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 81224738..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.
@@ -127,7 +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.
+* `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 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
@@ -142,8 +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.
@@ -201,4 +205,4 @@ npm -v # should print `10.9.0`
 ```
 
 ## Contributing
-Contributions are welcome! 
+Contributions are welcome!          
diff --git a/examples/poem-generation/poem.py b/examples/poem-generation/poem.py
index 1b59e562..af1cbae2 100644
--- a/examples/poem-generation/poem.py
+++ b/examples/poem-generation/poem.py
@@ -35,6 +35,27 @@ class Poems(BaseModel):
     poems_list: List[str] = Field(description="A list of poems.")
 
 
+from typing import Any, Dict, List, Union
+
+# Type alias for input/output types
+_DictOrBaseModel = Union[Dict[str, Any], BaseModel]
+
+
+def parse_poems(row: _DictOrBaseModel, poems: _DictOrBaseModel) -> _DictOrBaseModel:
+    """Parse the poems from the LLM response.
+
+    Args:
+        row: The input row containing the topic
+        poems: The structured output from the LLM (Poems model)
+
+    Returns:
+        A list of dictionaries containing the topic and poem
+    """
+    if isinstance(poems, Poems):
+        return [{"topic": row["topic"], "poem": p} for p in poems.poems_list]
+    return []  # Handle edge case where poems is not a Poems instance
+
+
 # We define an `LLM` object that generates poems which gets applied to the topics dataset.
 poet = curator.LLM(
     # The prompt_func takes a row of the dataset as input.
@@ -42,8 +63,8 @@ class Poems(BaseModel):
     prompt_func=lambda row: f"Write two poems about {row['topic']}.",
     model_name="gpt-4o-mini",
     response_format=Poems,
-    # `row` is the input row, and `poems` is the Poems class which is parsed from the structured output from the LLM.
-    parse_func=lambda row, poems: [{"topic": row["topic"], "poem": p} for p in poems.poems_list],
+    # Use the module-level parse function which supports type annotations
+    parse_func=parse_poems,
 )
 
 # We apply the prompter to the topics dataset.
diff --git a/poetry.lock b/poetry.lock
index df3d0c0c..8c24abfb 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
 
 [[package]]
 name = "aiofiles"
@@ -171,13 +171,13 @@ vertex = ["google-auth (>=2,<3)"]
 
 [[package]]
 name = "anyio"
-version = "4.7.0"
+version = "4.8.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "anyio-4.7.0-py3-none-any.whl", hash = "sha256:ea60c3723ab42ba6fff7e8ccb0488c898ec538ff4df1f1d5e642c3601d07e352"},
-    {file = "anyio-4.7.0.tar.gz", hash = "sha256:2f834749c602966b7d456a7567cafcb309f96482b5081d14ac93ccd457f9dd48"},
+    {file = "anyio-4.8.0-py3-none-any.whl", hash = "sha256:b5011f270ab5eb0abf13385f851315585cc37ef330dd88e27ec3d34d651fd47a"},
+    {file = "anyio-4.8.0.tar.gz", hash = "sha256:1d9fe889df5212298c0c0723fa20479d1b94883a2df44bd3897aa91083316f7a"},
 ]
 
 [package.dependencies]
@@ -188,7 +188,7 @@ typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
 
 [package.extras]
 doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
 trio = ["trio (>=0.26.1)"]
 
 [[package]]
@@ -487,6 +487,17 @@ files = [
 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
+[[package]]
+name = "cloudpickle"
