Repository: whittle-org/whittle (language: python)

Issue:
Write API from our supernet to LM-Eval-Harness
LiTGPT writes checkpoints to a directory, converts it into an HF model and evaluates it. We evaluate a lot of models during search and want this to be as seamless as possible. Writing an api for our models in lm-eval-harness would make evaluation during search much faster. Following are the list of checks necessary to integrate this:
1. Write a custom API following https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/interface.md#external-library-usage
2. Test that predictions/logits for an HF model and our model matches with a pretrained checkpoint


Discussion / hints:

Fix patch:
diff --git a/poetry.lock b/poetry.lock
index de6385a..9db1d93 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -24,18 +24,18 @@ files = [
 
 [[package]]
 name = "accelerate"
-version = "0.31.0"
+version = "0.33.0"
 description = "Accelerate"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "accelerate-0.31.0-py3-none-any.whl", hash = "sha256:0fc608dc49584f64d04711a39711d73cb0ad4ef3d21cddee7ef2216e29471144"},
-    {file = "accelerate-0.31.0.tar.gz", hash = "sha256:b5199865b26106ccf9205acacbe8e4b3b428ad585e7c472d6a46f6fb75b6c176"},
+    {file = "accelerate-0.33.0-py3-none-any.whl", hash = "sha256:0a7f33d60ba09afabd028d4f0856dd19c5a734b7a596d637d9dd6e3d0eadbaf3"},
+    {file = "accelerate-0.33.0.tar.gz", hash = "sha256:11ba481ed6ea09191775df55ce464aeeba67a024bd0261a44b77b30fb439e26a"},
 ]
 
 [package.dependencies]
-huggingface-hub = "*"
-numpy = ">=1.17"
+huggingface-hub = ">=0.21.0"
+numpy = ">=1.17,<2.0.0"
 packaging = ">=20.0"
 psutil = "*"
 pyyaml = "*"
@@ -256,57 +256,6 @@ files = [
 future = ">=0.15.2"
 numpy = ">=1.12"
 
-[[package]]
-name = "awscrt"
-version = "0.20.11"
-description = "A common runtime for AWS Python projects"
-optional = false
-python-versions = ">=3.7"
-files = [
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-    {file = "awscrt-0.20.11-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3b4ec0b471cf7d6a7a0950553ddf97d58a0caf4a8350da9ca12250c7df6add94"},
-    {file = "awscrt-0.20.11-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a6b91cac82abf9718657e0694f90334e4ef4b2ef32061938ff0ceed67e302469"},
-    {file = "awscrt-0.20.11-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:0261ef47f5000d5ce069dec05edf9d803a3ff89c02bd574ec0585e2e4447aca6"},
-    {file = "awscrt-0.20.11-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:81167651ccd45af55fa659a09b415eba881a9892415e465b6432a4f336311711"},
-    {file = "awscrt-0.20.11-cp310-cp310-win32.whl", hash = "sha256:fb316c27110a19917a45dc7b678349bc329c98ac1b95d5bd872f0ad37300e725"},
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-    {file = "awscrt-0.20.11-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:7f68ce98ee54b634fbe48689aa4610965e3af0e5e1a11da82a791057c741fb99"},
-    {file = "awscrt-0.20.11-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:4404f6e517a5f0871943463fe59cec657586b2d0d1e6e2efbe9dbb0b42f5b3e8"},
-    {file = "awscrt-0.20.11-cp39-cp39-win32.whl", hash = "sha256:8e95ce32b03006097f833b539d1bc3ea503379d880751ddbfd7bb0440e93c0c4"},
-    {file = "awscrt-0.20.11-cp39-cp39-win_amd64.whl", hash = "sha256:20b00d68a90575121cf04250c93aa4874f7d1f7d2d81f37511c12a157be7421c"},
-    {file = "awscrt-0.20.11.tar.gz", hash = "sha256:c3dbfb7f1909457952e645373e72b69f90c50c465ee6a46d9bbdc12acb79803c"},
-]
-
 [[package]]
 name = "bitsandbytes"
 version = "0.42.0"
@@ -323,20 +272,17 @@ scipy = "*"
 
 [[package]]
 name = "boto3"
-version = "1.34.137"
+version = "1.34.147"
 description = "The AWS SDK for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "boto3-1.34.137-py3-none-any.whl", hash = "sha256:7cb697d67fd138ceebc6f789919ae370c092a50c6b0ccc4ef483027935502eab"},
-    {file = "boto3-1.34.137.tar.gz", hash = "sha256:0b21b84db4619b3711a6f643d465a5a25e81231ee43615c55a20ff6b89c6cc3c"},
+    {file = "boto3-1.34.147-py3-none-any.whl", hash = "sha256:e1cef9a1a301866bcdee32ae0c699465eb2345f9a8e613a5835821430165ff6d"},
+    {file = "boto3-1.34.147.tar.gz", hash = "sha256:9ec1c6ab22588242a47549f51a63dfc7c21fdf95a94820fc6e629ab060c38bd9"},
 ]
 
 [package.dependencies]
-botocore = [
-    {version = ">=1.34.137,<1.35.0"},
-    {version = ">=1.21.0,<2.0a0", extras = ["crt"], optional = true, markers = "extra == \"crt\""},
-]
+botocore = ">=1.34.147,<1.35.0"
 jmespath = ">=0.7.1,<2.0.0"
 s3transfer = ">=0.10.0,<0.11.0"
 
@@ -345,17 +291,16 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.34.137"
+version = "1.34.147"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "botocore-1.34.137-py3-none-any.whl", hash = "sha256:a980fa4adec4bfa23fff70a3512622e9412c69c791898a52cafc2458b0be6040"},
-    {file = "botocore-1.34.137.tar.gz", hash = "sha256:e29c8e9bfda0b20a1997792968e85868bfce42fefad9730f633a81adcff3f2ef"},
+    {file = "botocore-1.34.147-py3-none-any.whl", hash = "sha256:be94a2f4874b1d1705cae2bd512c475047497379651678593acb6c61c50d91de"},
+    {file = "botocore-1.34.147.tar.gz", hash = "sha256:2e8f000b77e4ca345146cb2edab6403769a517b564f627bb084ab335417f3dbe"},
 ]
 
 [package.dependencies]
-awscrt = {version = "0.20.11", optional = true, markers = "extra == \"crt\""}
 jmespath = ">=0.7.1,<2.0.0"
 python-dateutil = ">=2.1,<3.0.0"
 urllib3 = [
@@ -368,13 +313,13 @@ crt = ["awscrt (==0.20.11)"]
 
 [[package]]
 name = "certifi"
-version = "2024.6.2"
+version = "2024.7.4"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2024.6.2-py3-none-any.whl", hash = "sha256:ddc6c8ce995e6987e7faf5e3f1b02b302836a0e5d98ece18392cb1a36c72ad56"},
-    {file = "certifi-2024.6.2.tar.gz", hash = "sha256:3cd43f1c6fa7dedc5899d69d3ad0398fd018ad1a17fba83ddaf78aa46c747516"},
+    {file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"},
+    {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"},
 ]
 
 [[package]]
@@ -872,13 +817,13 @@ torch = ["torch"]
 
 [[package]]
 name = "exceptiongroup"
-version = "1.2.1"
+version = "1.2.2"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "exceptiongroup-1.2.1-py3-none-any.whl", hash = "sha256:5258b9ed329c5bbdd31a309f53cbfb0b155341807f6ff7606a1e801a891b29ad"},
-    {file = "exceptiongroup-1.2.1.tar.gz", hash = "sha256:a4785e48b045528f5bfe627b6ad554ff32def154f42372786903b7abcfe1aa16"},
+    {file = "exceptiongroup-1.2.2-py3-none-any.whl", hash = "sha256:3111b9d131c238bec2f8f516e123e14ba243563fb135d3fe885990585aa7795b"},
+    {file = "exceptiongroup-1.2.2.tar.gz", hash = "sha256:47c2edf7c6738fafb49fd34290706d1a1a2f4d1c6df275526b62cbb4aa5393cc"},
 ]
 
 [package.extras]
@@ -886,13 +831,13 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "fastapi"
-version = "0.111.0"
+version = "0.111.1"
 description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fastapi-0.111.0-py3-none-any.whl", hash = "sha256:97ecbf994be0bcbdadedf88c3150252bed7b2087075ac99735403b1b76cc8fc0"},
-    {file = "fastapi-0.111.0.tar.gz", hash = "sha256:b9db9dd147c91cb8b769f7183535773d8741dd46f9dc6676cd82eab510228cd7"},
+    {file = "fastapi-0.111.1-py3-none-any.whl", hash = "sha256:4f51cfa25d72f9fbc3280832e84b32494cf186f50158d364a8765aabf22587bf"},
+    {file = "fastapi-0.111.1.tar.gz", hash = "sha256:ddd1ac34cb1f76c2e2d7f8545a4bcb5463bce4834e81abf0b189e0c359ab2413"},
 ]
 
 [package.dependencies]
@@ -900,12 +845,10 @@ email_validator = ">=2.0.0"
 fastapi-cli = ">=0.0.2"
 httpx = ">=0.23.0"
 jinja2 = ">=2.11.2"
-orjson = ">=3.2.1"
 pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0 || >2.0.0,<2.0.1 || >2.0.1,<2.1.0 || >2.1.0,<3.0.0"
 python-multipart = ">=0.0.7"
 starlette = ">=0.37.2,<0.38.0"
 typing-extensions = ">=4.8.0"
-ujson = ">=4.0.1,<4.0.2 || >4.0.2,<4.1.0 || >4.1.0,<4.2.0 || >4.2.0,<4.3.0 || >4.3.0,<5.0.0 || >5.0.0,<5.1.0 || >5.1.0"
 uvicorn = {version = ">=0.12.0", extras = ["standard"]}
 
 [package.extras]
@@ -946,53 +889,53 @@ typing = ["typing-extensions (>=4.8)"]
 
 [[package]]
 name = "fonttools"
-version = "4.53.0"
+version = "4.53.1"
 description = "Tools to manipulate font files"
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "fonttools-4.53.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:715b41c3e231f7334cbe79dfc698213dcb7211520ec7a3bc2ba20c8515e8a3b5"},
-    {file = "fonttools-4.53.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:74ae2441731a05b44d5988d3ac2cf784d3ee0a535dbed257cbfff4be8bb49eb9"},
-    {file = "fonttools-4.53.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:95db0c6581a54b47c30860d013977b8a14febc206c8b5ff562f9fe32738a8aca"},
-    {file = "fonttools-4.53.0-cp310-cp310-win32.whl", hash = "sha256:9cd7a6beec6495d1dffb1033d50a3f82dfece23e9eb3c20cd3c2444d27514068"},
-    {file = "fonttools-4.53.0-cp310-cp310-win_amd64.whl", hash = "sha256:daaef7390e632283051e3cf3e16aff2b68b247e99aea916f64e578c0449c9c68"},
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-    {file = "fonttools-4.53.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:eceef49f457253000e6a2d0f7bd08ff4e9fe96ec4ffce2dbcb32e34d9c1b8161"},
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-protobuf = ["grpcio-tools (>=1.64.1)"]
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+    {file = "hf_transfer-0.1.8-cp39-none-win_amd64.whl", hash = "sha256:05ea16307bf4a5eb097cbc6e5057e4eb5e080a138af23ef639fd38857723c288"},
+    {file = "hf_transfer-0.1.8-pp310-pypy310_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:928ff036c3e98e10dcfbdb4fcdfc4592d37a5cc8e365a7ba8dfd4337e849d675"},
+    {file = "hf_transfer-0.1.8-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d49ba3ce67035f460ae1924fe2feafec155cb535eec7f31ed5109c19064cd294"},
+    {file = "hf_transfer-0.1.8-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b01f5872c62cfee3ec9ca5c738818296f69f8adf84b4d8d15f2a5601d9dda339"},
+    {file = "hf_transfer-0.1.8-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:659d4212d50847a5165666bf43d67727679b4f694ef9c413613cc27093136527"},
+    {file = "hf_transfer-0.1.8.tar.gz", hash = "sha256:26d229468152e7a3ec12664cac86b8c2800695fd85f9c9a96677a775cc04f0b3"},
 ]
 
 [[package]]
@@ -1396,13 +1331,13 @@ socks = ["socksio (==1.*)"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.23.4"
+version = "0.24.2"
 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-0.23.4-py3-none-any.whl", hash = "sha256:3a0b957aa87150addf0cc7bd71b4d954b78e749850e1e7fb29ebbd2db64ca037"},
-    {file = "huggingface_hub-0.23.4.tar.gz", hash = "sha256:35d99016433900e44ae7efe1c209164a5a81dbbcd53a52f99c281dcd7ce22431"},
+    {file = "huggingface_hub-0.24.2-py3-none-any.whl", hash = "sha256:abdf3244d3a274c4b1fbc5c4a1ef700032b3f60ba93cc63e4f036fd082aa2805"},
+    {file = "huggingface_hub-0.24.2.tar.gz", hash = "sha256:92be892405d2f6a7a8479016f9a5662354f202b2c6c1ff499609621aed1fae10"},
 ]
 
 [package.dependencies]
@@ -1416,28 +1351,28 @@ tqdm = ">=4.42.1"
 typing-extensions = ">=3.7.4.3"
 
