Phyoe717/Llama-3-Groq-70B-Tool-Use

TEXT GENERATIONPricing:Input $3.5 / Cached $0.7 / Output $8.3Concurrent Unit Cost:4Model Size:70BQuant:FP8Context Size:8kTool Calling:SupportedPublished:Aug 4, 2026License:llama3Architecture:Transformer Featherless Exclusive Cold

Phyoe717/Llama-3-Groq-70B-Tool-Use is a 70 billion parameter Llama 3 model, fine-tuned by Groq, specifically optimized for advanced tool use and function calling tasks. This model excels at API interactions and structured data manipulation, achieving an overall accuracy of 90.76% on the Berkeley Function Calling Leaderboard, making it the top-performing open-source 70B LLM in this category. It is designed for research and development in scenarios requiring precise function execution and tool integration.

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Llama-3-Groq-70B-Tool-Use: Advanced Function Calling Model

This model is the 70 billion parameter version of the Llama 3 Groq Tool Use model, meticulously fine-tuned for advanced tool use and function calling. It leverages a full fine-tuning approach combined with Direct Preference Optimization (DPO) on the Llama 3 70B base model.

Key Capabilities

  • Exceptional Tool Use: Specifically designed to excel in scenarios requiring API interactions, structured data manipulation, and complex tool execution.
  • Leading Performance: Achieves an impressive 90.76% overall accuracy on the Berkeley Function Calling Leaderboard (BFCL), positioning it as the best-performing open-source 70B LLM for function calling.
  • Optimized Architecture: Built on an optimized transformer architecture, processing text input to generate text output with enhanced tool-use capabilities.

When to Use This Model

  • Function Calling Applications: Ideal for developing applications that require precise and reliable function calls based on natural language prompts.
  • API Integration: Excellent for tasks involving interaction with external APIs and services through structured function calls.
  • Research & Development: A strong candidate for research into advanced agentic workflows and complex tool orchestration.

Important Considerations

  • While highly specialized for tool use, for general knowledge or open-ended conversational tasks, a general-purpose LLM might be more suitable.
  • The model's performance can be sensitive to sampling configurations; recommended starting parameters are temperature=0.5 and top_p=0.65.

Finetunes
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