meta-llama/Llama-3.1-70B

Llama 3.1-70B: Multilingual Instruction-Tuned LLM

Meta's Llama 3.1-70B is a 70 billion parameter instruction-tuned large language model, part of the Llama 3.1 series, designed for multilingual dialogue. This model leverages an optimized transformer architecture with Grouped-Query Attention (GQA) and boasts an extended context length of 128k tokens. It was trained on over 15 trillion tokens of a new mix of publicly available online data, with a knowledge cutoff of December 2023.

Key Capabilities

• Multilingual Performance: Outperforms many open-source and closed chat models on common industry benchmarks across supported languages including English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
• Enhanced Instruction Following: Instruction-tuned using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) for improved alignment with human preferences, leading to significant gains in MMLU, MATH, and tool-use benchmarks compared to Llama 3.
• Code Generation: Demonstrates strong performance in code generation tasks, achieving 80.5 pass@1 on HumanEval and 86.0 pass@1 on MBPP++.
• Tool Use: Shows substantial improvements in tool-use capabilities, with scores like 90.0 on API-Bank and 29.7 on Gorilla Benchmark API Bench.

Good For

• Assistant-like Chat: Optimized for multilingual dialogue and assistant applications.
• Commercial and Research Use: Intended for a wide range of commercial and research applications, including synthetic data generation and model distillation.
• Multilingual Applications: Ideal for applications requiring robust performance in the 8 explicitly supported languages, with potential for fine-tuning in others.