+version = "3.1.0"
+description = "Pickler class to extend the standard pickle.Pickler functionality"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "cloudpickle-3.1.0-py3-none-any.whl", hash = "sha256:fe11acda67f61aaaec473e3afe030feb131d78a43461b718185363384f1ba12e"},
+    {file = "cloudpickle-3.1.0.tar.gz", hash = "sha256:81a929b6e3c7335c863c771d673d105f02efdb89dfaba0c90495d1c64796601b"},
+]
+
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -1030,13 +1041,13 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.27.0"
+version = "0.27.1"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "huggingface_hub-0.27.0-py3-none-any.whl", hash = "sha256:8f2e834517f1f1ddf1ecc716f91b120d7333011b7485f665a9a412eacb1a2a81"},
-    {file = "huggingface_hub-0.27.0.tar.gz", hash = "sha256:902cce1a1be5739f5589e560198a65a8edcfd3b830b1666f36e4b961f0454fac"},
+    {file = "huggingface_hub-0.27.1-py3-none-any.whl", hash = "sha256:1c5155ca7d60b60c2e2fc38cbb3ffb7f7c3adf48f824015b219af9061771daec"},
+    {file = "huggingface_hub-0.27.1.tar.gz", hash = "sha256:c004463ca870283909d715d20f066ebd6968c2207dae9393fdffb3c1d4d8f98b"},
 ]
 
 [package.dependencies]
@@ -1112,18 +1123,18 @@ files = [
 
 [[package]]
 name = "instructor"
-version = "1.7.1"
+version = "1.7.2"
 description = "structured outputs for llm"
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
-    {file = "instructor-1.7.1-py3-none-any.whl", hash = "sha256:f95d77c2c0efaf8bf65f3f2acf26e250a4c0614fda6558b354cd03eb14f0cd9b"},
-    {file = "instructor-1.7.1.tar.gz", hash = "sha256:41bd04b97f0709a569b36d749161b55aa2e5d7ce7451d728d4404a76834a5bdd"},
+    {file = "instructor-1.7.2-py3-none-any.whl", hash = "sha256:cb43d27f6d7631c31762b936b2fcb44d2a3f9d8a020430a0f4d3484604ffb95b"},
+    {file = "instructor-1.7.2.tar.gz", hash = "sha256:6c01b2b159766df24865dc81f7bf8457cbda88a3c0bbc810da3467d19b185ed2"},
 ]
 
 [package.dependencies]
 aiohttp = ">=3.9.1,<4.0.0"
-docstring-parser = ">=0.16,<0.17"
+docstring-parser = ">=0.16,<1.0"
 jinja2 = ">=3.1.4,<4.0.0"
 jiter = ">=0.6.1,<0.9"
 openai = ">=1.52.0,<2.0.0"
@@ -1135,15 +1146,13 @@ tenacity = ">=9.0.0,<10.0.0"
 typer = ">=0.9.0,<1.0.0"
 
 [package.extras]
-anthropic = ["anthropic (>=0.36.2,<0.41.0)", "xmltodict (>=0.13,<0.15)"]
-cerebras-cloud-sdk = ["cerebras_cloud_sdk (>=1.5.0,<2.0.0)"]
+anthropic = ["anthropic (==0.42.0)", "xmltodict (>=0.13,<0.15)"]
+cerebras-cloud-sdk = ["cerebras-cloud-sdk (>=1.5.0,<2.0.0)"]
 cohere = ["cohere (>=5.1.8,<6.0.0)"]
-fireworks-ai = ["fireworks-ai (>=0.15.4,<0.16.0)"]
-google-generativeai = ["google-generativeai (>=0.8.2,<0.9.0)"]
+fireworks-ai = ["fireworks-ai (>=0.15.4,<1.0.0)"]
+google-generativeai = ["google-generativeai (>=0.8.2,<1.0.0)", "jsonref (>=1.1.0,<2.0.0)"]
 groq = ["groq (>=0.4.2,<0.14.0)"]
-litellm = ["litellm (>=1.35.31,<2.0.0)"]
-mistralai = ["mistralai (>=1.0.3,<2.0.0)"]
-test-docs = ["anthropic (>=0.36.2,<0.41.0)", "cohere (>=5.1.8,<6.0.0)", "diskcache (>=5.6.3,<6.0.0)", "fastapi (>=0.109.2,<0.116.0)", "groq (>=0.4.2,<0.14.0)", "litellm (>=1.35.31,<2.0.0)", "mistralai (>=1.0.3,<2.0.0)", "pandas (>=2.2.0,<3.0.0)", "pydantic_extra_types (>=2.6.0,<3.0.0)", "redis (>=5.0.1,<6.0.0)", "tabulate (>=0.9.0,<0.10.0)"]
+test-docs = ["diskcache (>=5.6.3,<6.0.0)", "fastapi (>=0.109.2,<0.116.0)", "litellm (>=1.35.31,<2.0.0)", "mistralai (>=1.0.3,<2.0.0)", "pandas (>=2.2.0,<3.0.0)", "pydantic-extra-types (>=2.6.0,<3.0.0)", "redis (>=5.0.1,<6.0.0)", "tabulate (>=0.9.0,<1.0.0)"]
