This is just like `local` using `meta-reference` for everything except
it uses `vllm` for inference.
Docker works, but So far, `conda` is a bit easier to use with the vllm
provider. The default container base image does not include all the
necessary libraries for all vllm features. More cuda dependencies are
necessary.
I started changing this base image used in this template, but it also
required changes to the Dockerfile, so it was getting too involved to
include in the first PR.
Working so far:
* `python -m llama_stack.apis.inference.client localhost 5000 --model Llama3.2-1B-Instruct --stream True`
* `python -m llama_stack.apis.inference.client localhost 5000 --model Llama3.2-1B-Instruct --stream False`
Example:
```
$ python -m llama_stack.apis.inference.client localhost 5000 --model Llama3.2-1B-Instruct --stream False
User>hello world, write me a 2 sentence poem about the moon
Assistant>
The moon glows bright in the midnight sky
A beacon of light,
```
I have only tested these models:
* `Llama3.1-8B-Instruct` - across 4 GPUs (tensor_parallel_size = 4)
* `Llama3.2-1B-Instruct` - on a single GPU (tensor_parallel_size = 1)
* fixing safety inference and safety adapter for new API spec. Pinned the llama_models version to 0.0.24 as the latest version 0.0.35 has the model descriptor name changed. I was getting the missing package error during runtime as well, hence added the dependency to requirements.txt
* support Llama 3.2 models in Together inference adapter and cleanup Together safety adapter
* fixing model names
* adding vision guard to Together safety
We should use Inference APIs to execute Llama Guard instead of directly needing to use HuggingFace modeling related code. The actual inference consideration is handled by Inference.
I got this error message and tried to the run the command presented
and it didn't work. The model needs to be give with `--model-id`
instead of as a positional argument.
Signed-off-by: Russell Bryant <rbryant@redhat.com>