 [package.extras]
-all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.3.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 cli = ["InquirerPy (==0.3.4)"]
-dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.3.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
 hf-transfer = ["hf-transfer (>=0.1.4)"]
 inference = ["aiohttp", "minijinja (>=1.0)"]
-quality = ["mypy (==1.5.1)", "ruff (>=0.3.0)"]
+quality = ["mypy (==1.5.1)", "ruff (>=0.5.0)"]
 tensorflow = ["graphviz", "pydot", "tensorflow"]
 tensorflow-testing = ["keras (<3.0)", "tensorflow"]
-testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
-torch = ["safetensors", "torch"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
+torch = ["safetensors[torch]", "torch"]
 typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
 
 [[package]]
 name = "identify"
-version = "2.5.36"
+version = "2.6.0"
 description = "File identification library for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "identify-2.5.36-py2.py3-none-any.whl", hash = "sha256:37d93f380f4de590500d9dba7db359d0d3da95ffe7f9de1753faa159e71e7dfa"},
-    {file = "identify-2.5.36.tar.gz", hash = "sha256:e5e00f54165f9047fbebeb4a560f9acfb8af4c88232be60a488e9b68d122745d"},
+    {file = "identify-2.6.0-py2.py3-none-any.whl", hash = "sha256:e79ae4406387a9d300332b5fd366d8994f1525e8414984e1a59e058b2eda2dd0"},
+    {file = "identify-2.6.0.tar.gz", hash = "sha256:cb171c685bdc31bcc4c1734698736a7d5b6c8bf2e0c15117f4d469c8640ae5cf"},
 ]
 
 [package.extras]
@@ -1456,13 +1391,13 @@ files = [
 
 [[package]]
 name = "importlib-metadata"
-version = "8.0.0"
+version = "8.2.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "importlib_metadata-8.0.0-py3-none-any.whl", hash = "sha256:15584cf2b1bf449d98ff8a6ff1abef57bf20f3ac6454f431736cd3e660921b2f"},
-    {file = "importlib_metadata-8.0.0.tar.gz", hash = "sha256:188bd24e4c346d3f0a933f275c2fec67050326a856b9a359881d7c2a697e8812"},
+    {file = "importlib_metadata-8.2.0-py3-none-any.whl", hash = "sha256:11901fa0c2f97919b288679932bb64febaeacf289d18ac84dd68cb2e74213369"},
+    {file = "importlib_metadata-8.2.0.tar.gz", hash = "sha256:72e8d4399996132204f9a16dcc751af254a48f8d1b20b9ff0f98d4a8f901e73d"},
 ]
 
 [package.dependencies]
@@ -1557,13 +1492,13 @@ files = [
 
 [[package]]
 name = "jsonargparse"
-version = "4.31.0"
+version = "4.32.0"
 description = "Implement minimal boilerplate CLIs derived from type hints and parse from command line, config files and environment variables."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "jsonargparse-4.31.0-py3-none-any.whl", hash = "sha256:b9a834f28fed4b0e45e1e06e1579dfc22a7471920f1f44770aa77d224778fd34"},
-    {file = "jsonargparse-4.31.0.tar.gz", hash = "sha256:313ffa1abaf61cdc4f52a8b8a60f6541733b1ddb7dfe9932a3c85a086022edac"},
+    {file = "jsonargparse-4.32.0-py3-none-any.whl", hash = "sha256:2da92d37be875b91672ce117eecfcd4ed9841fae099d8081d3cf819703d7de1a"},
+    {file = "jsonargparse-4.32.0.tar.gz", hash = "sha256:a93f29506e9780b28fc6854c3b3718f4a74d92bf7f0c1f4c34e3047cd5d8be42"},
 ]
 
 [package.dependencies]
@@ -1721,18 +1656,18 @@ files = [
 
 [[package]]
 name = "lightning"
-version = "2.3.0.dev20240428"
+version = "2.3.3"
 description = "The Deep Learning framework to train, deploy, and ship AI products Lightning fast."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "lightning-2.3.0.dev20240428-py3-none-any.whl", hash = "sha256:f27f2e7c88d6fb337b84319da2d8161b810df311ea8c186c11dfe6bca809a31e"},
-    {file = "lightning-2.3.0.dev20240428.tar.gz", hash = "sha256:d23a94b314b510df40af9e68ba177a9d37ee163e8d6eb02a4e97c6abe0a90963"},
+    {file = "lightning-2.3.3-py3-none-any.whl", hash = "sha256:5b4950f20b5117f30aad7820709cfa56669567cf4ded93df502b3dee443a6a5d"},
+    {file = "lightning-2.3.3.tar.gz", hash = "sha256:7f454711895c1c6e455766f01fa39522e25e5ab54c15c5e5fbad342fa92bc93c"},
 ]
 
 [package.dependencies]
-fsspec = {version = ">=2022.5.0,<2025.0", extras = ["http"]}
-lightning-utilities = ">=0.8.0,<2.0"
+fsspec = {version = ">=2022.5.0,<2026.0", extras = ["http"]}
+lightning-utilities = ">=0.10.0,<2.0"
 numpy = ">=1.17.2,<3.0"
 packaging = ">=20.0,<25.0"
 pytorch-lightning = "*"
@@ -1743,45 +1678,36 @@ tqdm = ">=4.57.0,<6.0"
 typing-extensions = ">=4.4.0,<6.0"
 
