Basic-UI-for-GPT-J-6B-with-low-vram
clip-italian
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| Basic-UI-for-GPT-J-6B-with-low-vram | clip-italian | |
|---|---|---|
| 4 | 1 | |
| 113 | 171 | |
| - | 2.3% | |
| 0.0 | 2.0 | |
| over 2 years ago | 12 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | - |
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Basic-UI-for-GPT-J-6B-with-low-vram
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How to run this service with a local GPU?
You need a lot of VRAM to run the AI models, scaling somewhat with the amount of parameters a model uses. The most advanced model Pygmalion has is 6 billion parameters, which requires a minimum of 16GB of VRAM to run locally at decent speeds. There are methods of running 6b locally on low VRAM machines as listed here: https://github.com/arrmansa/Basic-UI-for-GPT-J-6B-with-low-vram but even then, the generations would be excruciatingly slow, and the lowest VRAM card used with this method has 6GB of VRAM.
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Tesla M40 and GPT-J-6B
While waiting however I came across https://github.com/arrmansa/Basic-UI-for-GPT-J-6B-with-low-vram which allows you to use some of system memory to run the model. I was able to get a version working with 2.7B on my 2060 6GB with KoboldAI. The github above has an error that prevents it from working (https://github.com/arrmansa/Basic-UI-for-GPT-J-6B-with-low-vram/issues/1), but other than that it works.
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How is any of this even possible?
Just to add to this, there is a low VRAM version of GPT-J here (suggest 16GB RAM + 8GB GPU).
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GPT-J 6B locally on my computer
I found this yesterday, is it somehow possible to use this with KoboldAI to run GPT-J on weaker graphics cards?
clip-italian
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[N][P] We built the Italian CLIP 🇮🇹
The demo and paper links are coming out soon! Github: https://github.com/clip-italian/clip-italian Twitter: https://twitter.com/peppeatta/status/1419593282682773507
What are some alternatives?
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clip-retrieval - Easily compute clip embeddings and build a clip retrieval system with them
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Transformer-MM-Explainability - [ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.
Behavior-Sequence-Transformer-Pytorch - This is a pytorch implementation for the BST model from Alibaba https://arxiv.org/pdf/1905.06874.pdf
TargetCLIP - [ECCV 2022] Official PyTorch implementation of the paper Image-Based CLIP-Guided Essence Transfer.
pytorch-sentiment-analysis - Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
browser-ml-inference - Edge Inference in Browser with Transformer NLP model
nn - 🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
pytorch-generative - Easy generative modeling in PyTorch.
Eleya - Artificial Intelligence That Generate Novel Biomedical Text