🔧 Fine-Tuning Demo Overview
🎯 Purpose of the Sample
The Fine-Tuning Demo showcases how to use the LM-Kit.NET SDK to fine-tune large language models (LLMs) for specific tasks. This sample demonstrates the process of fine-tuning models to enhance their performance in tasks such as sentiment analysis, sarcasm detection, and functioning as a chemistry assistant.
👥 Industry Target Audience
This sample is particularly beneficial for developers and organizations in the following sectors:
- 🔬 Machine Learning and AI Research: Researchers looking to optimize models for specific tasks.
- 💻 Software Development: Developers aiming to integrate task-specific language models into their applications.
- 📞 Customer Support: Enhance automated customer support systems with models fine-tuned for specific queries and responses.
- 🏫 Education: Develop educational tools with models fine-tuned to provide accurate and relevant information.
🚀 Problem Solved
Pre-trained language models are powerful, but their performance can be suboptimal for specific tasks without further training. The Fine-Tuning Demo addresses this problem by demonstrating how to fine-tune models for specific applications, thereby significantly improving their accuracy and effectiveness.
💻 Sample Application Description
The Fine-Tuning Demo is a console application that allows users to fine-tune language models for specific tasks. The demo includes fine-tuning experiments for sentiment analysis, sarcasm detection, and functioning as a chemistry assistant.
✨ Key Features
- 📈 Model Fine-Tuning: Fine-tune language models for specific tasks using the LoRA (Low-Rank Adaptation) technique.
- 🔍 Task-Specific Training: Demonstrates fine-tuning for tasks such as sentiment analysis, sarcasm detection, and chemistry assistance.
- 📊 Progress Tracking: Displays progress, loss, and accuracy metrics during the fine-tuning process.
- 💾 Checkpointing: Supports saving and loading training checkpoints to resume training sessions.
🧠 Supported Models
The sample supports the following state-of-the-art model for fine-tuning:
- TinyLLaMA 1.1B 1T OpenOrca
🛠️ Getting Started
📋 Prerequisites
- .NET Framework 4.6.2 or .NET 6.0
📥 Download the Project
▶️ Running the Application
📂 Clone the repository:
git clone https://github.com/LM-Kit/lm-kit-net-samples.git
📁 Navigate to the project directory:
cd lm-kit-net-samples/console_framework_4.62/finetuning
or
cd lm-kit-net-samples/console_net6/finetuning
🔨 Build and run the application:
dotnet build dotnet run
🔍 Select the Fine-Tuning Experiment:
- Uncomment the desired fine-tuning experiment in
Program.cs
. - Available experiments: Sentiment Analysis, Sarcasm Detection, Chemistry Assistant.
- Uncomment the desired fine-tuning experiment in
💡 Example Usage
Set the License Key (if available):
LMKit.Licensing.LicenseManager.SetLicenseKey(""); // Set an optional license key here if available.
Uncomment the Desired Experiment:
// Uncomment the fine-tuning experiment you want to run: SentimentAnalysisFinetuning.RunTraining(); //SarcasmDetectionFinetuning.RunTraining(); //ChemistryAssistantFinetuning.RunTraining();
Run the Application:
dotnet run
Monitor Progress: The console will display the progress, loss, and accuracy metrics during the fine-tuning process.
🛠️ Special Commands
- Resume Training: Load training checkpoints to resume previous training sessions.
- Early Stop Conditions: Automatically stop training based on loss or maximum training duration.
🔬 Fine-Tuning Experiments
🧾 Sentiment Analysis Fine-Tuning
- Purpose: Enhance the accuracy of LMKit's sentiment analysis engine, using a tiny LLaMA model.
- Initial Accuracy: ~46%
- Target Accuracy: 95% - 98%
🗨️ Sarcasm Detection Fine-Tuning
- Purpose: Improve the accuracy of LMKit's sarcasm detection engine, using a tiny LLaMA model.
- Initial Accuracy: ~50%
- Target Accuracy: 85%+
⚗️ Chemistry Assistant Fine-Tuning
- Purpose: Fine-tune a small LLaMA model to function as a chemistry assistant.
- Initial Accuracy: 16.67%
- Target Accuracy:
- Windows: 25.93% at iteration 3651
- macOS: 38.89% at iteration 2570
By following these steps, developers can explore the functionalities of LM-Kit.NET and integrate advanced fine-tuning techniques into their applications, enhancing the accuracy and relevance of language models for specific tasks.