 vertexai = ["google-cloud-aiplatform (>=1.53.0,<2.0.0)", "jsonref (>=1.1.0,<2.0.0)"]
 writer = ["writer-sdk (>=1.2.0,<2.0.0)"]
 
@@ -1940,13 +1949,13 @@ files = [
 
 [[package]]
 name = "openai"
-version = "1.58.1"
+version = "1.59.3"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "openai-1.58.1-py3-none-any.whl", hash = "sha256:e2910b1170a6b7f88ef491ac3a42c387f08bd3db533411f7ee391d166571d63c"},
-    {file = "openai-1.58.1.tar.gz", hash = "sha256:f5a035fd01e141fc743f4b0e02c41ca49be8fab0866d3b67f5f29b4f4d3c0973"},
+    {file = "openai-1.59.3-py3-none-any.whl", hash = "sha256:b041887a0d8f3e70d1fc6ffbb2bf7661c3b9a2f3e806c04bf42f572b9ac7bc37"},
+    {file = "openai-1.59.3.tar.gz", hash = "sha256:7f7fff9d8729968588edf1524e73266e8593bb6cab09298340efb755755bb66f"},
 ]
 
 [package.dependencies]
@@ -2060,93 +2069,89 @@ files = [
 
 [[package]]
 name = "pillow"
-version = "11.0.0"
+version = "11.1.0"
 description = "Python Imaging Library (Fork)"
 optional = false
 python-versions = ">=3.9"
 files = [
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+    {file = "pillow-11.1.0-cp39-cp39-manylinux_2_28_x86_64.whl", hash = "sha256:54251ef02a2309b5eec99d151ebf5c9904b77976c8abdcbce7891ed22df53884"},
+    {file = "pillow-11.1.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:5bb94705aea800051a743aa4874bb1397d4695fb0583ba5e425ee0328757f196"},
+    {file = "pillow-11.1.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:89dbdb3e6e9594d512780a5a1c42801879628b38e3efc7038094430844e271d8"},
+    {file = "pillow-11.1.0-cp39-cp39-win32.whl", hash = "sha256:e5449ca63da169a2e6068dd0e2fcc8d91f9558aba89ff6d02121ca8ab11e79e5"},
+    {file = "pillow-11.1.0-cp39-cp39-win_amd64.whl", hash = "sha256:3362c6ca227e65c54bf71a5f88b3d4565ff1bcbc63ae72c34b07bbb1cc59a43f"},
+    {file = "pillow-11.1.0-cp39-cp39-win_arm64.whl", hash = "sha256:b20be51b37a75cc54c2c55def3fa2c65bb94ba859dde241cd0a4fd302de5ae0a"},
+    {file = "pillow-11.1.0-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:8c730dc3a83e5ac137fbc92dfcfe1511ce3b2b5d7578315b63dbbb76f7f51d90"},
+    {file = "pillow-11.1.0-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:7d33d2fae0e8b170b6a6c57400e077412240f6f5bb2a342cf1ee512a787942bb"},
+    {file = "pillow-11.1.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a8d65b38173085f24bc07f8b6c505cbb7418009fa1a1fcb111b1f4961814a442"},
+    {file = "pillow-11.1.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:015c6e863faa4779251436db398ae75051469f7c903b043a48f078e437656f83"},
+    {file = "pillow-11.1.0-pp310-pypy310_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:d44ff19eea13ae4acdaaab0179fa68c0c6f2f45d66a4d8ec1eda7d6cecbcc15f"},
+    {file = "pillow-11.1.0-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:d3d8da4a631471dfaf94c10c85f5277b1f8e42ac42bade1ac67da4b4a7359b73"},
+    {file = "pillow-11.1.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:4637b88343166249fe8aa94e7c4a62a180c4b3898283bb5d3d2fd5fe10d8e4e0"},
+    {file = "pillow-11.1.0.tar.gz", hash = "sha256:368da70808b36d73b4b390a8ffac11069f8a5c85f29eff1f1b01bcf3ef5b2a20"},
 ]
 
 [package.extras]
 docs = ["furo", "olefile", "sphinx (>=8.1)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinxext-opengraph"]
