Basic-UI-for-GPT-J-6B-with-low-vram
pytorch-sentiment-analysis
Our great sponsors
| Basic-UI-for-GPT-J-6B-with-low-vram | pytorch-sentiment-analysis | |
|---|---|---|
| 4 | 2 | |
| 113 | 4,225 | |
| - | - | |
| 0.0 | 4.0 | |
| over 2 years ago | about 1 month ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Basic-UI-for-GPT-J-6B-with-low-vram
-
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.
-
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.
-
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).
-
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?
pytorch-sentiment-analysis
-
Ich habe einen Bot gebastelt für Ovalwichs
z.B. https://github.com/bentrevett/pytorch-sentiment-analysis
- German language sentiment classification - NLP Deep Learning
What are some alternatives?
gpt-neo_dungeon - Colab notebooks to run a basic AI Dungeon clone using gpt-neo-2.7B
spark-nlp - State of the Art Natural Language Processing
adaptnlp - An easy to use Natural Language Processing library and framework for predicting, training, fine-tuning, and serving up state-of-the-art NLP models.
Time-Series-Forecasting-Using-LSTM - Time-Series Forecasting on Stock Prices using LSTM
Behavior-Sequence-Transformer-Pytorch - This is a pytorch implementation for the BST model from Alibaba https://arxiv.org/pdf/1905.06874.pdf
clip-italian - CLIP (Contrastive Language–Image Pre-training) for Italian
malaya - Natural Language Toolkit for Malaysian language, https://malaya.readthedocs.io/
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, ... 🧠
afinn - AFINN sentiment analysis in Python
pytorch-generative - Easy generative modeling in PyTorch.
n4m-sentiment - Sentiment Analysis for your MaxMSP patches - made easy.