Review of Top Open Source AI Models
In recent years, there has been remarkable progress in the field of Artificial Intelligence, profoundly reshaping our interactions with technology. AI has seamlessly integrated into our daily lives, from chatbots and virtual assistants to automated customer support, becoming an indispensable part of our technological world. While OpenAI’s achievements, especially in the realm of language generation, have been impressive, the closed nature of Chat GPT has generated increasing interest in open-source AI alternatives.
These alternatives not only provide transparency and flexibility but also invite developers and researchers to actively participate in the advancement of AI. They offer a pathway towards not only utilizing AI but actively shaping its evolution.
ColossalChat
https://github.com/binmakeswell/ColossalChat
ColossalChat Leveraging the Colossal-AI project and grounded in the Meta model, LLaMA, ColossalChat marks a significant milestone as the first practical open-source initiative encompassing the entire RLHF (Reinforcement Learning from Human Feedback) process, allowing for the creation of models akin to ChatGPT. In RLHF, language models are incentivized when they provide accurate responses to queries.
ColossalChat, much like ChatGPT, boasts the ability to field questions, compose emails, and even generate code. However, a few distinctions set it apart. Notably, ColossalChat’s knowledge base is limited to information up until 2019, rendering it incapable of engaging in discussions on current events. Furthermore, the accessibility aspect is a game-changer – no account creation is necessary to utilize ColossalChat. Just launch your browser, start typing, and there’s no need for a premium ColossalChat+ subscription, which means no cost or queues for advanced AI features.
Alpaca
https://github.com/tloen/alpaca-lora
Alpaca, an AI language model, leverages the power of LLaMA, which is derived from Meta’s extensive language model architecture. It takes OpenAI’s GPT, specifically the text-davinci-003 version, to refine the 7-billion-parameter LLaMA model. Importantly, it is freely available for academic and research purposes, offering a solution with modest computational demands.
The development journey of Alpaca commenced with the LLaMA 7-billion-parameter model, which underwent pre-training with an astounding 1 trillion tokens. This initial phase involved kickstarting the model with 175 instruction-output pairs crafted by human experts, subsequently using ChatGPT’s API to generate additional pairs based on this foundation. This effort culminated in the accumulation of an impressive dataset of 52,000 sample conversations, which played a pivotal role in further fine-tuning the LLaMA model.
The versatility of LLaMA models is reflected in their various versions, ranging from 7 billion to 65 billion parameters, and Alpaca demonstrates adaptability across these versions. Importantly, it excels in terms of efficiency, delivering faster performance and requiring less memory, making it compatible with consumer-grade hardware. Additionally, the output produced is considerably more compact, measured in megabytes rather than gigabytes. Alpaca extends its utility by enabling the combination of multiple fine-tuned models during runtime, offering a potent and flexible AI solution.
Dolly
https://github.com/databrickslabs/dolly
Dolly, a substantial language model specializing in instruction-following, is the result of intensive training on the Databricks machine learning platform. Derived from the pythia-12b framework, Dolly’s training is grounded in approximately 15,000 finely tuned instruction-response records known as “databricks-dolly-15k.” These records are the brainchild of Databricks employees, encompassing a diverse range of capability domains. These domains include brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization.
It’s important to note that dolly-v2-12b may not be considered a state-of-the-art model, but it stands out due to its unexpectedly high-quality performance in following instructions. This unique ability sets it apart from the foundational model upon which it is built.
Vicuna-13B
https://github.com/vicuna-tools/vicuna-installation-guide
Vicuna, an open-source chatbot, undergoes training by fine-tuning the LLaMA model with user-contributed conversations harvested from ShareGPT. Initial assessments, employing GPT-4 as an evaluator, reveal that Vicuna-13B attains a quality level exceeding 90% when compared to OpenAI’s ChatGPT and Google Bard. Moreover, it demonstrates superior performance over other models, such as LLaMA and Stanford Alpaca, in over 90% of the cases.
The code and model weights, alongside an accessible online demonstration, are made publicly available for non-commercial use.
What sets Vicuna apart is primarily twofold: the extensive context it handles, allowing for 2048 tokens compared to the standard 512, and its data source. While prior models like Alpaca, gpt4all, and Dolly relied on synthetic datasets generated by ChatGPT, Vicuna draws its strength from ShareGPT—a platform where individuals freely shared their most engaging interactions with ChatGPT. This essentially creates a form of crowdsourcing, resulting in a more diverse selection of conversations with extended dialogues, inadvertently enriching the model’s learning experience through reinforcement from human feedback.
Raven RWKV
https://github.com/BlinkDL/ChatRWKV
Raven represents a novel foundational model, presently undergoing training on a considerably more expansive and diverse dataset. This dataset encompasses a remarkable array of samples from over a hundred languages, fostering multilingual capabilities. The training process adopts a partially instruction-driven approach, fine-tuning its responses to instructions and user input.