 fpx = ["olefile"]
 mic = ["olefile"]
-tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
+tests = ["check-manifest", "coverage (>=7.4.2)", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout", "trove-classifiers (>=2024.10.12)"]
 typing = ["typing-extensions"]
 xmp = ["defusedxml"]
 
@@ -2485,13 +2490,13 @@ typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
 
 [[package]]
 name = "pygments"
-version = "2.18.0"
+version = "2.19.1"
 description = "Pygments is a syntax highlighting package written in Python."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pygments-2.18.0-py3-none-any.whl", hash = "sha256:b8e6aca0523f3ab76fee51799c488e38782ac06eafcf95e7ba832985c8e7b13a"},
-    {file = "pygments-2.18.0.tar.gz", hash = "sha256:786ff802f32e91311bff3889f6e9a86e81505fe99f2735bb6d60ae0c5004f199"},
+    {file = "pygments-2.19.1-py3-none-any.whl", hash = "sha256:9ea1544ad55cecf4b8242fab6dd35a93bbce657034b0611ee383099054ab6d8c"},
+    {file = "pygments-2.19.1.tar.gz", hash = "sha256:61c16d2a8576dc0649d9f39e089b5f02bcd27fba10d8fb4dcc28173f7a45151f"},
 ]
 
 [package.extras]
@@ -2499,13 +2504,13 @@ windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
 name = "pyparsing"
-version = "3.2.0"
+version = "3.2.1"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "pyparsing-3.2.0-py3-none-any.whl", hash = "sha256:93d9577b88da0bbea8cc8334ee8b918ed014968fd2ec383e868fb8afb1ccef84"},
-    {file = "pyparsing-3.2.0.tar.gz", hash = "sha256:cbf74e27246d595d9a74b186b810f6fbb86726dbf3b9532efb343f6d7294fe9c"},
+    {file = "pyparsing-3.2.1-py3-none-any.whl", hash = "sha256:506ff4f4386c4cec0590ec19e6302d3aedb992fdc02c761e90416f158dacf8e1"},
+    {file = "pyparsing-3.2.1.tar.gz", hash = "sha256:61980854fd66de3a90028d679a954d5f2623e83144b5afe5ee86f43d762e5f0a"},
 ]
 
 [package.extras]
@@ -3011,23 +3016,23 @@ jeepney = ">=0.6"
 
 [[package]]
 name = "setuptools"
-version = "75.6.0"
+version = "75.7.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "setuptools-75.6.0-py3-none-any.whl", hash = "sha256:ce74b49e8f7110f9bf04883b730f4765b774ef3ef28f722cce7c273d253aaf7d"},
-    {file = "setuptools-75.6.0.tar.gz", hash = "sha256:8199222558df7c86216af4f84c30e9b34a61d8ba19366cc914424cdbd28252f6"},
+    {file = "setuptools-75.7.0-py3-none-any.whl", hash = "sha256:84fb203f278ebcf5cd08f97d3fb96d3fbed4b629d500b29ad60d11e00769b183"},
+    {file = "setuptools-75.7.0.tar.gz", hash = "sha256:886ff7b16cd342f1d1defc16fc98c9ce3fde69e087a4e1983d7ab634e5f41f4f"},
 ]
 
 [package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)", "ruff (>=0.7.0)"]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)", "ruff (>=0.8.0)"]
 core = ["importlib_metadata (>=6)", "jaraco.collections", "jaraco.functools (>=4)", "jaraco.text (>=3.7)", "more_itertools", "more_itertools (>=8.8)", "packaging", "packaging (>=24.2)", "platformdirs (>=4.2.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
 cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
 enabler = ["pytest-enabler (>=2.2)"]
-test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
-type = ["importlib_metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (>=1.12,<1.14)", "pytest-mypy"]
+test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.7.2)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
+type = ["importlib_metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.14.*)", "pytest-mypy"]
 
