Cookbooks ↗

Documentation Index#

Fetch the complete documentation index at: https://docs.fireworks.ai/llms.txt Use this file to discover all available pages before exploring further.

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#

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

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#

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)

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

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

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#

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