diff --git a/.vscode/cspell.json b/.vscode/cspell.json
index 6f3eae5e..d12ead35 100644
--- a/.vscode/cspell.json
+++ b/.vscode/cspell.json
@@ -51,7 +51,6 @@
         "jaxtyping",
         "kaiming",
         "keepdim",
-        "logit",
         "lognormal",
         "loguniform",
         "loguniformvalues",
@@ -74,7 +73,6 @@
         "neox",
         "nonlinerity",
         "numel",
-        "onebit",
         "openwebtext",
         "optim",
         "penality",
@@ -118,7 +116,6 @@
         "venv",
         "virtualenv",
         "virtualenvs",
-        "wandb",
-        "zoadam"
+        "wandb"
     ]
 }
diff --git a/poetry.lock b/poetry.lock
index f98130e5..ce2b723f 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2,13 +2,13 @@
 
 [[package]]
 name = "accelerate"
-version = "0.26.1"
+version = "0.25.0"
 description = "Accelerate"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "accelerate-0.26.1-py3-none-any.whl", hash = "sha256:04df826b84ac7bad8a0a8ab90e6aeacdecb1ea5a2d744d7e94f6735c29183227"},
-    {file = "accelerate-0.26.1.tar.gz", hash = "sha256:bf63716b6bd9460d87da970cf4d833abb824ca0aa633be36b741e63a1b504f89"},
+    {file = "accelerate-0.25.0-py3-none-any.whl", hash = "sha256:c7bb817eb974bba0ff3ea1ba0f24d55afb86d50e3d4fe98d6922dc69cf2ccff1"},
+    {file = "accelerate-0.25.0.tar.gz", hash = "sha256:ecf55b0ab278a1dac8539dde0d276977aff04683f07ede73eaf02478538576a1"},
 ]
 
 [package.dependencies]
@@ -367,22 +367,19 @@ test-tox-coverage = ["coverage (>=5.5)"]
 
 [[package]]
 name = "beautifulsoup4"
-version = "4.12.3"
+version = "4.12.2"
 description = "Screen-scraping library"
 optional = false
 python-versions = ">=3.6.0"
 files = [
-    {file = "beautifulsoup4-4.12.3-py3-none-any.whl", hash = "sha256:b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed"},
-    {file = "beautifulsoup4-4.12.3.tar.gz", hash = "sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051"},
+    {file = "beautifulsoup4-4.12.2-py3-none-any.whl", hash = "sha256:bd2520ca0d9d7d12694a53d44ac482d181b4ec1888909b035a3dbf40d0f57d4a"},
+    {file = "beautifulsoup4-4.12.2.tar.gz", hash = "sha256:492bbc69dca35d12daac71c4db1bfff0c876c00ef4a2ffacce226d4638eb72da"},
 ]
 
 [package.dependencies]
 soupsieve = ">1.2"
 
 [package.extras]
-cchardet = ["cchardet"]
-chardet = ["chardet"]
-charset-normalizer = ["charset-normalizer"]
 html5lib = ["html5lib"]
 lxml = ["lxml"]
 
@@ -769,41 +766,6 @@ files = [
     {file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"},
 ]
 
-[[package]]
-name = "deepspeed"
-version = "0.12.6"
-description = "DeepSpeed library"
-optional = false
-python-versions = "*"
-files = [
-    {file = "deepspeed-0.12.6.tar.gz", hash = "sha256:69ea07c65ef6414f9cd67746672f1c23b4b629dc14c9177de103ac0c5b2e0ce4"},
-]
-
-[package.dependencies]
-hjson = "*"
-ninja = "*"
-numpy = "*"
-packaging = ">=20.0"
-psutil = "*"
-py-cpuinfo = "*"
-pydantic = "*"
-pynvml = "*"
-torch = "*"
-tqdm = "*"
-
-[package.extras]
-1bit-mpi = ["mpi4py"]
-all = ["accelerate", "autodoc_pydantic", "clang-format (==16.0.2)", "coverage", "deepspeed-kernels", "diffusers", "docutils (<0.18)", "future", "google", "hjson", "importlib-metadata (>=4)", "lm-eval (==0.3.0)", "mpi4py", "mup", "neural-compressor (==2.1.0)", "packaging", "pre-commit (>=2.20.0)", "protobuf", "psutil", "py-cpuinfo", "pydantic (<2.0.0)", "pytest", "pytest-forked", "pytest-randomly", "pytest-xdist", "recommonmark", "sphinx", "sphinx-rtd-theme", "sphinx_rtd_theme", "tabulate", "tensorboard", "torch", "torchvision", "tqdm", "transformers (>=4.32.1)", "transformers[sentencepiece]", "triton (==1.0.0)", "triton (>=2.1.0)", "wandb", "xgboost"]
-autotuning = ["tabulate"]
-autotuning-ml = ["hjson", "tabulate", "xgboost"]
-dev = ["accelerate", "clang-format (==16.0.2)", "coverage", "deepspeed-kernels", "docutils (<0.18)", "future", "importlib-metadata (>=4)", "mup", "pre-commit (>=2.20.0)", "pytest", "pytest-forked", "pytest-randomly", "pytest-xdist", "recommonmark", "sphinx", "sphinx-rtd-theme", "tensorboard", "torchvision", "transformers (>=4.32.1)", "wandb"]
-inf = ["google", "lm-eval (==0.3.0)", "protobuf", "transformers (>=4.32.1)", "transformers[sentencepiece]"]
-readthedocs = ["autodoc_pydantic", "docutils (<0.18)", "hjson", "packaging", "psutil", "py-cpuinfo", "pydantic (<2.0.0)", "recommonmark", "sphinx_rtd_theme", "torch", "tqdm"]
-sd = ["diffusers", "triton (>=2.1.0)"]
-sparse = ["neural-compressor (==2.1.0)"]
-sparse-attn = ["triton (==1.0.0)"]
-triton = ["triton (>=2.1.0)"]
-
 [[package]]
 name = "defusedxml"
 version = "0.7.1"
@@ -1117,46 +1079,35 @@ smmap = ">=3.0.1,<6"
 
 [[package]]
 name = "gitpython"
-version = "3.1.41"
+version = "3.1.40"
 description = "GitPython is a Python library used to interact with Git repositories"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "GitPython-3.1.41-py3-none-any.whl", hash = "sha256:c36b6634d069b3f719610175020a9aed919421c87552185b085e04fbbdb10b7c"},
-    {file = "GitPython-3.1.41.tar.gz", hash = "sha256:ed66e624884f76df22c8e16066d567aaa5a37d5b5fa19db2c6df6f7156db9048"},
+    {file = "GitPython-3.1.40-py3-none-any.whl", hash = "sha256:cf14627d5a8049ffbf49915732e5eddbe8134c3bdb9d476e6182b676fc573f8a"},
+    {file = "GitPython-3.1.40.tar.gz", hash = "sha256:22b126e9ffb671fdd0c129796343a02bf67bf2994b35449ffc9321aa755e18a4"},
 ]
 
 [package.dependencies]
 gitdb = ">=4.0.1,<5"
 
 [package.extras]
-test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "sumtypes"]
+test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest", "pytest-cov", "pytest-instafail", "pytest-subtests", "pytest-sugar"]
 
 [[package]]
 name = "griffe"
-version = "0.39.0"
+version = "0.38.1"
 description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "griffe-0.39.0-py3-none-any.whl", hash = "sha256:b5e2f249d86feaad1d3068b33b1c8c2ecf39cb870bf292f2af3a4311891a4835"},
-    {file = "griffe-0.39.0.tar.gz", hash = "sha256:34461730c975a31c4ffa413bdad841f217a5d1a1c611b95f6110ed0f2b2fc04d"},
+    {file = "griffe-0.38.1-py3-none-any.whl", hash = "sha256:334c79d3b5964ade65c05dfcaf53518c576dedd387aaba5c9fd71212f34f1483"},
+    {file = "griffe-0.38.1.tar.gz", hash = "sha256:bd68d7da7f3d87bc57eb9962b250db123efd9bbcc06c11c1a91b6e583b2a9361"},
 ]
 
 [package.dependencies]
 colorama = ">=0.4"
 
-[[package]]
-name = "hjson"
-version = "3.1.0"
-description = "Hjson, a user interface for JSON."
-optional = false
-python-versions = "*"
-files = [
-    {file = "hjson-3.1.0-py3-none-any.whl", hash = "sha256:65713cdcf13214fb554eb8b4ef803419733f4f5e551047c9b711098ab7186b89"},
-    {file = "hjson-3.1.0.tar.gz", hash = "sha256:55af475a27cf83a7969c808399d7bccdec8fb836a07ddbd574587593b9cdcf75"},
-]
-
 [[package]]
 name = "huggingface-hub"
 version = "0.20.2"
@@ -1213,13 +1164,13 @@ files = [
 
