Compare PygmalionAI vs. Stable LM

PygmalionAI is a vibrant community focused on the development of open-source initiatives utilizing EleutherAI's GPT-J 6B and Meta's LLaMA models. Essentially, Pygmalion specializes in crafting AI tailored for engaging conversations and roleplaying. The actively maintained Pygmalion AI model currently features the 7B variant, derived from Meta AI's LLaMA model. Requiring a mere 18GB (or even less) of VRAM, Pygmalion demonstrates superior chat functionality compared to significantly larger language models, all while utilizing relatively limited resources. Our meticulously assembled dataset, rich in high-quality roleplaying content, guarantees that your AI companion will be the perfect partner for roleplaying scenarios. Both the model weights and the training code are entirely open-source, allowing you the freedom to modify and redistribute them for any purpose you desire. Generally, language models, such as Pygmalion, operate on GPUs, as they require swift memory access and substantial processing power to generate coherent text efficiently. As a result, users can expect a smooth and responsive interaction experience when employing Pygmalion's capabilities.

Stable LM represents a significant advancement in the field of language models by leveraging our previous experience with open-source initiatives, particularly in collaboration with EleutherAI, a nonprofit research organization. This journey includes the development of notable models such as GPT-J, GPT-NeoX, and the Pythia suite, all of which were trained on The Pile open-source dataset, while many contemporary open-source models like Cerebras-GPT and Dolly-2 have drawn inspiration from this foundational work. Unlike its predecessors, Stable LM is trained on an innovative dataset that is three times the size of The Pile, encompassing a staggering 1.5 trillion tokens. We plan to share more information about this dataset in the near future. The extensive nature of this dataset enables Stable LM to excel remarkably in both conversational and coding scenarios, despite its relatively modest size of 3 to 7 billion parameters when compared to larger models like GPT-3, which boasts 175 billion parameters. Designed for versatility, Stable LM 3B is a streamlined model that can efficiently function on portable devices such as laptops and handheld gadgets, making us enthusiastic about its practical applications and mobility. Overall, the development of Stable LM marks a pivotal step towards creating more efficient and accessible language models for a wider audience.