Build with Fireworks
Step-by-step guides for hands-on exploration, ideal for interactive learning of AI techniques.
Inference
Explore notebooks and projects showcasing how to run generative AI models on Fireworks, demonstrating both third-party integrations and innovative applications with industry-leading speed and flexibility.
LLMs
Dive into examples that utilize Fireworks for deploying and fine-tuning large language models (LLMs), featuring integrations with popular libraries and cutting-edge use cases.
Notebooks
Notebook: Fireworks Model Comparison App
(Python) An interactive Streamlit app for comparing LLMs on Fireworks with parameter tuning and LLM-as-a-Judge functionality.
Notebook: Structured Response with Llama 3.1
(Python) Demonstrates structured responses using Llama 3.1, covering Grammar Mode and JSON Mode for consistent output formats.
Notebook: Llama 3.1 Synthetic Data Generation
(Python) Explores generating synthetic data with Llama 3.1 models on Fireworks, including structured outputs for quizzes.
Apps
Visual-language
Discover projects combining vision and language capabilities using Fireworks, integrating external frameworks for seamless multimodal understanding.
Audio
Explore real-time audio transcription, processing, and generation examples using Fireworks’ advanced audio models and integrations.
Notebooks
Notebook: Concise Real-time Audio Transcription & Alignment
A notebook demonstrating real-time audio transcription using Fireworks’ Whisper-v3-turbo model. The project includes streaming audio input, transcribing speech, and aligning timestamps, making it ideal for tasks requiring accurate and responsive audio processing.
Notebook: Verbose Real-Time Audio Transcription & Alignment
Learn how to perform real-time audio transcription and timestamp alignment using Fireworks’ Whisper-v3-turbo model. This notebook demonstrates streaming audio input, transcription, and precise word-level alignment, ideal for applications needing accurate speech processing.
Image
Experiment with image-based projects using Fireworks’ models, enhanced with third-party libraries for innovative applications in image creation, manipulation, and recognition.
Multimodal
Learn from complex multimodal examples that blend text, audio, and image inputs, demonstrating the full potential of Fireworks combined with external tools for interactive AI experiences.
Fine-tuning
Access notebooks that demonstrate efficient model fine-tuning on Fireworks, utilizing both internal capabilities and third-party tools like Axolotl for custom optimization.
Function calling
Explore examples of function-calling workflows using Fireworks, showcasing how to integrate with external APIs and tools for sophisticated, multi-step AI operations.
Notebooks
Notebook: Fireworks LangChain Tool Usage
Demonstrates Function-Calling with LangChain integration, including custom tool routing and query handling. (Python)
Notebook: Fireworks & LangChain Tools Integration
Explore the integration of Fireworks’ function-calling model with LangChain tools. This notebook demonstrates building basic agents using firefunction-v1 for tasks like answering questions, retrieving stock prices, and generating images with the Fireworks SDXL API (Javascript).
Notebook: Fireworks LangGraph Tool Usage
Showcases Function-Calling with LangGraph integration for graph-based agent systems and tool queries. (Python)
Notebook: Fireworks Function-Calling QA with OpenAI
Uses Fireworks’ Function-Calling for structured QA with OpenAI, featuring multi-turn conversation handling. (Python)
Notebook: Fireworks Function-Calling Demo
Demonstrates querying financial data using Fireworks’ Function-Calling API with integrated tool setup. (Python)
Notebook: Fireworks Function-Calling for Information Extraction
Extracts structured information from web content using Fireworks’ Function-Calling API. (Python)
Notebook: Fireworks AutoGen Stock Chart Demo
Generates stock charts using Fireworks’ Function-Calling API with AutoGen integration. (Python)
Apps
RAG
Build retrieval-augmented generation (RAG) systems with Fireworks, featuring projects that connect with vector databases and search tools for enhanced, context-aware AI responses.
Notebooks
Notebook: Simple RAG with Chroma for League of Legends
A basic RAG implementation using ChromaDB with League of Legends data, comparing responses across multiple models. (Python)
Notebook: RAG Paper Title Generator
An agentic system using RAG for generating catchy research paper titles with embeddings and LLM completions. (Python)
Notebook: MongoDB RAG Movie Recommender
A movie recommendation system using Fireworks’ function-calling models and MongoDB Atlas for personalized, real-time suggestions. (Python)
Apps
Integration partners
We welcome contributions from integration partners! Follow these steps:
- Clone the Repo: Fireworks Cookbook repo
- Create Folder: Add your company/tool under
integrations - Add Examples: Include code, notebooks, or demos
- Use Template: Fill out the integration guide
- Submit PR: Create a pull request
- Review: Fireworks will review and merge
Support
For help or feedback:
- Discord: Join us
- Email: Contact us
Resources:
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