Cookbooks

Interactive Jupyter notebooks demonstrating advanced use cases and best practices with Fireworks AI

Explore our collection of notebooks that showcase real-world applications, best practices, and advanced techniques for building with Fireworks AI.

Fine-Tuning & Training

Knowledge Distillation

Transfer large model capabilities to efficient models using a two-stage SFT + RFT approach.

Techniques: Supervised Fine-Tuning (SFT) + Reinforcement Fine-Tuning (RFT)

Results: 52% → 70% accuracy on GSM8K mathematical reasoning

VLM Fine-tuning + Evals

Beat frontier closed-source models for product catalog cleansing with vision-language model fine-tuning.

Techniques: Supervised Fine-Tuning (SFT)

Results: 48% increase in quality from base model

Multimodal AI

NVIDIA Nemotron VL for Document Intelligence

Extract structured data from invoices, forms, and financial documents using state-of-the-art OCR and document understanding.

Use Cases: Forms, invoices, financial documents, product catalogs

Results: 90.8% accuracy on invoice extraction (100% on invoice numbers and dates)

Audio Streaming Speech-to-Text

Real-time audio transcription with streaming support and low latency.

Features: Streaming support, low-latency transcription, production-ready

Chat with Video using Qwen3 Omni

Analyze video and audio content with Qwen3 Omni, a multimodal model supporting video, audio, and text inputs.

Features: Video captioning, scene analysis, content understanding, multimodal Q&A

API Features

Fireworks MCP Examples

Leverage Model Context Protocol (MCP) for GitHub repository analysis, code search, and documentation Q&A.

Features: Repository analysis, code search, documentation Q&A, GitMCP integration

Models: Qwen 3 235B with external tool support