 [package.extras]
-all = ["Jinja2 (<4.0)", "PyYAML (<7.0)", "aiohttp (>=3.8.0,<4.0)", "arrow (>=1.2.0,<2.0)", "backoff (>=2.2.1,<3.0)", "beautifulsoup4 (>=4.8.0,<5.0)", "bitsandbytes (>=0.42.0,<1.0)", "click (<9.0)", "croniter (>=1.3.0,<1.5.0)", "dateutils (<1.0)", "deepdiff (>=5.7.0,<7.0)", "deepspeed (>=0.8.2,<=0.9.3)", "docker (>=5.0.0,<7.0)", "fastapi (>=0.92.0,<1.0)", "fsspec[http] (>=2022.5.0,<2024.0)", "gym[classic-control] (>=0.17.0,<1.0)", "hydra-core (>=1.0.5,<2.0)", "inquirer (>=2.10.0,<4.0)", "ipython[all] (<9.0)", "jsonargparse[signatures] (>=4.27.7,<5.0)", "lightning-api-access (>=0.0.3)", "lightning-cloud (==0.5.67)", "lightning-fabric (>=1.9.0)", "lightning-utilities (>=0.8.0,<1.0)", "matplotlib (>3.1,<4.0)", "omegaconf (>=2.0.5,<3.0)", "packaging", "psutil (<6.0)", "pydantic (>=1.7.4)", "python-multipart (>=0.0.5,<1.0)", "pytorch-lightning (>=1.9.0)", "redis (>=4.0.1,<6.0)", "requests (<3.0)", "rich (>=12.3.0,<14.0)", "s3fs (>=2022.5.0,<2024.0)", "starlette", "tensorboardX (>=2.2,<3.0)", "torchmetrics (>=0.10.0,<2.0)", "torchvision (>=0.15.0,<1.0)", "traitlets (>=5.3.0,<6.0)", "typing-extensions (>=4.4.0,<5.0)", "urllib3 (<3.0)", "uvicorn (<1.0)", "websocket-client (<2.0)", "websockets (<12.0)"]
-app = ["Jinja2 (<4.0)", "PyYAML (<7.0)", "arrow (>=1.2.0,<2.0)", "backoff (>=2.2.1,<3.0)", "beautifulsoup4 (>=4.8.0,<5.0)", "click (<9.0)", "croniter (>=1.3.0,<1.5.0)", "dateutils (<1.0)", "deepdiff (>=5.7.0,<7.0)", "fastapi (>=0.92.0,<1.0)", "fsspec[http] (>=2022.5.0,<2024.0)", "inquirer (>=2.10.0,<4.0)", "lightning-cloud (==0.5.67)", "lightning-utilities (>=0.8.0,<1.0)", "packaging", "psutil (<6.0)", "pydantic (>=1.7.4)", "python-multipart (>=0.0.5,<1.0)", "requests (<3.0)", "rich (>=12.3.0,<14.0)", "starlette", "traitlets (>=5.3.0,<6.0)", "typing-extensions (>=4.4.0,<5.0)", "urllib3 (<3.0)", "uvicorn (<1.0)", "websocket-client (<2.0)", "websockets (<12.0)"]
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-app-test = ["coverage (==7.3.1)", "httpx (==0.25.0)", "pandas", "playwright (==1.38.0)", "psutil (<6.0)", "pympler", "pytest (==7.4.0)", "pytest-asyncio (==0.21.1)", "pytest-cov (==4.1.0)", "pytest-doctestplus (==1.0.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "pytest-xdist (==3.3.1)", "requests-mock (==1.11.0)", "setuptools (<69.0)", "trio (<0.22.0)"]
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+all = ["bitsandbytes (>=0.42.0,<1.0)", "deepspeed (>=0.8.2,<=0.9.3)", "hydra-core (>=1.2.0,<2.0)", "ipython[all] (<9.0)", "jsonargparse[signatures] (>=4.27.7,<5.0)", "lightning-utilities (>=0.8.0,<1.0)", "matplotlib (>3.1,<4.0)", "omegaconf (>=2.2.3,<3.0)", "requests (<3.0)", "rich (>=12.3.0,<14.0)", "tensorboardX (>=2.2,<3.0)", "torchmetrics (>=0.10.0,<2.0)", "torchvision (>=0.15.0,<1.0)"]
+app = ["lightning-app (>=2.3.3,<3.0)"]
 data = ["litdata (>=0.2.0rc,<1.0)"]
-dev = ["Jinja2 (<4.0)", "PyYAML (<7.0)", "aiohttp (>=3.8.0,<4.0)", "arrow (>=1.2.0,<2.0)", "backoff (>=2.2.1,<3.0)", "beautifulsoup4 (>=4.8.0,<5.0)", "bitsandbytes (>=0.42.0,<1.0)", "click (<9.0)", "click (==8.1.7)", "cloudpickle (>=1.3,<3.0)", "coverage (==7.3.1)", "croniter (>=1.3.0,<1.5.0)", "dateutils (<1.0)", "deepdiff (>=5.7.0,<7.0)", "deepspeed (>=0.8.2,<=0.9.3)", "docker (>=5.0.0,<7.0)", "fastapi", "fastapi (>=0.92.0,<1.0)", "fsspec[http] (>=2022.5.0,<2024.0)", "gym[classic-control] (>=0.17.0,<1.0)", "httpx (==0.25.0)", "hydra-core (>=1.0.5,<2.0)", "inquirer (>=2.10.0,<4.0)", "ipython[all] (<9.0)", "jsonargparse[signatures] (>=4.27.7,<5.0)", "lightning-api-access (>=0.0.3)", "lightning-cloud (==0.5.67)", "lightning-fabric (>=1.9.0)", "lightning-utilities (>=0.8.0,<1.0)", "matplotlib (>3.1,<4.0)", "omegaconf (>=2.0.5,<3.0)", "onnx (>=0.14.0,<2.0)", "onnxruntime (>=0.15.0,<2.0)", "packaging", "pandas", "pandas (>1.0,<3.0)", "playwright (==1.38.0)", "psutil (<6.0)", "pydantic (>=1.7.4)", "pympler", "pytest (==7.4.0)", "pytest-asyncio (==0.21.1)", "pytest-cov (==4.1.0)", "pytest-doctestplus (==1.0.0)", "pytest-random-order (==1.1.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "pytest-xdist (==3.3.1)", "python-multipart (>=0.0.5,<1.0)", "pytorch-lightning (>=1.9.0)", "redis (>=4.0.1,<6.0)", "requests (<3.0)", "requests-mock (==1.11.0)", "rich (>=12.3.0,<14.0)", "s3fs (>=2022.5.0,<2024.0)", "scikit-learn (>0.22.1,<2.0)", "setuptools (<69.0)", "starlette", "tensorboard (>=2.9.1,<3.0)", "tensorboardX (>=2.2,<3.0)", "torchmetrics (>=0.10.0,<2.0)", "torchmetrics (>=0.7.0,<2.0)", "torchvision (>=0.15.0,<1.0)", "traitlets (>=5.3.0,<6.0)", "trio (<0.22.0)", "typing-extensions (>=4.4.0,<5.0)", "urllib3 (<3.0)", "uvicorn", "uvicorn (<1.0)", "websocket-client (<2.0)", "websockets (<12.0)"]
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-extra = ["Jinja2 (<4.0)", "PyYAML (<7.0)", "aiohttp (>=3.8.0,<4.0)", "arrow (>=1.2.0,<2.0)", "backoff (>=2.2.1,<3.0)", "beautifulsoup4 (>=4.8.0,<5.0)", "bitsandbytes (>=0.42.0,<1.0)", "click (<9.0)", "croniter (>=1.3.0,<1.5.0)", "dateutils (<1.0)", "deepdiff (>=5.7.0,<7.0)", "docker (>=5.0.0,<7.0)", "fastapi (>=0.92.0,<1.0)", "fsspec[http] (>=2022.5.0,<2024.0)", "hydra-core (>=1.0.5,<2.0)", "inquirer (>=2.10.0,<4.0)", "jsonargparse[signatures] (>=4.27.7,<5.0)", "lightning-api-access (>=0.0.3)", "lightning-cloud (==0.5.67)", "lightning-fabric (>=1.9.0)", "lightning-utilities (>=0.8.0,<1.0)", "matplotlib (>3.1,<4.0)", "omegaconf (>=2.0.5,<3.0)", "packaging", "psutil (<6.0)", "pydantic (>=1.7.4)", "python-multipart (>=0.0.5,<1.0)", "pytorch-lightning (>=1.9.0)", "redis (>=4.0.1,<6.0)", "requests (<3.0)", "rich (>=12.3.0,<14.0)", "s3fs (>=2022.5.0,<2024.0)", "starlette", "tensorboardX (>=2.2,<3.0)", "traitlets (>=5.3.0,<6.0)", "typing-extensions (>=4.4.0,<5.0)", "urllib3 (<3.0)", "uvicorn (<1.0)", "websocket-client (<2.0)", "websockets (<12.0)"]
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+examples = ["ipython[all] (<9.0)", "lightning-utilities (>=0.8.0,<1.0)", "requests (<3.0)", "torchmetrics (>=0.10.0,<2.0)", "torchvision (>=0.15.0,<1.0)"]
+extra = ["bitsandbytes (>=0.42.0,<1.0)", "hydra-core (>=1.2.0,<2.0)", "jsonargparse[signatures] (>=4.27.7,<5.0)", "matplotlib (>3.1,<4.0)", "omegaconf (>=2.2.3,<3.0)", "rich (>=12.3.0,<14.0)", "tensorboardX (>=2.2,<3.0)"]
 fabric-all = ["bitsandbytes (>=0.42.0,<1.0)", "deepspeed (>=0.8.2,<=0.9.3)", "lightning-utilities (>=0.8.0,<1.0)", "torchmetrics (>=0.10.0,<2.0)", "torchvision (>=0.15.0,<1.0)"]
 fabric-dev = ["bitsandbytes (>=0.42.0,<1.0)", "click (==8.1.7)", "coverage (==7.3.1)", "deepspeed (>=0.8.2,<=0.9.3)", "lightning-utilities (>=0.8.0,<1.0)", "pytest (==7.4.0)", "pytest-cov (==4.1.0)", "pytest-random-order (==1.1.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "tensorboardX (>=2.2,<3.0)", "torchmetrics (>=0.10.0,<2.0)", "torchmetrics (>=0.7.0,<2.0)", "torchvision (>=0.15.0,<1.0)"]
 fabric-examples = ["lightning-utilities (>=0.8.0,<1.0)", "torchmetrics (>=0.10.0,<2.0)", "torchvision (>=0.15.0,<1.0)"]
 fabric-strategies = ["bitsandbytes (>=0.42.0,<1.0)", "deepspeed (>=0.8.2,<=0.9.3)"]
 fabric-test = ["click (==8.1.7)", "coverage (==7.3.1)", "pytest (==7.4.0)", "pytest-cov (==4.1.0)", "pytest-random-order (==1.1.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "tensorboardX (>=2.2,<3.0)", "torchmetrics (>=0.7.0,<2.0)"]
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-pytorch-dev = ["bitsandbytes (>=0.42.0,<1.0)", "cloudpickle (>=1.3,<3.0)", "coverage (==7.3.1)", "deepspeed (>=0.8.2,<=0.9.3)", "fastapi", "gym[classic-control] (>=0.17.0,<1.0)", "hydra-core (>=1.0.5,<2.0)", "ipython[all] (<9.0)", "jsonargparse[signatures] (>=4.27.7,<5.0)", "lightning-utilities (>=0.8.0,<1.0)", "matplotlib (>3.1,<4.0)", "omegaconf (>=2.0.5,<3.0)", "onnx (>=0.14.0,<2.0)", "onnxruntime (>=0.15.0,<2.0)", "pandas (>1.0,<3.0)", "psutil (<6.0)", "pytest (==7.4.0)", "pytest-cov (==4.1.0)", "pytest-random-order (==1.1.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "requests (<3.0)", "rich (>=12.3.0,<14.0)", "scikit-learn (>0.22.1,<2.0)", "tensorboard (>=2.9.1,<3.0)", "tensorboardX (>=2.2,<3.0)", "torchmetrics (>=0.10.0,<2.0)", "torchvision (>=0.15.0,<1.0)", "uvicorn"]
-pytorch-examples = ["gym[classic-control] (>=0.17.0,<1.0)", "ipython[all] (<9.0)", "lightning-utilities (>=0.8.0,<1.0)", "requests (<3.0)", "torchmetrics (>=0.10.0,<2.0)", "torchvision (>=0.15.0,<1.0)"]
-pytorch-extra = ["bitsandbytes (>=0.42.0,<1.0)", "hydra-core (>=1.0.5,<2.0)", "jsonargparse[signatures] (>=4.27.7,<5.0)", "matplotlib (>3.1,<4.0)", "omegaconf (>=2.0.5,<3.0)", "rich (>=12.3.0,<14.0)", "tensorboardX (>=2.2,<3.0)"]
+pytorch-all = ["bitsandbytes (>=0.42.0,<1.0)", "deepspeed (>=0.8.2,<=0.9.3)", "hydra-core (>=1.2.0,<2.0)", "ipython[all] (<9.0)", "jsonargparse[signatures] (>=4.27.7,<5.0)", "lightning-utilities (>=0.8.0,<1.0)", "matplotlib (>3.1,<4.0)", "omegaconf (>=2.2.3,<3.0)", "requests (<3.0)", "rich (>=12.3.0,<14.0)", "tensorboardX (>=2.2,<3.0)", "torchmetrics (>=0.10.0,<2.0)", "torchvision (>=0.15.0,<1.0)"]
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+pytorch-examples = ["ipython[all] (<9.0)", "lightning-utilities (>=0.8.0,<1.0)", "requests (<3.0)", "torchmetrics (>=0.10.0,<2.0)", "torchvision (>=0.15.0,<1.0)"]
+pytorch-extra = ["bitsandbytes (>=0.42.0,<1.0)", "hydra-core (>=1.2.0,<2.0)", "jsonargparse[signatures] (>=4.27.7,<5.0)", "matplotlib (>3.1,<4.0)", "omegaconf (>=2.2.3,<3.0)", "rich (>=12.3.0,<14.0)", "tensorboardX (>=2.2,<3.0)"]
 pytorch-strategies = ["deepspeed (>=0.8.2,<=0.9.3)"]
 pytorch-test = ["cloudpickle (>=1.3,<3.0)", "coverage (==7.3.1)", "fastapi", "onnx (>=0.14.0,<2.0)", "onnxruntime (>=0.15.0,<2.0)", "pandas (>1.0,<3.0)", "psutil (<6.0)", "pytest (==7.4.0)", "pytest-cov (==4.1.0)", "pytest-random-order (==1.1.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "scikit-learn (>0.22.1,<2.0)", "tensorboard (>=2.9.1,<3.0)", "uvicorn"]
-store = ["Jinja2 (<4.0)", "PyYAML (<7.0)", "arrow (>=1.2.0,<2.0)", "backoff (>=2.2.1,<3.0)", "beautifulsoup4 (>=4.8.0,<5.0)", "click (<9.0)", "croniter (>=1.3.0,<1.5.0)", "dateutils (<1.0)", "deepdiff (>=5.7.0,<7.0)", "fastapi (>=0.92.0,<1.0)", "fsspec[http] (>=2022.5.0,<2024.0)", "inquirer (>=2.10.0,<4.0)", "lightning-cloud (==0.5.67)", "lightning-utilities (>=0.8.0,<1.0)", "packaging", "psutil (<6.0)", "pydantic (>=1.7.4)", "python-multipart (>=0.0.5,<1.0)", "requests (<3.0)", "rich (>=12.3.0,<14.0)", "starlette", "traitlets (>=5.3.0,<6.0)", "typing-extensions (>=4.4.0,<5.0)", "urllib3 (<3.0)", "uvicorn (<1.0)", "websocket-client (<2.0)", "websockets (<12.0)"]
 store-test = ["coverage (==7.3.1)", "pytest (==7.4.0)", "pytest-cov (==4.1.0)", "pytest-random-order (==1.1.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)"]
 strategies = ["bitsandbytes (>=0.42.0,<1.0)", "deepspeed (>=0.8.2,<=0.9.3)"]
-test = ["click (==8.1.7)", "cloudpickle (>=1.3,<3.0)", "coverage (==7.3.1)", "fastapi", "httpx (==0.25.0)", "onnx (>=0.14.0,<2.0)", "onnxruntime (>=0.15.0,<2.0)", "pandas", "pandas (>1.0,<3.0)", "playwright (==1.38.0)", "psutil (<6.0)", "pympler", "pytest (==7.4.0)", "pytest-asyncio (==0.21.1)", "pytest-cov (==4.1.0)", "pytest-doctestplus (==1.0.0)", "pytest-random-order (==1.1.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "pytest-xdist (==3.3.1)", "requests-mock (==1.11.0)", "scikit-learn (>0.22.1,<2.0)", "setuptools (<69.0)", "tensorboard (>=2.9.1,<3.0)", "tensorboardX (>=2.2,<3.0)", "torchmetrics (>=0.7.0,<2.0)", "trio (<0.22.0)", "uvicorn"]
+test = ["click (==8.1.7)", "cloudpickle (>=1.3,<3.0)", "coverage (==7.3.1)", "fastapi", "onnx (>=0.14.0,<2.0)", "onnxruntime (>=0.15.0,<2.0)", "pandas (>1.0,<3.0)", "psutil (<6.0)", "pytest (==7.4.0)", "pytest-cov (==4.1.0)", "pytest-random-order (==1.1.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "scikit-learn (>0.22.1,<2.0)", "tensorboard (>=2.9.1,<3.0)", "tensorboardX (>=2.2,<3.0)", "torchmetrics (>=0.7.0,<2.0)", "uvicorn"]
 
 [[package]]
 name = "lightning-utilities"
-version = "0.11.3.post0"
+version = "0.11.6"
 description = "Lightning toolbox for across the our ecosystem."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "lightning_utilities-0.11.3.post0-py3-none-any.whl", hash = "sha256:2aec1d067e5ab61a8978f879998850a97f9a3764ee54aade329552706b0d189b"},
-    {file = "lightning_utilities-0.11.3.post0.tar.gz", hash = "sha256:7485fad0e3c5607a6bde4507935689c553a2c91325de2127b4bb8171a601e236"},
+    {file = "lightning_utilities-0.11.6-py3-none-any.whl", hash = "sha256:ecd9953c316cbaf56ad820fbe7bd062187b9973c4a23d47b076cd59dc080a310"},
+    {file = "lightning_utilities-0.11.6.tar.gz", hash = "sha256:79fc27ef8ec8b8d55a537920f2c7610270c0c9e037fa6efc78f1aa34ec8cdf04"},
 ]
 
 [package.dependencies]
@@ -1796,31 +1722,31 @@ typing = ["mypy (>=1.0.0)", "types-setuptools"]
 
 [[package]]
 name = "litdata"
-version = "0.2.6"
+version = "0.2.16"
 description = "The Deep Learning framework to train, deploy, and ship AI products Lightning fast."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "litdata-0.2.6-py3-none-any.whl", hash = "sha256:068cde13f2249876397168154007f9dfd09649aab94cc46c1234e9c8b9c89890"},
-    {file = "litdata-0.2.6.tar.gz", hash = "sha256:67647a9dbcc7ccfbb01efc39b1bfedce2d47765b1b8fa39f1c11fcb7fd2994c3"},
+    {file = "litdata-0.2.16-py3-none-any.whl", hash = "sha256:c33a72fff777f095287e6c7fdf2ef076a58a1e975c46ab14b3348ecf0d4ebeb6"},
+    {file = "litdata-0.2.16.tar.gz", hash = "sha256:86061ffd60df23678af144ba7be05fa59b7dff333ec4519aa85eb90b0a86d121"},
 ]
 
 [package.dependencies]
-boto3 = {version = "*", extras = ["crt"]}
+boto3 = "*"
 filelock = "*"
-numpy = "*"
+numpy = "<2.0.0"
 requests = "*"
 torch = "*"
 
 [[package]]
 name = "litgpt"
-version = "0.4.2"
+version = "0.4.5"
 description = "Hackable implementation of state-of-the-art open-source LLMs"
 optional = false
 python-versions = "*"
 files = [
-    {file = "litgpt-0.4.2-py3-none-any.whl", hash = "sha256:398bc9786bfda1c50df6999d0906862a963f391f30e8241bd696b160821a104e"},
-    {file = "litgpt-0.4.2.tar.gz", hash = "sha256:377c05a324857e5023a34c33ed8568eca687a4b55bcadaa0061effef3079d198"},
+    {file = "litgpt-0.4.5-py3-none-any.whl", hash = "sha256:682267c3f178604744d848b54e1c39c66a2df3eec13610873ca3c0ea4788c1f5"},
+    {file = "litgpt-0.4.5.tar.gz", hash = "sha256:4cd586ae7aea712cf83fbb6738c121bc455eba971a65bb823870b000cc0ea3e3"},
 ]
 
