[Docs] Add notes on ROCm-supported models (#2087)

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Woosuk Kwon 2023-12-13 09:45:34 -08:00 committed by GitHub
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@ -73,6 +73,10 @@ If your model uses one of the above model architectures, you can seamlessly run
Otherwise, please refer to :ref:`Adding a New Model <adding_a_new_model>` for instructions on how to implement support for your model. Otherwise, please refer to :ref:`Adding a New Model <adding_a_new_model>` for instructions on how to implement support for your model.
Alternatively, you can raise an issue on our `GitHub <https://github.com/vllm-project/vllm/issues>`_ project. Alternatively, you can raise an issue on our `GitHub <https://github.com/vllm-project/vllm/issues>`_ project.
.. note::
Currently, the ROCm version of vLLM does not support Mixtral.
Additionally, it only supports Mistral for context lengths up to 4096.
.. tip:: .. tip::
The easiest way to check if your model is supported is to run the program below: The easiest way to check if your model is supported is to run the program below:
@ -84,12 +88,17 @@ Alternatively, you can raise an issue on our `GitHub <https://github.com/vllm-pr
output = llm.generate("Hello, my name is") output = llm.generate("Hello, my name is")
print(output) print(output)
To use model from www.modelscope.cn If vLLM successfully generates text, it indicates that your model is supported.
.. tip::
To use models from `ModelScope <www.modelscope.cn>`_ instead of HuggingFace Hub, set an environment variable:
.. code-block:: shell .. code-block:: shell
$ export VLLM_USE_MODELSCOPE=True $ export VLLM_USE_MODELSCOPE=True
And use with :code:`trust_remote_code=True`.
.. code-block:: python .. code-block:: python
from vllm import LLM from vllm import LLM
@ -97,5 +106,3 @@ Alternatively, you can raise an issue on our `GitHub <https://github.com/vllm-pr
llm = LLM(model=..., revision=..., trust_remote_code=True) # Name or path of your model llm = LLM(model=..., revision=..., trust_remote_code=True) # Name or path of your model
output = llm.generate("Hello, my name is") output = llm.generate("Hello, my name is")
print(output) print(output)
If vLLM successfully generates text, it indicates that your model is supported.