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Fireworks AI Cookbook

Fireworks AI Cookbook is an open-source Python repository of runnable training loops, worked examples, evaluation resources and agent skills for fine-tuning and post-training generative AI models through Fireworks.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Fireworks AI Cookbook — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use the cookbook when planning or implementing a Fireworks model-training workflow, or when seeking agent-based guidance to research a training approach, prepare a plan or investigate a run.

Who it is for

  • Developers seeking examples for supervised fine-tuning, reinforcement learning, preference optimization, distillation or embedding fine-tuning.
  • Teams evaluating or troubleshooting Fireworks training workflows.
  • Users of compatible Claude Code, Cursor or Codex environments who want agent guidance for researching, configuring or debugging training.

Documented uses

  • Find a relevant training method and a close runnable cookbook entry for a task.
  • Review or adapt a training loop and its supporting utilities.
  • Prepare a supported Fireworks training plan for review before spend or resource changes.
  • Diagnose a training run using runtime evidence.
  • Use evaluation resources to export validated trajectories for reinforcement-learning workflows.
Before you choose

Requirements & limits

The repository is public and open source under the Apache-2.0 license. The README documents installation paths for Claude Code, Cursor and Codex.

  • The recipe collection and Training API guidance are centered on Fireworks; reuse with another training service requires modification.
  • The research skill recommends a method and cookbook entry but does not launch training.
  • The configure skill describes plan review and approval before spending or resource mutation.
  • The documented quick start uses Git, uv and Python 3.12.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the training cookbook environment

Clone the repository and install its training package in a Python 3.12 virtual environment.

Before you start

  • Git
  • uv
  • Python 3.12

Steps

  1. Clone https://github.com/fw-ai/cookbook.git.

  2. Change into cookbook/training.

  3. Create and activate a Python 3.12 virtual environment with uv.

  4. Install the package in editable mode with uv pip install -e .

  5. Consult training/README.md for recipes and examples.

Expected outcome

The training package is installed in the local virtual environment, and the repository points to its training README for recipe details.

Guide limitations

  • This setup procedure does not itself run a training job.
  • The recipes are built around Fireworks; adapting them to another service requires changes to APIs and data formats.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
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Sources checked
13 October 2026
Original website title
fw-ai/cookbook · Official homepage title · retained in its source language
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