gpt-3
gpt-2
gpt-3 | gpt-2 | |
---|---|---|
41 | 64 | |
9,406 | 21,146 | |
- | 1.1% | |
3.5 | 2.5 | |
over 3 years ago | 27 days ago | |
Python | ||
- | GNU General Public License v3.0 or later |
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gpt-3
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GPT4.5 or GPT5 being tested on LMSYS?
>I wasn't talking about "state of the art LLMs," I am aware that commercial offerings are much better trained in Spanish. This was a thought experiment based on comments from people testing GPT-3.5 with Swahili.
A thought experiment from other people comments on another language. So...No. Fabricating failure modes from their constructed ideas about how LLMs work seems to be a frustratingly common occurrence in these kinds of discussions.
>Frustratingly, just few months ago I read a paper describing how LLMs excessively rely on English-language representations of ideas, but now I can't find it.
Most LLMs are trained on English overwhelmingly. GPT-3 had a 92.6% English dataset. https://github.com/openai/gpt-3/blob/master/dataset_statisti...
That the models are as proficient as they are is evidence enough of knowledge transfer clearly happening. https://arxiv.org/abs/2108.13349. If you trained a model on the Catalan tokens GPT-3 was trained on alone, you'd just get a GPT-2 level gibberish model at best.
anyway. These are some interesting papers
How do languages influence each other? Studying cross-lingual data sharing during LLM fine-tuning - https://arxiv.org/pdf/2305.13286
Teaching Llama a New Language Through Cross-Lingual Knowledge Transfer - https://arxiv.org/abs/2404.04042
Multilingual LLMs are Better Cross-lingual In-context Learners with Alignment - https://arxiv.org/abs/2305.05940
It's not like there is perfect transfer but the idea that there's none at all seemed so ridiculous to me (and why i asked the first question). Models would be utterly useless in multilingual settings if that were really the case.
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What are LLMs? An intro into AI, models, tokens, parameters, weights, quantization and more
Large models: Everything above 10B of parameters. This is where Llama 3, Llama 2, Mistral 8x22B, GPT 3, and most likely GPT 4 sit.
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Can ChatGPT improve my L2 grammar?
Are generative AI models useful for learning a language, and if so which languages? Over 90% of ChatGPT's training data was in English. The remaining 10% of data was split unevenly between 100+ languages. This suggests that the quality of the outputs will vary from language to language.
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GPT4 Can’t Ace MIT
I have doubts it was extensively trained on German data. Who knows about GPT4, but GPT3 is ~92% of English and ~1.5% of German, which means it saw more "die, motherfucker, die" than on "die Mutter".
(https://github.com/openai/gpt-3/blob/master/dataset_statisti...)
- Necesito ayuda.
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[R] PaLM 2 Technical Report
Catalan was 0.018 % of GPT-3's training corpus. https://github.com/openai/gpt-3/blob/master/dataset_statistics/languages_by_word_count.csv.
- I'm seriously concerned that if I lost ChatGPT-4 I would be handicapped
- The responses I got from bard after asking why 100 times… he was pissed 😂
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BharatGPT: India's Own ChatGPT
>Certainly it is pleasing that they are not just doing Hindi, but some of these languages must be represented online by a very small corpus of text indeed. I wonder how effectively an LLM can be trained on such a small training set for any given language?
as long as it's not the main language it doesn't really matter. Besides English(92.6%), the biggest language by representation (word count) is taken up by french at 1.8%. Most of the languages GPT-3 knows are sitting at <0.2% representation.
https://github.com/openai/gpt-3/blob/master/dataset_statisti...
Competence in the main language will bleed into the rest.
- GPT-4 gets a B on Scott Aaronson's quantum computing final exam
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.
What are some alternatives?
dalle-mini - DALL·E Mini - Generate images from a text prompt
DALL-E - PyTorch package for the discrete VAE used for DALL·E.
minGPT - A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training
DALLE-mtf - Open-AI's DALL-E for large scale training in mesh-tensorflow.
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
stylegan2-pytorch - Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement
gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.
v-diffusion-pytorch - v objective diffusion inference code for PyTorch.
sentencepiece - Unsupervised text tokenizer for Neural Network-based text generation.
dalle-2-preview
jukebox - Code for the paper "Jukebox: A Generative Model for Music"