Non-core Model Serving

TGI supports various LLM architectures (see full list here). If you wish to serve a model that is not one of the supported models, TGI will fallback to the transformers implementation of that model. This means you will be unable to use some of the features introduced by TGI, such as tensor-parallel sharding or flash attention. However, you can still get many benefits of TGI, such as continuous batching or streaming outputs.

You can serve these models using the same Docker command-line invocation as with fully supported models 👇

docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id gpt2

If the model you wish to serve is a custom transformers model, and its weights and implementation are available in the Hub, you can still serve the model by passing the --trust-remote-code flag to the docker run command like below 👇

docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id <CUSTOM_MODEL_ID> --trust-remote-code

Finally, if the model is not on Model Database Hub but on your local, you can pass the path to the folder that contains your model like below 👇

# Make sure your model is in the $volume directory
docker run --shm-size 1g -p 8080:80 -v $volume:/data  ghcr.io/huggingface/text-generation-inference:latest --model-id /data/<PATH-TO-FOLDER>

You can refer to transformers docs on custom models for more information.