Fire Function

Customized, production ready inference with open source models: Dmytro (Dima) Dzhulgakov

Feb 16, 2025 · 18:55

Dmytro (Dima) Dzhulgakov, co-founder and CTO of Fireworks AI, argues that open source models are the future for production Gen AI applications, and Fireworks provides a platform to make them customized and production-ready. He explains that while proprietary models like GPT-4 are powerful, they are too large, expensive, and slow for many use cases, whereas open models like Llama or Gemma can be fine-tuned for specific domains to achieve better quality at up to 10x speed and lower cost. The main challenges of using open models—complex setup, performance tuning, and production scaling—are addressed by Fireworks' custom serving stack, which achieves the fastest long-prompt inference and serves SDXL fastest among providers, handling over 150 billion tokens per day. The platform supports fine-tuning and serving thousands of LoRA adapters on the same GPU with serverless pay-per-token pricing. Dzhulgakov also highlights the emerging architecture of compound AI systems, where function calling—exemplified by the open-source Fire Function model—connects LLMs to external tools and knowledge sources, enabling agentic applications like stock querying and chart generation. The episode…

Build an AI Research Agent: Apoorva Joshi

Oct 25, 2024 · 27:33

In this workshop, Apoorva Joshi, an AI Developer Advocate at MongoDB, teaches how to build an AI research agent using MongoDB as the memory provider and knowledge store, open-source LLMs from Fireworks AI (Fire Function V1) as the agent’s brain, and LangChain to orchestrate the workflow. The agent searches for research papers, summarizes them, and answers questions based on past research, using tools like ArXivLoader and a MongoDB vector store. Joshi explains key agent concepts—planning with chain of thought and react patterns, short-term and long-term memory, and tool creation—then guides attendees through hands-on sections to build the agent step by step. Attendees learn to create agent tools, implement reasoning with react, and add short-term memory persisted to MongoDB. The workshop emphasizes that agents enable complex, multi-step tasks through iterative reasoning, tool use, and memory, trading higher cost and latency for improved accuracy.

Architecting and Testing Controllable Agents: Lance Martin

Oct 11, 2024 · 2:21:54

Lance Martin presents LangGraph, a graph-based framework for building controllable agents that trade some open-ended flexibility for significantly higher reliability compared to classic React agents, achieving 100% consistent tool-calling trajectories even with an 8B local model. He demonstrates self-corrective RAG patterns like Corrective RAG, SelfRAG, and Adaptive RAG, where the agent grades retrieved documents, checks for hallucinations, and routes to web search when needed. Martin also covers three testing loops: in-app error handling with LangGraph, pre-production evaluation using LangSmith to compare agent answers and tool trajectories against ground-truth datasets, and production monitoring with online evaluators that flag retrieval quality, answer relevance, and hallucinations without reference answers. He shares results from a five-question evaluation showing LangGraph agents achieve 80% answer accuracy and 100% tool-trajectory correctness with Fire Function V2, while React agents with GPT-4o degrade in tool reliability. The talk addresses practical concerns like handling many tools (suggesting RAG for tool selection), multi-turn conversations, and the importance of…