diff --git a/python/python/lancedb/embeddings/fine_tuner/README.md b/python/python/lancedb/embeddings/fine_tuner/README.md
index 7cee0cf9..afad9a70 100644
--- a/python/python/lancedb/embeddings/fine_tuner/README.md
+++ b/python/python/lancedb/embeddings/fine_tuner/README.md
@@ -1,3 +1,81 @@
+### Fine-tuning workflow
+The fine-tuning workflow is as follows:
+1. Create a `QADataset` object.
+2. Initialize any embedding function using LanceDB embedding API
+3. Call `finetune` method on the embedding object with the `QADataset` object as an argument.
+4. Evaluate the fine-tuned model using the `evaluate` method in the embedding API.
+
+# End-to-End Examples
+The following is an example of how to fine-tune an embedding model using the LanceDB embedding API.
+
+## Example 1: Fine-tuning from a synthetic dataset
+
+```python
+import os
+import pandas as pd
+
+from lancedb.embeddings.fine_tuner.llm import Openai
+from lancedb.embeddings.fine_tuner.dataset import QADataset, TextChunk
+from lancedb.pydantic import LanceModel, Vector
+from llama_index.core import SimpleDirectoryReader
+from llama_index.core.node_parser import SentenceSplitter
+from llama_index.core.schema import MetadataMode
+from lancedb.embeddings import get_registry
+
+dataset = "Uber10KDataset2021"
+lance_dataset_dir = dataset + "_lance"
+valset_dir = dataset + "_lance_val"
+finetuned_model_path = "./model_finetuned"
+
+# 1. Create a QADataset object. See all datasets on llama-index here: https://github.com/run-llama/llama_index/tree/main/llama-datasets
+
+if not os.path.exists(f"./data/{dataset}"):
+    os.system(
+        f"llamaindex-cli download-llamadataset {dataset} --download-dir ./data/{dataset}"
+    )
+docs = SimpleDirectoryReader(input_dir=f"./data/{dataset}/source_files").load_data()
+
+parser = SentenceSplitter()
+nodes = parser.get_nodes_from_documents(docs)
+# convert Llama-index TextNode to TextChunk
+chunks = [TextChunk.from_llama_index_node(node) for node in nodes]
+llm = Openai()
+
+if os.path.exists(lance_dataset_dir):
+    trainset = QADataset.load(lance_dataset_dir)
+else:
+    trainset = QADataset.from_llm(chunks, llm, num_questions_per_chunk=2)
+    trainset.save(lance_dataset_dir)
+
+# Ideally, we should have a standard dataset for validation, but here we're just generating a synthetic dataset.
+if os.path.exists(valset_dir):
+    valset = QADataset.load(valset_dir)
+else:
+    valset = QADataset.from_llm(chunks, llm, num_questions_per_chunk=4)
+    valset.save(valset_dir)
+
+# 2. Initialize the embedding model
+model = get_registry().get("sentence-transformers").create(name="sentence-transformers/multi-qa-MiniLM-L6-cos-v1")
+
+# 3. Fine-tune the model
+model.finetune(trainset=trainset, path=finetuned_model_path, epochs=4)
+
+# 4. Evaluate the fine-tuned model
+base = get_registry().get("sentence-transformers").create(name="sentence-transformers/multi-qa-MiniLM-L6-cos-v1")
+base_results = base.evaluate(valset, top_k=5)
+
+tuned = get_registry().get("sentence-transformers").create(name=finetuned_model_path)
+tuned_results = tuned.evaluate(valset, top_k=5)
+
+openai = get_registry().get("openai").create(name="text-embedding-3-small")
+openai_results = openai.evaluate(valset, top_k=5)
+
+
+print("openai-embedding-v3 hit-rate  - ", pd.DataFrame(openai_results)["is_hit"].mean())
+print("fine-tuned hit-rate  - ", pd.DataFrame(tuned_results)["is_hit"].mean())
+print("Base model hite-rate - ", pd.DataFrame(base_results)["is_hit"].mean())
+```
+
 Fine-tuning workflow for embeddings consists for the following parts:
 
 ### QADataset
@@ -66,69 +144,4 @@ class BaseEmbeddingTuner(ABC):
 ```
 
 ### Embedding API finetuning implementation
-Each embedding API needs to implement `finetune` method in order to support fine-tuning. A vanilla evaluation technique has been implemented in the `BaseEmbedding` class that calculates hit_rate @ `top_k`.
-
-### Fine-tuning workflow
-The fine-tuning workflow is as follows:
-1. Create a `QADataset` object.
-2. Initialize any embedding function using LanceDB embedding API
-3. Call `finetune` method on the embedding object with the `QADataset` object as an argument.
-4. Evaluate the fine-tuned model using the `evaluate` method in the embedding API.
-
-# End-to-End Examples
-The following is an example of how to fine-tune an embedding model using the LanceDB embedding API.
-
-## Example 1: Fine-tuning from a synthetic dataset
-
-```python
-import pandas as pd
-
-from lancedb.embeddings.fine_tuner.llm import Openai
-from lancedb.embeddings.fine_tuner.dataset import QADataset, TextChunk
-from lancedb.pydantic import LanceModel, Vector
-from llama_index.core import SimpleDirectoryReader
-from llama_index.core.node_parser import SentenceSplitter
-from llama_index.core.schema import MetadataMode
-from lancedb.embeddings import get_registry
-
-# 1. Create a QADataset object
-url = "uber10k.pdf"
-reader = SimpleDirectoryReader(input_files=url)
-docs = reader.load_data()
-
-parser = SentenceSplitter()
-nodes = parser.get_nodes_from_documents(docs)
-
-if os.path.exists(name):
-    ds = QADataset.load(name)
-else:
-    llm = Openai()
-    
-    # convert Llama-index TextNode to TextChunk
-    chunks = [TextChunk.from_llama_index_node(node) for node in nodes]
-
-    ds = QADataset.from_llm(chunks, llm)
-    ds.save(name)
-
-# 2. Initialize the embedding model
-model = get_registry().get("sentence-transformers").create()
-
-# 3. Fine-tune the model
-model.finetune(trainset=ds, path="model_finetuned", epochs=4)
-
-# 4. Evaluate the fine-tuned model
-base = get_registry().get("sentence-transformers").create()
-tuned = get_registry().get("sentence-transformers").create(name="./model_finetuned_1")
-openai = get_registry().get("openai").create(name="text-embedding-3-large")
-
-
-rs1 = base.evaluate(trainset, path="val_res")
-rs2 = tuned.evaluate(trainset, path="val_res")
-rs3 = openai.evaluate(trainset)
-
-print("openai-embedding-v3 hit-rate  - ", pd.DataFrame(rs3)["is_hit"].mean())
-print("fine-tuned hit-rate  - ", pd.DataFrame(rs2)["is_hit"].mean())
-print("Base model hite-rate - ", pd.DataFrame(rs1)["is_hit"].mean())
-```
-
-
+Each embedding API needs to implement `finetune` method in order to support fine-tuning. A vanilla evaluation technique has been implemented in the `BaseEmbedding` class that calculates hit_rate @ `top_k`.
\ No newline at end of file