SillyTilly/Meta-Llama-3.1-70B-Instruct
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. This model utilizes an optimized transformer architecture with Grouped-Query Attention and a 128k token context length, trained on over 15 trillion tokens. It is specifically optimized for multilingual dialogue use cases, supporting English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai, and excels in general reasoning, code generation, and tool use benchmarks.
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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 features an optimized transformer architecture with Grouped-Query Attention and a substantial 128k token context length. The model was trained on over 15 trillion tokens, with a data cutoff of December 2023, and fine-tuned using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) for helpfulness and safety.
Key Capabilities
- Multilingual Support: Optimized for 8 languages including English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai, with potential for fine-tuning in others.
- Extended Context Window: Features a 128k token context length, enabling processing of longer inputs.
- Enhanced Performance: Demonstrates strong performance across various benchmarks, including MMLU (83.6%), HumanEval (80.5% pass@1), and MATH (68.0% final_em).
- Tool Use: Shows significant improvements in tool use benchmarks like API-Bank (90.0%) and Nexus (56.7%).
Good For
- Commercial and Research Use: Intended for a wide range of applications in both commercial and academic settings.
- Assistant-like Chat: Instruction-tuned for effective and safe conversational AI.
- Code Generation: Strong performance in coding tasks, including HumanEval and MBPP.
- Multilingual Applications: Ideal for developing applications requiring robust performance across its supported languages.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.