 [[package]]
 name = "ipykernel"
-version = "6.29.0"
+version = "6.28.0"
 description = "IPython Kernel for Jupyter"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "ipykernel-6.29.0-py3-none-any.whl", hash = "sha256:076663ca68492576f051e4af7720d33f34383e655f2be0d544c8b1c9de915b2f"},
-    {file = "ipykernel-6.29.0.tar.gz", hash = "sha256:b5dd3013cab7b330df712891c96cd1ab868c27a7159e606f762015e9bf8ceb3f"},
+    {file = "ipykernel-6.28.0-py3-none-any.whl", hash = "sha256:c6e9a9c63a7f4095c0a22a79f765f079f9ec7be4f2430a898ddea889e8665661"},
+    {file = "ipykernel-6.28.0.tar.gz", hash = "sha256:69c11403d26de69df02225916f916b37ea4b9af417da0a8c827f84328d88e5f3"},
 ]
 
 [package.dependencies]
@@ -1242,17 +1193,17 @@ cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
 pyqt5 = ["pyqt5"]
 pyside6 = ["pyside6"]
-test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (==0.23.2)", "pytest-cov", "pytest-timeout"]
+test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "ipython"
-version = "8.20.0"
+version = "8.19.0"
 description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.10"
 files = [
-    {file = "ipython-8.20.0-py3-none-any.whl", hash = "sha256:bc9716aad6f29f36c449e30821c9dd0c1c1a7b59ddcc26931685b87b4c569619"},
-    {file = "ipython-8.20.0.tar.gz", hash = "sha256:2f21bd3fc1d51550c89ee3944ae04bbc7bc79e129ea0937da6e6c68bfdbf117a"},
+    {file = "ipython-8.19.0-py3-none-any.whl", hash = "sha256:2f55d59370f59d0d2b2212109fe0e6035cfea436b1c0e6150ad2244746272ec5"},
+    {file = "ipython-8.19.0.tar.gz", hash = "sha256:ac4da4ecf0042fb4e0ce57c60430c2db3c719fa8bdf92f8631d6bd8a5785d1f0"},
 ]
 
 [package.dependencies]
@@ -1352,13 +1303,13 @@ testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
 
 [[package]]
 name = "jinja2"
-version = "3.1.3"
+version = "3.1.2"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "Jinja2-3.1.3-py3-none-any.whl", hash = "sha256:7d6d50dd97d52cbc355597bd845fabfbac3f551e1f99619e39a35ce8c370b5fa"},
-    {file = "Jinja2-3.1.3.tar.gz", hash = "sha256:ac8bd6544d4bb2c9792bf3a159e80bba8fda7f07e81bc3aed565432d5925ba90"},
+    {file = "Jinja2-3.1.2-py3-none-any.whl", hash = "sha256:6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61"},
+    {file = "Jinja2-3.1.2.tar.gz", hash = "sha256:31351a702a408a9e7595a8fc6150fc3f43bb6bf7e319770cbc0db9df9437e852"},
 ]
 
 [package.dependencies]
@@ -1394,13 +1345,13 @@ files = [
 
 [[package]]
 name = "jsonschema"
-version = "4.21.0"
+version = "4.20.0"
 description = "An implementation of JSON Schema validation for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jsonschema-4.21.0-py3-none-any.whl", hash = "sha256:70a09719d375c0a2874571b363c8a24be7df8071b80c9aa76bc4551e7297c63c"},
-    {file = "jsonschema-4.21.0.tar.gz", hash = "sha256:3ba18e27f7491ea4a1b22edce00fb820eec968d397feb3f9cb61d5894bb38167"},
+    {file = "jsonschema-4.20.0-py3-none-any.whl", hash = "sha256:ed6231f0429ecf966f5bc8dfef245998220549cbbcf140f913b7464c52c3b6b3"},
+    {file = "jsonschema-4.20.0.tar.gz", hash = "sha256:4f614fd46d8d61258610998997743ec5492a648b33cf478c1ddc23ed4598a5fa"},
 ]
 
 [package.dependencies]
@@ -1503,13 +1454,13 @@ test = ["flaky", "pexpect", "pytest"]
 
 [[package]]
 name = "jupyter-core"
-version = "5.7.1"
+version = "5.7.0"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_core-5.7.1-py3-none-any.whl", hash = "sha256:c65c82126453a723a2804aa52409930434598fd9d35091d63dfb919d2b765bb7"},
-    {file = "jupyter_core-5.7.1.tar.gz", hash = "sha256:de61a9d7fc71240f688b2fb5ab659fbb56979458dc66a71decd098e03c79e218"},
+    {file = "jupyter_core-5.7.0-py3-none-any.whl", hash = "sha256:16eea462f7dad23ba9f86542bdf17f830804e2028eb48d609b6134d91681e983"},
+    {file = "jupyter_core-5.7.0.tar.gz", hash = "sha256:cb8d3ed92144d2463a3c5664fdd686a3f0c1442ea45df8babb1c1a9e6333fe03"},
 ]
 
 [package.dependencies]
@@ -1562,13 +1513,13 @@ jupyter-server = ">=1.1.2"
 
 [[package]]
 name = "jupyter-server"
-version = "2.12.5"
+version = "2.12.2"
 description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_server-2.12.5-py3-none-any.whl", hash = "sha256:184a0f82809a8522777cfb6b760ab6f4b1bb398664c5860a27cec696cb884923"},
-    {file = "jupyter_server-2.12.5.tar.gz", hash = "sha256:0edb626c94baa22809be1323f9770cf1c00a952b17097592e40d03e6a3951689"},
+    {file = "jupyter_server-2.12.2-py3-none-any.whl", hash = "sha256:abcfa33f98a959f908c8733aa2d9fa0101d26941cbd49b148f4cef4d3046fc61"},
+    {file = "jupyter_server-2.12.2.tar.gz", hash = "sha256:5eae86be15224b5375cdec0c3542ce72ff20f7a25297a2a8166a250bb455a519"},
 ]
 
 [package.dependencies]
@@ -1695,13 +1646,13 @@ files = [
 
 [[package]]
 name = "markdown"
-version = "3.5.2"
+version = "3.5.1"
 description = "Python implementation of John Gruber's Markdown."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "Markdown-3.5.2-py3-none-any.whl", hash = "sha256:d43323865d89fc0cb9b20c75fc8ad313af307cc087e84b657d9eec768eddeadd"},
-    {file = "Markdown-3.5.2.tar.gz", hash = "sha256:e1ac7b3dc550ee80e602e71c1d168002f062e49f1b11e26a36264dafd4df2ef8"},
+    {file = "Markdown-3.5.1-py3-none-any.whl", hash = "sha256:5874b47d4ee3f0b14d764324d2c94c03ea66bee56f2d929da9f2508d65e722dc"},
+    {file = "Markdown-3.5.1.tar.gz", hash = "sha256:b65d7beb248dc22f2e8a31fb706d93798093c308dc1aba295aedeb9d41a813bd"},
 ]
 
 [package.extras]
@@ -1940,13 +1891,13 @@ mkdocs = ">=1.0.3"
 
 [[package]]
 name = "mkdocs-material"
-version = "9.5.4"
+version = "9.5.3"
 description = "Documentation that simply works"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_material-9.5.4-py3-none-any.whl", hash = "sha256:efd7cc8ae03296d728da9bd38f4db8b07ab61f9738a0cbd0dfaf2a15a50e7343"},
-    {file = "mkdocs_material-9.5.4.tar.gz", hash = "sha256:3d196ee67fad16b2df1a458d650a8ac1890294eaae368d26cee71bc24ad41c40"},
+    {file = "mkdocs_material-9.5.3-py3-none-any.whl", hash = "sha256:76c93a8525cceb0b395b9cedab3428bf518cf6439adef2b940f1c1574b775d89"},
+    {file = "mkdocs_material-9.5.3.tar.gz", hash = "sha256:5899219f422f0a6de784232d9d40374416302ffae3c160cacc72969fcc1ee372"},
 ]
 
 [package.dependencies]
@@ -2021,13 +1972,13 @@ python-legacy = ["mkdocstrings-python-legacy (>=0.2.1)"]
 
