6 Ways to run a Local LLM (a.k.a. How to Use HuggingFace)

Dec 14, 2023 ·

Local Language Model (LLM) is a powerful tool for natural language processing. It enables developers and data scientists to quickly build and experiment with state-of-the-art language models, without needing to go through the complex process of training a model from scratch.

The key advantage of Local Language Models is that they are low cost and easy to use. This makes them ideal for applications that require frequently changing or updating models. They also require only a small amount of data to get started, so they can be used effectively even if there is limited data available. Additionally, they are built on open source frameworks and promote collaboration between developers and data scientists.

One example of a popular Local Language Model is ELMo. ELMo is an open source framework built by Allen Research Institute and released in 2018. It uses deep bidirectional language models to provide contextualized word representations. ELMo is built on top of TensorFlow and has been used successfully in multiple tasks, such as question answering, machine translation and sentiment analysis.

In addition to ELMo, other popular Local Language Models include BERT (Bidirectional Encoder Representations from Transformers) and GPT-2 (Generative Pre-trained Transformer 2). BERT is a transformer-based language model that was developed by Google in 2018. It has been used in many tasks, such as text classification, question answering, and document summarization. GPT-2 is a large transformer-based language model released by OpenAI in 2019. It has also been used in many tasks, including text generation and article summarization.

Overall, Local Language Models are an effective and efficient way to quickly develop and experiment with language models. Their open source frameworks enable developers and data scientists to collaboratively build, test, and experiment on state-of-the-art models in a low cost and easy-to-use environment.