Use powerful but expensive LLMs to fine-tune smaller and cheaper models suited to your exact needs. Evaluate model and prompt combinations in the playground. Query your past requests and export optimized training data. Try it out at https://app.openpipe.ai or run it locally.
Features
Experiment
Bulk-test wide-reaching scenarios using code templating.
Seamlessly translate prompts across different model APIs.
Tap into autogenerated scenarios for fresh test perspectives.
Fine-Tune (Beta)
Easy integration with OpenPipe's SDK in both Python and JS.
Swiftly query logs using intuitive built-in filters.
Export data in multiple training formats, including Alpaca and ChatGPT, with deduplication.
Sample Experiments
These are sample experiments users have created that show how OpenPipe works. Feel free to fork them and start experimenting yourself.
Install NodeJS 20 (earlier versions will very likely work but aren't tested).
Install pnpm: npm i -g pnpm
Clone this repository: git clone https://github.com/openpipe/openpipe
Install the dependencies: cd openpipe && pnpm install
Create a .env file (cp .env.example .env) and enter your OPENAI_API_KEY.
Update DATABASE_URL if necessary to point to your Postgres instance and run pnpm prisma migrate dev to create the database.
Create a GitHub OAuth App and update the GITHUB_CLIENT_ID and GITHUB_CLIENT_SECRET values. (Note: a PR to make auth optional when running locally would be a great contribution!)