# Open-source LLM

The world's first 6-billion-parameter Llama-3 base model, created using the downgrade loop technique from Meta-Llama-3-8B and continuously pretrained on 1 billion English text tokens

OpenELM is a set of open-source and efficient language models. It adopts a hierarchical parameter allocation strategy to improve model accuracy and includes pre-trained and instruction-tuned versions with 270 million to 3 billion parameters.

Llammas is an open-source conversational model adapted for Estonian based on the Llama-2-7B model, achieving cross-lingual knowledge transfer through two-stage training

AgentLM-70B is a large language model obtained by mixed training on the AgentInstruct dataset and the ShareGPT dataset based on the Llama-2-chat model, focusing on enhancing agent capabilities and general language abilities.

EM German is a family of German large language models based on Mistral/LeoLM, fine-tuned on German instruction data, excelling in understanding and generating German text.

The MathCoder series of open-source large language models, specifically designed for general mathematical problem solving, fine-tuned based on Llama-2

KoLlama2 is an open-source project aimed at improving the Korean performance of the English-based large language model Llama2 and bringing a better language interaction experience to Korean users.

OpenChat is a series of open-source language models that have been fine-tuned on diverse and high-quality multi-round dialogue datasets, aiming to achieve high performance with limited data.

A 7B-parameter LLaMa model fine-tuned on the Stanford Alpaca dataset, focusing on open-source instruction tuning