diff --git a/CHANGELOG.md b/CHANGELOG.md
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,9 @@
 # Revision history for ollama-haskell
 
+## 0.1.0.1 -- 2024-10-18
+
+* Renaming Lib.hs to OllamaExamples.hs as it was conflicting `Lib.hs` name
+
 ## 0.1.0.0 -- YYYY-mm-dd
 
 * First version. Released on an unsuspecting world.
diff --git a/ollama-haskell.cabal b/ollama-haskell.cabal
--- a/ollama-haskell.cabal
+++ b/ollama-haskell.cabal
@@ -1,6 +1,6 @@
 cabal-version:      3.4
 name:               ollama-haskell
-version:            0.1.0.0
+version:            0.1.0.1
 synopsis:           Ollama Haskell library
 -- description:
 license:            MIT
@@ -40,7 +40,7 @@
                     , Data.Ollama.Pull
                     , Data.Ollama.Push
                     , Data.Ollama.Show
-                    , Lib
+                    , OllamaExamples
     -- other-modules:
     -- other-extensions:
     build-depends:    base ^>=4.18
diff --git a/src/Lib.hs b/src/Lib.hs
deleted file mode 100644
--- a/src/Lib.hs
+++ /dev/null
@@ -1,138 +0,0 @@
-{-# LANGUAGE OverloadedStrings #-}
-{-# LANGUAGE RecordWildCards #-}
-
-module Lib where
-
-import Control.Monad (void)
-import Data.List.NonEmpty (NonEmpty((:|)))
-import Data.Maybe (fromMaybe)
-import Data.Ollama.Chat qualified as Chat
-import Data.Text.IO qualified as T
-import Ollama (GenerateOps(..), Role(..), chat, defaultChatOps, defaultGenerateOps, generate)
-import Ollama qualified
-
-main :: IO ()
-main = do
-  -- Example 1: Streamed Text Generation
-  -- This example demonstrates how to generate text using a model and stream the output directly to the console.
-  -- The `stream` option enables processing of each chunk of the response as it arrives.
-  void $
-    generate
-      defaultGenerateOps
-        { modelName = "llama3.2"
-        , prompt = "what is functional programming?"
-        , stream = Just (T.putStr . Ollama.response_, pure ())
-        }
-
-  -- Example 2: Non-streamed Text Generation
-  -- This example shows how to generate text and handle the complete response.
-  -- The result is either an error message or the generated text.
-  eRes <-
-    generate
-      defaultGenerateOps
-        { modelName = "llama3.2"
-        , prompt = "What is 2+2?"
-        }
-  case eRes of
-    Left e -> putStrLn e
-    Right Ollama.GenerateResponse {..} -> T.putStrLn response_
-
-  -- Example 3: Chat with Streaming
-  -- This example demonstrates setting up a chat session with streaming enabled.
-  -- As messages are received, they are printed to the console.
-  let msg = Ollama.Message User "What is functional programming?" Nothing
-      defaultMsg = Ollama.Message User "" Nothing
-  void $
-    chat
-      defaultChatOps
-        { Chat.chatModelName = "llama3.2"
-        , Chat.messages = msg :| []
-        , Chat.stream =
-            Just (T.putStr . Chat.content . fromMaybe defaultMsg . Chat.message, pure ())
-        }
-
-  -- Example 4: Non-streamed Chat
-  -- Here, we handle a complete chat response, checking for potential errors.
-  eRes1 <-
-    chat
-      defaultChatOps
-        { Chat.chatModelName = "llama3.2"
-        , Chat.messages = msg :| []
-        }
-  case eRes1 of
-    Left e -> putStrLn e
-    Right r -> do
-      let mMessage = Ollama.message r
-      case mMessage of
-        Nothing -> putStrLn "Something went wrong"
-        Just res -> T.putStrLn $ Ollama.content res
-
-  -- Example 5: Check Model Status (ps)
-  -- This example checks the status of models using the `ps` function.
-  -- It outputs the status or details of the available models.
-  res <- Ollama.ps
-  print res
-
-  -- Example 6: Simple Embedding
-  -- This demonstrates how to request embeddings for a given text using a specific model.
-  void $ Ollama.embedding "llama3.1" "What is 5+2?"
