Fine-Tuning LLMs: Generating JSONL Files with FragenAntwortLLMCPU

Introduction

Fine-tuning Large Language Models (LLMs) involves adapting a pre-trained model to perform specific tasks more effectively by providing additional training data. The "FragenAntwortLLMCPU" is an inexpensive library that does not need a GPU to facilitate the initial step of fine-tuning the LLM process by generating question-answer (QA) sets in JSON format. "FragenAntwortLLMCPU" uses only CPUs. This tutorial will guide you through the library's installation, setup, and usage to generate the JSONL files required for fine-tuning LLMs.

Prerequisites

Before you begin, ensure you have the following:

• Basic understanding of Python programming and LLMs.

Understanding LLMs and QA Sets

Large Language Models (LLMs)

LLMs, like Mistral, can understand and generate human-like text by being trained on vast amounts of data. Fine-tuning these models involves providing them with additional specific data to improve their performance on particular tasks, such as answering questions from documents, summarizing text, or engaging in dialogue.

Question-Answer (QA) Sets

QA sets are pairs of questions and answers derived from a text. They are crucial for training and fine-tuning LLMs to understand and respond to queries based on document content. Generating high-quality QA sets is critical in creating an effective fine-tuned model.

Installation

To install the FragenAntwortLLMCPU Library, use the following pip command:

pip install FragenAntwortLLMCPU

This command will also install the necessary dependencies, such as PyMuPDF, tokenizers, semantic-text-splitter, langchain, and others.

Basic Usage

Step 1: Import the Library

First, import the necessary components from the library

from FragenAntwortLLMCPU import DocumentProcessor

Step 2: Initialize the Document Processor

Create an instance of the DocumentProcessor Class with the required parameters:

pythdeprocessor = DocumentProcessor(
    book_path="path/to/your/document/",  # Directory path without ".pdf" term
    temp_folder="path/to/temp/folder",
    output_file="path/to/output/QA.jsonl",
    book_name="YourDocument.pdf",
    start_page=2,
    end_page=4,
    number_Q_A="one",  # This should be a written number like "one", "two", etc.
    target_information="specific information you need",
    max_new_tokens=1000,
    temperature=0.1,
    context_length=2100,
    max_tokens_chunk=400,
    arbitrary_prompt=""
)

Explanation of Parameters

• book_path: Directory path to the document without the ".pdf" extension.
• temp_folder: Path to a temporary folder for intermediate processing.
• output_file: Path where the output JSONL file will be saved.
• book_name: Name of the document file.
• start_page: Starting page number for processing.
• end_page: Ending page number for processing.
• number_Q_A: Number of question-answer pairs to generate (as a written number).
• target_information: Specific information you want to extract QA pairs from the document.
• max_new_tokens: Maximum number of new tokens to generate.
• temperature: the temperature rate (creativity) for the language model.
• context_length: Maximum context length for the model.
• max_tokens_chunk: Maximum number of tokens per chunk.
• arbitrary_prompt: Any additional prompt you want to use.

Step 3: Load and Process the Document

Load the document and process it to extract text:

processor.process_book()

Step 4: Generate Prompts

Generate prompts from the processed text:

prompts = processor.generate_prompts()
print(prompts)

Step 5: Save the Prompts to a File

Save the generated prompts to a JSONL file:

processor.save_to_jsonl()

Full Example

Here’s a complete example of putting all the steps together:

from FragenAntwortLLMCPU import DocumentProcessor
# Initialize the processor
processor = DocumentProcessor(
    book_path="path/to/your/document/",
    temp_folder="path/to/temp/folder",
    output_file="path/to/output/QA.jsonl",
    book_name="YourDocument.pdf",
    start_page=2,
    end_page=4,
    number_Q_A="one",
    target_information="locations and foods",
    max_new_tokens=1000,
    temperature=0.1,
    context_length=2100,
    max_tokens_chunk=400,
    arbitrary_prompt=""
)
# Process the document
processor.process_book()
# Generate prompts
prompts = processor.generate_prompts()
print(prompts)
# Save prompts to a JSONL file
processor.save_to_jsonl()

Advanced Usage

Customizing the Processor

You can customize the behavior of the DocumentProcessor by providing additional parameters during initialization or method calls. For detailed options and configurations, refer to the library’s documentation.

Integration with Other Tools

The FragenAntwortLLMCPU The library is designed to work seamlessly with other NLP and ML tools. You can integrate it into your existing workflows to enhance text processing and question-answering capabilities.

Conclusion

The FragenAntwortLLMCPU The library offers a powerful and flexible way to generate JSONL files for fine-tuning large language models. This tutorial covered the library's basic and advanced usage. For more information and detailed documentation, visit the PyPI page.