Exploring the Realm of Open Source Alternatives to ChatGPT/GPT-4
The demand for ChatGPT and GPT-4-like models has risen immensely following their skyrocketing popularity. As OpenAI does not open-source these models, many institutions, organizations, and enterprises globally are working feverishly to create similar versions. These efforts aim to close the technology gap and offer competitive models that could benefit academia, industry, and various technological applications. Below are some noteworthy projects tackling these challenges through autonomous models and more.
Autonomous Models
ChatYuan
ChatYuan, developed by the team at YuanYu Intelligence, is acclaimed as one of the first functional dialogue models from China, targeting various applications such as writing articles, completing assignments, poetry, and translating between Chinese and English. It also offers insights into specific fields like law. Built on a 700 million parameter T5 model foundation, ChatYuan utilizes supervised fine-tuning via PromptClue. Although it implements the first step of the ChatGPT technical framework, it lacks the reward model training and PPO reinforcement learning phases. More details can be found on their GitHub.
Colossal AI
Colossal AI introduced an open-source implementation of ChatGPT, detailing a comprehensive three-step technical approach reminiscent of GPT’s core strategy. Stage one involves fine-tuning with Huggingface's Trainer function; stage two focuses on reward model training; and stage three constitutes reinforcement learning from human feedback (RLHF). Colossal AI supports models like GPT2, OPT, and BLOOM. For more technical details, visit their GitHub.
ChatGLM
Brought to the scene by the Tsinghua-founded company Zhipu AI, ChatGLM supports both Chinese and English languages with a 6.2 billion parameter model. It optimizes the model architecture for lower deployment and application thresholds, with ongoing development for a larger 130 billion parameter model. It stands out in natural language processing, evidence extraction, and more, aligning well with human values. Technical resources are accessible here.
VisualGLM and ChatGLM2 are noteworthy expansions offering multimodal conversation capabilities and improved performances with longer context support and speed optimizations. Explore these advancements for VisualGLM on GitHub and ChatGLM2 here.
PaLM-rlhf-pytorch
As an ambitious ChatGPT alternative, this project utilizes Google's PaLM large model architecture alongside RLHF techniques, spotlighting its remarkable few-shot learning performance across diverse tasks. More insights are available on their GitHub.
OpenFlamingo
OpenFlamingo serves as a framework for training large multimodal models, inspired by Flamingo from DeepMind, and is positioned as a GPT-4 counterpart. It equips users to deal with complex interactions between text and images. Their GitHub holds comprehensive project details.
Other Models
The document also mentions numerous other projects such as MOSS, mPLUG-Owl, PandaLM, and more. These projects contribute in various ways, from evaluating large model content preferences to facilitating automatic evaluation, enhancing text, speech, and visual model capabilities.
For instance, MOSS by Fudan University, supporting bilingual capabilities, and mPLUG-Owl focusing on multimodal instruction understanding are pushing the envelope in dialogue model technology. Notably, projects like CoDi aim to seamlessly integrate multi-modal inputs and outputs, whereas ImageBind explores cross-modal integration across six modalities.
Explore these groundbreaking projects and find more about their technological feats on their respective GitHub pages.
In summary, the landscape for open-source models mimicking ChatGPT and GPT-4 is rapidly evolving. As ongoing advancements are realized, the gap in AI tech capabilities witnessed between OpenAI and global competitors is gradually diminishing. Each of these initiatives shows promise in revolutionizing AI applications, offering myriad capabilities from creating conversational models to multimodal frameworks that innovate how language models interact with data and users.