gpt-2
gpt-2 | dalle-2-preview | |
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64 | 61 | |
21,146 | 1,049 | |
1.1% | 0.0% | |
2.5 | 1.8 | |
28 days ago | almost 2 years ago | |
Python | ||
GNU General Public License v3.0 or later | - |
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gpt-2
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What are LLMs? An intro into AI, models, tokens, parameters, weights, quantization and more
Medium models: Roughly between 1B to 10B parameters. This is where Mistral 7B, Phi-3, Gemma from Google DeepMind, and wizardlm2 sit. Fun fact: GPT 2 was a medium sized model, much smaller than its latest versions.
- Sam Altman is still trying to return as OpenAI CEO
- Build Personal ChatGPT Using Your Data
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Are the recent advancements in AI technology primarily driven by recent discoveries or the progress in hardware capabilities and the abundance of available data?
"Our model, called GPT-2 (a successor to GPT), was trained simply to predict the next word in 40GB of Internet text. Due to our concerns about malicious applications of the technology, we are not releasing the trained model. As an experiment in responsible disclosure, we are instead releasing a much smaller model for researchers to experiment with, as well as a technical paper. "
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BING IS NOW THE DEFAULT SEARCH FOR CHATGPT
They did release GPT-2 under the MIT License.
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Don Knuth Plays with ChatGPT
Did you arrive at this certainty through reading something other than what OpenAI has published? The document [0] that describes the training data for GPT-2 makes this assertion hilarious to me.
[0]: https://github.com/openai/gpt-2/blob/master/model_card.md#da...
- Was frustriert euch an der Nutzung oder der Diskussion um KI?
- The AI
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Help with pet project to learn - Running ChatGPT-2 at home
I made a clone of https://github.com/openai/gpt-2 on my local laptop
- По поводу опасности ИИ и предложений остановить разработки на 6 месяцев.
dalle-2-preview
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Microsoft-backed OpenAI to let users customize ChatGPT | Reuters
We believe that many decisions about our defaults and hard bounds should be made collectively, and while practical implementation is a challenge, we aim to include as many perspectives as possible. As a starting point, we’ve sought external input on our technology in the form of red teaming. We also recently began soliciting public input on AI in education (one particularly important context in which our technology is being deployed).
- OpenAI AI not available for Algeria, gotta love Algeria
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The argument against the use of datasets seems ultimately insincere and pointless
From this OpenAI document:
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Dalle-2 is > 1,000x as dollar efficient as hiring a human illustrator.
It's also of note that you can't sell a game using this method, as Dalle-2's terms of service prevent use in commercial projects. It's hard to justify rate of return considering you can only ever give it away for free, and even in that case there are some uncertain legal elements regarding copyright and the images that are used to train the dataset.
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It's pretty obvious where dalle-2 gets some of their training data from! Anyone else had the Getty Images watermark? Prompt was "man in a suit standing in a fountain with his hair on fire."
On their GitHub https://github.com/openai/dalle-2-preview/blob/main/system-card.md I can only see references to v1.
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“Pinterest” for Dalle-2 images and prompts
"b) Exploration of the bolded part of OpenAI's comment "Each generated image includes a signature in the lower right corner, with the goal of indicating when DALL·E 2 helped generate a certain image." (source)." (source link: https://github.com/openai/dalle-2-preview/blob/main/system-c...)
I feel the DALL-E 2 watermark signature could be a seed or something.
- I’m an outsider to digital art and have a couple questions about A.I created art.
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The AI Art Apocalypse
DALL-E's docs for example mention it can output whole copyrighted logos and characters[1] and understands it's possible to generate human faces that are bear the likeness of those in the training data. We've also seen people recently critique Stable Diffusion's output for attempting to recreate artists' signatures that came from the commercial trained data.
That said by a certain point the kinks will be ironed out and likely skirt around such issues by only incorporating/manipulating just enough to be considered fair use and creative transformation.
[1] "The model can generate known entities including trademarked logos and copyrighted characters." https://github.com/openai/dalle-2-preview/blob/main/system-c...
- Trabalhei no projeto Dall-e, me pergunte qualquer coisa (AMA)
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Official Dalle server: Why “furry art” is a banned phrase
Some types of content were purposely excluded from the training dataset(s) (source).
What are some alternatives?
dalle-mini - DALL·E Mini - Generate images from a text prompt
minGPT - A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training
DALL-E - PyTorch package for the discrete VAE used for DALL·E.
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models
gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
sentencepiece - Unsupervised text tokenizer for Neural Network-based text generation.
disco-diffusion
jukebox - Code for the paper "Jukebox: A Generative Model for Music"
glide-text2im - GLIDE: a diffusion-based text-conditional image synthesis model