 [package.dependencies]
@@ -1828,8 +1754,8 @@ bitsandbytes = {version = "0.42.0", optional = true, markers = "extra == \"all\"
 datasets = {version = ">=2.18.0", optional = true, markers = "extra == \"all\""}
 huggingface-hub = {version = ">=0.21.0", extras = ["hf-transfer"], optional = true, markers = "extra == \"all\""}
 jsonargparse = {version = ">=4.27.6", extras = ["signatures"]}
-lightning = "2.3.0.dev20240428"
-litdata = {version = "0.2.6", optional = true, markers = "extra == \"all\""}
+lightning = "2.3.3"
+litdata = {version = "0.2.16", optional = true, markers = "extra == \"all\""}
 litserve = {version = ">=0.1.2", optional = true, markers = "extra == \"all\""}
 lm-eval = {version = ">=0.4.2", optional = true, markers = "extra == \"all\""}
 numpy = {version = "<2.0.0", optional = true, markers = "extra == \"all\""}
@@ -1842,22 +1768,23 @@ tensorboard = {version = ">=2.14.0", optional = true, markers = "extra == \"all\
 tokenizers = {version = ">=0.15.2", optional = true, markers = "extra == \"all\""}
 torch = ">=2.2.0"
 torchmetrics = {version = ">=1.3.1", optional = true, markers = "extra == \"all\""}
+tqdm = {version = ">=4.66.0", optional = true, markers = "extra == \"all\""}
 transformers = {version = ">=4.38.0", optional = true, markers = "extra == \"all\""}
 zstandard = {version = ">=0.22.0", optional = true, markers = "extra == \"all\""}
 
 [package.extras]
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+all = ["bitsandbytes (==0.42.0)", "datasets (>=2.18.0)", "huggingface-hub[hf-transfer] (>=0.21.0)", "litdata (==0.2.16)", "litserve (>=0.1.2)", "lm-eval (>=0.4.2)", "numpy (<2.0.0)", "pandas (>=1.9.0)", "pyarrow (>=15.0.2)", "requests (>=2.31.0)", "safetensors (>=0.4.3)", "sentencepiece (>=0.2.0)", "tensorboard (>=2.14.0)", "tokenizers (>=0.15.2)", "torchmetrics (>=1.3.1)", "tqdm (>=4.66.0)", "transformers (>=4.38.0)", "zstandard (>=0.22.0)"]
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 [package.dependencies]
@@ -2706,6 +2633,7 @@ description = "Nvidia JIT LTO Library"
 optional = false
 python-versions = ">=3"
 files = [
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@@ -2721,61 +2649,6 @@ files = [
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 transformers = "*"
 
 [package.extras]
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 [package.dependencies]
 numpy = ">=1.16.6"
 
+[package.extras]
+test = ["cffi", "hypothesis", "pandas", "pytest", "pytz"]
+
 [[package]]
 name = "pyarrow-hotfix"
 version = "0.6"
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 [package.dependencies]
@@ -3418,13 +3295,13 @@ yaml = ["PyYAML (>=3.11,<7)"]
 
 [[package]]
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-version = "8.2.2"
+version = "8.3.1"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
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 iniconfig = "*"
 packaging = "*"
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 tomli = {version = ">=1", markers = "python_version < \"3.11\""}
 
 [package.extras]
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 [[package]]
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 python-versions = ">=3.8"
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 [package.dependencies]
@@ -3947,8 +3825,8 @@ threadpoolctl = ">=3.1.0"
 
 [package.extras]
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-build = ["cython (>=3.0.10)", "meson-python (>=0.15.0)", "numpy (>=1.19.5)", "scipy (>=1.6.0)"]
-docs = ["Pillow (>=7.1.2)", "matplotlib (>=3.3.4)", "memory_profiler (>=0.57.0)", "numpydoc (>=1.2.0)", "pandas (>=1.1.5)", "plotly (>=5.14.0)", "polars (>=0.20.23)", "pooch (>=1.6.0)", "scikit-image (>=0.17.2)", "seaborn (>=0.9.0)", "sphinx (>=6.0.0)", "sphinx-copybutton (>=0.5.2)", "sphinx-gallery (>=0.15.0)", "sphinx-prompt (>=1.3.0)", "sphinxext-opengraph (>=0.4.2)"]
+build = ["cython (>=3.0.10)", "meson-python (>=0.16.0)", "numpy (>=1.19.5)", "scipy (>=1.6.0)"]
+docs = ["Pillow (>=7.1.2)", "matplotlib (>=3.3.4)", "memory_profiler (>=0.57.0)", "numpydoc (>=1.2.0)", "pandas (>=1.1.5)", "plotly (>=5.14.0)", "polars (>=0.20.23)", "pooch (>=1.6.0)", "pydata-sphinx-theme (>=0.15.3)", "scikit-image (>=0.17.2)", "seaborn (>=0.9.0)", "sphinx (>=7.3.7)", "sphinx-copybutton (>=0.5.2)", "sphinx-design (>=0.5.0)", "sphinx-gallery (>=0.16.0)", "sphinx-prompt (>=1.4.0)", "sphinx-remove-toctrees (>=1.0.0.post1)", "sphinxcontrib-sass (>=0.3.4)", "sphinxext-opengraph (>=0.9.1)"]
 examples = ["matplotlib (>=3.3.4)", "pandas (>=1.1.5)", "plotly (>=5.14.0)", "pooch (>=1.6.0)", "scikit-image (>=0.17.2)", "seaborn (>=0.9.0)"]
 install = ["joblib (>=1.2.0)", "numpy (>=1.19.5)", "scipy (>=1.6.0)", "threadpoolctl (>=3.1.0)"]
 maintenance = ["conda-lock (==2.5.6)"]
@@ -4060,18 +3938,19 @@ files = [
 
 [[package]]
 name = "setuptools"
-version = "70.2.0"
+version = "71.1.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "setuptools-70.2.0-py3-none-any.whl", hash = "sha256:b8b8060bb426838fbe942479c90296ce976249451118ef566a5a0b7d8b78fb05"},
-    {file = "setuptools-70.2.0.tar.gz", hash = "sha256:bd63e505105011b25c3c11f753f7e3b8465ea739efddaccef8f0efac2137bac1"},
+    {file = "setuptools-71.1.0-py3-none-any.whl", hash = "sha256:33874fdc59b3188304b2e7c80d9029097ea31627180896fb549c578ceb8a0855"},
+    {file = "setuptools-71.1.0.tar.gz", hash = "sha256:032d42ee9fb536e33087fb66cac5f840eb9391ed05637b3f2a76a7c8fb477936"},
 ]
 
 [package.extras]
+core = ["importlib-metadata (>=6)", "importlib-resources (>=5.10.2)", "jaraco.text (>=3.7)", "more-itertools (>=8.8)", "ordered-set (>=3.1.1)", "packaging (>=24)", "platformdirs (>=2.6.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
 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"]
-test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test", "mypy (==1.10.0)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (>=0.3.2)", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
+test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test", "mypy (==1.11.*)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (<0.4)", "pytest-ruff (>=0.2.1)", "pytest-ruff (>=0.3.2)", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
 
 [[package]]
 name = "shellingham"
@@ -4147,17 +4026,20 @@ full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.7
 
 [[package]]
 name = "sympy"
-version = "1.12.1"
+version = "1.13.1"
 description = "Computer algebra system (CAS) in Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "sympy-1.12.1-py3-none-any.whl", hash = "sha256:9b2cbc7f1a640289430e13d2a56f02f867a1da0190f2f99d8968c2f74da0e515"},
-    {file = "sympy-1.12.1.tar.gz", hash = "sha256:2877b03f998cd8c08f07cd0de5b767119cd3ef40d09f41c30d722f6686b0fb88"},
+    {file = "sympy-1.13.1-py3-none-any.whl", hash = "sha256:db36cdc64bf61b9b24578b6f7bab1ecdd2452cf008f34faa33776680c26d66f8"},
+    {file = "sympy-1.13.1.tar.gz", hash = "sha256:9cebf7e04ff162015ce31c9c6c9144daa34a93bd082f54fd8f12deca4f47515f"},
 ]
 
 [package.dependencies]
-mpmath = ">=1.1.0,<1.4.0"
+mpmath = ">=1.1.0,<1.4"
+
+[package.extras]
+dev = ["hypothesis (>=6.70.0)", "pytest (>=7.1.0)"]
 
 [[package]]
 name = "syne-tune"
@@ -4589,19 +4471,19 @@ dev = ["twine"]
 
 [[package]]
 name = "transformers"
-version = "4.42.3"
+version = "4.43.2"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "transformers-4.42.3-py3-none-any.whl", hash = "sha256:a61a0df9609b7d69229d941b2fd857c841ba3043d6da503d0da1a4b133f65b92"},
-    {file = "transformers-4.42.3.tar.gz", hash = "sha256:7539873ff45809145265cbc94ea4619d2713c41ceaa277b692d8b0be3430f7eb"},
+    {file = "transformers-4.43.2-py3-none-any.whl", hash = "sha256:283c8b47cf38640c5c0caea60be0dfa948669fa48e9739b03717cbf5e8b20f11"},
+    {file = "transformers-4.43.2.tar.gz", hash = "sha256:99dbbdeef9d451cdbc1c5316dce3af3dd5bb56b6cda5d0c564253e8fa7ccaf02"},
 ]
 
 [package.dependencies]
 filelock = "*"
 huggingface-hub = ">=0.23.2,<1.0"
-numpy = ">=1.17,<2.0"
+numpy = ">=1.17"
 packaging = ">=20.0"
 pyyaml = ">=5.1"
 regex = "!=2019.12.17"
@@ -4613,14 +4495,14 @@ tqdm = ">=4.27"
 [package.extras]
 accelerate = ["accelerate (>=0.21.0)"]
 agents = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch"]
-all = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm (<=0.9.16)", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision"]
+all = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm (<=0.9.16)", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision"]
 audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 benchmark = ["optimum-benchmark (>=0.2.0)"]
 codecarbon = ["codecarbon (==1.2.0)"]
 deepspeed = ["accelerate (>=0.21.0)", "deepspeed (>=0.9.3)"]
 deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.21.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "optuna", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
-dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
-dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.19,<0.20)", "urllib3 (<2.0.0)"]
+dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.19,<0.20)", "urllib3 (<2.0.0)"]
 dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
 flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"]
 flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
@@ -4643,15 +4525,15 @@ sigopt = ["sigopt"]
 sklearn = ["scikit-learn"]
 speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
 testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
-tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
-tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"]
+tf = ["keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
+tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 timm = ["timm (<=0.9.16)"]
 tokenizers = ["tokenizers (>=0.19,<0.20)"]
 torch = ["accelerate (>=0.21.0)", "torch"]
 torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
 torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
-torchhub = ["filelock", "huggingface-hub (>=0.23.2,<1.0)", "importlib-metadata", "numpy (>=1.17,<2.0)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"]
+torchhub = ["filelock", "huggingface-hub (>=0.23.2,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"]
 video = ["av (==9.2.0)", "decord (==0.6.0)"]
 vision = ["Pillow (>=10.0.1,<=15.0)"]
 
@@ -4718,13 +4600,13 @@ typing-extensions = ">=3.7.4.3"
 
 [[package]]
 name = "typeshed-client"
-version = "2.5.1"
+version = "2.7.0"
 description = "A library for accessing stubs in typeshed."
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -5536,4 +5382,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.9, <3.12"
-content-hash = "196cc6b73812333254a74bcb2be3ce37c0dd79ac4b4206a2af66dad8c0835588"
+content-hash = "6bcc8bfef33bdcf88efbebef0d747e4551d66b7db86926712548bbf2aff02e0b"
diff --git a/pyproject.toml b/pyproject.toml
index 22a20e0..b3bc408 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -14,7 +14,7 @@ matplotlib = "^3.9.0"
 transformers = "^4.42.3"
 typing-extensions = "^4.12.2"
 torch = ">=2.2.0"
-litgpt = { extras = ["all"], version = "^0.4.2" }
+litgpt = { extras = ["all"], version = "0.4.5" }
 syne-tune = { extras = ["moo"], version = "^0.13.0" }
 torchvision = "^0.18.1"
 
