https://github.com/simonpierreboucher/llm_multiple_generation

This repository contains Jupyter notebooks demonstrating various generation tasks with Large Language Models (LLMs). It provides examples for summarization, text generation, few-shot learning, and translation, utilizing different LLM APIs to showcase the capabilities of multiple providers.
https://github.com/simonpierreboucher/llm_multiple_generation

anthropic few-shot few-shot-prompting large-language-models llm mistral openai summary translation

Last synced: 26 days ago
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This repository contains Jupyter notebooks demonstrating various generation tasks with Large Language Models (LLMs). It provides examples for summarization, text generation, few-shot learning, and translation, utilizing different LLM APIs to showcase the capabilities of multiple providers.

• Host: GitHub
• URL: https://github.com/simonpierreboucher/llm_multiple_generation
• Owner: simonpierreboucher
• Created: 2024-11-13T22:49:50.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-11-13T23:07:53.000Z (about 2 months ago)
• Last Synced: 2024-11-13T23:29:34.960Z (about 2 months ago)
• Topics: anthropic, few-shot, few-shot-prompting, large-language-models, llm, mistral, openai, summary, translation
• Language: Jupyter Notebook
• Homepage:
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# LLM Multiple Generation Notebook

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This repository contains Jupyter notebooks demonstrating various generation tasks with Large Language Models (LLMs). It provides examples for summarization, text generation, few-shot learning, translation, and question-answering, utilizing different LLM APIs to showcase the capabilities of multiple providers.

## Repository Structure

- **[summarize_llm_function.ipynb](https://github.com/simonpierreboucher/llm_multiple_generation/blob/main/summarize_llm_function.ipynb)**: Demonstrates text summarization using LLMs, with examples for generating concise summaries from longer texts.

- **[generate_llm_function.ipynb](https://github.com/simonpierreboucher/llm_multiple_generation/blob/main/generate_llm_function.ipynb)**: Provides examples of text generation, covering creative and informative text outputs.

- **[fewshot_llm_function.ipynb](https://github.com/simonpierreboucher/llm_multiple_generation/blob/main/fewshot_llm_function.ipynb)**: Explores few-shot learning techniques, where the model is given a limited number of examples to guide its responses in specific tasks.

- **[translate_llm_function.ipynb](https://github.com/simonpierreboucher/llm_multiple_generation/blob/main/translate_llm_function.ipynb)**: Illustrates language translation capabilities using LLMs, allowing for translations across multiple language pairs.

- **[QA_llm_function.ipynb](https://github.com/simonpierreboucher/llm_multiple_generation/blob/main/QA_llm_function.ipynb)**: Demonstrates question-answering using LLMs, where the model provides accurate responses to queries based on context or a document.

## Getting Started

### Prerequisites

To run these notebooks, you will need:

- **Python 3.8+**

- **Jupyter Notebook**

- API keys for any relevant LLM services (e.g., OpenAI, Cohere, Anthropic)

- Required dependencies as listed in `requirements.txt`

### Installation

1. Clone the repository:

   ```bash

   git clone https://github.com/simonpierreboucher/llm_multiple_generation.git

   cd llm_multiple_generation

   ```

2. Install the dependencies:

   ```bash

   pip install -r requirements.txt

   ```

### Running the Notebooks

1. **Start Jupyter Notebook**: Open Jupyter by navigating to the repository folder and running:

   ```bash

   jupyter notebook

   ```

2. **Select a Notebook**: Open any of the notebooks (summarize, generate, few-shot, translate, or QA) to explore its functionality.

3. **Follow Instructions**: Each notebook includes instructions and code for working with LLMs in the context of specific generation tasks.

## Use Cases

- **Summarization**: Generate concise summaries for documents, articles, or other text inputs.

- **Text Generation**: Create new content, stories, or information-rich text with minimal input.

- **Few-Shot Learning**: Provide a few examples to guide the model's responses for targeted tasks, such as answering specific questions or following a template.

- **Translation**: Translate text across multiple languages using LLMs, useful for multilingual applications.

- **Question-Answering**: Use LLMs to provide context-based answers to questions, enhancing automated support or knowledge retrieval tasks.

## Contributing

We welcome contributions! Feel free to submit issues or pull requests to enhance functionality, add features, or address bugs.

## License

This repository is licensed under the MIT License.