diff --git a/README.md b/README.md
index b383808..d5509fa 100644
--- a/README.md
+++ b/README.md
@@ -10,18 +10,21 @@ This repository implements LLaMPPL for use with HuggingFace Transformers.
 
 If you just want to try out LLaMPPL, check out our [demo notebook on Colab](https://colab.research.google.com/drive/1uJEC-U8dcwsTWccCDGVexpgXexzZ642n?usp=sharing), which performs a simple constrained generation task using GPT-2. (Larger models may require more RAM or GPU resources than Colab's free version provides.)
 
-To get started on your own machine, clone this repository and run `pip install .` (or `pip install -e .` if you plan to modify the library).
+> [!NOTE]
+> We use [poetry](https://python-poetry.org/) to manage dependencies. If you don't have poetry installed, you can install it with `pip install poetry`.
+
+To get started on your own machine, clone this repository and run `poetry install` to install `hfppl` and its dependencies.
 
 ```
 git clone https://github.com/probcomp/hfppl
 cd hfppl
-pip install .
+poetry install
 ```
 
 Then, try running an example. Note that this will cause the weights for Vicuna-7b-v1.5 to be downloaded.
 
 ```
-python examples/hard_constraints.py
+poetry run python examples/hard_constraints.py
 ```
 
 If everything is working, you should see the model generate political news using words that are at most five letters long (e.g., "Dr. Jill Biden may still be a year away from the White House but she is set to make her first trip to the U.N. today.").
@@ -44,18 +47,18 @@ class MyModel(Model):
         # A stateful context object for the LLM, initialized with the prompt
         self.context = LMContext(lm, prompt)
         self.lm = lm
-        
+
         # The forbidden letter
         self.forbidden_tokens = [i for (i, v) in enumerate(lm.vocab)
                                    if forbidden_letter in v]
-    
+
     # The step method is used to perform a single 'step' of generation.
     # This might be a single token, a single phrase, or any other division.
     # Here, we generate one token at a time.
     async def step(self):
         # Sample a token from the LLM -- automatically extends `self.context`.
         # We use `await` so that LLaMPPL can automatically batch language model calls.
-        token = await self.sample(self.context.next_token(), 
+        token = await self.sample(self.context.next_token(),
                                   proposal=self.proposal())
 
         # Condition on the token not having the forbidden letter
@@ -65,7 +68,7 @@ class MyModel(Model):
         if token.token_id == self.lm.tokenizer.eos_token_id or str(token) in ['.', '!', '?']:
             # Finish generation
             self.finish()
-    
+
     # Helper method to define a custom proposal
     def proposal(self):
         logits = self.context.next_token_logprobs.copy()
diff --git a/docs/getting_started.md b/docs/getting_started.md
index 476bb56..ea9603f 100644
--- a/docs/getting_started.md
+++ b/docs/getting_started.md
@@ -6,18 +6,20 @@ One easy way to try LLaMPPL out is to use a Colab notebook. We have [a demo note
 
 ## Installing LLaMPPL
 
-To get started, clone the `hfppl` repository and install the `hfppl` package:
+To get started, clone the `hfppl` repository and install the `hfppl` package.
 
 ```bash
 git clone https://github.com/probcomp/hfppl
 cd hfppl
-pip install .
+poetry install
 ```
 
+We use [poetry](https://python-poetry.org/) to manage dependencies. If you don't have poetry installed, you can install it with `pip install poetry`.
+
 You can then run an example. The first time you run it, the example may ask to downlaod model weights from the HuggingFace model repository.
 
