This is a pretty important requirement. The streaming response type is
an AsyncGenerator while the non-stream one is a single object. So far
this has worked _sometimes_ due to various pre-existing hacks (and in
some cases, just failed.)
Also introduce a gross hack (to cover grosser(?) hack) to ensure
non-stream requests don't send back responses in SSE format. Not sure
which of these hacks is grosser.
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)
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>
