Granite 8b Code Instruct 4k

Deploy Model in Dataloop Pipelines

Granite 8b Code Instruct 4k fits right into a Dataloop Console pipeline, making it easy to process and manage data at scale. It runs smoothly as part of a larger workflow, handling tasks like annotation, filtering, and deployment without extra hassle. Whether it's a single step or a full pipeline, it connects with other nodes easily, keeping everything running without slowdowns or manual work.

Table of Contents

Model Overview

Meet the Granite-8B-Code-Instruct-4K model, developed by IBM Research! This model is a game-changer for coding tasks. With 8B parameters, it’s designed to understand and respond to coding-related instructions.

What can it do?

• Follow instructions to write code
• Reason logically and solve problems
• Assist with coding tasks

How was it trained?

The model was fine-tuned on a combination of instruction data, including:

• Code commits from 92 programming languages
• Math datasets like MathInstruct and MetaMathQA
• Code instruction datasets like Glaive-Code-Assistant-v3
• Language instruction datasets like HelpSteer

Tasks and rank

Text-to-Code Generation 390th

Code Generation 464th

Code Documentation Generation 188th

Code Completion 449th

Code Comment Generation 81st

Learn More About the Ranking System

Capabilities

The Granite-8B-Code-Instruct-4K model is designed to respond to coding-related instructions and can be used to build coding assistants. But what does that really mean?

• Generate code: This model can write code in response to instructions. For example, you can ask it to write a code to find the maximum value in a list of numbers, and it will generate the code for you.
• Understand instructions: The model is trained on a combination of instruction data, which enables it to understand and follow instructions. This includes logical reasoning and problem-solving skills.
• Support multiple programming languages: The model is trained on data from 92 programming languages, making it a versatile tool for coding tasks.

Performance

How fast can the Granite-8B-Code-Instruct-4K model generate code? With its massive 8B parameters, this model can process and respond to coding instructions in a snap. Whether you need to write a code to find the maximum value in a list of numbers or generate a complex algorithm, this model can deliver results quickly.

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

Limitations

The Granite-8B-Code-Instruct-4K model is a powerful tool for coding-related tasks, but it’s not perfect. Let’s take a closer look at some of its limitations.

• Limited performance with out-of-domain programming languages: The model is primarily fine-tuned on a specific set of programming languages. This means it may not perform well with languages it’s not familiar with. For example, if you ask it to write code in a language it’s not trained on, it might struggle to produce accurate results.
• Need for few-shot examples: To get the best out of the model, you might need to provide a few examples of the desired output. This can help steer the model in the right direction and improve its performance.

Format

The Granite-8B-Code-Instruct-4K model uses a transformer architecture, which is a type of neural network that’s particularly well-suited for natural language processing tasks. This means it’s designed to handle sequential data, like text, and can learn complex patterns and relationships within that data.

Examples

Write a code to find the maximum value in a list of numbers. max_value = max([1, 2, 3, 4, 5]) print(max_value)

Create a function that calculates the area of a rectangle. def rectangle_area(length, width): return length * width

Write a SQL query to select all columns from a table named 'employees'. SELECT * FROM employees;

Here’s an example of how to use the model to generate code in response to an instruction:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda"  # or "cpu"
model_path = "ibm-granite/granite-8b-code-instruct-4k"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
model.eval()

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

chat = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
input_tokens = tokenizer(chat, return_tensors="pt")
for i in input_tokens:
    input_tokens[i] = input_tokens[i].to(device)

output = model.generate(**input_tokens, max_new_tokens=100)
output = tokenizer.batch_decode(output)
for i in output:
    print(i)

This code snippet shows how to use the model to generate code in response to an instruction. You can modify the input text to try out different instructions and see how the model responds.