ML Training Example Projects

Purpose: Provide complete, runnable example projects demonstrating ML training workflows from data preparation through deployment.

Activation Triggers:

• User requests example projects or starter templates

• User wants to see working sentiment classification code

• User needs text generation training examples

• User mentions RedAI trade classifier

• User wants reference implementations

• User needs to understand complete training workflows

Key Resources:

• scripts/setup-example.sh

• Initialize and setup any example project

• scripts/run-training.sh

• Execute training for any example

• scripts/test-inference.sh

• Test trained models

• examples/sentiment-classification/

• Binary sentiment classification (IMDB-style)

• examples/text-generation/

• GPT-style text generation with LoRA

• examples/redai-trade-classifier/

• Financial trade classification with Modal deployment

• templates/

• Scaffolding for new projects

Available Example Projects

1. Sentiment Classification

Use Case: Binary sentiment analysis (positive/negative reviews)

Features:

• DistilBERT fine-tuning for text classification

• Custom dataset loading from JSON

• Training with validation metrics

• Model saving and inference

• Production-ready inference API

Files:

• train.py

• Complete training script

• data.json

• Sample training data (50 examples)

• inference.py

• Inference server

• README.md

• Setup and usage guide

Dataset Format:

{"text": "This movie was amazing!", "label": 1} {"text": "Terrible waste of time", "label": 0}

2. Text Generation

Use Case: Fine-tune GPT-2 for custom text generation

Features:

• GPT-2 small model fine-tuning

• LoRA (Low-Rank Adaptation) for efficient training

• Custom tokenization

• Generation with temperature/top-p sampling

• Modal deployment configuration

Files:

• train.py

• LoRA training script

• config.yaml

• Hyperparameters and model config

• generate.py

• Text generation script

• modal_deploy.py

• Modal deployment

• README.md

• Complete guide

Config Structure:

model: name: gpt2 max_length: 512 training: epochs: 3 batch_size: 4 learning_rate: 2e-4 lora: r: 8 alpha: 16 dropout: 0.1

3. RedAI Trade Classifier

Use Case: Financial trade classification (buy/sell/hold)

Features:

• Multi-class classification for trading signals

• Feature engineering from market data

• Class imbalance handling

• Modal deployment for production inference

• Real-time prediction API

Files:

• train.py

• Training with class weighting

• modal_deploy.py

• Complete Modal deployment

• data_preprocessing.py

• Feature engineering

• README.md

• Trading strategy guide

Model Input:

• Price features (open, high, low, close)

• Volume indicators

• Technical indicators (RSI, MACD, moving averages)

• Sentiment scores

Quick Start

Setup Any Example

Initialize example project

./scripts/setup-example.sh <project-name>

Options: sentiment-classification, text-generation, redai-trade-classifier

./scripts/setup-example.sh sentiment-classification

What it does:

• Creates project directory

• Copies example files

• Installs dependencies

• Downloads/prepares sample data

• Validates environment

Run Training

Train model for any example

./scripts/run-training.sh <project-name>

Examples:

./scripts/run-training.sh sentiment-classification ./scripts/run-training.sh text-generation ./scripts/run-training.sh redai-trade-classifier

Monitors:

• Training progress

• Loss curves

• Validation metrics

• GPU utilization

• Checkpoint saving

Test Inference

Test trained model

./scripts/test-inference.sh <project-name> <input>

Examples:

./scripts/test-inference.sh sentiment-classification "This product is great!" ./scripts/test-inference.sh text-generation "Once upon a time" ./scripts/test-inference.sh redai-trade-classifier market_data.json

Common Workflows

Start From Example Template

Choose example based on use case:

• Classification → sentiment-classification

• Generation → text-generation

• Financial ML → redai-trade-classifier

Setup project:

./scripts/setup-example.sh <example-name>

Customize for your data:

• Update data loading in train.py

• Modify model architecture if needed

• Adjust hyperparameters in config

Run training:

./scripts/run-training.sh <example-name>

Deploy:

• Local: Use inference.py

• Production: Use modal_deploy.py

Extend Example with Custom Data

• Prepare data in example format

• Replace data files (data.json, config.yaml)

• Update preprocessing if needed

• Train with same script

• Test inference with new data

Deploy Example to Production

All examples include Modal deployment:

Deploy to Modal

cd examples/<project-name> modal deploy modal_deploy.py

Get endpoint URL

modal app show <app-name>

Example Comparison

Feature Sentiment Text Gen Trade Classifier

Task Type Binary Classification Generation Multi-class

Model DistilBERT GPT-2 + LoRA Custom Transformer

Training Time 5-10 min 15-30 min 10-20 min

GPU Required Optional Recommended Required

Modal Deploy ✅ ✅ ✅

Custom Data Easy Moderate Advanced

Customization Guide

Sentiment Classification

Change dataset:

In train.py, update load_data()

def load_data(path): # Your custom loading logic return texts, labels

Change model:

Replace DistilBERT with other models

model_name = "bert-base-uncased" # or roberta-base, etc.

Text Generation

Change generation style:

In config.yaml

generation: temperature: 0.8 # Higher = more creative top_p: 0.9 # Nucleus sampling max_length: 200 # Output length

Add custom prompts:

In generate.py

prompts = [ "Your custom prompt here", "Another prompt" ]

Trade Classifier

Add features:

In data_preprocessing.py

def engineer_features(df): df['rsi'] = calculate_rsi(df['close']) df['macd'] = calculate_macd(df['close']) # Add your custom indicators return df

Change strategy:

Update labels in train.py

0 = sell, 1 = hold, 2 = buy

labels = your_strategy(prices, indicators)

Dependencies

Each example includes its own requirements.txt :

Sentiment Classification:

• transformers

• torch

• datasets

• scikit-learn

Text Generation:

• transformers

• peft (LoRA)

• torch

• modal (deployment)

Trade Classifier:

• transformers

• pandas

• numpy

• modal

• ta (technical analysis)

Troubleshooting

Training Fails

Issue: Out of memory Fix: Reduce batch size in config

Issue: CUDA not available Fix: Use CPU or install CUDA toolkit

Inference Errors

Issue: Model not found Fix: Check checkpoint path in inference script

Issue: Wrong input format Fix: Validate input matches training data format

Deployment Issues

Issue: Modal authentication Fix: Run modal token new to authenticate

Issue: Dependency conflicts Fix: Use exact versions from requirements.txt

Resources

Scripts: All scripts are in scripts/ with execution permissions

Examples: Complete projects in examples/ directory

Templates: Scaffolding in templates/ for creating new projects

Documentation: Each example has detailed README.md

Supported Frameworks: PyTorch, Transformers, PEFT Deployment Platforms: Modal, Local, FastAPI Version: 1.0.0