PragmaticLM: Bridging User Intent with Intelligent Prompt Refinement

PragmaticLM is an advanced language model designed to extract hidden user intent and transform raw prompts into structured, contextually enriched queries. By leveraging cutting-edge transformer architecture and pragmatic reasoning, PragmaticLM refines ambiguous inputs to optimize the performance of downstream large language models (LLMs) such as Llama.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Training & Fine-Tuning
• Contributing

Introduction

In today's era of AI-driven interactions, understanding the subtle nuances in user prompts is crucial. PragmaticLM bridges this gap by:

• Extracting latent user intent.
• Refining and restructuring prompts.
• Enhancing inference quality when interfacing with external LLMs.

This makes PragmaticLM ideal for applications in conversational agents, decision support systems, and creative content generation.

Features

• Pragmatic Understanding: Captures implicit intent and refines ambiguous prompts.
• Modular Architecture: Built on a transformer backbone with additional layers/adapters for enhanced reasoning.
• Task-Specific Fine-Tuning: Easily fine-tune on custom datasets for tasks like prompt refinement.
• Seamless Integration: Outputs structured prompts for downstream models like Llama.
• Open Source: Designed to be extensible and community-friendly.

Installation

Clone the repository and install the required dependencies:

git clone https://github.com/mohammad17ali/PragmaticLM/.git
cd PragmaticLM
pip install -r requirements.txt

Usage

Below is a quick example of how to use PragmaticLM for prompt refinement:

from pragmaticlm import PragmaticLM, load_tokenizer

# Load the model and tokenizer
model = PragmaticLM.from_pretrained("path/to/pretrained/model")
tokenizer = load_tokenizer("path/to/tokenizer")

# Example prompt
raw_prompt = "summarize: The T5 model is a transformer-based model that was pre-trained on a mixture of tasks."

# Refine the prompt using PragmaticLM
refined_prompt = model.refine_prompt(raw_prompt)
print("Refined Prompt:", refined_prompt)

Training & Fine-Tuning

For fine-tuning PragmaticLM on your specific dataset, refer to our training guide for detailed instructions on setting up the environment, preparing data, and initiating training.

Contributing

We welcome contributions from the community! If you'd like to contribute:

Fork the repository. Create a new branch for your feature or bug fix. Submit a pull request with a clear description of your changes. Please read our CONTRIBUTING.md for more details.