diff --git a/whittle/eval/utils.py b/whittle/eval/utils.py
new file mode 100644
index 0000000..72fd7b4
--- /dev/null
+++ b/whittle/eval/utils.py
@@ -0,0 +1,105 @@
+import json
+import os
+from pathlib import Path
+from pprint import pprint
+from typing import Optional, Union
+import torch
+from whittle.models.gpt import GPT
+from whittle.eval.whittle_llms import WhittleLM
+
+
+def prepare_results(results, save_filepath, print_results=True):
+    from lm_eval.utils import make_table
+
+    if print_results:
+        print(make_table(results))
+        if "groups" in results:
+            print(make_table(results, "groups"))
+
+    json_result = json.dumps(results, indent=2, ensure_ascii=False, default=str)
+    save_filepath.open("w", encoding="utf-8").write(json_result)
+
+
+def convert_and_evaluate(
+    model: GPT,
+    tasks: Optional[str] = None,
+    out_dir=None,
+    force_conversion: bool = False,
+    num_fewshot: Optional[int] = None,
+    batch_size: Union[int, str] = 1,
+    device: Optional[str] = None,
+    dtype: Optional[Union[str, torch.dtype]] = None,
+    limit: Optional[float] = None,
+    seed: int = 1234,
+    save_filepath: Optional[Path] = None,
+    access_token: Optional[str] = None,
+) -> None:
+    """Evaluate a model with the LM Evaluation Harness.
+
+    Arguments:
+        checkpoint_dir: Directory where the `lit_model.pth` and tokenizer files are located.
+        out_dir: Directory in which to save the converted checkpoints for evaluation.
+            Saves to `checkpoint_dir`/evaluate by default.
+        force_conversion: Set to `True` to reconvert the model and override
+            an existing model.pth from a previous evaluation call.
+        tasks: CSV of task names to evaluate. Example: "hellaswag,truthfulqa_mc2,mmlu"
+        num_fewshot: Number of examples in few-shot context.
+        batch_size: Batch size configuration as positive integer value (default: 1),
+            "auto", in the format 'auto:N', where 'auto:4' recomputes the batch size 4 times.
+        device: Device to use for evaluation, for example, "cuda" or "cuda:0".
+        limit: Limit on number of examples per task.
+        seed: Random seed.
+        save_filepath: The file where the results will be saved.
+            Saves to `out_dir/results.json` by default.
+        access_token: Optional API token to access models with restrictions.
+    """
+    if tasks is None:
+        from lm_eval.tasks import TaskManager
+
+        taskm = TaskManager()
+        print("\n".join(taskm.task_index.keys()))
+        print(
+            "\n\nTo evaluate multiple tasks, you can chain the task names "
+            "listed above via a comma-separated list."
+            "\nFor example: `--tasks 'hellaswag,truthfulqa_mc2,mmlu'`. "
+            "\nTo search for a specific task, use `litgpt evaluate list | grep task_name`."
+        )
+        return
+
+    pprint(locals())
+
+    if not (isinstance(batch_size, int) and batch_size > 0) and not (
+        isinstance(batch_size, str) and batch_size.startswith("auto")
+    ):
+        raise ValueError(
+            "batch_size must be a positive integer, 'auto', or in the format 'auto:N'."
+        )
+
+    from lm_eval import evaluator
+
+    if device is None:
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    model = WhittleLM(
+        pretrained=model, device=device, batch_size=batch_size, dtype=dtype
+    )
+
+    os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+    results = evaluator.simple_evaluate(
+        model=model,
+        tasks=tasks.split(","),
+        num_fewshot=num_fewshot,
+        batch_size=batch_size,
+        device=device,
+        limit=limit,
+        random_seed=seed,
+        numpy_random_seed=seed,
+        torch_random_seed=seed,
+    )
+    out_dir = Path(out_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+    save_filepath = (
+        out_dir / Path("results.json") if save_filepath is None else Path(save_filepath)
+    )
+    prepare_results(results, save_filepath)
diff --git a/whittle/eval/whittle_llms.py b/whittle/eval/whittle_llms.py
new file mode 100644
index 0000000..6d785c1
--- /dev/null
+++ b/whittle/eval/whittle_llms.py
@@ -0,0 +1,1062 @@
+import copy
+from typing import Dict, List, Literal, Optional, Tuple, Union
+from tqdm import tqdm
+import torch
+import torch.nn.functional as F
+import transformers
+from accelerate import (
+    find_executable_batch_size,
+)
+
+from transformers.models.auto.modeling_auto import (
+    MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
+    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
+)
+
+from lm_eval import utils
+from lm_eval.api.instance import Instance
+from lm_eval.api.model import TemplateLM
+from lm_eval.api.registry import register_model
+from lm_eval.models.utils import (
+    Collator,
+    clear_torch_cache,
+    pad_and_concat,
+)
+from litgpt.generate.base import generate
+from transformers import PreTrainedTokenizerBase
+from transformers.configuration_utils import PretrainedConfig
+from whittle.models.gpt import GPT
+
+eval_logger = utils.eval_logger
+
+
+def _get_accelerate_args(
+    device_map_option="auto",
+    max_memory_per_gpu=None,
+    max_cpu_memory=None,
+    offload_folder="./offload",
+    gpus=None,
+):
+    """Returns the kwargs needed to apply `accelerate` in `AutoModel.from_pretrained`."""
+    max_memory = {}
+    if max_memory_per_gpu is not None:
+        max_memory_per_gpu_map = {
+            device_idx: max_memory_per_gpu for device_idx in range(gpus)
+        }
+        max_memory.update(max_memory_per_gpu_map)
+    if max_cpu_memory is not None:
+        max_memory["cpu"] = max_cpu_memory
+
+    args = {}
+    if max_memory:
+        args["max_memory"] = max_memory
+    args["device_map"] = device_map_option
+    args["offload_folder"] = offload_folder
+    return args
+
+
+def configure_pad_token(
+    tokenizer: "PreTrainedTokenizerBase",
+    model_config: Optional["PretrainedConfig"] = None,
+) -> "PreTrainedTokenizerBase":
+    """
+    This function checks if the (Hugging Face) tokenizer has a padding token and sets it if not present.
+    Some tokenizers require special handling.
+
+    Args:
+        tokenizer: The tokenizer for which the padding token is to be handled.
+        model_config: The configuration of the model. Default is None.
+
+    Returns:
+        The tokenizer after the padding token has been handled.
+
+    Raises:
+        AssertionError: If the tokenizer is of type RWKVWorldTokenizer or Rwkv5Tokenizer and the padding token id is not 0.
+    """
+    if tokenizer.pad_token:
+        pass
+    elif tokenizer.unk_token:
+        tokenizer.pad_token_id = tokenizer.unk_token_id
+    elif tokenizer.eos_token:
+        tokenizer.pad_token_id = tokenizer.eos_token_id
+    else:
+        # handle special cases
+        if model_config and getattr(model_config, "model_type", None) == "qwen":
+            # Qwen's trust_remote_code tokenizer does not allow for adding special tokens
+            tokenizer.pad_token = "
"
+        elif (
+            tokenizer.__class__.__name__ == "RWKVWorldTokenizer"
+            or tokenizer.__class__.__name__ == "Rwkv5Tokenizer"
+        ):
+            # The RWKV world tokenizer, does not allow for adding special tokens / setting the pad token (which is set as 0)
+            # The additional tokenizer name check is needed, as there exists rwkv4 models with neox tokenizer
+            # ---
+            # Note that the world tokenizer class name, might change in the future for the final huggingface merge
+            # https://github.com/huggingface/transformers/pull/26963
+            assert tokenizer.pad_token_id == 0
+        else:
+            tokenizer.add_special_tokens({"pad_token": "
"})
+
+    return tokenizer
+
+
+@register_model("whittle")
+class WhittleLM(TemplateLM):
+    """
+    An abstracted whittle model class. Enables usage with both models of `whittle.models.gpt.GPT`
+    """
+
+    AUTO_MODEL_CLASS = GPT
+    _DEFAULT_MAX_LENGTH = 2048
+
+    def __init__(
+        self,
+        pretrained: GPT,
+        backend: Optional[Literal["default", "causal", "seq2seq"]] = "default",
+        # override whether the model should be treated as decoder-only (causal) or encoder-decoder (seq2seq)
+        revision: Optional[str] = "main",
+        subfolder=None,
+        tokenizer: Optional[
+            Union[
+                str,
+                transformers.PreTrainedTokenizer,
+                transformers.PreTrainedTokenizerFast,
+            ]
+        ] = None,
+        truncation: Optional[bool] = False,
+        logits_cache: bool = True,
+        max_length=None,
+        device="cuda",
+        dtype="auto",
+        batch_size=1,
+        max_batch_size=64,
+        trust_remote_code: Optional[bool] = False,
+        use_fast_tokenizer: Optional[bool] = True,
+        add_bos_token: Optional[bool] = False,
+        prefix_token_id: Optional[int] = None,
+        # arguments used for splitting a model across GPUs naively.
+        # only used if `parallelize=True`.
+        parallelize: Optional[bool] = False,
+        device_map_option="auto",
+        max_memory_per_gpu=None,
+        max_cpu_memory=None,
+        offload_folder="./offload",
+        # PEFT, delta weights and quantization options
+        peft: Optional[str] = None,
+        delta: Optional[str] = None,
+        autogptq: Optional[Union[bool, str]] = False,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+
+        # optionally: take in an already-initialized transformers.PreTrainedModel
+        eval_logger.warning(
+            "`pretrained` model kwarg is not of type `str`. Many other model arguments may be ignored. Please do not launch via accelerate or use `parallelize=True` if passing an existing model this way."
+        )
+        assert not parallelize, "`parallelize=True` is not compatible with passing pre-initialized model to `pretrained`"
+        self._model = pretrained
+        self._device = self._model.device
+        self._config = self._model.config
+
+        if tokenizer:
+            assert isinstance(
+                tokenizer, transformers.PreTrainedTokenizer
+            ) or isinstance(tokenizer, transformers.PreTrainedTokenizerFast)
+            self.tokenizer = tokenizer
+        else:
+            # Get tokenizer
+            model_name = self._model.name_or_path
+            self.tokenizer = transformers.AutoTokenizer.from_pretrained(
+                model_name,
+                revision=revision,
+                trust_remote_code=trust_remote_code,
+                use_fast=use_fast_tokenizer,
+            )
+
+        # determine which of 'causal' and 'seq2seq' backends to use
+        self._get_backend(
+            config=self.config, backend=backend, trust_remote_code=trust_remote_code
+        )
+
+        # load tokenizer so we know tokenizer vocabulary size before loading model and PEFT
+        self._create_tokenizer(
+            pretrained,
+            tokenizer,
+            revision=revision,
+            trust_remote_code=trust_remote_code,
+            use_fast_tokenizer=use_fast_tokenizer,
+        )
+
+        # access self._model through self.model property outside this method
+        if isinstance(self.model, torch.nn.Module):
+            self.model.eval()
+            self.model.tie_weights()
+        self.truncation = truncation
+        self.logits_cache = logits_cache
+        self.vocab_size = self.tokenizer.vocab_size
+        # select (or create) a pad token to use
+        self.tokenizer = configure_pad_token(self.tokenizer, model_config=self.config)
+
+        self.add_bos_token = add_bos_token
+        if "gemma" in getattr(self.config, "model_type", ""):
+            self.add_bos_token = True
+            eval_logger.info(
+                f"Model type is '{self.config.model_type}', part of the Gemma family--a BOS token will be used as Gemma underperforms without it."
+            )
+
+        self._max_length = max_length
+        self.pretrained = pretrained
+        self.delta = delta
+        self.peft = peft
+        self.revision = revision
+        self.batch_schedule: float = 1
+        self.batch_sizes: Dict = {}
+        self.max_batch_size = max_batch_size
+
+        if str(batch_size).startswith("auto"):
+            batch_size = batch_size.split(":")
+            self.batch_size_per_gpu = batch_size[0]
+            self.batch_schedule = float(batch_size[1]) if len(batch_size) > 1 else 1
+        else:
+            self.batch_size_per_gpu = int(batch_size)
+
+        # if a PreTrainedModel was passed into HFLM, we forgo distributed setup.
+        eval_logger.warning(
+            "Passed an already-initialized model through `pretrained`, assuming single-process call to evaluate() or custom distributed integration"
+        )
+        self._rank = 0
+        self._world_size = 1
+
+        self.custom_prefix_token_id = prefix_token_id
+        if prefix_token_id is not None:
+            eval_logger.info(
+                f"Loglikelihood prefix token id used in evaluation: {self.prefix_token_id}"
+            )
+
+    @property
+    def config(self):
+        # return the associated transformers.AutoConfig for the given pretrained model.
+        return self._config
+
+    @property
+    def model(self):
+        # returns the model, unwrapping it if using Accelerate
+        if hasattr(self, "accelerator"):
+            return self.accelerator.unwrap_model(self._model)
+        else:
+            return self._model
+
+    @property
+    def eot_token_id(self):
+        # we use EOT because end of *text* is more accurate for what we're doing than end of *sentence*
+        return self.tokenizer.eos_token_id
+
+    @property
+    def prefix_token_id(self):
+        # it is used as prefix for loglikelihood
+        if self.custom_prefix_token_id is not None:
+            return self.custom_prefix_token_id
+        if self.tokenizer.bos_token_id is not None:
+            return self.tokenizer.bos_token_id
+        return self.tokenizer.eos_token_id
+
+    @property
+    def max_length(self):
+        if self._max_length:  # if max length manually set, return it
+            return self._max_length
+        seqlen_config_attrs = ("n_positions", "max_position_embeddings", "n_ctx")
+        for attr in seqlen_config_attrs:
+            if hasattr(self.model.config, attr):
+                return getattr(self.model.config, attr)
+        if hasattr(self.tokenizer, "model_max_length"):
+            if self.tokenizer.model_max_length == 1000000000000000019884624838656:
+                return self._DEFAULT_MAX_LENGTH
+            return self.tokenizer.model_max_length
+        return self._DEFAULT_MAX_LENGTH
+
+    @property
+    def max_gen_toks(self) -> int:
+        return 256
+
+    @property
+    def batch_size(self):
+        return self.batch_size_per_gpu
+
+    @property
+    def device(self):
+        return self._device
+
+    @property
+    def rank(self):
+        return self._rank
+
+    @property
+    def world_size(self):
+        return self._world_size
+
+    @property
+    def tokenizer_name(self) -> str:
+        return self.tokenizer.name_or_path.replace("/", "__")
+
+    @property
+    def chat_template(self) -> str:
+        if self.tokenizer.chat_template is not None:
+            return self.tokenizer.chat_template
+        return self.tokenizer.default_chat_template
+
+    def _get_backend(
+        self,
+        config: Union[transformers.PretrainedConfig, transformers.AutoConfig],
+        backend: Optional[Literal["default", "causal", "seq2seq"]] = "default",
+        trust_remote_code=False,
+    ) -> None:
+        """
+        Helper method during initialization.
+        Determines the backend ("causal" (decoder-only) or "seq2seq" (encoder-decoder))
+        model type to be used.
+        """
+        assert backend in ["default", "causal", "seq2seq"]
+
+        if backend != "default":
+            # if we've settled on non-default backend, use that manually
+            if backend == "causal":
+                self.AUTO_MODEL_CLASS = GPT