A distinctive feature of Raven is its inclusion in the ChatRWKV ecosystem, an open-source model similar to ChatGPT but distinctively powered by the RWKV (100% RNN) language model, diverging from the more prevalent transformer-based models. The integration of Recurrent Neural Networks (RNNs) in Raven enables it to deliver levels of quality and scalability that are on par with transformer models. However, it comes with the added advantages of enhanced processing speed and judicious utilization of VRAM resources.
To ensure its proficiency in following instructions, Raven has undergone a meticulous fine-tuning process, drawing from datasets such as Stanford Alpaca and code-alpaca, among others, which collectively enrich its capacity to respond effectively to user directives and interactions.
GPT4ALL
https://github.com/nomic-ai/gpt4all
GPT4ALL emerges as a conversational AI, crafted by the innovative Nomic AI Team, forged from a vast and carefully curated dataset encompassing various forms of assisted interactions, including word problems, code snippets, narratives, visual depictions, and multi-turn dialogues. This rich dataset serves as the foundation for GPT4ALL’s exceptional capabilities.
The underlying model architecture of GPT4ALL draws from the LLaMa framework, while its implementation leverages low-latency machine-learning accelerators, enabling swift inferences, especially on standard CPUs.
With GPT4ALL, you gain access to a comprehensive toolkit, including a Python client, support for both GPU and CPU inference, Typescript bindings, a user-friendly chat interface, and a robust Langchain backend, culminating in a versatile and powerful conversational AI solution.
OpenChatKit
https://github.com/togethercomputer/OpenChatKit
OpenChatKit offers a strong open-source foundation, serving as a versatile platform for creating both specialized and general-purpose models, designed to cater to a multitude of applications. The toolkit comprises finely-tuned language models for responding to instructions, a moderation model for maintaining a safe environment, and an adaptable retrieval system, capable of seamlessly integrating up-to-date responses from custom repositories. Notably, OpenChatKit’s models have been meticulously trained on the OIG-43M training dataset, a collaborative effort involving Together, LAION, and Ontocord.ai.
With OpenChatKit, the possibilities are boundless. It enables the swift and effortless development of chatbots adept at addressing customer inquiries, providing educational support, or even functioning as personal assistants. These chatbots are characterized by their high degree of flexibility and can be precisely tailored to suit your distinct requirements, ensuring a seamless alignment with your specific needs and objectives.
Flan-T5-XXL
https://github.com/google-research/t5x
T5X represents an innovative, modular, and research-oriented framework, meticulously designed to facilitate the high-performance, adaptable, and self-service training, evaluation, and inference of sequence models, with a particular emphasis on language models at diverse scales. This framework, rooted in JAX and Flax, stands as a notable improvement and evolution of the original T5 codebase, which was initially based on Mesh TensorFlow. For a more in-depth understanding of T5X, please refer to the comprehensive T5X Paper.
Notably, Flan-T5-XXL is the outcome of fine-tuning T5 models that have undergone extensive training on a vast array of datasets, presented in the form of instructional guidance. This unique fine-tuning approach has yielded substantial enhancements in performance across various model categories, including PaLM, T5, and U-PaLM. Furthermore, the Flan-T5-XXL model has been subject to additional fine-tuning, encompassing over 1,000 distinct tasks that span multiple languages, further broadening its capabilities and versatility.
Baize
https://github.com/project-baize/baize-chatbot
Highly customizable Open Source ChatGPT alternative, making it a wonderful choice for those who need tailored solutions. Baize exhibits impressive performance in multi-turn dialogues thanks to its guardrails that help mitigate potential risks. It has achieved this through a high-quality multi-turn chat corpus, which was developed by leveraging ChatGPT to facilitate conversations with itself. Baize code source, model, and dataset are released under a non-commercial (research purposes) license.
Open Assistant
https://github.com/LAION-AI/Open-Assistant
Open Assistant embodies the essence of a genuinely open-source initiative, a commitment to granting unrestricted access to cutting-edge, chat-based large language models. Its overarching goal is to instigate a transformative wave of innovation within the realm of language. This vision is brought to life through the empowerment of individuals, offering them the ability to seamlessly engage with third-party systems, access dynamic information, and foster the development of novel applications utilizing the power of language.
Notably, Open Assistant is characterized by its efficiency, as it can operate on a single high-end consumer GPU, making it an accessible resource for a broad spectrum of users. Furthermore, the code, models, and data integral to Open Assistant are generously licensed under open-source licenses, reinforcing its commitment to fostering a collaborative and open environment for the community.
This exemplifies how open-source solutions can pose a genuine challenge to the paid and ad-supported AI chatbots offered by industry giants.
