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Update Fireworks + Togther documentation

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Ashwin Bharambe 2024-11-18 12:52:23 -08:00
parent 1ecaf2cb3c
commit a562668dcd
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llama_stack/templates/tgi/doc_template.md
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@ -22,13 +22,13 @@ The following environment variables can be configured:
Please check the [TGI Getting Started Guide](https://github.com/huggingface/text-generation-inference?tab=readme-ov-file#get-started) to get a TGI endpoint. Here is a sample script to start a TGI server locally via Docker:
```bash
export TGI_INFERENCE_PORT=8080
export INFERENCE_PORT=8080
export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
export CUDA_VISIBLE_DEVICES=0
docker run --rm -it \
-v $HOME/.cache/huggingface:/data \
-p $TGI_INFERENCE_PORT:$TGI_INFERENCE_PORT \
-p $INFERENCE_PORT:$INFERENCE_PORT \
--gpus $CUDA_VISIBLE_DEVICES \
ghcr.io/huggingface/text-generation-inference:2.3.1 \
--dtype bfloat16 \
@ -36,29 +36,29 @@ docker run --rm -it \
--sharded false \
--cuda-memory-fraction 0.7 \
--model-id $INFERENCE_MODEL \
--port $TGI_INFERENCE_PORT
--port $INFERENCE_PORT
```
If you are using Llama Stack Safety / Shield APIs, then you will need to also run another instance of a TGI with a corresponding safety model like `meta-llama/Llama-Guard-3-1B` using a script like:
```bash
export TGI_SAFETY_PORT=8081
export SAFETY_PORT=8081
export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
export CUDA_VISIBLE_DEVICES=1
docker run --rm -it \
-v $HOME/.cache/huggingface:/data \
-p $TGI_SAFETY_PORT:$TGI_SAFETY_PORT \
-p $SAFETY_PORT:$SAFETY_PORT \
--gpus $CUDA_VISIBLE_DEVICES \
ghcr.io/huggingface/text-generation-inference:2.3.1 \
--dtype bfloat16 \
--usage-stats off \
--sharded false \
--model-id $SAFETY_MODEL \
--port $TGI_SAFETY_PORT
--port $SAFETY_PORT
```
## Running Llama Stack with TGI as the inference provider
## Running Llama Stack
Now you are ready to run Llama Stack with TGI as the inference provider. You can do this via Conda (build code) or Docker which has a pre-built image.
@ -69,7 +69,6 @@ This method allows you to get started quickly without having to build the distri
```bash
LLAMA_STACK_PORT=5001
docker run \
--network host \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run.yaml:/root/my-run.yaml \
@ -77,14 +76,13 @@ docker run \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env TGI_URL=http://host.docker.internal:$TGI_INFERENCE_PORT
--env TGI_URL=http://host.docker.internal:$INFERENCE_PORT
```
If you are using Llama Stack Safety / Shield APIs, use:
```bash
docker run \
--network host \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run-with-safety.yaml:/root/my-run.yaml \
@ -92,9 +90,9 @@ docker run \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env TGI_URL=http://host.docker.internal:$TGI_INFERENCE_PORT \
--env TGI_URL=http://host.docker.internal:$INFERENCE_PORT \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env TGI_SAFETY_URL=http://host.docker.internal:$TGI_SAFETY_PORT
--env TGI_SAFETY_URL=http://host.docker.internal:$SAFETY_PORT
```
### Via Conda
@ -106,7 +104,7 @@ llama stack build --template {{ name }} --image-type conda
llama stack run ./run.yaml
--port 5001
--env INFERENCE_MODEL=$INFERENCE_MODEL
--env TGI_URL=http://127.0.0.1:$TGI_INFERENCE_PORT
--env TGI_URL=http://127.0.0.1:$INFERENCE_PORT
```
If you are using Llama Stack Safety / Shield APIs, use:
@ -115,7 +113,7 @@ If you are using Llama Stack Safety / Shield APIs, use:
llama stack run ./run-with-safety.yaml
--port 5001
--env INFERENCE_MODEL=$INFERENCE_MODEL
--env TGI_URL=http://127.0.0.1:$TGI_INFERENCE_PORT
--env TGI_URL=http://127.0.0.1:$INFERENCE_PORT
--env SAFETY_MODEL=$SAFETY_MODEL
--env TGI_SAFETY_URL=http://127.0.0.1:$TGI_SAFETY_PORT
--env TGI_SAFETY_URL=http://127.0.0.1:$SAFETY_PORT
```