pytorch-sentiment-analysis
Tutorials on getting started with PyTorch and TorchText for sentiment analysis. (by bentrevett)
Basic-UI-for-GPT-J-6B-with-low-vram
A repository to run gpt-j-6b on low vram machines (4.2 gb minimum vram for 2000 token context, 3.5 gb for 1000 token context). Model loading takes 12gb free ram. (by arrmansa)
| pytorch-sentiment-analysis | Basic-UI-for-GPT-J-6B-with-low-vram | |
|---|---|---|
| 2 | 4 | |
| 4,571 | 113 | |
| 0.5% | -0.9% | |
| 4.0 | 0.0 | |
| over 1 year ago | about 4 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | Apache License 2.0 |
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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.
pytorch-sentiment-analysis
Posts with mentions or reviews of pytorch-sentiment-analysis. We have used some of these posts to build our list of alternatives and similar projects.
- Ich habe einen Bot gebastelt für Ovalwichs
z.B. https://github.com/bentrevett/pytorch-sentiment-analysis
- German language sentiment classification - NLP Deep Learning
Basic-UI-for-GPT-J-6B-with-low-vram
Posts with mentions or reviews of Basic-UI-for-GPT-J-6B-with-low-vram. We have used some of these posts to build our list of alternatives and similar projects.
- 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?
What are some alternatives?
When comparing pytorch-sentiment-analysis and Basic-UI-for-GPT-J-6B-with-low-vram you can also consider the following projects:
spark-nlp - State of the Art Natural Language Processing
clip-italian - CLIP (Contrastive Language–Image Pre-training) for Italian
Time-Series-Forecasting-Using-LSTM - Time-Series Forecasting on Stock Prices using LSTM
gpt-neo_dungeon - Colab notebooks to run a basic AI Dungeon clone using gpt-neo-2.7B
afinn - AFINN sentiment analysis in Python
Behavior-Sequence-Transformer-Pytorch - This is a pytorch implementation for the BST model from Alibaba https://arxiv.org/pdf/1905.06874.pdf
pytorch-sentiment-analysis vs spark-nlp Basic-UI-for-GPT-J-6B-with-low-vram vs clip-italian pytorch-sentiment-analysis vs Time-Series-Forecasting-Using-LSTM Basic-UI-for-GPT-J-6B-with-low-vram vs gpt-neo_dungeon pytorch-sentiment-analysis vs afinn Basic-UI-for-GPT-J-6B-with-low-vram vs Behavior-Sequence-Transformer-Pytorch