diff --git a/docs/docs/getting_started/starter_example_local.md b/docs/docs/getting_started/starter_example_local.md
index 24a0978e58c0a..cd42f4443bea7 100644
--- a/docs/docs/getting_started/starter_example_local.md
+++ b/docs/docs/getting_started/starter_example_local.md
@@ -3,7 +3,7 @@
 !!! tip
     Make sure you've followed the [custom installation](installation.md) steps first.
 
-This is our famous "5 lines of code" starter example with local LLM and embedding models. We will use `BAAI/bge-small-en-v1.5` as our embedding model and `Mistral-7B` served through `Ollama` as our LLM.
+This is our famous "5 lines of code" starter example with local LLM and embedding models. We will use `nomic-embed-text` as our embedding model and `Llama3`, both served through `Ollama`.
 
 ## Download data
 
@@ -17,26 +17,34 @@ Ollama is a tool to help you get set up with LLMs locally (currently supported o
 
 Follow the [README](https://github.com/jmorganca/ollama) to learn how to install it.
 
-To load in a Mistral-7B model just do `ollama pull mistral`
+To download the Llama3 model just do `ollama pull llama3`.
+
+To download the nomic embeddings, just do `ollama pull nomic-embed-text`
 
 **NOTE**: You will need a machine with at least 32GB of RAM.
 
+To import `llama_index.llms.ollama`, you should run `pip install llama-index-llms-ollama`.
+
+To import `llama_index.embeddings.ollama`, you should run `pip install llama-index-embeddings-ollama`.
+
+More integrations are all listed on https://llamahub.ai.
+
 ## Load data and build an index
 
 In the same folder where you created the `data` folder, create a file called `starter.py` file with the following:
 
 ```python
 from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
-from llama_index.core.embeddings import resolve_embed_model
+from llama_index.embeddings.ollama import OllamaEmbedding
 from llama_index.llms.ollama import Ollama
 
 documents = SimpleDirectoryReader("data").load_data()
 
-# bge embedding model
-Settings.embed_model = resolve_embed_model("local:BAAI/bge-small-en-v1.5")
+# nomic embedding model
+Settings.embed_model = OllamaEmbedding(model_name="nomic-embed-text")
 
 # ollama
-Settings.llm = Ollama(model="mistral", request_timeout=30.0)
+Settings.llm = Ollama(model="llama3", request_timeout=360.0)
 
 index = VectorStoreIndex.from_documents(
     documents,
@@ -53,7 +61,7 @@ Your directory structure should look like this:
     └── paul_graham_essay.txt
 </pre>
 
-We use the `BAAI/bge-small-en-v1.5` model through `resolve_embed_model`, which resolves to our HuggingFaceEmbedding class. We also use our `Ollama` LLM wrapper to load in the mistral model.
+We use the `nomic-embed-text` from our `Ollama` embedding wrapper. We also use our `Ollama` LLM wrapper to load in the Llama3 model.
 
 ## Query your data