-
-  -- Example 7: Embedding with Options
-  -- This example uses the `embeddingOps` function, allowing for additional configuration like options and streaming.
-  void $ Ollama.embeddingOps "llama3.1" "What is 5+2?" Nothing Nothing
-
-{-
-Scotty example:
-{-# LANGUAGE OverloadedStrings #-}
-
-module Main where
-
-import Web.Scotty
-import Control.Monad.IO.Class (liftIO)
-import Data.Aeson (FromJSON, ToJSON)
-import Data.Text (Text)
-import Data.Text qualified as T
-import Database.SQLite.Simple
-import Ollama (GenerateOps(..), defaultGenerateOps, generate)
-import Data.Maybe (fromRight)
-
-data PromptInput = PromptInput
-  { conversation_id :: Int
-  , prompt :: Text
-  } deriving (Show, Generic)
-
-instance FromJSON PromptInput
-instance ToJSON PromptInput
-
-main :: IO ()
-main = do
-  conn <- open "chat.db"
-  execute_ conn "CREATE TABLE IF NOT EXISTS conversation (convo_id INTEGER PRIMARY KEY, convo_title TEXT)"
-  execute_ conn "CREATE TABLE IF NOT EXISTS chats (chat_id INTEGER PRIMARY KEY, convo_id INTEGER, role TEXT, message TEXT, FOREIGN KEY(convo_id) REFERENCES conversation(convo_id))"
-  
-  scotty 3000 $ do
-    post "/chat" $ do
-      p <- jsonData :: ActionM PromptInput
-      let cId = conversation_id p
-      let trimmedP = T.dropEnd 3 $ T.drop 3 $ prompt p
-      newConvoId <- case cId of
-        -1 -> do
-          liftIO $ execute conn "INSERT INTO conversation (convo_title) VALUES (?)" (Only ("latest title" :: String))
-          [Only convoId] <- liftIO $ query_ conn "SELECT last_insert_rowid()" :: ActionM [Only Int]
-          pure convoId
-        _ -> pure cId
-
-      liftIO $ execute conn "INSERT INTO chats (convo_id, role, message) VALUES (?, 'user', ?)" (newConvoId, trimmedP)
-      
-      stream $ \sendChunk flush -> do
-        eRes <- generate defaultGenerateOps
-                { modelName = "llama3.2"
-                , prompt = prompt p
-                , stream = Just (sendChunk . T.pack, flush)
-                }
-        case eRes of
-            Left e -> return ()
-            Right r -> do
-                let res = response_ r
-                liftIO $ execute conn "INSERT INTO chats (convo_id, role, message) VALUES (?, 'ai', ?)" (newConvoId, res)
--}
diff --git a/src/OllamaExamples.hs b/src/OllamaExamples.hs
new file mode 100644
--- /dev/null
+++ b/src/OllamaExamples.hs
@@ -0,0 +1,138 @@
+{-# LANGUAGE OverloadedStrings #-}
+{-# LANGUAGE RecordWildCards #-}
+
+module OllamaExamples where
+
+import Control.Monad (void)
+import Data.List.NonEmpty (NonEmpty((:|)))
+import Data.Maybe (fromMaybe)
+import Data.Ollama.Chat qualified as Chat
+import Data.Text.IO qualified as T
+import Ollama (GenerateOps(..), Role(..), chat, defaultChatOps, defaultGenerateOps, generate)
+import Ollama qualified
+
+main :: IO ()
+main = do
+  -- Example 1: Streamed Text Generation
+  -- This example demonstrates how to generate text using a model and stream the output directly to the console.
+  -- The `stream` option enables processing of each chunk of the response as it arrives.
+  void $
+    generate
+      defaultGenerateOps
+        { modelName = "llama3.2"
+        , prompt = "what is functional programming?"
+        , stream = Just (T.putStr . Ollama.response_, pure ())
+        }
+
+  -- Example 2: Non-streamed Text Generation
+  -- This example shows how to generate text and handle the complete response.
+  -- The result is either an error message or the generated text.
+  eRes <-
+    generate
+      defaultGenerateOps
+        { modelName = "llama3.2"
+        , prompt = "What is 2+2?"
