# 🌟 Batch LLM Inference with Ray Data LLM 🌟
## From Simple to Advanced

Welcome to the **Batch LLM Inference with Ray Data LLM** repository! This project aims to simplify and enhance the use of large language models (LLMs) for batch inference in various applications. By leveraging the capabilities of Ray Data and Ray Serve, this repository enables efficient parallel processing and distributed computing.

![Ray Logo](https://raw.githubusercontent.com/ray-project/ray/master/python/ray/assets/ray_logo.svg)

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## 📚 Table of Contents

- [Features](#features)
- [Topics](#topics)
- [Getting Started](#getting-started)
- [Installation](#installation)
- [Usage](#usage)
- [Examples](#examples)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)

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## ⚡ Features

- **Batch Inference**: Process multiple inputs in a single call for efficiency.
- **Distributed Computing**: Scale your applications seamlessly across multiple nodes.
- **Support for Large Language Models**: Utilize state-of-the-art models for natural language processing tasks.
- **Parallel Processing**: Speed up tasks by processing data in parallel.
- **Integration with Ray Data and Ray Serve**: Take advantage of robust tools for distributed applications.

## 🏷️ Topics

This repository covers a variety of topics related to large language models and distributed computing, including:

- batch-inference
- distributed-computing
- large-language-models
- llm
- llm-api
- nlp
- parallel-processing
- ray
- ray-data
- ray-serve
- vllm

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## 🚀 Getting Started

To begin, you need to set up your environment. Follow the installation instructions below to get started quickly.

### Prerequisites

- Python 3.7 or later
- pip
- Access to a GPU (optional, but recommended for performance)

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## 🛠️ Installation

Clone the repository to your local machine:

```bash
git clone https://github.com/ARYAN555279/Batch_LLM_Inference_with_Ray_Data_LLM.git
cd Batch_LLM_Inference_with_Ray_Data_LLM

Install the required packages:

pip install -r requirements.txt

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📖 Usage

To use the batch inference feature, follow these simple steps. First, load your model and prepare your data.

Example Code

Here’s a basic example to get you started:

from ray import serve

# Initialize Ray
import ray
ray.init()

# Load your LLM
@serve.deployment
def llm_inference(request):
    # Logic for LLM inference here
    pass

# Deploy the service
serve.run(llm_inference.bind())

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📊 Examples

Explore practical examples to see how batch inference works with different LLMs. Each example demonstrates a specific feature or use case.

Example 1: Sentiment Analysis

Use the LLM to analyze the sentiment of a batch of texts:

texts = ["I love this!", "This is terrible."]
results = llm_inference(texts)
print(results)

Example 2: Text Generation

Generate text based on a prompt:

prompts = ["Once upon a time", "In a galaxy far away"]
generated_texts = llm_inference(prompts)
print(generated_texts)

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🤝 Contributing

Contributions are welcome! Please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes and commit them (git commit -m 'Add new feature').
4. Push to your branch (git push origin feature-branch).
5. Create a pull request.

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📄 License

This project is licensed under the MIT License. See the LICENSE file for more details.

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📬 Contact

For questions or support, feel free to reach out:

• GitHub: ARYAN555279
• Email: your_email@example.com

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📦 Releases

You can find the latest releases here. Make sure to download and execute the necessary files to get started.

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🎉 Conclusion

Thank you for checking out the Batch LLM Inference with Ray Data LLM repository. We hope this project aids in your journey with large language models. Happy coding!