Quick Start Guide¶

Get up and running with IdeaWeaver in minutes! This guide walks you through the essential features.

Prerequisites¶

Make sure you have completed the Installation Guide and can run:

source ideaweaver-env/bin/activate
ideaweaver --help

Available Commands¶

IdeaWeaver provides comprehensive CLI commands for AI model operations:

Commands:
  agent       Intelligent agent workflows for creative and analytical tasks.
  download    Download a model from Hugging Face Hub
  evaluate    Evaluate a model using lm-evaluation-harness with...
  finetune    Supervised fine-tuning commands with LoRA, QLoRA, and full...
  list-tasks  List all available evaluation tasks from lm-evaluation-harness
  mcp         Model Context Protocol (MCP) integration commands
  rag         RAG (Retrieval-Augmented Generation) commands
  train       Train a model with AutoTrain Advanced.
  validate    Validate a configuration file

Download Your First Model¶

Start by downloading a model from Hugging Face:

# Download a small model for quick testing
ideaweaver download microsoft/DialoGPT-medium

# Use the `--save-path` option to specify the directory where the downloaded model will be stored.
ideaweaver download microsoft/DialoGPT-medium --save-path ./models/my-model

Basic Model Training¶

1. Prepare Your Dataset¶

Create a simple training configuration:

# config/training_config.yaml
model:
  name: "microsoft/DialoGPT-medium"
  task: "text-generation"

dataset:
  name: "your-dataset-name"
  split: "train"

training:
  output_dir: "./results"
  num_train_epochs: 3
  per_device_train_batch_size: 4
  learning_rate: 5e-5
  save_steps: 500
  logging_steps: 100

2. Start Training¶

# Basic training
ideaweaver train --config config/training_config.yaml

RAG (Retrieval-Augmented Generation)¶

1. Set Up Your First RAG System¶

# 1. Create a knowledge base
ideaweaver rag create-kb --name mykb --embedding-model sentence-transformers/all-MiniLM-L6-v2

# 2. Ingest documents into the knowledge base
ideaweaver rag ingest --kb mykb --source ./documents/

# 3. Query the knowledge base
ideaweaver rag query --kb mykb --question "What is machine learning?"

🔌 MCP (Model Context Protocol) Integration¶

1. List Available MCP Servers¶

# See all available MCP integrations
ideaweaver mcp list-servers

2. Set Up GitHub Integration¶

# 1. Set up GitHub authentication (will prompt for your token)
ideaweaver mcp setup-auth github

# 2. Enable the GitHub MCP server
ideaweaver mcp enable github

# 3. List available MCP servers (to verify)
ideaweaver mcp list-servers

# 4. Call a tool on the GitHub MCP server (example: list issues)
ideaweaver mcp call-tool github list_issues --args '{"owner": "your-username/org name", "repo": "your-repo"}'

Model Evaluation¶

1. Evaluate with Standard Benchmarks¶

# Run evaluation on specific tasks
ideaweaver evaluate ./downloaded_model --tasks hellaswag,arc_easy,winogrande --output-path results.json

Agents¶

Agents for Multi-Agent Workflows¶

# Generate storybooks
ideaweaver agent generate_storybook --theme "brave little mouse" --target-age "3-5"

Model Fine-tuning¶

ideaweaver finetune full \
  --model microsoft/DialoGPT-small \
  --dataset datasets/instruction_following_sample.json \
  --output-dir ./test_full_basic \
  --epochs 5 \
  --batch-size 2 \
  --gradient-accumulation-steps 2 \
  --learning-rate 5e-5 \
  --max-seq-length 256 \
  --gradient-checkpointing \
  --verbose

Configuration Validation¶

Always validate your configurations before running:

# Validate training configuration
ideaweaver validate config/training_config.yaml

Pro Tips¶

Environment Variables: Set API keys in your shell profile for persistence
Configuration Files: Use YAML configs for complex setups - easier to reproduce
Logging: Add --verbose flag to most commands for detailed output
GPU Usage: Commands automatically detect and use available GPUs
Caching: Models and datasets are cached locally to speed up repeated runs

🚨 Common Issues¶

Command not found: Make sure your virtual environment is activated

source ideaweaver-env/bin/activate

Import errors: Reinstall requirements if packages are missing

pip install -r requirements.txt --force-reinstall

GPU not detected: Check CUDA installation

python -c "import torch; print(torch.cuda.is_available())"

⚠️ Note: IdeaWeaver is currently in alpha. Expect a few bugs, and please report any issues you find. If you like the project, drop a ⭐ on GitHub!

Next Steps¶

Now that you've got the basics down:

📖 Explore detailed Tutorials
🔧 Check the CLI Reference
🤝 Join our Community
🚀 Deploy to Production

Ready to build something amazing? Let's go! 🚀