+        }
+  case eRes of
+    Left e -> putStrLn e
+    Right Ollama.GenerateResponse {..} -> T.putStrLn response_
+
+  -- Example 3: Chat with Streaming
+  -- This example demonstrates setting up a chat session with streaming enabled.
+  -- As messages are received, they are printed to the console.
+  let msg = Ollama.Message User "What is functional programming?" Nothing
+      defaultMsg = Ollama.Message User "" Nothing
+  void $
+    chat
+      defaultChatOps
+        { Chat.chatModelName = "llama3.2"
+        , Chat.messages = msg :| []
+        , Chat.stream =
+            Just (T.putStr . Chat.content . fromMaybe defaultMsg . Chat.message, pure ())
+        }
+
+  -- Example 4: Non-streamed Chat
+  -- Here, we handle a complete chat response, checking for potential errors.
+  eRes1 <-
+    chat
+      defaultChatOps
+        { Chat.chatModelName = "llama3.2"
+        , Chat.messages = msg :| []
+        }
+  case eRes1 of
+    Left e -> putStrLn e
+    Right r -> do
+      let mMessage = Ollama.message r
+      case mMessage of
+        Nothing -> putStrLn "Something went wrong"
+        Just res -> T.putStrLn $ Ollama.content res
+
+  -- Example 5: Check Model Status (ps)
+  -- This example checks the status of models using the `ps` function.
+  -- It outputs the status or details of the available models.
+  res <- Ollama.ps
+  print res
+
+  -- Example 6: Simple Embedding
+  -- This demonstrates how to request embeddings for a given text using a specific model.
+  void $ Ollama.embedding "llama3.1" "What is 5+2?"
+
+  -- Example 7: Embedding with Options
+  -- This example uses the `embeddingOps` function, allowing for additional configuration like options and streaming.
+  void $ Ollama.embeddingOps "llama3.1" "What is 5+2?" Nothing Nothing
+
+{-
+Scotty example:
+{-# LANGUAGE OverloadedStrings #-}
+
+module Main where
+
+import Web.Scotty
+import Control.Monad.IO.Class (liftIO)
+import Data.Aeson (FromJSON, ToJSON)
+import Data.Text (Text)
+import Data.Text qualified as T
+import Database.SQLite.Simple
+import Ollama (GenerateOps(..), defaultGenerateOps, generate)
+import Data.Maybe (fromRight)
+
+data PromptInput = PromptInput
+  { conversation_id :: Int
+  , prompt :: Text
+  } deriving (Show, Generic)
+
+instance FromJSON PromptInput
+instance ToJSON PromptInput
+
+main :: IO ()
+main = do
+  conn <- open "chat.db"
+  execute_ conn "CREATE TABLE IF NOT EXISTS conversation (convo_id INTEGER PRIMARY KEY, convo_title TEXT)"
+  execute_ conn "CREATE TABLE IF NOT EXISTS chats (chat_id INTEGER PRIMARY KEY, convo_id INTEGER, role TEXT, message TEXT, FOREIGN KEY(convo_id) REFERENCES conversation(convo_id))"
+  
+  scotty 3000 $ do
+    post "/chat" $ do
+      p <- jsonData :: ActionM PromptInput
+      let cId = conversation_id p
+      let trimmedP = T.dropEnd 3 $ T.drop 3 $ prompt p
+      newConvoId <- case cId of
+        -1 -> do
+          liftIO $ execute conn "INSERT INTO conversation (convo_title) VALUES (?)" (Only ("latest title" :: String))
+          [Only convoId] <- liftIO $ query_ conn "SELECT last_insert_rowid()" :: ActionM [Only Int]
+          pure convoId
+        _ -> pure cId
+
+      liftIO $ execute conn "INSERT INTO chats (convo_id, role, message) VALUES (?, 'user', ?)" (newConvoId, trimmedP)
+      
+      stream $ \sendChunk flush -> do
+        eRes <- generate defaultGenerateOps
+                { modelName = "llama3.2"
+                , prompt = prompt p
+                , stream = Just (sendChunk . T.pack, flush)
+                }
+        case eRes of
+            Left e -> return ()
+            Right r -> do
+                let res = response_ r
+                liftIO $ execute conn "INSERT INTO chats (convo_id, role, message) VALUES (?, 'ai', ?)" (newConvoId, res)
+-}