diff --git a/.python-version b/.python-version
new file mode 100644
index 00000000..d4b278f0
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.11.7
diff --git a/README.md b/README.md
index 23621c67..41e3c76f 100644
--- a/README.md
+++ b/README.md
@@ -70,7 +70,7 @@ print(poems)
 ```
 Note that retries and caching are enabled by default.
 So now if you run the same prompt again, you will get the same response, pretty much instantly.
-You can delete the cache at `~/.cache/curator`.
+You can delete the cache at the path specified by `os.path.join(os.path.expanduser("~"), ".cache", "curator")`.
 
 #### Use LiteLLM backend for calling other models
 You can use the [LiteLLM](https://docs.litellm.ai/docs/providers) backend for calling other models.
@@ -127,10 +127,11 @@ poet = curator.LLM(
 Here:
 * `prompt_func` takes a row of the dataset as input and returns the prompt for the LLM.
 * `response_format` is the structured output class we defined above.
-* `parse_func` takes the input (`row`) and the structured output (`poems`) and converts it to a list of dictionaries. This is so that we can easily convert the output to a HuggingFace Dataset object. For best practices with type annotations:
+* `parse_func` takes the input (`row`) and the structured output (`poems`) and converts it to a list of dictionaries. This is so that we can easily convert the output to a HuggingFace Dataset object. For best practices:
   * Define `parse_func` as a module-level function rather than a lambda to ensure proper serialization
   * Use the `_DictOrBaseModel` type alias for input/output types: `def parse_func(row: _DictOrBaseModel, response: _DictOrBaseModel) -> _DictOrBaseModel`
-  * Type annotations are now fully supported thanks to cloudpickle serialization
+  * Type annotations are fully supported through our CustomPickler implementation
+  * Function hashing is path-independent, ensuring consistent caching across different environments (e.g., Ray clusters)
 
 Now we can apply the `LLM` object to the dataset, which reads very pythonic.
 ```python
@@ -145,8 +146,8 @@ print(poem.to_pandas())
 ```
 Note that `topics` can be created with `curator.LLM` as well,
 and we can scale this up to create tens of thousands of diverse poems.
-You can see a more detailed example in the [examples/poem.py](https://github.com/bespokelabsai/curator/blob/mahesh/update_doc/examples/poem.py) file,
-and other examples in the [examples](https://github.com/bespokelabsai/curator/blob/mahesh/update_doc/examples) directory.
+You can see a more detailed example in the [examples/poem-generation/poem.py](https://github.com/bespokelabsai/curator/blob/main/examples/poem-generation/poem.py) file,
+and other examples in the [examples](https://github.com/bespokelabsai/curator/blob/main/examples) directory.
 
 See the [docs](https://docs.bespokelabs.ai/) for more details as well as 
 for troubleshooting information.
@@ -204,4 +205,4 @@ npm -v # should print `10.9.0`
 ```
 
 ## Contributing
-Contributions are welcome!       
+Contributions are welcome!          
diff --git a/src/bespokelabs/curator/llm/llm.py b/src/bespokelabs/curator/llm/llm.py
index 147d90a4..96ed9ca1 100644
--- a/src/bespokelabs/curator/llm/llm.py
+++ b/src/bespokelabs/curator/llm/llm.py
@@ -7,11 +7,12 @@
 from io import BytesIO
 from typing import Any, Callable, Dict, Iterable, Optional, Type, TypeVar, Union
 
-import cloudpickle
 from datasets import Dataset
 from pydantic import BaseModel
 from xxhash import xxh64
 
+from bespokelabs.curator.utils.custom_pickler import CustomPickler
+
 from bespokelabs.curator.db import MetadataDB
 from bespokelabs.curator.llm.prompt_formatter import PromptFormatter
 from bespokelabs.curator.request_processor import (
@@ -285,13 +286,15 @@ def __call__(
 def _get_function_hash(func) -> str:
     """Get a hash of a function's source code.
 
-    Uses cloudpickle to properly handle functions with type annotations and closure variables.
+    Uses CustomPickler to properly handle both:
+    1. Path normalization (from HuggingFace's Pickler)
+    2. Type annotations and closure variables (from cloudpickle)
     """
     if func is None:
         return xxh64("").hexdigest()
 
     file = BytesIO()
-    file.write(cloudpickle.dumps(func))
+    CustomPickler(file, recurse=True).dump(func)
     return xxh64(file.getvalue()).hexdigest()
 
 
diff --git a/src/bespokelabs/curator/utils/custom_pickler.py b/src/bespokelabs/curator/utils/custom_pickler.py
new file mode 100644
index 00000000..f971bf6e
--- /dev/null
+++ b/src/bespokelabs/curator/utils/custom_pickler.py
@@ -0,0 +1,79 @@
+"""Custom Pickler that combines HuggingFace's path normalization with type annotation support.
+
+This module provides a CustomPickler class that extends HuggingFace's Pickler to support
+both path normalization (for consistent function hashing across different environments)
+and type annotations in function signatures.
+"""
+
+import os
+from io import BytesIO
+from typing import Any, Optional, Type, TypeVar, Union
+
+import cloudpickle
+from datasets.utils._dill import Pickler as HFPickler
+
+
+class CustomPickler(HFPickler):
+    """A custom pickler that combines HuggingFace's path normalization with type annotation support.
+
+    This pickler extends HuggingFace's Pickler to:
+    1. Preserve path normalization for consistent function hashing
+    2. Support type annotations in function signatures
+    3. Handle closure variables properly
+    """
+
+    def __init__(self, file: BytesIO, recurse: bool = True):
+        """Initialize the CustomPickler.
+
+        Args:
+            file: The file-like object to pickle to
+            recurse: Whether to recursively handle object attributes
+        """
+        super().__init__(file, recurse=recurse)
+
+    def save(self, obj: Any, save_persistent_id: bool = True) -> None:
+        """Save an object, handling type annotations properly.
+
+        This method attempts to use cloudpickle's type annotation handling while
+        preserving HuggingFace's path normalization logic.
+
+        Args:
+            obj: The object to pickle
+            save_persistent_id: Whether to save persistent IDs
+        """
+        try:
+            # First try HuggingFace's pickler for path normalization
+            super().save(obj, save_persistent_id=save_persistent_id)
+        except Exception as e:
+            if "No default __reduce__ due to non-trivial __cinit__" in str(e):
+                # If HF's pickler fails with type annotation error, use cloudpickle
+                cloudpickle.dump(obj, self._file)
+            else:
+                # Re-raise other exceptions
+                raise
+
+
+def dumps(obj: Any) -> bytes:
+    """Pickle an object to bytes using CustomPickler.
+
+    Args:
+        obj: The object to pickle
+
+    Returns:
+        The pickled object as bytes
+    """
+    file = BytesIO()
+    CustomPickler(file, recurse=True).dump(obj)
+    return file.getvalue()
+
+
+def loads(data: bytes) -> Any:
+    """Unpickle an object from bytes.
+
+    Args:
+        data: The pickled data
+
+    Returns:
+        The unpickled object
+    """
+    return cloudpickle.loads(data)