Efficient multilingual LLM for diverse NLP tasks with 3B parameters.
Model Name: Llama 3.2 3B Instruct Turbo
Developer/Creator: Meta
Release Date: September 2024
Version: 3.2
Model Type: Text-to-Text Language Model
Llama 3.2 3B Instruct Turbo is a large language model (LLM) optimized for instruction-following tasks, striking a balance between computational efficiency and high-quality performance. It excels in multilingual tasks, offering a lightweight solution without compromising on quality.
Llama 3.2 3B Instruct Turbo is designed for various natural language processing tasks, including:
The model demonstrates strong multilingual capabilities, with benchmark scores available for:
Llama 3.2 3B Instruct Turbo uses an optimized transformer architecture with auto-regressive language modeling. It incorporates:
Overview: Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. The training process involved:
Data Source and Size: A new mix of publicly available online data, with up to 9T tokens used in training.
Knowledge Cutoff: December 2023
Diversity and Bias: The model's strong performance across multiple languages suggests a diverse training dataset.
Llama 3.2 3B Instruct Turbo demonstrates impressive performance across various benchmarks:
Multilingual performance (MMLU benchmark):
Accuracy: Llama 3.2 3B Instruct Turbo shows competitive performance, especially considering its smaller size. For example, it achieves 63.4% on the MMLU benchmark, compared to 69.4% for the larger Llama 3.1 8B model.
Speed: The model demonstrates high performance with an output speed of 131.7 tokens per second, which is faster compared to the average.Robustness: Its strong performance across multiple languages and various tasks indicates good generalization capabilities.
While specific ethical guidelines are not detailed in the search results, the usage terms prohibit:
Llama 3.2 3B Instruct Turbo is released under an open license by Meta. However, users should refer to the official licensing terms for specific usage rights and restrictions.