llama-stack-provider-lmeval 0.1.8

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TrustyAI LM-Eval as and Out-of-Tree Llama Stack Provider

About

This repository implements TrustyAI's LM-Eval as an out-of-tree Llama Stack remote provider.

Use

Prerequsites

• Admin access to an OpenShift cluster with RHOAI installed
• Login to your OpenShift cluster with oc login --token=<TOKEN> --server=<SERVER>
• Installation of uv
• Installation of oc cli tool
• Installation of llama stack cli tool

1. Clone this repository

   git clone https://github.com/trustyai-explainability/llama-stack-provider-lmeval.git
   

2. Set llama-stack-provider-lmeval as your working directory.

   cd llama-stack-provider-lmeval
   

3. Deploy microsoft/Phi-3-mini-4k-instruct on vLLM Serving Runtime

   a. Create a namespace with a name of your choice

   TEST_NS=<NAMESPACE>
   oc create ns $TEST_NS
   oc get ns $TEST_NS
   

   b. Deploy the model via vLLM

   oc apply -k demos/resources/kustomization.yaml
   

4. Before continuing, preform a sanity check to make sure the model was sucessfully deployed

   oc get pods | grep "predictor"
   

   Expected output:

   phi-3-predictor-00002-deployment-794fb6b4b-clhj7   3/3     Running   0          5h55m
   

5. Create and activate a virtual enviornment

   uv venv .llamastack-venv
   

   source .llamastack-venv/bin/activate
   

6. Install the required libraries

   uv pip install -e .
   

7. Define the following ennvironment variables

   export VLLM_URL=https://$(oc get $(oc get ksvc -o name | grep predictor) --template={{.status.url}})/v1/completions
   
   export TRUSTYAI_LM_EVAL_NAMESPACE=$(oc project | cut -d '"' -f2)
   

8. Start the llama stack server in a virtual enviornment

   llama stack run run.yaml --image-type venv
   

   Expected output:

   INFO:     Application startup complete.
   INFO:     Uvicorn running on http://['::', '0.0.0.0']:8321 (Press CTRL+C to quit)
   

9. Navigate to demos/ to run the demo notebooks