 [[package]]
 name = "mkdocstrings-python"
-version = "1.8.0"
+version = "1.7.5"
 description = "A Python handler for mkdocstrings."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocstrings_python-1.8.0-py3-none-any.whl", hash = "sha256:4209970cc90bec194568682a535848a8d8489516c6ed4adbe58bbc67b699ca9d"},
-    {file = "mkdocstrings_python-1.8.0.tar.gz", hash = "sha256:1488bddf50ee42c07d9a488dddc197f8e8999c2899687043ec5dd1643d057192"},
+    {file = "mkdocstrings_python-1.7.5-py3-none-any.whl", hash = "sha256:5f6246026353f0c0785135db70c3fe9a5d9318990fc7ceb11d62097b8ffdd704"},
+    {file = "mkdocstrings_python-1.7.5.tar.gz", hash = "sha256:c7d143728257dbf1aa550446555a554b760dcd40a763f077189d298502b800be"},
 ]
 
 [package.dependencies]
@@ -2051,34 +2002,6 @@ markdown = ">=3.3.3"
 mkdocs = ">=1.5.0"
 nbconvert = ">=6.0.0"
 
-[[package]]
-name = "mpi4py"
-version = "3.1.5"
-description = "Python bindings for MPI"
-optional = true
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-files = [
-    {file = "mpi4py-3.1.5-cp27-cp27m-win32.whl", hash = "sha256:dbc47296a560d2a5aa50d716cb9bf90552349687ee068a493d646e3f6c20a9a4"},
-    {file = "mpi4py-3.1.5-cp27-cp27m-win_amd64.whl", hash = "sha256:b3e06c9ef1d0a2da3d5c7431bbf4af50a01ac9915fb4ab33409a21cd9e67be9c"},
-    {file = "mpi4py-3.1.5-cp310-cp310-win32.whl", hash = "sha256:f39df0d985cb6fb342ee6c6902cadf21b2d828d7df00b182573da0242646b715"},
-    {file = "mpi4py-3.1.5-cp310-cp310-win_amd64.whl", hash = "sha256:aec0e6238ed76c930c07df7dcea19f3be5ca958fb76353e668b19511ed4c86d7"},
-    {file = "mpi4py-3.1.5-cp311-cp311-win32.whl", hash = "sha256:f73686e3ff8f76bacb9ecacba0515f84392ad4c561b76603f9680f0fe64ef0ed"},
-    {file = "mpi4py-3.1.5-cp311-cp311-win_amd64.whl", hash = "sha256:d854dae2e62042a0355fa24ef7bea50b5380414806319240a57e654be1e59d9c"},
-    {file = "mpi4py-3.1.5-cp312-cp312-win32.whl", hash = "sha256:17cc793bf2fe3921f2c3cda59a2a708d2e0c68ce07c8b9d2b6ee1a9adc28fe3d"},
-    {file = "mpi4py-3.1.5-cp312-cp312-win_amd64.whl", hash = "sha256:8b38ad45a843bcbd7d11e23fd901016bb8069f35a9d4500666090465a2f734f2"},
-    {file = "mpi4py-3.1.5-cp35-cp35m-win32.whl", hash = "sha256:61d6c5df1803002cf6c61523417d48f9ecf64b55808e3d9d47815c174d7125dd"},
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@@ -2231,13 +2154,13 @@ test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=
 
 [[package]]
 name = "nbconvert"
-version = "7.14.2"
+version = "7.14.0"
 description = "Converting Jupyter Notebooks"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -2289,13 +2212,13 @@ test = ["pep440", "pre-commit", "pytest", "testpath"]
 
 [[package]]
 name = "nest-asyncio"
-version = "1.5.9"
+version = "1.5.8"
 description = "Patch asyncio to allow nested event loops"
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 python-versions = ">=3.5"
 files = [
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 [[package]]
@@ -2316,33 +2239,6 @@ doc = ["nb2plots (>=0.7)", "nbconvert (<7.9)", "numpydoc (>=1.6)", "pillow (>=9.
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-
 [[package]]
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 version = "1.8.0"
@@ -2837,22 +2733,22 @@ wcwidth = "*"
 
 [[package]]
 name = "protobuf"
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 python-versions = ">=3.8"
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 [[package]]
@@ -2908,17 +2804,6 @@ files = [
 [package.extras]
 tests = ["pytest"]
 
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 [[package]]
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 version = "14.0.2"
@@ -3127,13 +3012,13 @@ typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
 
 [[package]]
 name = "pydoclint"
-version = "0.3.9"
+version = "0.3.8"
 description = "A Python docstring linter that checks arguments, returns, yields, and raises sections"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -3177,26 +3062,15 @@ pyyaml = "*"
 [package.extras]
 extra = ["pygments (>=2.12)"]
 
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 [package.dependencies]
@@ -3615,13 +3489,13 @@ test = ["pytest (>=6,!=7.0.0,!=7.0.1)", "pytest-cov (>=3.0.0)", "pytest-qt"]
 
 [[package]]
 name = "referencing"
-version = "0.32.1"
+version = "0.32.0"
 description = "JSON Referencing + Python"
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 python-versions = ">=3.8"
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 [package.dependencies]
@@ -3796,136 +3670,136 @@ jupyter = ["ipywidgets (>=7.5.1,<9)"]
 
 [[package]]
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 python-versions = ">=3.8"
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 ]
 
 [[package]]
 name = "ruff"
-version = "0.1.13"
+version = "0.1.11"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "ruff-0.1.11.tar.gz", hash = "sha256:f9d4d88cb6eeb4dfe20f9f0519bd2eaba8119bde87c3d5065c541dbae2b5a2cb"},
 ]
 
 [[package]]
@@ -4065,13 +3939,13 @@ win32 = ["pywin32"]
 
 [[package]]
 name = "sentry-sdk"
-version = "1.39.2"
+version = "1.39.1"
 description = "Python client for Sentry (https://sentry.io)"
 optional = false
 python-versions = "*"
 files = [
-    {file = "sentry-sdk-1.39.2.tar.gz", hash = "sha256:24c83b0b41c887d33328a9166f5950dc37ad58f01c9f2fbff6b87a6f1094170c"},
-    {file = "sentry_sdk-1.39.2-py2.py3-none-any.whl", hash = "sha256:acaf597b30258fc7663063b291aa99e58f3096e91fe1e6634f4b79f9c1943e8e"},
+    {file = "sentry-sdk-1.39.1.tar.gz", hash = "sha256:320a55cdf9da9097a0bead239c35b7e61f53660ef9878861824fd6d9b2eaf3b5"},
+    {file = "sentry_sdk-1.39.1-py2.py3-none-any.whl", hash = "sha256:81b5b9ffdd1a374e9eb0c053b5d2012155db9cbe76393a8585677b753bd5fdc1"},
 ]
 
 [package.dependencies]
@@ -4097,7 +3971,7 @@ huey = ["huey (>=2)"]
 loguru = ["loguru (>=0.5)"]
 opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
 opentelemetry-experimental = ["opentelemetry-distro (>=0.40b0,<1.0)", "opentelemetry-instrumentation-aiohttp-client (>=0.40b0,<1.0)", "opentelemetry-instrumentation-django (>=0.40b0,<1.0)", "opentelemetry-instrumentation-fastapi (>=0.40b0,<1.0)", "opentelemetry-instrumentation-flask (>=0.40b0,<1.0)", "opentelemetry-instrumentation-requests (>=0.40b0,<1.0)", "opentelemetry-instrumentation-sqlite3 (>=0.40b0,<1.0)", "opentelemetry-instrumentation-urllib (>=0.40b0,<1.0)"]
-pure-eval = ["asttokens", "executing", "pure_eval"]
+pure-eval = ["asttokens", "executing", "pure-eval"]
 pymongo = ["pymongo (>=3.1)"]
 pyspark = ["pyspark (>=2.4.4)"]
 quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
@@ -4620,13 +4494,13 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
 
 [[package]]
 name = "transformer-lens"
-version = "1.12.1"
+version = "1.12.0"
 description = "An implementation of transformers tailored for mechanistic interpretability."
 optional = false
 python-versions = ">=3.8,<4.0"
 files = [
-    {file = "transformer_lens-1.12.1-py3-none-any.whl", hash = "sha256:ab11cc0089d29e59954c641ff1763752406b22c78b90cad5cc56ee87a6ccaef2"},
-    {file = "transformer_lens-1.12.1.tar.gz", hash = "sha256:98ba181fe6173085dd9f2c0d23a34d75db48c73c20439ac6ddc328f260c7f22e"},
+    {file = "transformer_lens-1.12.0-py3-none-any.whl", hash = "sha256:764740fc3bdf2ed5390d27cd2422eb9b161587ae39420722130a62f322aeef22"},
+    {file = "transformer_lens-1.12.0.tar.gz", hash = "sha256:8e9022e1c72deecc9b02eead0d4465d96361fecf1aa9f5a0179d363a850f6d79"},
 ]
 
 [package.dependencies]
@@ -4755,13 +4629,13 @@ test = ["mypy", "pytest", "typing-extensions"]
 
