Llama vs MPT-7B vs Vicuna-13B

In today’s data-driven and digitally interconnected world, large language models (LLMs) have emerged as a game-changing technology that is reshaping the landscape of business and industry. These AI models have the ability to understand and generate human-like text, allowing them to perform an array of tasks and help users and businesses stay competitive in their markets. Llama, MPT-7B, and Vicuna-13B are some of the models that have emerged as powerful tools in this AI revolution, enabling users to streamline their numerous tasks. In this article, we will compare these three models to help you understand which one is better suited to your needs.

Feature	Llama	MPT-7B	Vicuna-13B
Developer	Meta (formerly Facebook)	MosaicML	Students and faculty members from UC Berkeley, UCSD, CMU, MBZUAI
Model Type	Large language model	Decoder-style transformer model	Chatbot fine-tuned from Llama
Parameters	Various Sizes (7B, 13B, 33B, and 65B parameters)	6.7 billion parameters	13 billion parameters
Training Data	Unlabeled text data	1 trillion tokens of text and code	User-shared conversations
Main Use Cases	Research in AI	Natural language understanding, code-related tasks	Real-world chatbot interactions
Context Length	2048	Up to 65k tokens	2048
Open Source	Yes	Yes	Yes

What is Llama?

Llama is a state-of-the-art foundational large language model released by Meta designed to assist researchers in the field of artificial intelligence. It is trained on a substantial amount of unlabeled text data and is capable of generating text by predicting the next word in a sequence. Llama is designed to be more efficient in terms of computing power and resources compared to larger models, making it more accessible for researchers who may not have access to extensive computational resources.

Llama is available in different sizes, (7B, 13B, 33B, and 65B parameters) to cater to various research needs and use cases. This flexibility allows researchers to choose the model size that best suits their requirements.

What is MPT-7B?

MPT-7B is a decoder-style transformer model with 6.7 billion parameters developed by MosaicML. This model is trained on a dataset consisting of 1 trillion tokens of text and code, which was curated to emphasize English natural language text and provide diversity for various downstream uses. The dataset also incorporates elements from the RedPajama dataset, ensuring that the web crawl and Wikipedia portions of the data are up-to-date with information from 2023. MPT-7B incorporates features like FlashAttention for efficient training and inference and ALiBi for finetuning and extrapolation to long context lengths.

Thus, its extensive and diverse training data, make it a valuable resource for a wide range of natural language understanding and generation tasks, as well as code-related applications.

What is Vicuna-13B?

Vicuna-13B is an open-source chatbot developed by fine-tuning the Llama model and using approximately 70,000 user-shared conversations collected from ShareGPT.com. To ensure data quality, the collected conversations, originally in HTML format, are processed to convert them back to markdown. This conversion step helps standardize the data format, making it more suitable for training machine learning models. Additionally, during this process, conversations that contain inappropriate or low-quality content are filtered out. It has also made enhancements in multi-turn conversations to generate coherent and contextually relevant responses in the context of ongoing dialogues and made memory optimizations to expand the max context length from 512 in Alpaca to 2048.

Its data quality assurance measures, segmentation strategy for lengthy conversations, and memory optimizations make it a powerful tool for generating coherent and context-aware responses in real-world chatbot interactions.