+            elif backend == "seq2seq":
+                self.AUTO_MODEL_CLASS = transformers.AutoModelForSeq2SeqLM
+            eval_logger.info(
+                f"Overrode HF model backend type, and using type '{backend}'"
+            )
+        else:
+            # determine and use the default HF backend for this model, based on its config + metadata.
+            if (
+                getattr(config, "model_type")
+                in MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
+            ):
+                # first check if model type is listed under seq2seq models, since some
+                # models like MBart are listed in both seq2seq and causal mistakenly in HF transformers.
+                # these special cases should be treated as seq2seq models.
+                self.AUTO_MODEL_CLASS = transformers.AutoModelForSeq2SeqLM
+            elif (
+                getattr(self.config, "model_type") in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
+            ):
+                self.AUTO_MODEL_CLASS = GPT
+            else:
+                if not trust_remote_code:
+                    eval_logger.warning(
+                        "HF model type is neither marked as CausalLM or Seq2SeqLM. \
+                    This is expected if your model requires `trust_remote_code=True` but may be an error otherwise."
+                    )
+                # if model type is neither in HF transformers causal or seq2seq model registries
+                # then we default to GPT
+                self.AUTO_MODEL_CLASS = GPT
+
+        assert self.AUTO_MODEL_CLASS in [
+            GPT,
+            transformers.AutoModelForSeq2SeqLM,
+        ]
+        return None
+
+    def _create_tokenizer(
+        self,
+        pretrained: Union[str, transformers.PreTrainedModel],
+        tokenizer: Optional[
+            Union[
+                str,
+                transformers.PreTrainedTokenizer,
+                transformers.PreTrainedTokenizerFast,
+            ]
+        ],
+        revision: Optional[str] = "main",
+        trust_remote_code: Optional[bool] = False,
+        use_fast_tokenizer: Optional[bool] = True,
+    ) -> None:
+        """
+        Helper method during initialization.
+
+        Create a tokenizer object corresponding to the correct
+        tokenizer for value of `pretrained`, or use the pre-initialized tokenizer passed.
+        """
+
+        if tokenizer:
+            if isinstance(tokenizer, str):
+                self.tokenizer = transformers.AutoTokenizer.from_pretrained(
+                    tokenizer,
+                    revision=revision,
+                    trust_remote_code=trust_remote_code,
+                    use_fast=use_fast_tokenizer,
+                )
+            else:
+                assert isinstance(
+                    tokenizer, transformers.PreTrainedTokenizer
+                ) or isinstance(tokenizer, transformers.PreTrainedTokenizerFast)
+                self.tokenizer = tokenizer
+        else:
+            # Get tokenizer based on 'pretrained'
+            if isinstance(pretrained, str):
+                model_name = pretrained
+            else:
+                # get the HF hub name via accessor on model
+                model_name = self.model.name_or_path
+            self.tokenizer = transformers.AutoTokenizer.from_pretrained(
+                model_name,
+                revision=revision,
+                trust_remote_code=trust_remote_code,
+                use_fast=use_fast_tokenizer,
+            )
+        return None
+
+    def _detect_batch_size(self, requests=None, pos: int = 0):
+        if requests:
+            _, context_enc, continuation_enc = requests[pos]
+            max_length = len(
+                (context_enc + continuation_enc)[-(self.max_length + 1) :][:-1]
+            )
+            max_context_enc = len(context_enc[-(self.max_length + 1) :])
+            max_cont_enc = len(continuation_enc[-(self.max_length + 1) :])
+        else:
+            max_length = self.max_length
+            max_context_enc = max_length
+            max_cont_enc = max_length
+
+        # if OOM, then halves batch_size and tries again
+        @find_executable_batch_size(starting_batch_size=self.max_batch_size)
+        def forward_batch(batch_size):
+            if self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
+                length = max(max_context_enc, max_cont_enc)
+                batched_conts = torch.ones(
+                    (batch_size, length), device=self.device
+                ).long()
+                test_batch = torch.ones((batch_size, length), device=self.device).long()
+                call_kwargs = {
+                    "attn_mask": test_batch,
+                    "labels": batched_conts,
+                }
+            else:
+                call_kwargs = {}
+                test_batch = torch.ones(
+                    (batch_size, max_length), device=self.device
+                ).long()
+            for _ in range(5):
+                out = F.log_softmax(self._model_call(test_batch, **call_kwargs), dim=-1)  # noqa: F841
+
+            return batch_size
+
+        try:
+            batch_size = forward_batch()
+        except RuntimeError as e:
+            if "No executable batch size found" in str(e):
+                batch_size = 1
+            else:
+                raise
+
+        if self.world_size > 1:
+            # if multi-GPU, always take minimum over all selected batch sizes
+            max_rnk_bs = torch.tensor([batch_size], device=self.device)
+            gathered = (
+                self.accelerator.gather(max_rnk_bs).cpu().detach().numpy().tolist()
+            )
+            batch_size = min(gathered)
+            clear_torch_cache()
+            return batch_size
+
+        clear_torch_cache()
+        return batch_size
+
+    def tok_encode(
+        self, string: str, left_truncate_len=None, add_special_tokens=None
+    ) -> List[int]:
+        """ """
+        # default for None - empty dict, use predefined tokenizer param
+        # used for all models except for CausalLM or predefined value
+        special_tokens_kwargs = {}
+
+        # by default for CausalLM - false or self.add_bos_token is set
+        if add_special_tokens is None:
+            if self.AUTO_MODEL_CLASS == GPT:
+                special_tokens_kwargs = {
+                    "add_special_tokens": False or self.add_bos_token
+                }
+        # otherwise the method explicitly defines the value
+        else:
+            special_tokens_kwargs = {"add_special_tokens": add_special_tokens}
+
+        encoding = self.tokenizer.encode(string, **special_tokens_kwargs)
+
+        # left-truncate the encoded context to be at most `left_truncate_len` tokens long
+        if left_truncate_len:
+            encoding = encoding[-left_truncate_len:]
+
+        return encoding
+
+    def tok_batch_encode(
+        self,
+        strings,
+        padding_side="left",
+        left_truncate_len=None,
+        truncation=False,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # encode a batch of strings. converts to tensors and pads automatically, unlike tok_encode.
+        old_padding_side = self.tokenizer.padding_side
+        self.tokenizer.padding_side = padding_side
+
+        add_special_tokens = {}
+        if self.AUTO_MODEL_CLASS == GPT:
+            add_special_tokens = {"add_special_tokens": False or self.add_bos_token}
+
+        encoding = self.tokenizer(
+            strings,
+            truncation=truncation,
+            padding="longest",
+            return_tensors="pt",
+            **add_special_tokens,
+        )
+        if left_truncate_len:
+            encoding["input_ids"] = encoding["input_ids"][:, -left_truncate_len:]
+            encoding["attention_mask"] = encoding["attention_mask"][
+                :, -left_truncate_len:
+            ]
+        self.tokenizer.padding_side = old_padding_side
+
+        return encoding["input_ids"], encoding["attention_mask"]
+
+    def tok_decode(self, tokens, skip_special_tokens=True):
+        return self.tokenizer.decode(tokens, skip_special_tokens=skip_special_tokens)
+
+    def _model_call(self, inps, attn_mask=None, labels=None):
+        """
+        :param inps: torch.Tensor
+            A torch tensor of shape [batch, (sequence_ctx + sequence_cont)] or of shape
+            [batch, sequence_ctx]. the size of sequence may vary from call to call
+        :param attn_mask: torch.Tensor, optional
+            A torch tensor of shape [batch, (sequence_ctx + sequence_cont)]. Only passed
+            (and must be passed) if self.AUTO_MODEL_CLASS is transformers.AutoModelForSeq2SeqLM
+        :param labels: torch.Tensor, optional
+            A torch tensor of shape [batch, (sequence_ctx + sequence_cont)]. Only passed
+            (and must be passed) if self.AUTO_MODEL_CLASS is transformers.AutoModelForSeq2SeqLM
+        :return
+            A torch tensor of shape [batch, sequence, vocab] with the
+        logits returned from the model's decoder
+        """
+        with torch.no_grad():
+            if attn_mask is not None or labels is not None:
+                assert attn_mask is not None and labels is not None
+                assert self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM
+                return self.model(
+                    input_ids=inps, attention_mask=attn_mask, labels=labels
+                )
+            else:
+                assert self.AUTO_MODEL_CLASS == GPT
+                return self.model(inps)
+
+    def _model_generate(self, context, max_length, stop, **generation_kwargs):
+        # temperature = 0.0 if not set
+        # if do_sample is false and temp==0.0:
+        # remove temperature, as do_sample=False takes care of this
+        # and we don't want a warning from HF
+        generation_kwargs["temperature"] = generation_kwargs.get("temperature", 0.0)
+        temperature = generation_kwargs["temperature"]
+        do_sample = generation_kwargs.get("do_sample", None)
+
+        # The temperature has to be a strictly positive float -- if it is 0.0, use greedy decoding strategies
+        # print(generation_kwargs)
+        if generation_kwargs.get("temperature") == 0.0 and do_sample is None:
+            generation_kwargs["do_sample"] = do_sample = False
+        first_turn = self.model.mask_cache is None
+
+        if first_turn or max_length > self.model.max_seq_length:
+            self.model.max_seq_length = max_length
+            self.model.set_kv_cache(batch_size=1, device=self.device)
+        # print(generation_kwargs)
+        outputs = []
+        for i in range(context.shape[0]):
+            outputs.append(
+                generate(
+                    model=self.model,
+                    prompt=torch.squeeze(context[i]),
+                    max_returned_tokens=max_length,
+                    temperature=temperature,
+                    top_k=generation_kwargs.get("top_k"),
+                    top_p=generation_kwargs.get("top_p")
+                    if generation_kwargs.get("top_p") is not None
+                    else 0.0,
+                    eos_id=generation_kwargs.get("eos_token_id"),
+                    # pad_token_id=self.tokenizer.pad_token_id,
+                    # use_cache=True,
+                    # **generation_kwargs,
+                )
+                .data.cpu()
+                .numpy()
+            )
+        # print(outputs)
+        return torch.tensor(outputs).to(self.device)
+
+    def _select_cont_toks(
+        self, logits: torch.Tensor, contlen=None, inplen=None
+    ) -> torch.Tensor:
+        if self.AUTO_MODEL_CLASS == GPT:
+            assert (
+                contlen and inplen
+            ), "Must pass input len and cont. len to select scored logits for causal LM"
+            # discard right-padding.
+            # also discard the input/context tokens. we'll only score continuations.
+            logits = logits[inplen - contlen : inplen]
+        elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
+            assert (
+                contlen and not inplen
+            ), "Selecting scored logits for Seq2SeqLM requires only cont. len"
+            # only discard right-padding.
+            # the logits input to this fn only contain decoder-side tokens.
+            logits = logits[:contlen]
+
+        return logits
+
+    def loglikelihood_rolling(
+        self, requests: List[Instance], disable_tqdm: bool = False
+    ) -> List[float]:
+        loglikelihoods = []
+
+        adaptive_batch_size = None
+        if self.batch_size == "auto":
+            # using rolling window with maximum context
+            print("Passed argument batch_size = auto. Detecting largest batch size")
+            batch_size = self._detect_batch_size()
+            print(f"Determined Largest batch size: {batch_size}")
+            adaptive_batch_size = batch_size
+
+        for (string,) in tqdm(
+            [req.args for req in requests], disable=(disable_tqdm or (self.rank != 0))
+        ):
+            rolling_token_windows = list(
+                map(
+                    utils.make_disjoint_window,
+                    utils.get_rolling_token_windows(
+                        token_list=self.tok_encode(string),
+                        prefix_token=self.prefix_token_id,
+                        max_seq_len=self.max_length,
+                        context_len=1,
+                    ),
+                )
+            )
+
+            # TODO: Right now, we pass single EOT token to the Encoder and the full context to the decoder, in seq2seq case
+            rolling_token_windows = [(None,) + x for x in rolling_token_windows]
+
+            pad_amnt = 0
+            if self.world_size > 1:
+                # We pad out the external document-level iterator so the inner iterator doesn't hang
+                mytensor = torch.tensor(len(rolling_token_windows), device=self.device)
+                gathered = (
+                    self.accelerator.gather(mytensor).cpu().detach().numpy().tolist()
+                )
+
+                pad_amnt = max(gathered) - gathered[self.rank]
+                if pad_amnt > 0:
+                    rolling_token_windows += pad_amnt * [rolling_token_windows[0]]
+
+            string_nll = self._loglikelihood_tokens(
+                requests=rolling_token_windows,
+                disable_tqdm=True,
+                override_bs=adaptive_batch_size,
+            )
+
+            if (self.world_size > 1) and (pad_amnt > 0):
+                string_nll = [x[0] for x in string_nll[:-pad_amnt]]
+            else:
+                # discard is_greedy
+                string_nll = [x[0] for x in string_nll]
+
+            string_nll = sum(string_nll)
+            loglikelihoods.append(string_nll)
+
+        return loglikelihoods
+
+    def _batch_scheduler(self, pos, n_reordered_requests):
+        sched = pos // int(len(n_reordered_requests) / self.batch_schedule)
+        if sched in self.batch_sizes:
+            return self.batch_sizes[sched]
+        if (len(self.batch_sizes) > 1) and (
+            self.batch_sizes[sched - 1] == self.max_batch_size
+        ):
+            # if previous batch size is already maximal, skip recomputation
+            self.batch_sizes[sched] = self.max_batch_size
+            return self.batch_sizes[sched]
+        print(
+            f"Passed argument batch_size = auto:{self.batch_schedule}. Detecting largest batch size"
+        )
+        self.batch_sizes[sched] = self._detect_batch_size(n_reordered_requests, pos)
+        print(f"Determined largest batch size: {self.batch_sizes[sched]}")
+        return self.batch_sizes[sched]
+
+    def _loglikelihood_tokens(
+        self,
+        requests: List[Tuple[Tuple[str, str], List[int], List[int]]],
+        disable_tqdm=False,
+        override_bs=None,
+    ):
+        # TODO: implement some kind of efficient-request-middleware that lumps together requests with the same context
+        res = []
+
+        def _collate(req: Tuple[Tuple[str, str], List[int], List[int]]):