 [[package]]
 name = "types-python-dateutil"
-version = "2.8.19.20240106"
+version = "2.8.19.14"
 description = "Typing stubs for python-dateutil"
 optional = false
-python-versions = ">=3.8"
+python-versions = "*"
 files = [
-    {file = "types-python-dateutil-2.8.19.20240106.tar.gz", hash = "sha256:1f8db221c3b98e6ca02ea83a58371b22c374f42ae5bbdf186db9c9a76581459f"},
-    {file = "types_python_dateutil-2.8.19.20240106-py3-none-any.whl", hash = "sha256:efbbdc54590d0f16152fa103c9879c7d4a00e82078f6e2cf01769042165acaa2"},
+    {file = "types-python-dateutil-2.8.19.14.tar.gz", hash = "sha256:1f4f10ac98bb8b16ade9dbee3518d9ace017821d94b057a425b069f834737f4b"},
+    {file = "types_python_dateutil-2.8.19.14-py3-none-any.whl", hash = "sha256:f977b8de27787639986b4e28963263fd0e5158942b3ecef91b9335c130cb1ce9"},
 ]
 
 [[package]]
@@ -4818,13 +4692,13 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "wandb"
-version = "0.16.2"
+version = "0.16.1"
 description = "A CLI and library for interacting with the Weights & Biases API."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "wandb-0.16.2-py3-none-any.whl", hash = "sha256:6b119cf3c01f35e7276b62d052128e5320621d182c9eb5796a12cf62a9b3134f"},
-    {file = "wandb-0.16.2.tar.gz", hash = "sha256:e40cd79ea6272fe4762a80b9f47b172e141daeb3b56eb9d1e192ebd10752e64e"},
+    {file = "wandb-0.16.1-py3-none-any.whl", hash = "sha256:1d7423f92520984585bae9693bb637ae08d3e0c1d75ad4b34215bc44431f114c"},
+    {file = "wandb-0.16.1.tar.gz", hash = "sha256:ffe6e8dd8cc8fcd72010c1246fb3d6d226b37c4f111f3f94308a1c0ae28a2fec"},
 ]
 
 [package.dependencies]
@@ -4844,10 +4718,11 @@ setuptools = "*"
 async = ["httpx (>=0.23.0)"]
 aws = ["boto3"]
 azure = ["azure-identity", "azure-storage-blob"]
+core = ["wandb-core (>=0.17.0b2)"]
 gcp = ["google-cloud-storage"]
 kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
-launch = ["PyYAML (>=6.0.0)", "awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "typing-extensions"]
-media = ["bokeh", "moviepy", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit-pypi", "soundfile"]
+launch = ["PyYAML (>=6.0.0)", "awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "typing-extensions"]
+media = ["bokeh", "moviepy", "numpy", "pillow", "plotly", "rdkit-pypi", "soundfile"]
 models = ["cloudpickle"]
 perf = ["orjson"]
 sweeps = ["sweeps (>=0.2.0)"]
@@ -4893,13 +4768,13 @@ watchmedo = ["PyYAML (>=3.10)"]
 
 [[package]]
 name = "wcwidth"
-version = "0.2.13"
+version = "0.2.12"
 description = "Measures the displayed width of unicode strings in a terminal"
 optional = false
 python-versions = "*"
 files = [
-    {file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"},
-    {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"},
+    {file = "wcwidth-0.2.12-py2.py3-none-any.whl", hash = "sha256:f26ec43d96c8cbfed76a5075dac87680124fa84e0855195a6184da9c187f133c"},
+    {file = "wcwidth-0.2.12.tar.gz", hash = "sha256:f01c104efdf57971bcb756f054dd58ddec5204dd15fa31d6503ea57947d97c02"},
 ]
 
 [[package]]
@@ -5236,10 +5111,7 @@ cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\
 [package.extras]
 cffi = ["cffi (>=1.11)"]
 
-[extras]
-deepspeed = ["deepspeed", "mpi4py"]
-
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10, <3.12"
-content-hash = "73aa3dca010ec93be401cec32ecfcc67003fb95736c2e4c73835c66ab8a96e92"
+content-hash = "ecbd7cbeb4c7821efd7a3279e20893011882715222f6230b5fb552db82e83ffb"
diff --git a/pyproject.toml b/pyproject.toml
index edc5ac57..347bb10f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,12 +7,9 @@
     readme="README.md"
     version="0.0.0"
 
-    # Note: Zstandard is required for downloading datasets such as The Pile
     [tool.poetry.dependencies]
         datasets=">=2.15.0"
-        deepspeed={version=">=0.12.6", extras=["deepspeed"], optional=false}
         einops=">=0.6"
-        mpi4py={version=">=3.1.5", extras=["deepspeed"], optional=true}
         pydantic=">=2.5.2"
         python=">=3.10, <3.12"
         strenum=">=0.4.15"
@@ -20,7 +17,7 @@
         torch=">=2.1.1"
         transformers=">=4.35.2"
         wandb=">=0.16.1"
-        zstandard=">=0.22.0"
+        zstandard=">=0.22.0"    # Required for downloading datasets such as The Pile
 
     [tool.poetry.group]
         [tool.poetry.group.dev.dependencies]
@@ -57,9 +54,6 @@
             pymdown-extensions=">=10.5"
             pytkdocs-tweaks=">=0.0.7"
 
-[tool.poetry.extras]
-    deepspeed=["deepspeed", "mpi4py"]
-
 [tool.poetry.scripts]
     join-sae-sweep='sparse_autoencoder.train.join_sweep:run'
 
diff --git a/sparse_autoencoder/__init__.py b/sparse_autoencoder/__init__.py
index 53c258e5..963bb548 100644
--- a/sparse_autoencoder/__init__.py
+++ b/sparse_autoencoder/__init__.py
@@ -9,7 +9,6 @@
 from sparse_autoencoder.metrics.train.capacity import CapacityMetric
 from sparse_autoencoder.metrics.train.feature_density import TrainBatchFeatureDensityMetric
 from sparse_autoencoder.optimizer.adam_with_reset import AdamWithReset
-from sparse_autoencoder.optimizer.deepspeed_adam_with_reset import ZeroOneAdamWithReset
 from sparse_autoencoder.source_data.pretokenized_dataset import PreTokenizedDataset
 from sparse_autoencoder.source_data.text_dataset import TextDataset
 from sparse_autoencoder.train.pipeline import Pipeline
@@ -84,5 +83,4 @@
     "TensorActivationStore",
     "TextDataset",
     "TrainBatchFeatureDensityMetric",
-    "ZeroOneAdamWithReset",
 ]
diff --git a/sparse_autoencoder/activation_resampler/activation_resampler.py b/sparse_autoencoder/activation_resampler/activation_resampler.py
index 1524c879..e2562bcd 100644
--- a/sparse_autoencoder/activation_resampler/activation_resampler.py
+++ b/sparse_autoencoder/activation_resampler/activation_resampler.py
@@ -2,13 +2,11 @@
 from dataclasses import dataclass
 from typing import Annotated, NamedTuple
 
-from deepspeed import DeepSpeedEngine
 from einops import rearrange
 from jaxtyping import Bool, Float, Int64
 from pydantic import Field, NonNegativeInt, PositiveInt, validate_call
 import torch
 from torch import Tensor
-from torch.nn.parallel import DataParallel
 from torch.utils.data import DataLoader
 
 from sparse_autoencoder.activation_resampler.utils.component_slice_tensor import (
@@ -19,6 +17,7 @@
 from sparse_autoencoder.loss.abstract_loss import AbstractLoss
 from sparse_autoencoder.tensor_types import Axis
 from sparse_autoencoder.train.utils.get_model_device import get_model_device
+from sparse_autoencoder.utils.data_parallel import DataParallelWithModelAttributes
 
