gemma-template 0.1.6

Gemma Template is a lightweight Python library for generating templates to fine-tune models like Gemma, LLaMa, and others. It supports multilingual frameworks, offers advanced customization, and ensures precise, dynamic template creation.

Gemma Template

This library was developed for the Kaggle challenge: Google - Unlocking Global Communication with Gemma, sponsored by Google.

Credit Requirement

Important: If you are a participant in the competition and wish to use this source code in your submission, you must clearly credit the original author before the competition's end date, January 14, 2025.

Please include the following information in your submission:

Author: Tu Pham
Kaggle Username: [bigfishdev](https://www.kaggle.com/bigfishdev)
GitHub: [https://github.com/thewebscraping/gemma-template/](https://github.com/thewebscraping/gemma-template)
LinkedIn: [https://www.linkedin.com/in/thetwofarm](https://www.linkedin.com/in/thetwofarm)

Overview

Gemma Template is a lightweight and efficient Python library for generating templates to fine-tune models and craft prompts. Designed for flexibility, it seamlessly supports Gemma, LLaMA, and other language frameworks, offering fast, user-friendly customization. With multilingual capabilities and advanced configuration options, it ensures precise, professional, and dynamic template creation.

Learning Process and Acknowledgements

As a newbie, I created Gemma Template based on what I read and learned from the following sources:

• Google Gemma Cookbook: Advanced Prompting Techniques
• Google Gemma Cookbook: Finetune_with_LLaMA_Factory
• Google Gemma Cookbook: Finetuning Gemma for Function Calling
• Alpaca: Alpaca Lora Documentation
• Unsloth: Finetune Llama 3.2, Mistral, Phi-3.5, Qwen 2.5 & Gemma 2-5x faster with 80% less memory!

Gemma Template supports exporting dataset files in three formats: Text, Alpaca, and GPT conversions.

Multilingual Content Writing Assistant

This writing assistant is a multilingual professional writer specializing in crafting structured, engaging, and SEO-optimized content. It enhances text readability, aligns with linguistic nuances, and preserves original context across various languages.

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Key Features:

1. Creative and Engaging Rewrites

• Transforms input text into captivating and reader-friendly content.
• Utilizes vivid imagery and descriptive language to enhance engagement.

2. Advanced Text Analysis

• Processes text with unigrams, bigrams, and trigrams to understand linguistic patterns.
• Ensures language-specific nuances and cultural integrity are preserved.

3. SEO-Optimized Responses

• Incorporates keywords naturally to improve search engine visibility.
• Aligns rewritten content with SEO best practices for discoverability.

4. Professional and Multilingual Expertise

• Full support for creating templates in local languages.
• Supports multiple languages with advanced prompting techniques.
• Vocabulary and grammar enhancement with unigrams, bigrams, and trigrams instruction template.
• Supports hidden mask input text. Adapts tone and style to maintain professionalism and clarity.
• Full documentation with easy configuration prompts and examples.

5. Customize Advanced Response Structure and Dataset Format

• Supports advanced response structure format customization.
• Compatible with other models such as LLaMa.
• Enhances dynamic prompts using Round-Robin loops.
• Outputs multiple formats such as Text, Alpaca and GPT conversions.

Installation

To install the library, you can choose between two methods:

1. Install via PyPI:

pip install gemma-template

2. Install via GitHub Repository:

pip install git+https://github.com/thewebscraping/gemma-template.git

Quick Start

Start using Gemma Template with just a few lines of code:

from gemma_template.models import *

template_instance = Template(
         structure_field=StructureField(
         title=["Custom Title"],
         description=["Custom Description"],
         document=["Custom Article"],
         main_points=["Custom Main Points"],
         categories=["Custom Categories"],
         tags=["Custom Tags"],
    ),
)   # Create fully customized structured reminders.

response = template_instance.template(
    title="Gemma open models",
    description="Gemma: Introducing new state-of-the-art open models.",
    document="Gemma open models are built from the same research and technology as Gemini models. Gemma 2 comes in 2B, 9B and 27B and Gemma 1 comes in 2B and 7B sizes.",
    main_points=["Main point 1", "Main point 2"],
    categories=["Artificial Intelligence", "Gemma"],
    tags=["AI", "LLM", "Google"],
    output="A new family of open language models demonstrating strong performance across academic benchmarks for language understanding, reasoning, and safety.",
    max_hidden_words=.1,  # set 0 if you don't want to hide words.
    min_chars_length=2,  # Minimum character of a word, used to create unigrams, bigrams, and trigrams. Default is 2.
    max_chars_length=0,  # Maximum character of a word, used to create unigrams, bigrams and trigrams.. Default is 0.
 )  # remove kwargs if not used.
print(response)

Output:

<start_of_turn>user

You are a multilingual professional writer.

