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| layout: default | |
| title: Getting Started | |
| permalink: /getting_started/ | |
| # Getting Started with CLaRa | |
| This guide will help you get started with CLaRa, from installation to running your first training. | |
| ## Installation | |
| ### Prerequisites | |
| - Python 3.10+ | |
| - CUDA-compatible GPU (recommended) | |
| - PyTorch 2.0+ | |
| - CUDA 11.8 or 12.x | |
| ### Step 1: Create Conda Environment | |
| ```bash | |
| env=clara | |
| conda create -n $env python=3.10 -y | |
| conda activate $env | |
| ``` | |
| ### Step 2: Install Dependencies | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| Key dependencies include: | |
| - `torch>=2.0` | |
| - `transformers>=4.20` | |
| - `deepspeed>=0.18` | |
| - `flash-attn>=2.8.0` | |
| - `accelerate>=1.10.1` | |
| - `peft>=0.17.1` | |
| ### Step 3: Set Environment Variables | |
| ```bash | |
| export PYTHONPATH=/path/to/clara:$PYTHONPATH | |
| ``` | |
| ## Quick Start | |
| ### 1. Prepare Your Data | |
| CLaRa uses JSONL format for training data. See the [Training Guide](./training.md) for data format details. | |
| ### 2. Train Stage 1: Compression Pretraining | |
| ```bash | |
| bash scripts/train_pretraining.sh | |
| ``` | |
| ### 3. Train Stage 2: Instruction Tuning | |
| ```bash | |
| bash scripts/train_instruction_tuning.sh | |
| ``` | |
| ### 4. Train Stage 3: End-to-End Training | |
| ```bash | |
| bash scripts/train_stage_end_to_end.sh | |
| ``` | |
| ### 5. Run Inference | |
| See the [Inference Guide](./inference.md) for examples of using all three model stages. | |
| ## Next Steps | |
| - [Training Guide](./training.md) - Detailed training instructions and data formats | |
| - [Inference Guide](./inference.md) - Inference examples for all model stages | |