Overview

Meta Llama 3.1-70B-Instruct is a 70 billion parameter instruction-tuned model from Meta's Llama 3.1 family, designed for multilingual dialogue. It utilizes an optimized transformer architecture and has been fine-tuned using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. The model boasts a substantial 128K token context length and was trained on over 15 trillion tokens of publicly available online data with a knowledge cutoff of December 2023.

Key Capabilities

Multilingual Performance: Optimized for dialogue in English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai, with potential for other languages through fine-tuning.
Extended Context Window: Features a 128K token context length, significantly enhancing its ability to handle long-form interactions and complex queries.
Instruction Following: Excels in assistant-like chat scenarios due to extensive instruction tuning.
Tool Use: Demonstrates strong performance in tool-use benchmarks like API-Bank (90.0% accuracy) and BFCL (84.8% accuracy).
Reasoning and Math: Achieves high scores on reasoning benchmarks such as ARC-C (94.8% accuracy) and math benchmarks like GSM-8K (95.1% accuracy) and MATH (68.0% final_em).

Good For

Assistant-like Chatbots: Its instruction-tuned nature makes it highly suitable for conversational AI applications.
Multilingual Applications: Ideal for developing applications that require understanding and generating text in its supported languages.
Complex Problem Solving: The extended context window and strong reasoning capabilities benefit applications requiring detailed analysis or multi-turn interactions.
Code Generation: Shows solid performance in coding benchmarks like HumanEval (80.5% pass@1) and MBPP++ (86.0% pass@1).
Research and Commercial Use: Intended for a broad range of commercial and research applications, with a focus on responsible deployment and safety.