 
 @dataclass
@@ -208,7 +207,7 @@ def _get_dead_neuron_indices(
     def compute_loss_and_get_activations(
         self,
         store: ActivationStore,
-        autoencoder: SparseAutoencoder | DataParallel[SparseAutoencoder] | DeepSpeedEngine,
+        autoencoder: SparseAutoencoder | DataParallelWithModelAttributes[SparseAutoencoder],
         loss_fn: AbstractLoss,
         train_batch_size: int,
     ) -> LossInputActivationsTuple:
@@ -441,7 +440,7 @@ def renormalize_and_scale(
     def resample_dead_neurons(
         self,
         activation_store: ActivationStore,
-        autoencoder: SparseAutoencoder | DataParallel[SparseAutoencoder] | DeepSpeedEngine,
+        autoencoder: SparseAutoencoder | DataParallelWithModelAttributes[SparseAutoencoder],
         loss_fn: AbstractLoss,
         train_batch_size: int,
     ) -> list[ParameterUpdateResults]:
@@ -531,7 +530,7 @@ def step_resampler(
         self,
         batch_neuron_activity: Int64[Tensor, Axis.names(Axis.COMPONENT, Axis.LEARNT_FEATURE)],
         activation_store: ActivationStore,
-        autoencoder: SparseAutoencoder | DataParallel[SparseAutoencoder] | DeepSpeedEngine,
+        autoencoder: SparseAutoencoder | DataParallelWithModelAttributes[SparseAutoencoder],
         loss_fn: AbstractLoss,
         train_batch_size: int,
     ) -> list[ParameterUpdateResults] | None:
diff --git a/sparse_autoencoder/autoencoder/model.py b/sparse_autoencoder/autoencoder/model.py
index 34e031e6..0ae8a9a9 100644
--- a/sparse_autoencoder/autoencoder/model.py
+++ b/sparse_autoencoder/autoencoder/model.py
@@ -276,7 +276,7 @@ def load(
             The loaded model.
         """
         # Load the file
-        serialized_state = torch.load(file_path, map_location=torch.device("cpu"))
+        serialized_state = torch.load(file_path)
         state = SparseAutoencoderState.model_validate(serialized_state)
 
         # Initialise the model
diff --git a/sparse_autoencoder/optimizer/adam_with_reset.py b/sparse_autoencoder/optimizer/adam_with_reset.py
index 0f83bd34..4401c29f 100644
--- a/sparse_autoencoder/optimizer/adam_with_reset.py
+++ b/sparse_autoencoder/optimizer/adam_with_reset.py
@@ -42,7 +42,7 @@ def __init__(  # (extending existing implementation)
         lr: float | Float[Tensor, Axis.names(Axis.SINGLE_ITEM)] = 1e-3,
         betas: tuple[float, float] = (0.9, 0.999),
         eps: float = 1e-8,
-        weight_decay: float = 0.0,
+        weight_decay: float = 0,
         *,
         amsgrad: bool = False,
         foreach: bool | None = None,
diff --git a/sparse_autoencoder/optimizer/deepspeed_adam_with_reset.py b/sparse_autoencoder/optimizer/deepspeed_adam_with_reset.py
deleted file mode 100644
index 3365eb6a..00000000
--- a/sparse_autoencoder/optimizer/deepspeed_adam_with_reset.py
+++ /dev/null
@@ -1,194 +0,0 @@
-"""Deepspeed Zero One Adam Optimizer with a reset method.
-
-This reset method is useful when resampling dead neurons during training.
-"""
-from collections.abc import Iterator
-from typing import final
-
-from deepspeed.runtime.fp16.onebit.zoadam import ZeroOneAdam
-from jaxtyping import Int
-from torch import Tensor
-from torch.nn.parameter import Parameter
-from torch.optim.optimizer import params_t
-
-from sparse_autoencoder.optimizer.abstract_optimizer import AbstractOptimizerWithReset
-from sparse_autoencoder.tensor_types import Axis
-
-
-@final
-class ZeroOneAdamWithReset(ZeroOneAdam, AbstractOptimizerWithReset):
-    """Deepspeed Zero One Adam Optimizer with a reset method.
-
-    https://deepspeed.readthedocs.io/en/latest/optimizers.html#zerooneadam-gpu
-
-    The :meth:`reset_state_all_parameters` and :meth:`reset_neurons_state` methods are useful when
-    manually editing the model parameters during training (e.g. when resampling dead neurons). This
-    is because Adam maintains running averages of the gradients and the squares of gradients, which
-    will be incorrect if the parameters are changed.
-
-    Otherwise this is the same as the standard ZeroOneAdam optimizer.
-
-    Warning:
-        Requires a distributed torch backend.
-    """
-
-    parameter_names: list[str]
-    """Parameter Names.
-
-    The names of the parameters, so that we can find them later when resetting the state.
-    """
-
-    _has_components_dim: bool
-    """Whether the parameters have a components dimension."""
-
-    def __init__(
-        self,
-        params: params_t,
-        lr: float = 1e-3,
-        betas: tuple[float, float] = (0.9, 0.999),
-        eps: float = 1e-8,
-        weight_decay: float = 0.0,
-        *,
-        named_parameters: Iterator[tuple[str, Parameter]],
-        has_components_dim: bool,
-    ) -> None:
-        """Initialize the optimizer.
-
-        Warning:
-            Named parameters must be with default settings (remove duplicates and not recursive).
-
-        Args:
-            params: Iterable of parameters to optimize or dicts defining parameter groups.
-            lr: Learning rate. A Tensor LR is not yet fully supported for all implementations. Use a
-                float LR unless specifying fused=True or capturable=True.
-            betas: Coefficients used for computing running averages of gradient and its square.
-            eps: Term added to the denominator to improve numerical stability.
-            weight_decay: Weight decay (L2 penalty).
-            named_parameters: An iterator over the named parameters of the model. This is used to
-                find the parameters when resetting their state. You should set this as
-                `model.named_parameters()`.
-            has_components_dim: If the parameters have a components dimension (i.e. if you are
-                training an SAE on more than one component).
-
-
-        Raises:
-            ValueError: If the number of parameter names does not match the number of parameters.
-        """
-        # Initialise the parent class (note we repeat the parameter names so that type hints work).
-        super().__init__(
-            params=params,
-            lr=lr,
-            betas=betas,
-            eps=eps,
-            weight_decay=weight_decay,
-        )
-
-        self._has_components_dim = has_components_dim
-
-        # Store the names of the parameters, so that we can find them later when resetting the
-        # state.
-        self.parameter_names = [name for name, _value in named_parameters]
-
-        if len(self.parameter_names) != len(self.param_groups[0]["params"]):
-            error_message = (
-                "The number of parameter names does not match the number of parameters. "
-                "If using model.named_parameters() make sure remove_duplicates is True "
-                "and recursive is False (the default settings)."
-            )
-            raise ValueError(error_message)
-
-    def reset_state_all_parameters(self) -> None:
-        """Reset the state for all parameters.
-
-        Iterates over all parameters and resets both the running averages of the gradients and the
-        squares of gradients.
-        """
-        # Iterate over every parameter
-        for group in self.param_groups:
-            for parameter in group["params"]:
-                # Get the state
-                state = self.state[parameter]
-
-                # Check if state is initialized
-                if len(state) == 0:
-                    continue
-
-                # Reset running averages
-                exp_avg: Tensor = state["exp_avg"]
-                exp_avg.zero_()
-                exp_avg_sq: Tensor = state["exp_avg_sq"]
-                exp_avg_sq.zero_()
-
-                # If AdamW is used (weight decay fix), also reset the max exp_avg_sq
-                if "max_exp_avg_sq" in state:
-                    max_exp_avg_sq: Tensor = state["max_exp_avg_sq"]
-                    max_exp_avg_sq.zero_()
-
-    def reset_neurons_state(
-        self,
-        parameter: Parameter,
-        neuron_indices: Int[Tensor, Axis.names(Axis.LEARNT_FEATURE_IDX)],
-        axis: int,
-        component_idx: int = 0,
-    ) -> None:
-        """Reset the state for specific neurons, on a specific parameter.
-
-        Args:
-            parameter: The parameter to be reset. Examples from the standard sparse autoencoder
-                implementation  include `tied_bias`, `_encoder._weight`, `_encoder._bias`,
-            neuron_indices: The indices of the neurons to reset.
-            axis: The axis of the state values to reset (i.e. the input/output features axis, as
-                we're resetting all input/output features for a specific dead neuron).
-            component_idx: The component index of the state values to reset.
-
-        Raises:
-            ValueError: If the parameter has a components dimension, but has_components_dim is
-                False.
-        """
-        # Get the state of the parameter
-        state = self.state[parameter]
-
-        # If the number of dimensions is 3, we definitely have a components dimension. If 2, we may
-        # do (as the bias has 2 dimensions with components, but the weight has 2 dimensions without
-        # components).
-        definitely_has_components_dimension = 3
-        if (
-            not self._has_components_dim
-            and state["exp_avg"].ndim == definitely_has_components_dimension
-        ):
-            error_message = (
-                "The parameter has a components dimension, but has_components_dim is False. "
-                "This should not happen."
-            )
-            raise ValueError(error_message)
-
-        # Check if state is initialized
-        if len(state) == 0:
-            return
-
-        # Check there are any neurons to reset
-        if neuron_indices.numel() == 0:
-            return
-
-        # Move the neuron indices to the correct device
-        neuron_indices = neuron_indices.to(device=state["exp_avg"].device)
-
-        # Reset running averages for the specified neurons
-        if "exp_avg" in state:
-            if self._has_components_dim:
-                state["exp_avg"][component_idx].index_fill_(axis, neuron_indices, 0)
-            else:
-                state["exp_avg"].index_fill_(axis, neuron_indices, 0)
-
-        if "exp_avg_sq" in state:
-            if self._has_components_dim:
-                state["exp_avg_sq"][component_idx].index_fill_(axis, neuron_indices, 0)
-            else:
-                state["exp_avg_sq"].index_fill_(axis, neuron_indices, 0)
-
-        # If AdamW is used (weight decay fix), also reset the max exp_avg_sq
-        if "max_exp_avg_sq" in state:
-            if self._has_components_dim:
-                state["max_exp_avg_sq"][component_idx].index_fill_(axis, neuron_indices, 0)
-            else:
-                state["max_exp_avg_sq"].index_fill_(axis, neuron_indices, 0)
diff --git a/sparse_autoencoder/source_model/replace_activations_hook.py b/sparse_autoencoder/source_model/replace_activations_hook.py
index e1e56e80..e0cf6874 100644
--- a/sparse_autoencoder/source_model/replace_activations_hook.py
+++ b/sparse_autoencoder/source_model/replace_activations_hook.py
@@ -1,37 +1,33 @@
 """Replace activations hook."""
 from typing import TYPE_CHECKING
 
