StableLM vs ChatGPT language models compared and tested


StableLM vs ChatGPT language models compared and tested

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In the ever-evolving world of artificial intelligence, the latest development to capture attention is StableLM, a language model created by the team at Stability AI. This open-source project, available via GitHub, has been making waves in the AI community, particularly when compared to its counterpart, ChatGPT. The quick StableLM vs ChatGPT comparison offers a little more insight into both.

ChatGPT is a series of large-scale generative models developed by OpenAI. It belongs to the family of models trained using the Transformer architecture, introduced by Vaswani et al. in 2017. Below is a technical overview of ChatGPT, including its architecture, training data, methods, and other details.

StableLM is a significant contribution to the AI community, showcasing a transparent, accessible, and supportive approach to AI development. It aims to democratize AI technology and fosters a broader distribution of the economic benefits of AI. Through its focus on efficiency and adaptability, it promises to offer a scalable alternative to large proprietary models.

StableLM vs ChatGPT

The StableLM model was put to the test by Venelin Valkov using a prompt based on the character Michael Scott from the popular TV show, The Office. The responses generated by StableLM were then compared to those of ChatGPT. Interestingly, while ChatGPT provided more character-specific responses, the StableLM model took about eight seconds to generate a response, which was found to be more generic in nature. This pattern was consistent when the StableLM model was tested on other prompts, further fueling the StableLM vs ChatGPT debate.

StableLM, available in 3 billion and 7 billion parameters, with larger models on the horizon, is a testament to Stability AI’s commitment to making foundational AI technology accessible. The StableLM base models can be freely used and adapted for commercial or research purposes under the terms of the CC BY-SA-4.0 license. This follows the release of Stable Diffusion, an open and scalable image model.

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The versatility of StableLM is evident in its ability to generate text and code, making it a powerful tool for various downstream applications. This new model builds on Stability AI’s experience in open-sourcing language models with EleutherAI, including GPT-J, GPT-NeoX, and the Pythia suite.

One of the standout features of StableLM is its training dataset. Despite its smaller size compared to other models like GPT-3, StableLM delivers high performance in conversational and coding tasks due to the richness of its training dataset. This dataset is three times larger than The Pile, with 1.5 trillion content tokens.

In addition to StableLM, Stability AI is also releasing a set of research models that are fine-tuned instructions, using a combination of five recent open-source datasets for conversational agents. These fine-tuned models are intended for research use only and are released under a noncommercial CC BY-NC-SA 4.0 license.

AI community

Stability AI has built the StableLM language model with a commitment to transparency :

  • Transparent. We open-source our models to promote transparency and foster trust. Researchers can “look under the hood” to verify performance, work on interpretability techniques, identify potential risks, and help develop safeguards. Organizations across the public and private sectors can adapt (“fine-tune”) these open-source models for their own applications without sharing their sensitive data or giving up control of their AI capabilities.

  • Accessible. We design for the edge so everyday users can run our models on local devices. Using these models, developers can build independent applications compatible with widely-available hardware instead of relying on proprietary services from one or two companies. In this way, the economic benefits of AI are shared by a broad community of users and developers. Open, fine-grained access to our models allows the broad research and academic community to develop interpretability and safety techniques beyond what is possible with closed models.

  • Supportive. We build models to support our users, not replace them. We are focused on efficient, specialized, and practical AI performance – not a quest for god-like intelligence. We develop tools that help everyday people and everyday firms use AI to unlock creativity, boost their productivity, and open up new economic opportunities.

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The StableLM vs ChatGPT comparison provides valuable insights into the capabilities and potential of these two AI language models. While each has its strengths and weaknesses, the open-source nature of StableLM and its adaptability for various applications make it a promising contender in the AI landscape.

Source: YouTube

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