OpenPipe

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 Software engineers and data scientists use OpenPipe's intuitive fine-tuning and monitoring services to decrease the cost and latency of their LLM operations.  You can use OpenPipe to collect and analyze LLM logs, create fine-tuned models, and compare output from multiple models given the same input.

Introduction

OpenPipe Documentation

OpenPipe is a streamlined platform designed to help product-focused teams train specialized LLM models as replacements for slow and expensive prompts.

Overview

Train and compare across a range of the most powerful base models.

Base Models

Get started with OpenPipe in a few quick steps.

Quick Start

Installing the SDK

 Record production data to train and improve your models' performance.

Logging Requests

Logging Anthropic Requests

 Search and filter your past LLM requests to inspect your responses and build a training dataset.

Training on Logs

Pipelines

 Upload external data to kickstart your fine-tuning process. Use the OpenAI chat fine-tuning format.

Uploading Data

Use powerful models to generate new outputs for your data before training.

Relabeling Data

 Export your past requests as a JSONL file in their raw form.

Exporting Data

 Fine tune your models on filtered logs or uploaded datasets. Filter by prompt id and exclude requests with an undesirable output.

Webapp

Fine Tuning via Webapp

 Fine tune your models programmatically through our API.

API (Beta)

Fine Tuning via API (Beta)

 Evaluate your fine-tuned models against comparison LLMs like GPT-4 and GPT-4-Turbo. Add and remove models from the evaluation, and customize the evaluation criteria.

Evaluations

Chat Completions

Anthropic Proxy

Mixture of Agents

Mixture of Agents Chat Completions

Use Prompt Prefilling to control the initial output of the completion.

Prompt Prefilling

 Improve performance and reduce costs by caching previously generated responses.

Caching

Updating Tags

Decrease input token counts by pruning out chunks of static text.

Pruning Rules

 Safeguard your application against potential failures, timeouts, or instabilities that may occur when using experimental or newly released models.

Fallback

Fallback options

Use Mixture of Agents to increase quality beyond SOTA models.

Report

Record request logs from Anthropic models

Report Anthropic

OpenAI-compatible route for generating inference and optionally logging the request.

Update tags for logged calls matching the provided filters

Update Log Tags

Our SDK wraps calls and forwards requests

Welcome

Getting Started

Features

API Reference

FAQ

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