Optimized versions of the Llama, using techniques like Quantization-Aware Training with LoRA Adapters and SpinQuant, to reduce model size and memory usage while maintaining accuracy and performance, enabling deployment on resource-constrained devices like mobile phones.

Small and medium-sized vision LLMs (11B and 90B), and lightweight, text-only models (1B and 3B).

Additional experiments of adding multimodal capabilities to Llama3.

A family of multilingual language models ranging from 8B to 405B parameters, trained on a massive dataset of 15T tokens and achieving comparable performance to leading models like GPT-4 on various tasks.

A family of 8B and 70B parameter models trained on 15T tokens with a focus on data quality, demonstrating state-of-the-art performance on various benchmarks, improved reasoning capabilities.

A series of long context LLMs s that support effective context windows of up to 32,768 tokens.

LLaMA 2 based LLM for code.

LLaMA finetuned using Instrustion backtranslation.

Successor of LLaMA. LLaMA 2-Chat is optimized for dialogue use cases.

A LLaMa model fine-tuned on only 1,000 carefully curated prompts and responses, without any reinforcement learning or human preference modeling.

A collection of foundation LLMs by Meta ranging from 7B to 65B parameters, trained using publicly available datasets exclusively.