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Exploring Pygmalion: The New Contender in Language Models

Enthusiasm is building in the OpenAI community for Pygmalion , a cleverly named new language model. While initial responses vary, the community is undeniably eager to delve into its capabilities and quirks.

Pygmalion exhibits some unique characteristics, particularly in role-playing scenarios. It's been found to generate frequent emotive responses, similar to its predecessor, Pygmalion 7B from TavernAI. However, some users argue that it's somewhat less coherent than its cousin, Wizard Vicuna 13B uncensored, as it … click here to read


Running Large Language Models and Uncensored Content

WizardLM-7B-Uncensored is an uncensored large language model. This model aims to provide users with more freedom in generating content without censorship. While the original WizardLM allowed for open discussions, the uncensored version takes it a step further, enabling users to express themselves without limitations.

Users who encountered issues related to NSFW (Not Safe for Work) content can now make use of a bypass prompt to access such content. OpenAI emphasizes responsible use and ethical considerations when … click here to read


MiniGPT-4: Generating Witty and Sarcastic Text with Ease

If you've ever struggled with generating witty and sarcastic text, you're not alone. It can be a challenge to come up with clever quips or humorous responses on the fly. Fortunately, there's a solution: MiniGPT-4.

This language model uses a GPT-3.5 architecture and can generate coherent and relevant text for a variety of natural language processing tasks, including text generation, question answering, and language translation. What sets MiniGPT-4 apart is its smaller size and faster speed, making it a great … click here to read


HuggingFace and the Future of AI Hosting

The other day, I listened to an AI podcast where HuggingFace's Head of Product discussed their partnership with Amazon, which has been in place for years and has recently become closer. As I understand it, Amazon provides all their hosting, storage, and bandwidth via AWS, and part of that partnership is that they receive significant discounts compared to a regular company.

According to the interview, HuggingFace already has many thousands of paying customers, and they're aiming to be the easiest or … click here to read


Panoptic Segmentation: Segment Everything, Everywhere, All At Once

Panoptic Segmentation is a breakthrough technology that has the ability to segment every object with semantics, cover every pixel in the image, and support all compositions of prompts at once. The paper and GitHub repository provide more information on this technology, including a segmentation interface built with a single pre-trained model.

The GitHub repository for this technology, available at https://github.com/UX-Decoder/Segment-Everything-Everywhere-All-At-Once , contains the demo code, pre-trained models, and … click here to read


Exploring the Potential: Diverse Applications of Transformer Models

Users have been employing transformer models for various purposes, from building interactive games to generating content. Here are some insights:

  • OpenAI's GPT is being used as a game master in an infinite adventure game, generating coherent scenarios based on user-provided keywords. This application demonstrates the model's ability to synthesize a vast range of pop culture knowledge into engaging narratives.
  • A Q&A bot is being developed for the Army, employing a combination of … click here to read

Exciting News About StoryWriter Model from MosaicML!

There's plenty of excitement surrounding the StoryWriter model by MosaicML. Although it was pretrained on sequences of 2048 tokens, it can handle up to 65k of context! While there are questions about how the model manages long-range dependencies and the attention score decay, many users are optimistic about its potential.

Not only is the model impressive, but MosaicML's platform has also drawn attention. Despite some concerns about the necessity of format conversions, users are finding MosaicML … click here to read



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