Granite 20b Code Instruct 8k
Granite 20b Code Instruct 8k is a powerful AI model designed to respond to coding-related instructions and build coding assistants. What makes it unique is its ability to follow instructions, including logical reasoning and problem-solving skills, thanks to its fine-tuning on a combination of permissively licensed instruction data. With 20B parameters and trained on various code and math datasets, this model can efficiently handle tasks like generating code and providing solutions to programming problems. However, it's essential to note that its performance may be limited with out-of-domain programming languages, and developers should perform safety testing and target-specific tuning before deploying it on critical applications.
Table of Contents
Model Overview
The Granite-20B-Code-Instruct-8K model is a powerful tool for coding-related tasks. Developed by IBM Research, it’s a fine-tuned version of the Granite-20B-Code-Base-8K model, designed to excel in coding-related tasks.
Key Features
- 20B parameters: This model has been trained on a huge dataset to learn patterns and relationships in code.
- Instruction following: The model is designed to follow instructions and respond accordingly, making it perfect for building coding assistants.
- Logical reasoning and problem-solving: It’s not just about following instructions; this model can also think critically and solve problems.
Capabilities
The Granite-20B-Code-Instruct-8K model is a powerful tool designed to respond to coding-related instructions. It’s perfect for building coding assistants that can help with tasks like writing code to solve problems or find the maximum value in a list of numbers.
What can it do?
- Logical reasoning and problem-solving skills: This model is fine-tuned to enhance its instruction-following capabilities, making it great at understanding and solving complex problems.
- Code generation: It can generate code in response to instructions, making it a valuable tool for developers.
- Coding assistants: The model can be used to build coding assistants that can help with a variety of tasks, from writing code to debugging.
How does it work?
The model uses a combination of permissively licensed instruction data, including code commits, math datasets, and code instruction datasets. This data is used to fine-tune the model and enhance its instruction-following capabilities.
What kind of data is it trained on?
- Code Commits Datasets: The model is trained on code commits data from the CommitPackFT dataset, which includes data for 92 programming languages.
- Math Datasets: It’s also trained on high-quality math datasets, such as MathInstruct and MetaMathQA.
- Code Instruction Datasets: The model uses code instruction datasets, including Glaive-Code-Assistant-v3 and NL2SQL11.
- Language Instruction Datasets: It’s trained on high-quality language instruction datasets, such as HelpSteer and Platypus.
Performance
The Granite-20B-Code-Instruct-8K model is a powerhouse when it comes to coding tasks. But how fast is it? How accurate? And how efficient is it in different tasks? Let’s dive in and find out.
Speed
The Granite-20B-Code-Instruct-8K model is trained on a massive infrastructure with thousands of GPUs, making it incredibly fast. But what does that mean for you? It means you can get results quickly, whether you’re generating code or getting answers to your coding questions.
Accuracy
So, how accurate is the Granite-20B-Code-Instruct-8K model? The model is fine-tuned on a combination of instruction data, which enhances its instruction-following capabilities, including logical reasoning and problem-solving skills. This means it can understand complex coding tasks and provide accurate results.
Efficiency
But how efficient is the Granite-20B-Code-Instruct-8K model in different tasks? Here are some examples:
- Code Generation: The Granite-20B-Code-Instruct-8K model can generate high-quality code quickly and efficiently. Whether you need to write a script or a program, this model can help.
- Code Completion: The model can also complete partially written code, saving you time and effort.
- Code Explanation: If you’re struggling to understand a piece of code, the Granite-20B-Code-Instruct-8K model can explain it to you in simple terms.
| Task | Efficiency |
|---|---|
| Code Generation | High |
| Code Completion | High |
| Code Explanation | Medium |
Limitations
The Granite-20B-Code-Instruct-8K model is a powerful tool, but it’s not perfect. Let’s take a closer look at some of its limitations.
Out-of-Domain Programming Languages
The model is primarily fine-tuned on a specific set of programming languages. This means that its performance may suffer when dealing with languages outside of its training data. If you’re working with a language that’s not in the model’s repertoire, you might need to provide a few examples to help it get on track.
Safety and Security
As with any AI model, there are safety and security concerns to consider. The model’s output may not always be accurate or reliable, especially in critical applications. Developers should perform thorough safety testing and target-specific tuning before deploying the model in real-world scenarios.
Inherited Limitations
The Granite-20B-Code-Instruct-8K model is built on top of the Granite-20B-Code-Base-8K model, which means it inherits some of its limitations. If you’re interested in learning more about these limitations, be sure to check out the Granite-20B-Code-Base-8K model card.
Data Quality
The model’s performance is only as good as the data it’s trained on. If the training data contains biases or inaccuracies, these can be reflected in the model’s output. It’s essential to ensure that the data used to train the model is high-quality and representative of the task at hand.
Format
Granite-20B-Code-Instruct-8K Model Architecture
The Granite-20B-Code-Instruct-8K model is a type of transformer model, which is a popular architecture for natural language processing tasks. It’s designed to process sequential data, like text, and it’s particularly well-suited for tasks that require understanding the relationships between different parts of the input.
Supported Data Formats
This model supports input data in the form of text sequences, specifically designed for coding-related instructions. It can handle a variety of programming languages, including 92 languages from the CommitPackFT dataset.
Input Requirements
To use the Granite-20B-Code-Instruct-8K model, you’ll need to preprocess your input data into a specific format. Here’s an example of how to do this using the transformers library:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the model and tokenizer
model_path = "ibm-granite/granite-20b-code-instruct-8k"
tokenizer = AutoTokenizer.from_pretrained(model_path)
# Prepare your input text
chat = [
{"role": "user", "content": "Write a code to find the maximum value in a list of numbers."},
]
# Apply the chat template and tokenize the input
chat = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
input_tokens = tokenizer(chat, return_tensors="pt")
# Transfer the tokenized inputs to the device (e.g. GPU)
device = "cuda" # or "cpu"
for i in input_tokens:
input_tokens[i] = input_tokens[i].to(device)
Output Format
The model generates output in the form of text sequences, which can be decoded using the tokenizer object. Here’s an example of how to generate output and decode it:
# Generate output tokens
output = model.generate(**input_tokens, max_new_tokens=100)
# Decode the output tokens into text
output = tokenizer.batch_decode(output)
# Print the output
for i in output:
print(i)
Special Requirements
Keep in mind that the Granite-20B-Code-Instruct-8K model is primarily fine-tuned on instruction-response pairs for a specific set of programming languages. If you’re working with out-of-domain languages, you may need to provide few-shot examples to steer the model’s output. Additionally, be sure to perform safety testing and target-specific tuning before deploying this model in critical applications.
