Llama 3.3 70B Instruct: Multilingual Dialogue and Tool Use

Meta's Llama 3.3 70B Instruct is a 70 billion parameter instruction-tuned large language model designed for multilingual dialogue and generative tasks. It leverages an optimized transformer architecture with Grouped-Query Attention (GQA) for enhanced inference scalability and a substantial 32,768 token context window. The model was trained on over 15 trillion tokens of publicly available data, with a knowledge cutoff of December 2023, and fine-tuned using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

Key Capabilities

Multilingual Performance: Optimized for dialogue in English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai, outperforming many open-source and closed chat models on common industry benchmarks like MGSM (91.1% EM).
Advanced Reasoning: Demonstrates strong performance in reasoning benchmarks such as MMLU Pro (68.9% macro_avg/acc) and MATH (77.0% sympy_intersection_score).
Code Generation: Achieves high scores on coding tasks, including HumanEval (88.4% pass@1) and MBPP EvalPlus (87.6% pass@1).
Tool Use: Supports multiple tool use formats, enabling integration with external functions and services, with examples provided for transformers library integration.
Scalable Inference: Utilizes Grouped-Query Attention (GQA) for improved inference efficiency.

Good For

Commercial and Research Applications: Intended for a wide range of commercial and research uses, particularly in multilingual contexts.
Assistant-like Chatbots: Instruction-tuned for creating highly capable assistant-like chat applications.
Synthetic Data Generation: Can be used to generate synthetic data for improving other models.
Memory-Efficient Deployment: Supports 8-bit and 4-bit quantization with bitsandbytes for reduced memory footprint during deployment.