-```
-python examples/hard_constraints.py
+```bash
+poetry run python examples/hard_constraints.py
 ```
 
 Depending on your available GPU memory, you may wish to edit the example to change parameters such as the batch size, or which HuggingFace model to use. The `hard_constraints.py` example has been run successfully on an NVIDIA L4 GPU (with 24 GB of VRAM) on Google Cloud.
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..6c6731d
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,1327 @@
+# This file is automatically @generated by Poetry 1.7.0 and should not be changed by hand.
+
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+description = "Accelerate"
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+]
+
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+torch = ">=1.10.0"
+
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+testing = ["bitsandbytes", "datasets", "deepspeed", "evaluate", "parameterized", "pytest", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "tqdm", "transformers"]
+
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+    {file = "triton-2.1.0-0-pp37-pypy37_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:143582ca31dd89cd982bd3bf53666bab1c7527d41e185f9e3d8a3051ce1b663b"},
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+    {file = "triton-2.1.0-0-pp39-pypy39_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:81a96d110a738ff63339fc892ded095b31bd0d205e3aace262af8400d40b6fa8"},
+]
+
+[package.dependencies]
+filelock = "*"
+
+[package.extras]
+build = ["cmake (>=3.18)", "lit"]
+tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)"]
+tutorials = ["matplotlib", "pandas", "tabulate"]
+
+[[package]]
+name = "typing-extensions"
+version = "4.9.0"
+description = "Backported and Experimental Type Hints for Python 3.8+"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "typing_extensions-4.9.0-py3-none-any.whl", hash = "sha256:af72aea155e91adfc61c3ae9e0e342dbc0cba726d6cba4b6c72c1f34e47291cd"},
+    {file = "typing_extensions-4.9.0.tar.gz", hash = "sha256:23478f88c37f27d76ac8aee6c905017a143b0b1b886c3c9f66bc2fd94f9f5783"},
+]
+
+[[package]]
+name = "urllib3"
+version = "2.1.0"
+description = "HTTP library with thread-safe connection pooling, file post, and more."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "urllib3-2.1.0-py3-none-any.whl", hash = "sha256:55901e917a5896a349ff771be919f8bd99aff50b79fe58fec595eb37bbc56bb3"},
+    {file = "urllib3-2.1.0.tar.gz", hash = "sha256:df7aa8afb0148fa78488e7899b2c59b5f4ffcfa82e6c54ccb9dd37c1d7b52d54"},
+]
+
+[package.extras]
+brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
+socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
+zstd = ["zstandard (>=0.18.0)"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = "^3.10"
+content-hash = "a8bcc9ac554b57fef4d8c08d10dd66966bd274195674adb7df8ef2c263872c64"
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..6eee5f2
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,32 @@
+[tool.poetry]
+name = "hfppl"
+version = "0.1.0"
+description = "Probabilistic programming with HuggingFace Transformer models."
+authors = [
+    "Alex Lew <alexlew@mit.edu>",
+    "Gabriel Grand <grandg@mit.edu>",
+]
+license = "MIT"
+readme = "README.md"
+packages = [{include = "hfppl"}]
+
+[tool.poetry.dependencies]
+python = "^3.10"
+torch = "^2.1.2"
+numpy = "^1.26.2"
+transformers = "^4.36.2"
+bitsandbytes = "^0.41.3.post2"
+accelerate = "^0.25.0"
+sentencepiece = "^0.1.99"
+scipy = "^1.11.4"
+
+[tool.poetry.group.dev.dependencies]
+pytest = "^7.4.3"
+black = "^23.12.1"
+pre-commit = "^3.6.0"
+jupyterlab = "^4.0.9"
+ipywidgets = "^8.1.1"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
diff --git a/setup.py b/setup.py
deleted file mode 100644
index d6c933f..0000000
--- a/setup.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from setuptools import setup, find_packages
-
-setup(
-    name='hfppl',
-    version='0.1.0',
-    description='Probabilistic programming with HuggingFace Transformer models',
-    url='https://github.com/probcomp/hfppl',
-    author='Alex Lew',
-    author_email='alexlew@mit.edu',
-    license='MIT',
-    packages=find_packages(),
-    install_requires=['torch',
-                      'numpy',
-                      'transformers',
-                      'bitsandbytes',
-                      'accelerate',
-                      'sentencepiece'
-                      ],
-
-    classifiers=[
-        'Programming Language :: Python :: 3.10',
-    ],
-)