+            """Defines the key for the sorted method"""
+            # the negative sign on len(toks) sorts descending - this has a few advantages:
+            # - time estimates will always be over not underestimates, which is more useful for planning
+            # - to know the size of a batch when going through the list, you know the first one is always the batch
+            #   padded context length. this is useful to simplify the batching logic and more importantly to make
+            #   automatic adaptive batches much much easier to implement
+            # - any OOMs will happen right away rather than near the end
+
+            toks = req[1] + req[2]
+            return -len(toks), tuple(toks)
+
+        def _lookup_one_token_cont(req: Tuple[Tuple[str, str], List[int], List[int]]):
+            """Defines the key to group and lookup one-token continuations"""
+            # Use with group_by="contexts" (optional)"
+            # allows for the creation of a lookup, so we can reuse logits in case of one-token continuations.
+            # speeds up some multiple-choice tasks proportionally to the number of choices.
+            # groups requests by context+continuation[:-1] and infer on one request/group.
+            return req[-2] + req[-1][:-1]
+
+        re_ord = Collator(
+            requests,
+            sort_fn=_collate,
+            group_by="contexts"
+            if self.AUTO_MODEL_CLASS == GPT and self.logits_cache
+            else None,
+            group_fn=_lookup_one_token_cont,
+        )
+
+        # automatic (variable) batch size detection for vectorization
+        # pull longest context sample from request
+        n_reordered_requests = len(re_ord)
+        batch_size = (
+            self.batch_size
+            if self.batch_size != "auto"
+            else override_bs
+            if override_bs is not None
+            else 0
+        )
+        batch_fn = (
+            self._batch_scheduler
+            if self.batch_size == "auto"
+            and n_reordered_requests > 0
+            and not override_bs
+            else None
+        )
+
+        chunks = re_ord.get_batched(n=batch_size, batch_fn=batch_fn)
+        pbar = tqdm(
+            total=len(requests),
+            disable=(disable_tqdm or (self.rank != 0)),
+            desc="Running loglikelihood requests",
+        )
+        for chunk in chunks:
+            inps = []
+            cont_toks_list = []
+            inplens = []
+
+            conts = []
+            encoder_attns = []
+
+            padding_len_inp = 0
+            padding_len_cont = 0
+            # because vectorizing is annoying, we first convert each (context, continuation) pair to padded
+            # tensors, then we pack them together into a batch, call the model, and then pick it all apart
+            # again because vectorizing is annoying
+
+            for _, context_enc, continuation_enc in chunk:
+                # sanity check
+                assert len(context_enc) > 0
+                assert len(continuation_enc) > 0
+                assert len(continuation_enc) <= self.max_length
+
+                # how this all works (illustrated on a causal decoder-only setup):
+                #          CTX      CONT
+                # inp    0 1 2 3|4 5 6 7 8 9   <- last token is deleted by inp[:, :-1]
+                # model  \               \
+                # logits   1 2 3|4 5 6 7 8 9   <- the ctx half gets tossed out by the
+                # cont_toks      4 5 6 7 8 9      [:, -len(continuation_enc):, :self.vocab_size] slice
+
+                # when too long to fit in context, truncate from the left
+                if self.AUTO_MODEL_CLASS == GPT:
+                    inp = torch.tensor(
+                        (context_enc + continuation_enc)[-(self.max_length + 1) :][:-1],
+                        dtype=torch.long,
+                        device=self.device,
+                    )
+                    (inplen,) = inp.shape
+                elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
+                    inp = torch.tensor(
+                        (context_enc)[-self.max_length :],
+                        dtype=torch.long,
+                        device=self.device,
+                    )
+                    (inplen,) = inp.shape
+
+                    # build encoder attn masks
+                    encoder_attns.append(torch.ones_like(inp))
+
+                    cont = torch.tensor(
+                        (continuation_enc)[-self.max_length :],
+                        # TODO: left-shift these?
+                        # TODO: our code assumes we never end up truncating conts for either model type
+                        dtype=torch.long,
+                        device=self.device,
+                    )
+                    (contlen,) = cont.shape
+
+                    conts.append(cont)
+
+                    padding_len_cont = (
+                        max(padding_len_cont, contlen)
+                        if padding_len_cont is not None
+                        else contlen
+                    )
+
+                padding_len_inp = (
+                    max(padding_len_inp, inplen)
+                    if padding_len_inp is not None
+                    else inplen
+                )
+
+                inps.append(inp)  # [1, inp_length]
+                cont_toks_list.append(continuation_enc)
+                inplens.append(inplen)
+
+            # create encoder attn mask and batched conts, if seq2seq
+            call_kwargs = {}
+            if self.AUTO_MODEL_CLASS == GPT:
+                batched_inps = pad_and_concat(
+                    padding_len_inp, inps, padding_side="right"
+                )  # [batch, padding_len_inp]
+            elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
+                # TODO: left-pad encoder inps and mask?
+                batched_inps = pad_and_concat(
+                    padding_len_inp, inps
+                )  # [batch, padding_len_inp]
+                batched_conts = pad_and_concat(
+                    padding_len_cont, conts
+                )  # [batch, padding_len_cont]
+                batched_encoder_mask = pad_and_concat(
+                    padding_len_inp, encoder_attns
+                )  # [batch, padding_len_inp]
+                call_kwargs = {
+                    "attn_mask": batched_encoder_mask,
+                    "labels": batched_conts,
+                }
+
+            multi_logits = F.log_softmax(
+                self._model_call(batched_inps, **call_kwargs), dim=-1
+            )  # [batch, padding_length (inp or cont), vocab]
+
+            for (request_str, ctx_tokens, _), logits, inplen, cont_toks in zip(
+                chunk, multi_logits, inplens, cont_toks_list
+            ):
+                # Slice to original seq length
+                contlen = len(cont_toks)
+                # take only logits in the continuation
+                # (discard context toks if decoder-only ; discard right-padding)
+                # also discards + checks for "virtual tokens" in the causal LM's input window
+                # from prompt/prefix tuning tokens, if applicable
+                ctx_len = (
+                    inplen + (logits.shape[0] - padding_len_inp)
+                    if self.AUTO_MODEL_CLASS == GPT
+                    else None
+                )
+                logits = self._select_cont_toks(logits, contlen=contlen, inplen=ctx_len)
+                logits = logits.unsqueeze(0)  # [1, seq, vocab]
+
+                # Check if per-token argmax is exactly equal to continuation
+                greedy_tokens = logits.argmax(dim=-1)
+
+                # check for one-token continuation cache hits.
+                # noop in case group_by != "contexts" or no cache hit and returns the
+                # original args. Otherwise, expands the logits batch dimension and yields each
+                # batch along with matching continuation tokens and prompt strings.
+                # logits -> [1, seq, vocab]
+                for request_str, cont_toks, logits in re_ord.get_cache(
+                    req_str=request_str,
+                    cxt_toks=ctx_tokens,
+                    cont_toks=cont_toks,
+                    logits=logits,
+                ):
+                    cont_toks = torch.tensor(
+                        cont_toks, dtype=torch.long, device=self.device
+                    ).unsqueeze(0)  # [1, seq]
+                    max_equal = (greedy_tokens == cont_toks).all()
+
+                    # Obtain log-probs at the corresponding continuation token indices
+                    # last_token_slice = logits[:, -1, :].squeeze(0).tolist()
+                    logits = torch.gather(logits, 2, cont_toks.unsqueeze(-1)).squeeze(
+                        -1
+                    )  # [1, seq]
+
+                    # Answer: (log prob, is-exact-match)
+                    answer = (float(logits.sum()), bool(max_equal))
+
+                    res.append(answer)
+
+                    self.cache_hook.add_partial("loglikelihood", request_str, answer)
+                    pbar.update(1)
+
+        pbar.close()
+
+        return re_ord.get_original(res)
+
+    def set_sub_network(
+        self,
+        sub_network_n_embd: int,
+        sub_network_intermediate_size: list,
+        sub_network_num_heads: list,
+        sub_network_n_layers: int,
+        sample_random_indices: bool = False,
+    ) -> None:
+        self.model.set_sub_network(
+            sub_network_n_embd=sub_network_n_embd,
+            sub_network_intermediate_size=sub_network_intermediate_size,
+            sub_network_num_heads=sub_network_num_heads,
+            sub_network_n_layers=sub_network_n_layers,
+            sample_random_indices=sample_random_indices,
+        )
+
+    def generate_until(
+        self, requests: List[Instance], disable_tqdm: bool = False
+    ) -> List[str]:
+        res = []
+
+        # print(requests)
+        def _collate(req: Tuple[str, dict]):
+            """Defines the key for the sorted method"""
+            # the negative sign on len(toks) sorts descending - this has a few advantages:
+            # - time estimates will always be over not underestimates, which is more useful for planning
+            # - to know the size of a batch when going through the list, you know the first one is always the batch
+            #   padded context length. this is useful to simplify the batching logic and more importantly to make
+            #   automatic adaptive batches much much easier to implement
+            # - any OOMs will happen right away rather than near the end
+            toks = self.tok_encode(req[0])
+            return -len(toks), req[0]
+
+        pbar = tqdm(
+            total=len(requests),
+            disable=(disable_tqdm or (self.rank != 0)),
+            desc="Running generate_until requests",
+        )
+        adaptive_batch_size = None
+        if self.batch_size == "auto":
+            # using rolling window with maximum context
+            print("Passed argument batch_size = auto. Detecting largest batch size")
+            batch_size = self._detect_batch_size()
+            print(f"Determined Largest batch size: {batch_size}")
+            adaptive_batch_size = batch_size
+        # for each different set of kwargs, we execute all requests, by batch.
+        batch_size = (
+            self.batch_size
+            if self.batch_size != "auto"
+            else adaptive_batch_size
+            if adaptive_batch_size is not None
+            else 0
+        )
+        batch_fn = (
+            self._batch_scheduler
+            if self.batch_size == "auto" and not adaptive_batch_size
+            else None
+        )
+
+        # we group requests by their generation_kwargs,
+        # so that we don't try to execute e.g. greedy sampling and temp=0.8 sampling
+        # in the same batch.
+        # group_fn=lambda x: x[1] -> x=(context, gen_kwargs)
+        re_ords = Collator(
+            [reg.args for reg in requests],
+            sort_fn=_collate,
+            group_by="gen_kwargs",
+            group_fn=lambda x: x[1],
+        )
+        chunks = re_ords.get_batched(n=batch_size, batch_fn=batch_fn)
+        for chunk in chunks:
+            contexts, all_gen_kwargs = zip(*chunk)
+            # print(contexts)
+            # we assume all gen kwargs in the batch are the same
+            # this is safe to assume because the `grouper` object ensures it.
+            gen_kwargs = all_gen_kwargs[0]
+            # unpack our keyword arguments.
+            until = None
+            if isinstance(gen_kwargs, dict):
+                kwargs = copy.deepcopy(gen_kwargs)  # edge case for repeats > 1
+                if "until" in kwargs.keys():
+                    until = kwargs.pop("until")
+                    if isinstance(until, str):
+                        until = [until]
+                    elif not isinstance(until, list):
+                        raise ValueError(
+                            f"Expected `kwargs['until']` to be of type Union[str,list] but got {until}"
+                        )
+            else:
+                raise ValueError(
+                    f"Expected `kwargs` to be of type `dict` but got {type(gen_kwargs)}"
+                )
+            # add EOS token to stop sequences
+            eos = self.tok_decode(self.eot_token_id, skip_special_tokens=False)
+            if not until:
+                until = [eos]
+            else:
+                until.append(eos)
+            if "max_gen_toks" in kwargs.keys():
+                max_gen_toks = kwargs.pop("max_gen_toks")
+            else:
+                max_gen_toks = self.max_gen_toks
+            max_ctx_len = self.max_length - max_gen_toks
+
+            # encode, pad, and truncate contexts for this batch
+            context_enc, attn_masks = self.tok_batch_encode(
+                contexts,
+                left_truncate_len=max_ctx_len,
+                truncation=self.truncation,
+            )
+            context_enc = context_enc.to(self.device)
+            attn_masks = attn_masks.to(self.device)
+
+            if "max_length" not in kwargs:
+                kwargs["max_length"] = context_enc.shape[1] + max_gen_toks
+
+            # perform batched generation
+            # print(context_enc)
+            cont = self._model_generate(
+                context=context_enc,
+                attention_mask=attn_masks,
+                stop=until,
+                **kwargs,
+            )
+
+            cont_toks_list = cont.tolist()
+            for cont_toks, context in zip(cont_toks_list, contexts):
+                # discard context + left-padding toks if using causal decoder-only LM
+                if self.AUTO_MODEL_CLASS == GPT:
+                    cont_toks = cont_toks[context_enc.shape[1] :]
+
+                s = self.tok_decode(cont_toks)
+
+                # use secondary stop seqs to cut off should-have-been-stopped content post-hoc
+                for term in until:
+                    if len(term) > 0:
+                        # ignore '' separator,
+                        # for seq2seq case where self.tok_decode(self.eot_token_id) = ''
+                        s = s.split(term)[0]
+
+                res.append(s)
+
+                self.cache_hook.add_partial("generate_until", (context, gen_kwargs), s)
+                pbar.update(1)
+        # reorder this group of results back to original unsorted form
+        res = re_ords.get_original(res)
+
+        pbar.close()
+
+        return res
+
+    def apply_chat_template(self, chat_history: List[Dict[str, str]]) -> str:
+        """
+        Method to apply a chat template to a list of chat history between user and model.
+        """
+        return self.tokenizer.apply_chat_template(
+            chat_history, tokenize=False, add_generation_prompt=True
+        )
diff --git a/whittle/models/gpt/model.py b/whittle/models/gpt/model.py
index fccd711..b816f12 100644
--- a/whittle/models/gpt/model.py
+++ b/whittle/models/gpt/model.py
@@ -12,7 +12,6 @@
 