-from deepspeed import DeepSpeedEngine
-from jaxtyping import Float
 from torch import Tensor
-from torch.nn.parallel import DataParallel
 from transformer_lens.hook_points import HookPoint
 
 from sparse_autoencoder.autoencoder.model import SparseAutoencoder
+from sparse_autoencoder.utils.data_parallel import DataParallelWithModelAttributes
 
 
 if TYPE_CHECKING:
     from sparse_autoencoder.tensor_types import Axis
+from jaxtyping import Float
 
 
 def replace_activations_hook(
     value: Tensor,
     hook: HookPoint,  # noqa: ARG001
-    sparse_autoencoder: SparseAutoencoder | DataParallel[SparseAutoencoder] | DeepSpeedEngine,
+    sparse_autoencoder: SparseAutoencoder | DataParallelWithModelAttributes[SparseAutoencoder],
     component_idx: int | None = None,
-    n_components: int | None = None,
 ) -> Tensor:
     """Replace activations hook.
 
-    This should be pre-initialised with `functools.partial`.
-
     Args:
         value: The activations to replace.
         hook: The hook point.
-        sparse_autoencoder: The sparse autoencoder.
+        sparse_autoencoder: The sparse autoencoder. This should be pre-initialised with
+            `functools.partial`.
         component_idx: The component index to replace the activations with, if just replacing
             activations for a single component. Requires the model to have a component axis.
-        n_components: The number of components that the SAE is trained on.
 
     Returns:
         Replaced activations.
@@ -47,8 +43,11 @@ def replace_activations_hook(
     )
 
     if component_idx is not None:
-        if n_components is None:
-            error_message = "The number of model components must be set if component_idx is set."
+        if sparse_autoencoder.config.n_components is None:
+            error_message = (
+                "Cannot replace for a specific component, if the model does not have a "
+                "component axis."
+            )
             raise RuntimeError(error_message)
 
         # The approach here is to run a forward pass with dummy values for all components other than
@@ -57,7 +56,7 @@ def replace_activations_hook(
         # components.
         expanded_shape = [
             squashed_value.shape[0],
-            n_components,
+            sparse_autoencoder.config.n_components,
             squashed_value.shape[-1],
         ]
         expanded = squashed_value.unsqueeze(1).expand(*expanded_shape)
diff --git a/sparse_autoencoder/source_model/tests/test_replace_activations_hook.py b/sparse_autoencoder/source_model/tests/test_replace_activations_hook.py
index 165c74df..efee221f 100644
--- a/sparse_autoencoder/source_model/tests/test_replace_activations_hook.py
+++ b/sparse_autoencoder/source_model/tests/test_replace_activations_hook.py
@@ -66,12 +66,7 @@ def test_hook_replaces_activations_2_components() -> None:
         fwd_hooks=[
             (
                 "blocks.0.hook_mlp_out",
-                partial(
-                    replace_activations_hook,
-                    sparse_autoencoder=autoencoder,
-                    component_idx=1,
-                    n_components=2,
-                ),
+                partial(replace_activations_hook, sparse_autoencoder=autoencoder, component_idx=1),
             )
         ],
     )
diff --git a/sparse_autoencoder/train/pipeline.py b/sparse_autoencoder/train/pipeline.py
index 39fcbb04..bccad468 100644
--- a/sparse_autoencoder/train/pipeline.py
+++ b/sparse_autoencoder/train/pipeline.py
@@ -5,12 +5,10 @@
 from tempfile import gettempdir
 from typing import TYPE_CHECKING, final
 
-from deepspeed import DeepSpeedEngine
 from jaxtyping import Float, Int, Int64
 from pydantic import NonNegativeInt, PositiveInt, validate_call
 import torch
 from torch import Tensor
-from torch.nn.parallel import DataParallel
 from torch.optim.lr_scheduler import LRScheduler
 from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
@@ -34,6 +32,7 @@
 from sparse_autoencoder.source_model.zero_ablate_hook import zero_ablate_hook
 from sparse_autoencoder.tensor_types import Axis
 from sparse_autoencoder.train.utils.get_model_device import get_model_device
+from sparse_autoencoder.utils.data_parallel import DataParallelWithModelAttributes
 
 
 if TYPE_CHECKING:
@@ -52,15 +51,9 @@ class Pipeline:
     activation_resampler: ActivationResampler | None
     """Activation resampler to use."""
 
-    autoencoder: SparseAutoencoder | DataParallel[SparseAutoencoder] | DeepSpeedEngine
+    autoencoder: SparseAutoencoder | DataParallelWithModelAttributes[SparseAutoencoder]
     """Sparse autoencoder to train."""
 
-    n_input_features: int
-    """Number of input features in the sparse autoencoder."""
-
-    n_learned_features: int
-    """Number of learned features in the sparse autoencoder."""
-
     cache_names: list[str]
     """Names of the cache hook points to use in the source model."""
 
@@ -88,7 +81,7 @@ class Pipeline:
     source_dataset: SourceDataset
     """Source dataset to generate activation data from (tokenized prompts)."""
 
-    source_model: HookedTransformer | DataParallel[HookedTransformer]
+    source_model: HookedTransformer | DataParallelWithModelAttributes[HookedTransformer]
     """Source model to get activations from."""
 
     total_activations_trained_on: int = 0
@@ -104,15 +97,13 @@ def n_components(self) -> int:
     def __init__(
         self,
         activation_resampler: ActivationResampler | None,
-        autoencoder: SparseAutoencoder | DataParallel[SparseAutoencoder] | DeepSpeedEngine,
+        autoencoder: SparseAutoencoder | DataParallelWithModelAttributes[SparseAutoencoder],
         cache_names: list[str],
         layer: NonNegativeInt,
         loss: AbstractLoss,
         optimizer: AbstractOptimizerWithReset,
         source_dataset: SourceDataset,
-        source_model: HookedTransformer | DataParallel[HookedTransformer],
-        n_input_features: int,
-        n_learned_features: int,
+        source_model: HookedTransformer | DataParallelWithModelAttributes[HookedTransformer],
         run_name: str = "sparse_autoencoder",
         checkpoint_directory: Path = DEFAULT_CHECKPOINT_DIRECTORY,
         lr_scheduler: LRScheduler | None = None,
@@ -133,8 +124,6 @@ def __init__(
             optimizer: Optimizer to use.
             source_dataset: Source dataset to get data from.
             source_model: Source model to get activations from.
-            n_input_features: Number of input features in the sparse autoencoder.
-            n_learned_features: Number of learned features in the sparse autoencoder.
             run_name: Name of the run for saving checkpoints.
             checkpoint_directory: Directory to save checkpoints to.
             lr_scheduler: Learning rate scheduler to use.
@@ -157,8 +146,6 @@ def __init__(
         self.source_data_batch_size = source_data_batch_size
         self.source_dataset = source_dataset
         self.source_model = source_model
-        self.n_input_features = n_input_features
-        self.n_learned_features = n_learned_features
 
         # Create a stateful iterator
         source_dataloader = source_dataset.get_dataloader(
@@ -188,10 +175,9 @@ def generate_activations(self, store_size: PositiveInt) -> TensorActivationStore
             raise ValueError(error_message)
 
         # Setup the store
+        n_neurons: int = self.autoencoder.config.n_input_features
         source_model_device: torch.device = get_model_device(self.source_model)
-        store = TensorActivationStore(
-            store_size, self.n_input_features, n_components=self.n_components
-        )
+        store = TensorActivationStore(store_size, n_neurons, n_components=self.n_components)
 
