* docs: update readme * fix: do not exclude peft * Free disk space before docker building * fix: fix docker-compose * fix: dockercompose user to 1000 * fix dockerfile * fix: cachedir ownership |
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| tabby | ||
| tests | ||
| .dockerignore | ||
| .gitattributes | ||
| .gitignore | ||
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README.md
Warning Tabby is still in the alpha phrase
An opensource / on-prem alternative to GitHub Copilot.
Features
- Self-contained, with no need for a DBMS or cloud service
- Web UI for visualizing and configuration models and MLOps.
- OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE).
- Consumer level GPU supports (FP-16 weight loading with various optimization).
Get started
Docker
The easiest way of getting started is using the official docker image:
docker run \
-it --rm \
-v ./data:/data \
-v ./data/hf_cache:/root/.cache/huggingface \
-p 5000:5000 \
-p 8501:8501 \
-p 8080:8080 \
-e MODEL_NAME=TabbyML/J-350M \
tabbyml/tabby
You can then query the server using /v1/completions endpoint:
curl -X POST http://localhost:5000/v1/completions -H 'Content-Type: application/json' --data '{
"prompt": "def binarySearch(arr, left, right, x):\n mid = (left +"
}'
To use the GPU backend (triton) for a faster inference speed, use deployment/docker-compose.yml:
docker-compose up
Note: To use GPUs, you need to install the NVIDIA Container Toolkit. We also recommend using NVIDIA drivers with CUDA version 11.8 or higher.
We also provides an interactive playground in admin panel localhost:8501
Skypilot
See deployment/skypilot/README.md
API documentation
Tabby opens an FastAPI server at localhost:5000, which embeds an OpenAPI documentation of the HTTP API.
Development
Go to development directory.
make dev
or
make dev-python # Turn off triton backend (for non-cuda env developers)

