Remove HFTGI and Flan references

Author: guimou

bootstrap/ic-shared-llm/deployment-hftgi.yaml

@@ -17,9 +17,6 @@ resources:
   # wave 2
   - inference-service-granite-modelcar.yaml
   - inference-service-qwen-modelcar.yaml
-  - pvc-hftgi.yaml
-  - deployment-hftgi.yaml
-  - service-hftgi.yaml
 
 transformers:
   - namespace-transformer.yaml

bootstrap/ic-shared-llm/kustomization.yaml

@@ -30,7 +30,7 @@ Success: Minio is reachable on minio.ic-shared-minio.svc.cluster.local:9000
 Success: Gitea is reachable on gitea.gitea.svc.cluster.local:3000
 Success: Postgres Database is reachable on claimdb.ic-shared-db.svc.cluster.local:5432
 Success: LLM Service is reachable on llm.ic-shared-llm.svc.cluster.local:8000
-Success: LLM Service-FlanT5 is reachable on llm-flant5.ic-shared-llm.svc.cluster.local:3000
+Success: LLM Service-Qwen2.5 is reachable on qwen-predictor.ic-shared-llm.svc.cluster.local:8080
 Success: ModelMesh is reachable on modelmesh-serving.ic-shared-img-det.svc.cluster.local:8033
 Success: Milvus Vector DB is reachable on vectordb-milvus.ic-shared-milvus.svc.cluster.local:19530
 ----

bootstrap/ic-shared-llm/pvc-hftgi.yaml

@@ -3,7 +3,7 @@ include::_attributes.adoc[]
 
 So far, for this {ic-lab}, we have used the model https://huggingface.co/RedHatAI/granite-3.1-8b-instruct[Granite 3.1 8B Instruct,window=_blank]. Although lighter than other models, it is still quite heavy and we need a large GPU to run it. Would we get as good results with a smaller model running on a CPU only? Let's try!
 
-In this exercise, we'll pitch our previous model against a much smaller LLM called https://huggingface.co/google/flan-t5-large[flan-t5-large,window=_blank]. We'll compare the results and see if the smaller model is good enough for our use case.
+In this exercise, we'll pitch our previous model against a much smaller LLM called https://huggingface.co/RedHatAI/Qwen2.5-0.5B-quantized.w8a8[Qwen2.5 0.5B Quantized-w8a8,window=_blank]. We'll compare the results and see if the smaller model is good enough for our use case.
 
 From the `parasol-insurance/lab-materials/03` folder, please open the notebook called `03-04-comparing-model-servers.ipynb` and follow the instructions.