There are many available models to choose from, here's a few recommended list from Telus International. I've used the OpenAI GPT and Llama models. The GPT models is not free, but easy to configured. I will probably use the Open Source for other projects, because it is free to use while still building knowledge in Artificial Intelligence world.
Read on to get a sense of what's out there to choose from, as well as a list of key considerations when deciding on an LLM.
Available large language models
The first step in your generative AI journey is familiarizing yourself with available foundational models.
There are two types of LLMs: proprietary and open source. Proprietary LLMs require a license that may restrict how the LLM can be used, while open-source models can be used and modified by anyone, for any purpose.
With a seemingly endless list of models to choose from, the following provides just a taste of what's available.
- Llama 2: The next generation of Meta's open source LLM, Llama 2 has double the context length of Llama 1. Available in three size versions: 7 billion, 13 billion and 70 billion parameters. Open source.
- Falcon: Developed by the Technology Innovation Institute, Falcon is available in 1.3 billion, 7.5 billion, 40 billion and 180 billion parameter models. These models are multilingual and serve as bases that can be fine-tuned for specific requirements. Open source.
- PaLM 2: Trained on more than 100 languages, Google's LLM is available in four sizes (parameters not specified) that can be fine-tuned to support specific use cases. This next-generation model was developed using a larger range of datasets compared to PaLM and also features model architecture improvements. A technique called compute-optimal scaling, in which model size and the training dataset size are scaled proportionately, resulted in this model being smaller than PaLM, yet more efficient and boasting better overall performance. Open source.
- GPT models: OpenAI's range of models includes GPT-4, GPT-4 Turbo, GPT-3.5, GPT-3 (legacy model) and more, each with different capabilities and price points. Models can be customized with fine-tuning for your specific use case. GPT-3 has more than 175 billion parameters. The number of parameters in GPT-4 is not confirmed, but is estimated to be 1.76 trillion. Proprietary.
- Claude: To reduce the potential of an LLM to be harmful rather than helpful, the developers at Anthropic used a process they call "constitutional training" to guide this LLM to adhere to a "constitution" of desired behavior. This family of models includes Claude, Claude Instant and Claude 2. They were mostly trained in English but are also said to work well in other common languages. Proprietary.
A popular and up-to-the-minute resource is Hugging Face, which has an open-source LLM leaderboard where models are ranked based on general knowledge tests, multitasking capabilities, propensity to hallucinate, ability to make common sense inferences and more.
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