OpenChatKit Could be Your Open-Source Alternative to ChatGPT

It’s no surprise that LLMs like ChatGPT and Bard possess impressive capabilities. However, it’s important to note that they are proprietary software, subject to restrictions in terms of access and usage due to licensing constraints. Consequently, this limitation has generated significant interest within the open-source community, leading to the development of alternative solutions that emphasize freedom, transparency, and community-driven collaboration.

In recent months, the open-source community Together has introduced OpenChatKit, an alternative to ChatGPT, aimed at providing developers with a versatile chatbot solution. OpenChatKit utilizes the GPT-NeoX language model developed by EleutherAI, which consists of an impressive 20 billion parameters. Additionally, the model has been fine-tuned specifically for chat use with 43 million instructions. OpenChatKit's performance surpasses the base model in the industry-standard HELM benchmark, making it a promising tool for various applications.

OpenChatKit Components and Capabilities

OpenChatKit is accompanied by a comprehensive toolkit available on GitHub under the Apache 2.0 license. The toolkit includes several key components designed to enhance customization and performance:

1. Customization recipes: These recipes allow developers to fine-tune the language model for their specific tasks, resulting in higher accuracy and improved performance.
2. Extensible retrieval system: OpenChatKit enables developers to augment bot responses by integrating information from external sources such as document repositories, APIs, or live-updating data during inference time. This capability enhances the bot's ability to provide contextually relevant and accurate responses.
3. Moderation model: The toolkit incorporates a moderation model derived from GPT-JT-6B, fine-tuned to filter and determine which questions the bot should respond to. This feature helps ensure appropriate and safe interactions.
4. Feedback mechanisms: OpenChatKit provides built-in tools for users to provide feedback on the chatbot's responses and contribute new datasets. This iterative feedback loop allows for continuous improvement and refinement of the chatbot's performance.

OpenChatKit's Strengths and Limitations

Developers highlight OpenChatKit's strengths in specific tasks such as summarization, contextual question answering, information extraction, and text classification. It excels in these areas, offering accurate and relevant responses.

However, OpenChatKit's performance is comparatively weaker in tasks that require handling questions without context, coding, and creative writing—areas where ChatGPT has gained popularity. OpenChatKit may occasionally generate erroneous or misleading responses (hallucinations), a challenge that is also encountered in ChatGPT. Furthermore, OpenChatKit sometimes struggles with smoothly transitioning between conversation topics and may occasionally repeat answers.

Fine-Tuning and Specialized Use Cases

OpenChatKit's performance can be significantly improved by fine-tuning it for specific use cases. Together, the developers are actively working on their own chatbots designed for learning, financial advice, and support requests. By tailoring OpenChatKit to these specific domains, they aim to enhance its capabilities and deliver more accurate and contextually appropriate responses.

Comparing OpenChatKit with ChatGPT

In a brief evaluation, OpenChatKit did not demonstrate the same level of eloquence as ChatGPT. This discrepancy can partly be attributed to OpenChatKit's response limit of 256 tokens, which is less than the approximately 500 tokens in ChatGPT. As a result, OpenChatKit generates shorter responses. However, OpenChatKit outperforms ChatGPT in terms of response speed, generating replies at a faster rate. The language transition between different languages does not appear to pose any challenges for OpenChatKit, and it supports formatting options like lists and tables.

The Role of User Feedback in Improvement

Together recognizes the importance of user feedback in enhancing OpenChatKit's performance and plan to leverage it for further improvement. Actively involving users in providing feedback and suggestions ensures that OpenChatKit evolves to meet user expectations and becomes increasingly useful across a wide range of applications.

The Future of Decentralized Training for AI Models

The decentralized training approach employed in OpenChatKit, as previously seen with GPT-JT, represents a potential future for large-scale open-source projects. By distributing the computational load required for training across numerous machines instead of relying solely on a central data center, developers can leverage the combined power of multiple systems. This decentralized approach not only accelerates training but also promotes collaboration and accessibility within the open-source community.

Anticipating Future Developments

OpenChatKit is the pioneer among open-source alternatives to ChatGPT. However, it is likely that other similar projects will emerge. Notably, Meta's LLaMa models, which were leaked, boast parameters three times greater than GPT-NeoX-20B. With these advancements, it is only a matter of time before chatbots based on these models enter the scene.

Getting Started with OpenChatKit

Getting started with the program is simple:

1. Head to https://openchatkit.net/#demo in your browser
2. Read through the guidelines and agree to the Terms & Conditions
3. Type your prompt into the chatbox

Now you can test-drive the program and see how you like it compared to other LLMS. 

Summary

In summation, OpenChatKit presents an exciting alternative to ChatGPT. Leveraging the powerful GPT-NeoX language model and extensive fine-tuning for chat-based interactions, OpenChatKit demonstrates promising capabilities in tasks such as summarization, contextual question answering, information extraction, and text classification. While some limitations exist, such as occasional hallucinations and difficulty transitioning between topics, fine-tuning OpenChatKit for specific use cases significantly improves its performance. With the provided toolkit, developers can customize the chatbot to suit their needs, and user feedback plays a crucial role in the continuous refinement of OpenChatKit. As decentralized training becomes an increasingly prominent approach in open-source projects, OpenChatKit sets the stage for further innovations in the field, while also foreshadowing the emergence of more advanced chatbot models in the future.

Author Bio

Julian Melanson is one of the founders of Leap Year Learning. Leap Year Learning is a cutting-edge online school that specializes in teaching creative disciplines and integrating AI tools. We believe that creativity and AI are the keys to a successful future and our courses help equip students with the skills they need to succeed in a continuously evolving world. Our seasoned instructors bring real-world experience to the virtual classroom and our interactive lessons help students reinforce their learning with hands-on activities.

No matter your background, from beginners to experts, hobbyists to professionals, Leap Year Learning is here to bring in the future of creativity, productivity, and learning!