhance instruction following capabilities, making it great for tasks that require logical reasoning and problem-solving skills.
• Code Intelligence: With 8B parameters, this model is designed to understand and generate code, making it a great tool for developers and programmers.
• Instruction Following: Trained on a combination of permissively licensed instruction data, making it great for tasks that require following instructions and completing tasks.

Alternatives

DeepSeek Coder V2 Instruct 442 Code intelligence model

DeepSeek Coder V2 Instruct 0724 98 Code intelligence model

DeepSeek Coder V2 Instruct GGUF 27 Deep learning model

OpenCodeInterpreter CL 70B 24 Code generation model

DeepSeek Coder V2 Instruct GGUF 21 Code intelligence model

How Does it Compare to Other Models?

Compared to ==other models==, this model has some unique features that set it apart. For example:

• Quantization: This model was quantized by SanctumAI, which means it’s been optimized for performance and efficiency.
• Open-Source: This model is part of the Granite Code Models family, which is an open-source project that aims to provide a family of open foundation models for code intelligence.

Hardware Requirements

To run this model, you’ll need a device with the following hardware requirements:

Model	Quant Method	Size	Memory (RAM, vRAM) Required
granite-8b-code-instruct.Q2_K.gguf	Q2_K	3.06 GB	7.47 GB
granite-8b-code-instruct.Q3_K_S.gguf	Q3_K_S	3.55 GB	?
…	…	…	…

Performance

This model is a powerhouse of a model, fine-tuned to excel in instruction following, logical reasoning, and problem-solving skills. But how does it perform in real-world tasks?

• Speed: With various quantization methods, this model can achieve impressive speeds while maintaining accuracy.
• Accuracy: With its 8B parameters, this model can handle complex code instructions with ease.
• Efficiency: This model has been optimized to require minimal memory while maintaining its performance.

Examples

Write a Python function to calculate the area of a rectangle. def rectangle_area(length, width): return length * width

Estimate the required memory for the granite-8b-code-instruct.Q5_K_S.gguf model. 5.57 GB

Write a code snippet in Java to print 'Hello World'. public class HelloWorld { public static void main(String[] args) { System.out.println("Hello World"); } }

Example Use Case

Here’s an example of how you might use this model:

# Import the necessary libraries
import torch

# Load the model
model = torch.load('granite-8b-code-instruct.pt')

# Define your input prompt
system_prompt = 'Write a Python function to calculate the sum of two numbers.'
prompt = 'What is the sum of 2 and 3?'

# Pre-process your input data
input_data = {'system_prompt': system_prompt, 'prompt': prompt}

# Pass the input data to the model
output = model(input_data)

# Print the output
print(output)

Limitations

This model is a powerful tool, but it’s not perfect. Let’s take a closer look at some of its limitations.

• Hardware Requirements: This model requires a significant amount of memory (RAM and vRAM) to run efficiently.
• Data Limitations: This model was fine-tuned on a combination of permissively licensed instruction data, which may not cover all possible scenarios or domains.
• Logical Reasoning and Problem-Solving: While this model has been fine-tuned to enhance instruction following capabilities, including logical reasoning and problem-solving skills, it’s not a guarantee that it will always produce accurate or coherent outputs.