Directory Contents
# Tinker R&D Lab
This directory is a self-contained environment for data generation and model training using the `tinker-cookbook` library, based on the principles and documentation from `adam/v21.0`.
## Setup
1. **Activate Virtual Environment:**
```bash
source .venv/bin/activate
```
2. **Install Dependencies:**
If this is the first time, or if dependencies change, run:
```bash
pip install -e tinker-cookbook/
pip install jupyterlab pandas openai python-dotenv
```
3. **Set API Keys:**
Copy the `.env.example` file to a new file named `.env` and add your private API keys.
```bash
cp .env.example .env
nano .env
```
## How to Use
1. **Launch Jupyter:**
```bash
jupyter lab
```
2. **Run Notebooks:**
* **`01_Data_Generation.ipynb`**: Use this to generate `jsonl` training datasets.
* **`02_Model_Training.ipynb`**: Use this to load the generated data and run fine-tuning jobs.
## Output Structure
All artifacts are saved to the `outputs/` directory:
* `outputs/datasets/`: Contains generated `.jsonl` files for training.
* `outputs/model_weights/`: Contains logs and identifiers for trained models.
* `outputs/logs/`: Contains general-purpose logs.