metadata

title: OpenLLM Live Training Space
emoji: 🚀
colorFrom: green
colorTo: blue
sdk: gradio
sdk_version: 4.44.1
app_file: app.py
pinned: false
license: gpl-3.0

🚀 OpenLLM Live Training Space

📚 What is This Space?

Welcome to the OpenLLM Live Training Space! This is an interactive web application where you can train new language models from existing checkpoints with customizable parameters. Think of it as a "training playground" where you can experiment with different training configurations in real-time.

🎯 What Makes This Special?

Unlike most AI demos that only allow you to use pre-trained models, this space lets you actually train new models with your own settings:

Interactive Training: Configure and start training sessions in real-time
Parameter Experimentation: Try different learning rates, batch sizes, and optimization settings
Live Monitoring: Watch training progress and metrics as they happen
Educational: Learn how different parameters affect model training
No Setup Required: Train models without installing anything locally

🧠 Understanding Model Training

What is Model Training?

Model training is like teaching a student by showing them millions of examples. The model learns patterns from the data and gradually improves its ability to predict what comes next.

Example Training Process:

Input: "The weather today is..."
Model Prediction: "sunny" (might be wrong initially)
Correction: "Actually, it's rainy"
Learning: Model adjusts its "thinking" to do better next time
Repeat: Millions of times until the model gets good at predictions

How Does Training Work?

The Training Loop

Forward Pass: Model makes a prediction
Loss Calculation: Measure how wrong the prediction was
Backward Pass: Calculate how to adjust the model
Parameter Update: Update model weights to improve
Repeat: Continue until the model performs well

Key Parameters

Learning Rate: How big steps to take when learning (too big = overshooting, too small = slow learning)
Batch Size: How many examples to process at once (affects memory usage and training speed)
Training Steps: How long to train (more steps = potentially better performance)
Optimizer: Algorithm for updating model weights (AdamW, Adam, SGD)

🎮 How to Use This Space

Step-by-Step Guide

1. Configure Training Parameters

Learning Rate: Start with 3e-4 (0.0003) for most cases
Batch Size: Choose based on your memory constraints (8-16 is usually good)
Training Steps:
- 1000 steps = Quick experiment (10-30 minutes)
- 5000 steps = Medium training (1-3 hours)
- 10000 steps = Extended training (3-8 hours)

2. Start Training

Click the "🚀 Start Training" button
Watch the status updates in real-time
Monitor loss values and training progress

3. Monitor Progress

Loss: Should decrease over time (lower is better)
Learning Rate: May change based on scheduler
Steps: Current progress through training

4. Download Results

Once training completes, download your trained model
Use it for text generation or further fine-tuning

Training Scenarios

Quick Experiments (1000 steps)

Best for: Testing different learning rates and configurations
Duration: 10-30 minutes
Use case: Hyperparameter exploration and rapid prototyping

Medium Training (5000 steps)

Best for: Significant model improvement and fine-tuning
Duration: 1-3 hours
Use case: Model optimization and performance enhancement

Extended Training (10000 steps)

Best for: Maximum performance improvement
Duration: 3-8 hours
Use case: Production model development and research

📊 Understanding the Parameters

Learning Parameters

Learning Rate: Controls how fast the model learns
- Too high: Model might overshoot and never converge
- Too low: Training takes forever
- Sweet spot: Usually between 1e-4 and 1e-3
Batch Size: Number of examples processed together
- Larger: More stable gradients, but uses more memory
- Smaller: Less memory, but potentially less stable training

Optimization Settings

Gradient Accumulation: Simulates larger batch sizes with less memory
Optimizer: Algorithm for updating weights
- AdamW: Usually the best choice for transformers
- Adam: Good general-purpose optimizer
- SGD: Simple but may need more tuning
Scheduler: How learning rate changes over time
- Cosine: Smooth decrease, often works well
- Linear: Straight-line decrease
- Constant: No change (rarely used)

Advanced Options

Weight Decay: Prevents overfitting by penalizing large weights
Gradient Clipping: Prevents exploding gradients
Warmup Steps: Gradually increase learning rate at the start

🎓 Educational Value

What You'll Learn

1. Training Dynamics

How loss decreases over time
The relationship between learning rate and convergence
When to stop training (avoiding overfitting)

2. Hyperparameter Tuning

How different parameters affect training
The trade-offs between speed and quality
Best practices for different scenarios

3. Model Development

The complete training workflow
How to evaluate model performance
When to use different training strategies

4. Practical Skills

Reading training logs and metrics
Understanding model convergence
Debugging training issues

Learning Path

Beginner Level

Start with default parameters
Try different training step counts
Observe how loss changes over time

Intermediate Level

Experiment with different learning rates
Try different optimizers and schedulers
Understand the relationship between parameters

Advanced Level

Fine-tune all parameters for optimal performance
Understand the underlying training algorithms
Apply these concepts to your own projects

🔬 Research Applications

What Can You Do With This?

1. Hyperparameter Research

Study how different parameters affect training
Find optimal configurations for specific tasks
Understand parameter interactions

2. Training Methodologies

Compare different optimization strategies
Study learning rate schedules
Research training stability techniques

3. Model Development

Prototype new training approaches
Test different architectures
Develop custom training pipelines

4. Educational Research

Study how people learn about ML
Develop better teaching methods
Create interactive learning experiences

🛠️ Technical Details

Base Model

This space uses the lemms/openllm-small-extended-9k model as the starting point, which is our best-performing model with:

Architecture: GPT-style transformer
Parameters: ~35.8M
Training: 9,000 steps on SQUAD dataset
Performance: ~5.2 loss, ~177 perplexity

Training Infrastructure

Framework: PyTorch with custom training loop
Optimization: AdamW optimizer with cosine scheduling
Memory Management: Gradient checkpointing and accumulation
Monitoring: Real-time loss and metric tracking

Limitations

Demo Mode: This is a demonstration of training capabilities
Resource Constraints: Limited GPU time per session
Model Size: Currently supports small models only
Dataset: Uses pre-processed SQUAD dataset

🔗 Related Resources

OpenLLM Project

Model Demo Space - Test trained models
GitHub Repository - Source code and documentation
Training Documentation - Detailed training guide

Learning Resources

PyTorch Tutorials: Official PyTorch documentation
Transformer Papers: "Attention Is All You Need" and follow-ups
Training Guides: Hugging Face training tutorials

Community

GitHub Discussions: Ask questions and share results
Discord/Slack: Join our community chat
Twitter: Follow for updates and announcements

📞 Support and Contact

Getting Help

GitHub Issues: For bugs and feature requests
Discussions: For questions and general help
Email: louischua@gmail.com for private matters

Contact Information

Author: Louis Chua Bean Chong
GitHub: https://github.com/louischua/openllm
Model Demo: https://huggingface.co/spaces/lemms/llm

📄 License

This space is part of the OpenLLM project and is available under the GPLv3 license for open source use, with commercial licensing options available.

🎉 Start Training!

Ready to train your own language model? Configure your parameters and click "Start Training" to begin your AI learning journey!

Remember: This is a demonstration space. For production training, please refer to the full OpenLLM documentation and run training locally or on your own infrastructure.

This space is maintained by Louis Chua Bean Chong and the open-source community. Your feedback and contributions are welcome!