 [[package]]
 name = "shellingham"
@@ -3551,4 +3556,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "caf8273167768467fed7b1ad34cae5d9994e5aa552aab2bc04b58721d6daf265"
+content-hash = "9e43769b0240c18928f425b16d34fa52bc6b2bcaaffd30c974f132df8cbda617"
diff --git a/pyproject.toml b/pyproject.toml
index 50e2b68f..9cd3129d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -22,6 +22,7 @@ keywords = ["ai", "curator", "bespoke"]
 python = "^3.10"
 pydantic = ">=2.9.2"
 datasets = "^3.0.2"
+cloudpickle = "^3.0.0"
 instructor = "^1.6.3"
 pytest = "^8.3.3"
 pytest-asyncio = "^0.24.0"
diff --git a/src/bespokelabs/curator/llm/llm.py b/src/bespokelabs/curator/llm/llm.py
index b76b13cd..96ed9ca1 100644
--- a/src/bespokelabs/curator/llm/llm.py
+++ b/src/bespokelabs/curator/llm/llm.py
@@ -8,10 +8,11 @@
 from typing import Any, Callable, Dict, Iterable, Optional, Type, TypeVar, Union
 
 from datasets import Dataset
-from datasets.utils._dill import Pickler
 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 (
@@ -62,7 +63,9 @@ def __init__(
             prompt_func: A function that takes a single row
                 and returns either a string (assumed to be a user prompt) or messages list
             parse_func: A function that takes the input row and
-                response object and returns the parsed output
+                response object and returns the parsed output. Can use type annotations
+                (e.g., `def parse_func(row, response: ResponseModel) -> OutputType`)
+                as the function is serialized using cloudpickle for proper type annotation support.
             response_format: A Pydantic model specifying the
                 response format from the LLM.
             backend: The backend to use ("openai" or "litellm"). If None, will be auto-determined
@@ -281,12 +284,17 @@ def __call__(
 
 
 def _get_function_hash(func) -> str:
-    """Get a hash of a function's source code."""
+    """Get a hash of a function's source code.
+
+    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()
-    Pickler(file, recurse=True).dump(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)
diff --git a/tests/test_custom_pickler.py b/tests/test_custom_pickler.py
new file mode 100644
index 00000000..a8a2836b
--- /dev/null
+++ b/tests/test_custom_pickler.py
@@ -0,0 +1,101 @@
+import os
+import pytest
+from io import BytesIO
+from typing import List
+from pydantic import BaseModel
+
+from bespokelabs.curator.utils.custom_pickler import CustomPickler, dumps, loads
+
+
+class TestModel(BaseModel):
+    value: str
+    items: List[int]
+
+
+def test_custom_pickler_type_annotations():
+    """Test CustomPickler handles type annotations correctly."""
+
+    def func(x: TestModel) -> List[int]:
+        return x.items
+
+    # Test pickling and unpickling
+    pickled = dumps(func)
+    unpickled = loads(pickled)
+
+    # Test function still works
+    test_input = TestModel(value="test", items=[1, 2, 3])
+    assert unpickled(test_input) == [1, 2, 3]
+
+
+def test_custom_pickler_path_normalization():
+    """Test CustomPickler normalizes paths in function source."""
+    import tempfile
+    from pathlib import Path
+
+    def create_test_function(pkg_dir: Path):
+        """Create a test function in a specific package directory."""