         # Add the hook to the model (will automatically store the activations every time the model
         # runs)
@@ -239,9 +225,9 @@ def train_autoencoder(
         learned_activations_fired_count: Int64[
             Tensor, Axis.names(Axis.COMPONENT, Axis.LEARNT_FEATURE)
         ] = torch.zeros(
-            (self.n_components, self.n_learned_features),
+            (self.n_components, self.autoencoder.config.n_learned_features),
             dtype=torch.int64,
-            device=torch.device("cpu"),
+            device=autoencoder_device,
         )
 
         for store_batch in activations_dataloader:
@@ -274,7 +260,7 @@ def train_autoencoder(
             # Store count of how many neurons have fired
             with torch.no_grad():
                 fired = learned_activations > 0
-                learned_activations_fired_count.add_(fired.sum(dim=0).cpu())
+                learned_activations_fired_count.add_(fired.sum(dim=0))
 
             # Backwards pass
             total_loss.backward()
@@ -372,7 +358,6 @@ def validate_sae(self, validation_n_activations: PositiveInt) -> None:
                     replace_activations_hook,
                     sparse_autoencoder=self.autoencoder,
                     component_idx=component_idx,
-                    n_components=self.n_components,
                 )
 
                 with torch.no_grad():
diff --git a/sparse_autoencoder/train/sweep.py b/sparse_autoencoder/train/sweep.py
index d318421b..2adbdc43 100644
--- a/sparse_autoencoder/train/sweep.py
+++ b/sparse_autoencoder/train/sweep.py
@@ -1,16 +1,13 @@
 """Sweep."""
-from functools import partial
 from pathlib import Path
 import re
 import sys
 import traceback
 
-import deepspeed
-from deepspeed import DeepSpeedEngine
 import torch
-from torch.nn.parallel import DataParallel
 from torch.optim.lr_scheduler import CosineAnnealingLR, LRScheduler, ReduceLROnPlateau
 from transformer_lens import HookedTransformer
+from transformer_lens.utils import get_device
 from transformers import AutoTokenizer
 import wandb
 
@@ -25,13 +22,13 @@
     SparseAutoencoder,
 )
 from sparse_autoencoder.autoencoder.model import SparseAutoencoderConfig
-from sparse_autoencoder.optimizer.abstract_optimizer import AbstractOptimizerWithReset
 from sparse_autoencoder.source_data.abstract_dataset import SourceDataset
 from sparse_autoencoder.source_data.text_dataset import TextDataset
 from sparse_autoencoder.train.sweep_config import (
     RuntimeHyperparameters,
     SweepConfig,
 )
+from sparse_autoencoder.utils.data_parallel import DataParallelWithModelAttributes
 
 
 def setup_activation_resampler(hyperparameters: RuntimeHyperparameters) -> ActivationResampler:
@@ -74,14 +71,14 @@ def setup_source_model(hyperparameters: RuntimeHyperparameters) -> HookedTransfo
     )
 
 
-def setup_autoencoder_optimizer_scheduler(
-    hyperparameters: RuntimeHyperparameters, *, use_deepspeed: bool
-) -> tuple[DeepSpeedEngine | SparseAutoencoder, AdamWithReset, LRScheduler | None]:
+def setup_autoencoder(
+    hyperparameters: RuntimeHyperparameters, device: torch.device
+) -> SparseAutoencoder:
     """Setup the sparse autoencoder.
 
     Args:
         hyperparameters: The hyperparameters dictionary.
-        use_deepspeed: Whether to use deepspeed.
+        device: The computation device.
 
     Returns:
         The initialized sparse autoencoder.
@@ -94,12 +91,41 @@ def setup_autoencoder_optimizer_scheduler(
         n_learned_features=autoencoder_input_dim * expansion_factor,
         n_components=len(hyperparameters["source_model"]["cache_names"]),
     )
+    return SparseAutoencoder(config).to(device)
 
-    model = SparseAutoencoder(config)
 
+def setup_loss_function(hyperparameters: RuntimeHyperparameters) -> LossReducer:
+    """Setup the loss function for the autoencoder.
+
+    Args:
+        hyperparameters: The hyperparameters dictionary.
+
+    Returns:
+        The combined loss function.
+    """
+    return LossReducer(
+        LearnedActivationsL1Loss(
+            l1_coefficient=hyperparameters["loss"]["l1_coefficient"],
+        ),
+        L2ReconstructionLoss(),
+    )
+
+
+def setup_optimizer(
+    autoencoder: SparseAutoencoder, hyperparameters: RuntimeHyperparameters
+) -> tuple[AdamWithReset, LRScheduler | None]:
+    """Setup the optimizer for the autoencoder.
+
+    Args:
+        autoencoder: The sparse autoencoder model.
+        hyperparameters: The hyperparameters dictionary.
+
+    Returns:
+        The initialized optimizer & learning rate scheduler.
+    """
     optim = AdamWithReset(
-        params=model.parameters(),
-        named_parameters=model.named_parameters(),
+        params=autoencoder.parameters(),
+        named_parameters=autoencoder.named_parameters(),
         lr=hyperparameters["optimizer"]["lr"],
         betas=(
             hyperparameters["optimizer"]["adam_beta_1"],
@@ -122,37 +148,7 @@ def setup_autoencoder_optimizer_scheduler(
                 T_max=10,
             )
 
-    if use_deepspeed:
-        model_engine, optimizer_engine, _dataset, scheduler = deepspeed.initialize(
-            args={"local_rank": -1},
-            model=model,
-            optimizer=optim,
-            lr_scheduler=lr_scheduler,  # type: ignore
-            config={
-                "train_batch_size": hyperparameters["pipeline"]["train_batch_size"],
-            },
-        )
-
-        return (model_engine, optimizer_engine, scheduler)  # type: ignore
-
-    return (model, optim, lr_scheduler)
-
-
-def setup_loss_function(hyperparameters: RuntimeHyperparameters) -> LossReducer:
-    """Setup the loss function for the autoencoder.
-
-    Args:
-        hyperparameters: The hyperparameters dictionary.
-
-    Returns:
-        The combined loss function.
-    """
-    return LossReducer(
-        LearnedActivationsL1Loss(
-            l1_coefficient=hyperparameters["loss"]["l1_coefficient"],
-        ),
-        L2ReconstructionLoss(),
-    )
+    return optim, lr_scheduler
 
 
 def setup_source_data(hyperparameters: RuntimeHyperparameters) -> SourceDataset:
@@ -261,10 +257,10 @@ def stop_layer_from_cache_names(cache_names: list[str]) -> int:
 
 def run_training_pipeline(
     hyperparameters: RuntimeHyperparameters,
-    source_model: HookedTransformer | DataParallel[HookedTransformer],
-    autoencoder: SparseAutoencoder | DataParallel[SparseAutoencoder] | DeepSpeedEngine,
+    source_model: HookedTransformer | DataParallelWithModelAttributes[HookedTransformer],
+    autoencoder: SparseAutoencoder | DataParallelWithModelAttributes[SparseAutoencoder],
     loss: LossReducer,
-    optimizer: AbstractOptimizerWithReset,
+    optimizer: AdamWithReset,
     lr_scheduler: LRScheduler | None,
     activation_resampler: ActivationResampler,
     source_data: SourceDataset,
@@ -307,11 +303,6 @@ def run_training_pipeline(
         run_name=run_name,
         num_workers_data_loading=hyperparameters["pipeline"]["num_workers_data_loading"],
         lr_scheduler=lr_scheduler,
-        n_input_features=hyperparameters["source_model"]["hook_dimension"],
-        n_learned_features=int(
-            hyperparameters["autoencoder"]["expansion_factor"]
-            * hyperparameters["source_model"]["hook_dimension"]
-        ),
     )
 
     pipeline.run_pipeline(
@@ -324,28 +315,28 @@ def run_training_pipeline(
     )
 
 
-def train(*, use_deepspeed: bool) -> None:
-    """Train the sparse autoencoder using the hyperparameters from the WandB sweep.
-
-    Args:
-        use_deepspeed: Whether to use deepspeed.
-    """
+def train() -> None:
+    """Train the sparse autoencoder using the hyperparameters from the WandB sweep."""
     try:
         # Set up WandB
         hyperparameters = setup_wandb()
         run_name: str = wandb.run.name  # type: ignore
 
+        # Setup the device for training
+        device = get_device()
+
         # Set up the source model
         source_model = setup_source_model(hyperparameters)
 