Rewrite the text to be more search engine friendly. Incorporate relevant keywords naturally, improve readability, and ensure it aligns with SEO best practices.

# Role:
You are a highly skilled professional content writer, linguistic analyst, and multilingual expert specializing in structured writing and advanced text processing.

# Task:
Your primary objectives are:
1. Your primary task is to rewrite the provided content into a more structured, professional format that maintains its original intent and meaning.
2. Enhance vocabulary comprehension by analyzing text with unigrams (single words), bigrams (two words), and trigrams (three words).
3. Ensure your response adheres strictly to the prescribed structure format.
4. Respond in the primary language of the input text unless alternative instructions are explicitly given.

# Additional Expectations:
1. Provide a rewritten, enhanced version of the input text, ensuring professionalism, clarity, and improved structure.
2. Focus on multilingual proficiency, using complex vocabulary, grammar to improve your responses.
3. Preserve the context and cultural nuances of the original text when rewriting.

Topics: Artificial Intelligence, Gemma
Keywords: AI, LLM, Google

# Text Analysis:
Example 1: Unigrams (single words)
and => English
built => English
from => English
the => English
research => English
Text Analysis 3: These are common English words, indicating the text is in English.

Example 2: Bigrams (two words)
technology as => English
Text Analysis 2: Frequent bigrams in English confirm the language context.

Example 3: Trigrams (three words)
technology as Gemini => English
Text Analysis 3: Trigrams further validate the linguistic analysis and the necessity to respond in English.

# Conclusion of Text Analysis:
The linguistic analysis confirms the text is predominantly in English. Consequently, the response should be structured and written in English to align with the original text and context.

# Response Structure Format:
You must follow the response structure:
**Custom Title (Title):** Rewrite the title to reflect the main keyword and topic.
**Custom Description (Meta Description):** Rewrite the description with a bold claim or statistic to grab attention.
**Custom Article (Edit Article):** Reimagine this article with a more engaging and creative tone. Add metaphors, analogies, or storytelling elements to make it more captivating for readers.
**Custom Main Points (Highlights):** Summarize the main ideas into concise, actionable key points for added context to make them more engaging.
**Custom Categories (Topics):** Assign appropriate categories to the article based text or target audience.
**Custom Tags (Keywords):** Focus use tags that reflect the article’s subtopics or themes for better SEO.

By adhering to this format, the response will maintain linguistic integrity while enhancing professionalism, structure and alignment with user expectations.

# Text:
Gemma open models are built from the same research _____ technology as Gemini models. Gemma 2 comes in 2B, 9B and 27B _____ Gemma 1 comes in 2B _____ 7B sizes.

<end_of_turn>
<start_of_turn>model

## **Custom Title**:
### Gemma open models

## **Custom Description**:
Gemma: Introducing new state-of-the-art open models.

## **Custom Article**:
A new family of open language models demonstrating strong performance across academic benchmarks for language understanding, reasoning, and safety.

## **Custom Main Points**:
- Main point 1
- Main point 2

## **Custom Categories**:
- Artificial Intelligence
- Gemma

## **Custom Tags**:
- AI
- LLM
- Google<end_of_turn>

Load Dataset

Returns: Dataset: A Hugging Face Dataset or DatasetDict object containing the processed prompts.

Load Dataset from local file path

prompt_instance = Template()
data_dict = [
    {
        "id": "JnZJolR76_u2",
        "title": "Sample title",
        "description": "Sample description",
        "document": "Sample document",
        "categories": ["Topic 1", "Topic 2"],
        "tags": ["Tag 1", "Tag 2"],
        "output": "Sample output",
        "main_points": ["Main point 1", "Main point 2"],
    }
]
dataset = prompt_instance.load_dataset(data_dict, output_format='text')   # enum: text, gpt, alpaca
print(dataset['text'][0])

Load Dataset from HuggingFace

dataset = gemma_template.load_dataset(
    "your_huggingface_dataset",
    # enum: `text`, `alpaca` and `gpt`.
    output_format='text',
    # Template for instruction the user prompt.
    instruction_template=INSTRUCTION_TEMPLATE,
    # Template for structuring the user prompt.
    structure_template=STRUCTURE_TEMPLATE,
    # Percentage of documents that need to be word masked.
    # Min: 0, Max: 1. Default: 0.
    max_hidden_ratio=.1,
    # Replace 10% of words in the input document with '_____'.
    # Use int to extract the correct number of words. The `max_hidden_ratio` parameter must be greater than 0.
    max_hidden_words=.1,
    # Minimum character of a word, used to create unigrams, bigrams, and trigrams. Default is 2.
    min_chars_length=2,
    # Maximum character of a word, used to create unigrams, bigrams and trigrams. Default is 0.
    max_chars_length=8,
)