+        # Create a module file in the package directory
+        module_path = pkg_dir / "test_module.py"
+        with open(module_path, "w") as f:
+            f.write(
+                """
+def func():
+    path = os.path.join("/home", "user", "file.txt")
+    return path
+"""
+            )
+
+        # Import the function from the file
+        import importlib.util
+        from types import ModuleType
+
+        spec = importlib.util.spec_from_file_location("test_module", str(module_path))
+        if spec is None or spec.loader is None:
+            raise ImportError(f"Could not load module from {module_path}")
+
+        module: ModuleType = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(module)
+
+        if not hasattr(module, "func"):
+            raise AttributeError(f"Module {module_path} does not have 'func' attribute")
+
+        return module.func
+
+    # Create two identical functions in different Ray-like package directories
+    with tempfile.TemporaryDirectory() as tmp_dir1, tempfile.TemporaryDirectory() as tmp_dir2:
+        # Simulate Ray package paths
+        ray_pkg_dir1 = Path(tmp_dir1) / "ray" / "ray_pkg_123"
+        ray_pkg_dir2 = Path(tmp_dir2) / "ray" / "ray_pkg_456"
+        ray_pkg_dir1.mkdir(parents=True, exist_ok=True)
+        ray_pkg_dir2.mkdir(parents=True, exist_ok=True)
+
+        # Create and pickle functions from different directories
+        func1 = create_test_function(ray_pkg_dir1)
+        func2 = create_test_function(ray_pkg_dir2)
+
+        # Get hashes for both functions
+        pickled1 = dumps(func1)
+        pickled2 = dumps(func2)
+
+        # Hashes should match despite different Ray package paths
+        assert pickled1 == pickled2, "Function hashes should match regardless of Ray package path"
+
+
+def test_custom_pickler_hybrid_serialization():
+    """Test CustomPickler falls back to cloudpickle for type annotations."""
+
+    def func(x: TestModel, items: List[int]) -> List[int]:
+        return [i for i in items if i > int(x.value)]
+
+    # Test pickling with both type annotations and path-dependent code
+    file = BytesIO()
+    pickler = CustomPickler(file, recurse=True)
+    pickler.dump(func)
+
+    # Test unpickling
+    file.seek(0)
+    unpickled = loads(file.getvalue())
+
+    # Test function works
+    test_input = TestModel(value="2", items=[1, 2, 3])
+    assert unpickled(test_input, [1, 2, 3, 4]) == [3, 4]
diff --git a/tests/test_prompt.py b/tests/test_prompt.py
index f06af47d..bac082fa 100644
--- a/tests/test_prompt.py
+++ b/tests/test_prompt.py
@@ -1,5 +1,5 @@
 import os
-from typing import Optional
+from typing import Any, Dict, Optional, Union
 from unittest.mock import patch, MagicMock
 
 import pytest
@@ -8,6 +8,9 @@
 
 from bespokelabs.curator import LLM
 
+# Type alias for input/output types
+_DictOrBaseModel = Union[Dict[str, Any], BaseModel]
+
 
 class MockResponseFormat(BaseModel):
     """Mock response format for testing."""
@@ -93,8 +96,16 @@ def test_single_completion_batch(prompter: LLM):
     """
 
     # Create a prompter with batch=True
-    def simple_prompt_func():
-        return [
+    def simple_prompt_func(row: _DictOrBaseModel) -> _DictOrBaseModel:
+        """Generate a simple prompt for testing.
+
+        Args:
+            row: The input row (unused in this test)
+
+        Returns:
+            A list of messages for the LLM
+        """
+        messages = [
             {
                 "role": "user",
                 "content": "Write a test message",
@@ -104,7 +115,10 @@ def simple_prompt_func():
                 "content": "You are a helpful assistant.",
             },
         ]
+        return {"messages": messages}
 
+    # Set dummy OpenAI API key for testing
+    os.environ["OPENAI_API_KEY"] = "test-key"
     batch_prompter = LLM(
         model_name="gpt-4o-mini",
         prompt_func=simple_prompt_func,
@@ -142,8 +156,16 @@ def test_single_completion_no_batch(prompter: LLM):
     """
 
     # Create a prompter without batch parameter
-    def simple_prompt_func():
-        return [
+    def simple_prompt_func(row: _DictOrBaseModel) -> _DictOrBaseModel:
+        """Generate a simple prompt for testing.
+
+        Args:
+            row: The input row (unused in this test)
+
+        Returns:
+            A list of messages for the LLM
+        """
+        messages = [
             {
                 "role": "user",
                 "content": "Write a test message",
@@ -153,7 +175,10 @@ def simple_prompt_func():
                 "content": "You are a helpful assistant.",
             },
         ]
+        return {"messages": messages}
 
+    # Set dummy OpenAI API key for testing
+    os.environ["OPENAI_API_KEY"] = "test-key"
     non_batch_prompter = LLM(
         model_name="gpt-4o-mini",
         prompt_func=simple_prompt_func,