fireworks-ai/llama-3-firefunction-v2

Hugging Face
TEXT GENERATIONConcurrency Cost:4Model Size:70BQuant:FP8Ctx Length:8kPublished:Jun 5, 2024License:llama3Architecture:Transformer0.1K Warm

FireFunction V2 is a 70 billion parameter function-calling model developed by Fireworks AI, built upon the Llama 3 architecture. This model is optimized for robust function calling, including parallel function execution, and maintains strong general instruction-following capabilities with an 8192-token context window. It demonstrates competitive performance against leading models like GPT-4o in function-calling benchmarks, making it suitable for complex tool-use applications and structured information extraction.

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FireFunction V2: Advanced Function Calling

FireFunction V2, developed by Fireworks AI, is a 70 billion parameter model based on the Llama 3 architecture, specifically engineered for high-performance function calling. It represents a significant advancement over its predecessor, FireFunction v1, with substantial quality improvements across various metrics.

Key Capabilities

  • Parallel Function Calling: Unlike FireFunction v1, V2 supports parallel execution of multiple functions, enhancing efficiency for complex tasks.
  • Competitive Performance: Benchmarks show FireFunction V2 scoring 0.81 on public function-calling evaluations, competitive with GPT-4o's 0.80. It also retains strong instruction-following, scoring 0.84 on MT-bench.
  • Broad Use Cases: Optimized for general instruction following, multi-turn chat with function calls, single and parallel function calling, and structured information extraction.
  • Extensive Function Support: Capable of handling up to 20 function specifications simultaneously within its 8k context window.

Ideal Use Cases

  • Tool-use applications: Integrating external tools and APIs via function calls.
  • Complex conversational agents: Managing multi-turn dialogues that require dynamic function invocation.
  • Structured data extraction: Accurately extracting specific information from unstructured text using defined schemas.

This model is not optimized for scenarios requiring 100+ function specifications or nested function calling.

Popular Sampler Settings

Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.

temperature
top_p
top_k
frequency_penalty
presence_penalty
repetition_penalty
min_p