-        # Set up the autoencoder, optimizer and learning rate scheduler
-        autoencoder, optimizer, lr_scheduler = setup_autoencoder_optimizer_scheduler(
-            hyperparameters, use_deepspeed=use_deepspeed
-        )
+        # Set up the autoencoder
+        autoencoder = setup_autoencoder(hyperparameters, device)
 
         # Set up the loss function
         loss_function = setup_loss_function(hyperparameters)
 
+        # Set up the optimizer
+        optimizer, lr_scheduler = setup_optimizer(autoencoder, hyperparameters)
+
         # Set up the activation resampler
         activation_resampler = setup_activation_resampler(hyperparameters)
 
@@ -356,7 +347,7 @@ def train(*, use_deepspeed: bool) -> None:
         run_training_pipeline(
             hyperparameters=hyperparameters,
             source_model=source_model,
-            autoencoder=autoencoder,
+            autoencoder=DataParallelWithModelAttributes(autoencoder),
             loss=loss_function,
             optimizer=optimizer,
             lr_scheduler=lr_scheduler,
@@ -370,12 +361,7 @@ def train(*, use_deepspeed: bool) -> None:
         sys.exit(1)
 
 
-def sweep(
-    sweep_config: SweepConfig | None = None,
-    sweep_id: str | None = None,
-    *,
-    use_deepspeed: bool = False,
-) -> None:
+def sweep(sweep_config: SweepConfig | None = None, sweep_id: str | None = None) -> None:
     """Run the training pipeline with wandb hyperparameter sweep.
 
     Warning:
@@ -384,19 +370,16 @@ def sweep(
     Args:
         sweep_config: The sweep configuration.
         sweep_id: The sweep id for an existing sweep.
-        use_deepspeed: Whether to use deepspeed.
 
     Raises:
         ValueError: If neither sweep_config nor sweep_id is specified.
     """
     if sweep_id is not None:
-        wandb.agent(
-            sweep_id, partial(train, use_deepspeed=use_deepspeed), project="sparse-autoencoder"
-        )
+        wandb.agent(sweep_id, train, project="sparse-autoencoder")
 
     elif sweep_config is not None:
         sweep_id = wandb.sweep(sweep_config.to_dict(), project="sparse-autoencoder")
-        wandb.agent(sweep_id, partial(train, use_deepspeed=use_deepspeed))
+        wandb.agent(sweep_id, train)
 
     else:
         error_message = "Either sweep_config or sweep_id must be specified."
diff --git a/sparse_autoencoder/train/tests/test_pipeline.py b/sparse_autoencoder/train/tests/test_pipeline.py
index b6416244..fbb46588 100644
--- a/sparse_autoencoder/train/tests/test_pipeline.py
+++ b/sparse_autoencoder/train/tests/test_pipeline.py
@@ -70,8 +70,6 @@ def pipeline_fixture() -> Pipeline:
         source_dataset=source_data,
         source_model=src_model,
         source_data_batch_size=10,
-        n_input_features=src_model.cfg.d_model,
-        n_learned_features=int(src_model.cfg.d_model * 2),
     )
 
 
diff --git a/sparse_autoencoder/train/tests/test_sweep.py b/sparse_autoencoder/train/tests/test_sweep.py
index fbf8a772..4647ff75 100644
--- a/sparse_autoencoder/train/tests/test_sweep.py
+++ b/sparse_autoencoder/train/tests/test_sweep.py
@@ -2,10 +2,11 @@
 
 import pytest
 from syrupy.session import SnapshotSession
+import torch
 
 from sparse_autoencoder.train.sweep import (
     setup_activation_resampler,
-    setup_autoencoder_optimizer_scheduler,
+    setup_autoencoder,
     setup_loss_function,
 )
 from sparse_autoencoder.train.sweep_config import (
@@ -29,7 +30,7 @@ def dummy_hyperparameters() -> RuntimeHyperparameters:
         "optimizer": {
             "adam_beta_1": 0.9,
             "adam_beta_2": 0.99,
-            "adam_weight_decay": 0.0,
+            "adam_weight_decay": 0,
             "amsgrad": False,
             "fused": False,
             "lr": 1e-05,
@@ -80,9 +81,7 @@ def test_setup_autoencoder(
     dummy_hyperparameters: RuntimeHyperparameters, snapshot: SnapshotSession
 ) -> None:
     """Test the setup_autoencoder function."""
-    autoencoder, _, _ = setup_autoencoder_optimizer_scheduler(
-        dummy_hyperparameters, use_deepspeed=False
-    )
+    autoencoder = setup_autoencoder(dummy_hyperparameters, device=torch.device("cpu"))
     assert snapshot == str(autoencoder), "Autoencoder string representation has changed."
 
 
diff --git a/sparse_autoencoder/train/utils/get_model_device.py b/sparse_autoencoder/train/utils/get_model_device.py
index 82a265aa..0d49bbd5 100644
--- a/sparse_autoencoder/train/utils/get_model_device.py
+++ b/sparse_autoencoder/train/utils/get_model_device.py
@@ -1,11 +1,9 @@
 """Get the device that the model is on."""
-from deepspeed import DeepSpeedEngine
 import torch
 from torch.nn import Module
-from torch.nn.parallel import DataParallel
 
 
-def get_model_device(model: Module | DataParallel | DeepSpeedEngine) -> torch.device:
+def get_model_device(model: Module) -> torch.device:
     """Get the device on which a PyTorch model is on.
 
     Args:
@@ -17,10 +15,6 @@ def get_model_device(model: Module | DataParallel | DeepSpeedEngine) -> torch.de
     Raises:
         ValueError: If the model has no parameters.
     """
-    # Deepspeed models already have a device property, so just return that
-    if hasattr(model, "device"):
-        return model.device
-
     # Check if the model has parameters
     if len(list(model.parameters())) == 0:
         exception_message = "The model has no parameters."
diff --git a/sparse_autoencoder/train/utils/wandb_sweep_types.py b/sparse_autoencoder/train/utils/wandb_sweep_types.py
index a8530041..5df4aa5b 100644
--- a/sparse_autoencoder/train/utils/wandb_sweep_types.py
+++ b/sparse_autoencoder/train/utils/wandb_sweep_types.py
@@ -165,7 +165,7 @@ class Distribution(LowercaseStrEnum):
 class Controller:
     """Controller."""
 
-    type: ControllerType
+    type: ControllerType  # noqa: A003
 
 
 @dataclass
@@ -180,7 +180,7 @@ class HyperbandStopping:
         HyperbandStopping(type=hyperband)
     """
 
-    type: HyperbandStoppingType | None = HyperbandStoppingType.HYPERBAND
+    type: HyperbandStoppingType | None = HyperbandStoppingType.HYPERBAND  # noqa: A003
 
     eta: float | None = None
     """ETA.
@@ -286,10 +286,10 @@ class Parameter(Generic[ParamType]):
     Specifies the single valid value for this hyperparameter. Compatible with grid.
     """
 
-    max: ParamType | None = None
+    max: ParamType | None = None  # noqa: A003
     """Maximum value."""
 
-    min: ParamType | None = None
+    min: ParamType | None = None  # noqa: A003
     """Minimum value."""
 
     distribution: Distribution | None = None
diff --git a/sparse_autoencoder/utils/data_parallel.py b/sparse_autoencoder/utils/data_parallel.py
new file mode 100644
index 00000000..4895b412
--- /dev/null
+++ b/sparse_autoencoder/utils/data_parallel.py
@@ -0,0 +1,40 @@
+"""Data parallel utils."""
+from typing import Any, Generic, TypeVar
+
+from torch.nn import DataParallel, Module
+
+
+T = TypeVar("T", bound=Module)
+
+
+class DataParallelWithModelAttributes(DataParallel[T], Generic[T]):
+    """Data parallel with access to underlying model attributes/methods.
+
+    Allows access to underlying model attributes/methods, which is not possible with the default
+    `DataParallel` class. Based on:
+    https://pytorch.org/tutorials/beginner/former_torchies/parallelism_tutorial.html
+
+    Example:
+        >>> from sparse_autoencoder import SparseAutoencoder, SparseAutoencoderConfig
+        >>> model = SparseAutoencoder(SparseAutoencoderConfig(
+        ...     n_input_features=2,
+        ...     n_learned_features=4,
+        ... ))
+        >>> distributed_model = DataParallelWithModelAttributes(model)
+        >>> distributed_model.config.n_learned_features
+        4
+    """
+
+    def __getattr__(self, name: str) -> Any:  # noqa: ANN401
+        """Allow access to underlying model attributes/methods.
+
+        Args:
+            name: Attribute/method name.
+
+        Returns:
+            Attribute value/method.
+        """
+        try:
+            return super().__getattr__(name)
+        except AttributeError:
+            return getattr(self.module, name)