text-to-text-transfer-transformer
gemma_pytorch
| text-to-text-transfer-transformer | gemma_pytorch | |
|---|---|---|
| 29 | 6 | |
| 5,909 | 5,034 | |
| 1.1% | 3.0% | |
| 5.0 | 7.7 | |
| 3 months ago | 16 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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text-to-text-transfer-transformer
- T5: Text-to-Text-Transfer-Transformer
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Gemma: New Open Models
Google released the T5 paper about 5 years ago:
https://arxiv.org/abs/1910.10683
This included full model weights along with a detailed description of the dataset, training process, and ablations that led them to that architecture. T5 was state-of-the-art on many benchmarks when it was released, but it was of course quickly eclipsed by GPT-3.
Following GPT-3, it became much more common for labs to not release full details or model weights. Prior to that, it was common practice from Google (BERT, T5), Meta (BART), OpenAI (GPT1, GPT2) and others to release full training details and model weights.
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[P] Free and Fast LLM Finetuning
[2] - https://arxiv.org/abs/1910.10683
- Free and Fast LLM Finetuning
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[Discussion] Is there a better way than positional encodings in self attention?
T5-style relative encodings https://arxiv.org/abs/1910.10683
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What were the 40 research papers on the list Ilya Sutskever gave John Carmack?
11. T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer" (2020) - https://arxiv.org/abs/1910.10683 (Google Research)
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[P] T5 Implementation in PyTorch
You can find a link to the paper here: https://arxiv.org/abs/1910.10683
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Text-to-Text Transformer (T5-Base Model) Testing For Summarization, Sentiment Classification, and Translation Using Pytorch and Torchtext
The Text-to-Text Transformer is a type of neural network architecture that is particularly well-suited for natural language processing tasks involving the generation of text. It was introduced in the paper "Attention is All You Need" by Vaswani et al. and has since become a popular choice for many NLP tasks, including language translation, summarization, and text generation
- AlphaCode by DeepMind
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[R] LiBai: a large-scale open-source model training toolbox
Found relevant code at https://github.com/google-research/text-to-text-transfer-transformer + all code implementations here
gemma_pytorch
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Getting Started with Gemma Models
Gemma is a family of lightweight, open-source machine learning models developed by Google AI. These models are designed to be accessible and efficient, making AI development more available for a broad range of users. Released on February 21st, 2024, Gemma is built from the same research and technology that was used to create the Gemini models. Amongst the key features, which are being lightweight and open-source, Gemma is also text-based. It excels in tasks like text summarization, question answering, and reasoning.
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Gemma doesn't suck anymore – 8 bug fixes
Here are the missing links:
* Gemma, a family of open models from Google: https://ai.google.dev/gemma
* Unsloth is a tool/method for training models faster (IIUC): https://github.com/unslothai/unsloth
- The official PyTorch implementation of Google's Gemma models
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Gemma: New Open Models
The release page has comparisons to Mistral everywhere: https://ai.google.dev/gemma
What are some alternatives?
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
llama.cpp - LLM inference in C/C++
tortoise-tts - A multi-voice TTS system trained with an emphasis on quality
gemma - Open weights LLM from Google DeepMind.
DeepCreamPy - Decensoring Hentai with Deep Neural Networks
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
dalle-mini - DALL·E Mini - Generate images from a text prompt
gemma.cpp - lightweight, standalone C++ inference engine for Google's Gemma models.
latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models
ai-on-gke
majesty-diffusion - Majesty Diffusion by @Dango233(@Dango233max) and @apolinario (@multimodalart)
nlu - 1 line for thousands of State of The Art NLP models in hundreds of languages The fastest and most accurate way to solve text problems.