The Meta Llama 3.1-70B-Instruct is a 70 billion parameter instruction-tuned generative language model developed by Meta, part of the Llama 3.1 collection. Optimized for multilingual dialogue use cases, it features a 128K token context length and excels in assistant-like chat applications. This model outperforms many open-source and closed chat models on common industry benchmarks, supporting multiple languages including English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
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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.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.