What is Open Source LLM?
There are basically two types of LLMs which are proprietary LLM and open source LLM. Proprietary LLM (Large Language Models) are the ones which are being owned by big tech companies used for their own research and commercial purposes. On other hand Open source large language models are free to use for the public and can be used by anyone for research, development, modify or for any other purposes.
Also, Open source large language models are the kind of a generative AI which are being used to produce many results in the form of text, images or even videos nowadays by training these large language models with highly enormous amount of data (generally in the billions of bytes). And that is why these are called Large Language Models. And being open source, these AI models are researchable, modifiable and usable by everyone else that makes them more transparent and hence architecture and overall process can be learned and understood by everyone else.
Also read: What is Large Language Models(LLM) & how do they work?
Why to use open source LLM?
As we already discussed that open source LLMs are the more transparent as compared with the proprietary large language models. That means the open source large language models’ algorithms and their architecture as well as the training that which are being used to develop them can be known and understood by the researchers, students and developers.
Another very big advantage of using open source LLM is that it allows fine tuning that means through pre-trained LLM model one can add or modify the features of the respective LLM model as per the ease and use cases involved. Not only this but through fine tuning you can also train your model using your own data sets. This offers variety of customization options with the set of data that are being used to develop the respective AI model. In short you can fine tune your large language models with your own data.
Additionally, while using an open source model the community contribution becomes a very big factor and advantage in open source LLM. And so open source LLM can be benefited from many community providers & contributors that can be further used for experiments and other use cases in LLM which is why it has made so many organizations to use open source LLMs. Some very well known organizations includes NASA, IBM and also there are other healthcare companies using open source LLM for treatment optimizations.
Top 4 most powerful LLM models
Llama 2:
Llama 2 is family of Large language models released by Meta in a partnership with Microsoft. And this is said to be as one of the largest models which has more than 70 billion parameters and a contacts length of more than 4,000 tokens and though it is not as much powerful as GPT 4 or PALM 2 but its killer feature is that it’s been released with a commercial license. You can literally download the model and start using that night now or even play around them on Huggingface.
Also if you have an App with less than 700 million monthly active users then you can even self-host the model and use it commercially that gives you almost gpt 4 capabilities at a fraction of a cost as compared with the openAI API.
Vicuna:
Vicuna is another very popular open source LLM model which has been trained on over 13 billions parameters. Vicuna is an open source project which and a result of the collaboration between researchers from UC Berkeley, CMU, Stanford, & UC San Diego. The best part of Vicuna is that it supports fine tuning which means a regular improvements are being implemented to the model and also since Vicuna uses ShareGPT to gather training data and it has been trained on over 70k user shared chatgpt conversations via Sharegpt.
Bloom:
Bloom is one of the largest open source yet multilingual language model much like GPT3 band it was developed from the researchers from UC Berkeley, Stanford University, and the Allen Institute for Artificial Intelligence. Bloom is able to generate texts from 46 different languages and some of them includes Spanish, French and Arabic etc. Bloom has been developed from over 176 billion parameters and has been trained on a training data with more than 366 billion tokens from March 2022 to July 2022 and it was released for the public on July 12, 2022.
FinGPT:
FinGPT is an open source initiative by a group known as AI for financial foundation and they have provided us basically two things via their projects. First, they have given us how they’ve collected data have made it available. Secondly, they’ve made developed couple of models including FinGPT. So, basically FinGPT is an open source for finance. FinGPT basically works on its own 4 layers which includes Data Source layer, Data engineering layer, LLM layer and Application layer. Among all of these 4 layers, application layer is the one where you actually execute the code and get the results. Also, FinGPT has released its own paper which you explore here on what exactly FinGPT is all about.
Common risks associated with open source LLMs:
Although large language models’ results/outputs often sounds fluent and authoritative but this may not be the same case every time. Sometimes their results could be wrong too if the respective LLM is being trained with some incorrect, manipulated or biased data from any misunderstanding context. This could result into the hallucination of the output of an open source LLM and can even cause in the spread of the misinformation among the people using it. Bias generally happens from the source of data is not very diverse or representative. Also, if not trained well in the good contextualized manner then LLM can even lead to reveal the confidentiality of the data for any entity.
Sometimes LLM can raise security concerns that may include leaking of PII, and not only this but also nowadays LLMs can be misused in the activities like phishing and other online malicious tasks. But since we are currently in the beginning phase of LLM then it is important to mitigate the risks from the beginning.