Unify API sampler params into a superclass which should make them easier to manage and inherit generic functions from. Not all frontends expose all sampling parameters due to connections with OAI (that handles sampling themselves with the exception of a few sliders). Add the ability for the user to customize fallback parameters from server-side. In addition, parameters can be forced to a certain value server-side in case the repo automatically sets other sampler values in the background that the user doesn't want. Signed-off-by: kingbri <bdashore3@proton.me> |
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|---|---|---|
| .github/workflows | ||
| backends/exllamav2 | ||
| colab | ||
| common | ||
| docker | ||
| loras | ||
| models | ||
| OAI | ||
| sampler_overrides | ||
| templates | ||
| tests | ||
| .gitignore | ||
| .ruff.toml | ||
| config_sample.yml | ||
| formatting.bat | ||
| formatting.sh | ||
| LICENSE | ||
| main.py | ||
| README.md | ||
| requirements-amd.txt | ||
| requirements-cu118.txt | ||
| requirements-dev.txt | ||
| requirements-nowheel.txt | ||
| requirements.txt | ||
| start.bat | ||
| start.py | ||
| start.sh | ||
TabbyAPI
Note
Need help? Join the Discord Server and get the
Tabbyrole. Please be nice when asking questions.
A FastAPI based application that allows for generating text using an LLM (large language model) using the Exllamav2 backend
Disclaimer
This API is still in the alpha phase. There may be bugs and changes down the line. Please be aware that you might need to reinstall dependencies if needed.
Help Wanted
Please check the issues page for issues that contributors can help on. We appreciate all contributions. Please read the contributions section for more details about issues and pull requests.
If you want to add samplers, add them in the exllamav2 library and then link them to tabbyAPI.
Getting Started
Read the Wiki for more information. It contains user-facing documentation for installation, configuration, sampling, API usage, and so much more.
Supported Model Types
TabbyAPI uses Exllamav2 as a powerful and fast backend for model inference, loading, etc. Therefore, the following types of models are supported:
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Exl2 (Highly recommended)
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GPTQ
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FP16 (using Exllamav2's loader)
Alternative Loaders/Backends
If you want to use a different model type than the ones listed above, here are some alternative backends with their own APIs:
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GGUF + GGML - KoboldCPP
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AWQ - Aphrodite Engine
Contributing
If you have issues with the project:
-
Describe the issues in detail
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If you have a feature request, please indicate it as such.
If you have a Pull Request
- Describe the pull request in detail, what, and why you are changing something
Developers and Permissions
Creators/Developers: