Implementation:Microsoft DeepSpeedExamples Alpaca Training Dataset

Knowledge Sources	Microsoft_DeepSpeedExamples
Domains	Natural Language Processing, Training Data, Instruction Tuning
Last Updated	2026-02-07 12:00 GMT

Overview

A JSON data file containing the Alpaca instruction-following dataset used for training language models with tensor parallelism in DeepSpeed.

Description

This is a large JSON data file (approximately 260,000 lines) containing the Alpaca training dataset, a collection of instruction-following examples used for fine-tuning large language models. Each entry in the dataset is a JSON object with three fields: instruction (the task description), input (optional additional context), and output (the expected model response).

The dataset follows the Stanford Alpaca format and is designed for instruction tuning of language models. Examples range from simple factual questions ("What are the three primary colors?") to multi-step tasks ("Give three tips for staying healthy."). The input field is often empty for self-contained instructions but can contain supplementary context when the task requires it.

This data file serves as the training corpus for the tensor parallel training examples in DeepSpeed, where it is loaded and processed for distributed fine-tuning of large language models across multiple GPUs using DeepSpeed's tensor parallelism features.

Usage

Use this dataset as the training data source for instruction-tuning language models in the tensor parallel training examples. It is loaded by the training scripts in the training/tensor_parallel/ directory and should not be modified directly.

Code Reference

Source Location

• Repository: Microsoft_DeepSpeedExamples
• File: training/tensor_parallel/alpaca_data.json
• Lines: 1-260012

Signature

[
    {
        "instruction": "Give three tips for staying healthy.",
        "input": "",
        "output": "1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. ..."
    },
    ...
]

Import

import json

with open("training/tensor_parallel/alpaca_data.json", "r") as f:
    alpaca_data = json.load(f)

I/O Contract

Inputs

Name	Type	Required	Description
N/A	N/A	N/A	This is a static data file with no inputs

Outputs

Name	Type	Description
instruction	str	Task description or question for the model to respond to
input	str	Optional additional context or input for the task (may be empty)
output	str	Expected model response or answer

Usage Examples

import json

# Load the Alpaca dataset
with open("training/tensor_parallel/alpaca_data.json", "r") as f:
    dataset = json.load(f)

# Access individual examples
example = dataset[0]
print(f"Instruction: {example['instruction']}")
print(f"Input: {example['input']}")
print(f"Output: {example['output']}")

# Format as a prompt for training
def format_prompt(example):
    if example["input"]:
        return f"### Instruction:\n{example['instruction']}\n\n### Input:\n{example['input']}\n\n### Response:\n{example['output']}"
    else:
        return f"### Instruction:\n{example['instruction']}\n\n### Response:\n{example['output']}"