 from litgpt import Config
 from litgpt.model import build_rope_cache
-
 from whittle.models.gpt.blocks import Block
 from whittle.modules.embedding import Embedding
 from whittle.modules.linear import Linear
@@ -25,7 +24,7 @@ def __init__(self, config: Config) -> None:
         super().__init__()
         assert config.padded_vocab_size is not None
         self.config = config
-
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
         self.lm_head = Linear(
             config.n_embd, config.padded_vocab_size, bias=config.lm_head_bias
         )
@@ -48,6 +47,9 @@ def __init__(self, config: Config) -> None:
         self.cos: torch.Tensor
         self.sin: torch.Tensor
         self.random_layers = list(range(self.config.n_layer))
+        self.config.is_encoder_decoder = False
+        self.main_input_name = "input_pos"
+        self._supports_cache_class = True
         # self.transformer.wte.weight = self.lm_head.weight # weight tying: TODO: where does litgpt do this?
 
     @property
@@ -96,6 +98,10 @@ def _init_weights(self, module: nn.Module) -> None:
         elif isinstance(module, nn.Embedding):
             torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
 
+    def tie_weights(self) -> None:
+        if self.config.tie_embeddings:
+            self.transformer.wte.weight = self.lm_head.weight
+
     def rope_cache(
         self, device: Optional[torch.device] = None
     ) -> Tuple[torch.Tensor, torch.Tensor]:
@@ -197,27 +203,27 @@ def forward(
             if not self.config.fix_head_size:
                 if isinstance(self.sub_network_num_heads, list):
                     cos, sin = build_rope_cache(
-                        T,
+                        seq_len=self.max_seq_length,
                         n_elem=int(
                             self.config.rotary_percentage
                             * (self.sub_network_n_embd // self.sub_network_num_heads[i])
                         ),
-                        device=idx.device,
+                        device=self.device,
                     )
                 else:
                     cos, sin = build_rope_cache(
-                        T,
+                        seq_len=self.max_seq_length,
                         n_elem=int(
                             self.config.rotary_percentage
                             * (self.sub_network_n_embd // self.sub_network_num_heads)
                         ),
-                        device=idx.device,
+                        device=self.device,
                     )
             else:
                 cos, sin = build_rope_cache(
-                    T,
+                    seq_len=self.max_seq_length,
                     n_elem=int(self.config.rotary_percentage * (self.config.head_size)),
-                    device=idx.device,
+                    device=self.device,
                 )
 
             cos, sin, mask = self.process_rope_cache(cos, sin, input_pos, T)


Test patch:
diff --git a/test/test_api.py b/test/test_api.py
new file mode 100644
index 0000000..3d2d9de
--- /dev/null
+++ b/test/test_api.py
@@ -0,0 +1,276 @@
+import json
+import sys
+import numpy as np
+import pathlib
+import pytest
+import torch
+from litgpt import Config
+from litgpt.scripts.download import download_from_hub
+from lm_eval import tasks
+from lm_eval.api.instance import Instance
+
+from whittle.models.gpt import GPT
+
+from whittle.eval.whittle_llms import WhittleLM
+from whittle.eval.utils import convert_and_evaluate
+
+
+@pytest.fixture(scope="session")
+def checkpoint_dir(tmp_path_factory):
+    # img = compute_expensive_image()
+    checkpoint_dir = tmp_path_factory.getbasetemp()
+    download_from_hub(repo_id="EleutherAI/pythia-70m", checkpoint_dir=checkpoint_dir)
+    return pathlib.Path(checkpoint_dir) / "EleutherAI" / "pythia-70m"
+
+
+@pytest.fixture(scope="session")
+def out_dir(tmp_path_factory):
+    # img = compute_expensive_image()
+    out_dir = tmp_path_factory.getbasetemp()
+
+    return pathlib.Path(out_dir) / "out_dir"
+
+
+class Test_WhittleLM:
+    # torch.use_deterministic_algorithms(True)
+    task_manager = tasks.TaskManager()
+    task_list = task_manager.load_task_or_group(["arc_easy", "gsm8k", "wikitext"])
+    version_minor = sys.version_info.minor
+    multiple_choice_task = task_list["arc_easy"]  # type: ignore
+    multiple_choice_task.build_all_requests(limit=10, rank=0, world_size=1)
+    MULTIPLE_CH: list[Instance] = multiple_choice_task.instances
+    generate_until_task = task_list["gsm8k"]  # type: ignore
+    generate_until_task._config.generation_kwargs["max_gen_toks"] = 10
+    generate_until_task.set_fewshot_seed(1234)  # fewshot random generator seed
+    generate_until_task.build_all_requests(limit=10, rank=0, world_size=1)
+    generate_until: list[Instance] = generate_until_task.instances
+    rolling_task = task_list["wikitext"]  # type: ignore
+    rolling_task.build_all_requests(limit=10, rank=0, world_size=1)
+    ROLLING: list[Instance] = rolling_task.instances
+    TEST_STRING = "foo bar"
+    MULTIPLE_CH_RES = [
+        -41.902435302734375,
+        -42.939308166503906,
+        -33.914180755615234,
+        -37.07139205932617,
+        -22.95258331298828,
+        -20.342208862304688,
+        -14.818366050720215,
+        -27.942853927612305,
+        -15.80704116821289,
+        -15.936427116394043,
+        -13.052018165588379,
+        -18.04828453063965,
+        -13.345029830932617,
+        -13.366025924682617,
+        -12.127134323120117,
+        -11.872495651245117,
+        -47.10598373413086,
+        -47.76410675048828,
+        -36.4406852722168,
+        -50.0289421081543,
+        -16.72093963623047,
+        -18.535587310791016,
+        -26.46993637084961,
+        -20.355995178222656,
+        -17.757919311523438,
+        -21.80595588684082,
+        -33.1990852355957,
+        -39.28636932373047,
+        -14.759679794311523,
+        -16.753942489624023,
+        -11.486852645874023,
+        -15.42177677154541,
+        -13.15798282623291,
+        -15.887393951416016,
+        -15.28614616394043,
+        -12.339089393615723,
+        -44.59441375732422,
+        -55.40888214111328,
+        -52.70050811767578,
+        -56.25089645385742,
+    ]
+    generate_until_RES = [
+        " The average of $2.50 each is $",
+        " A robe takes 2 bolts of blue fiber and half",
+        " $50,000 in repairs.\n\nQuestion",
+        " He runs 1 sprint 3 times a week.",
+        " They feed each of her chickens three cups of mixed",
+        " The price of the glasses is $5, but",
+        " The total percentage of students who said they like to",
+        " Carla is downloading a 200 GB file. Normally",
+        " John drives for 3 hours at a speed of 60",
+        " Eliza sells 4 tickets to 5 friends so she",
+    ]
+    ROLLING_RES = [
+        -3603.6328125,
+        -19779.23974609375,
+        -8834.16455078125,
+        -27967.591796875,
+        -7636.794982910156,
+        -9491.93505859375,
+        -41043.4248046875,
+        -8397.689819335938,
+        -45969.47155761719,
+        -7158.90625,
+    ]
+
+    def test_logliklihood(self, checkpoint_dir, out_dir) -> None:
+        config = Config.from_file(str(checkpoint_dir / "model_config.yaml"))
+        config.fix_head_size = True
+        config.model_type = "gpt"
+        config.tie_embeddings = False
+        gpt = GPT(config)
+        gpt.to(gpt.device)
+        gpt.name_or_path = "EleutherAI/pythia-70m"
+
+        # model = LitGPT(config)
+        gpt.load_state_dict(torch.load(str(checkpoint_dir / "lit_model.pth")))
+        LM = WhittleLM(pretrained=gpt, dtype="float32")
+        res = LM.loglikelihood(self.MULTIPLE_CH)
+        _RES, _res = self.MULTIPLE_CH_RES, [r[0] for r in res]
+        # log samples to CI
+        dir_path = out_dir
+        dir_path.mkdir(parents=True, exist_ok=True)
+        assert np.allclose(_res, _RES, atol=1e-2)
+        # check indices for Multiple Choice
+        argmax_RES, argmax_res = (
+            np.argmax(np.array(_RES).reshape(-1, 4), axis=1),
+            np.argmax(np.array(_res).reshape(-1, 4), axis=1),
+        )
+        assert (argmax_RES == argmax_res).all()
+
+    def test_generate_until(self, checkpoint_dir) -> None:
+        config = Config.from_file(str(checkpoint_dir / "model_config.yaml"))
+        config.fix_head_size = True
+        config.model_type = "gpt"
+        config.tie_embeddings = False
+        gpt = GPT(config)
+        gpt.to(gpt.device)
+        gpt.name_or_path = "EleutherAI/pythia-70m"
+
+        # model = LitGPT(config)
+        gpt.load_state_dict(torch.load(str(checkpoint_dir / "lit_model.pth")))
+        LM = WhittleLM(pretrained=gpt, dtype="float32")
+        res = LM.generate_until(self.generate_until)
+        assert res == self.generate_until_RES
+
+    def test_logliklihood_rolling(self, checkpoint_dir) -> None:
+        config = Config.from_file(str(checkpoint_dir / "model_config.yaml"))
+        config.fix_head_size = True
+        config.model_type = "gpt"
+        config.tie_embeddings = False
+        gpt = GPT(config)
+        gpt.to(gpt.device)
+        gpt.name_or_path = "EleutherAI/pythia-70m"
+
+        # model = LitGPT(config)
+        gpt.load_state_dict(torch.load(str(checkpoint_dir / "lit_model.pth")))
+        LM = WhittleLM(pretrained=gpt, dtype="float32")
+        res = LM.loglikelihood_rolling(self.ROLLING)
+        assert np.allclose(res, self.ROLLING_RES, atol=1e-1)
+
+    def test_toc_encode(self, checkpoint_dir) -> None:
+        config = Config.from_file(str(checkpoint_dir / "model_config.yaml"))
+        config.fix_head_size = True
+        config.model_type = "gpt"
+        config.tie_embeddings = False
+        gpt = GPT(config)
+        gpt.to(gpt.device)
+        gpt.name_or_path = "EleutherAI/pythia-70m"
+
+        # model = LitGPT(config)
+        gpt.load_state_dict(torch.load(str(checkpoint_dir / "lit_model.pth")))
+        LM = WhittleLM(pretrained=gpt, dtype="float32")
+        res = LM.tok_encode(self.TEST_STRING)
+        assert res == [12110, 2534]
+
+    def test_toc_decode(self, checkpoint_dir) -> None:
+        config = Config.from_file(str(checkpoint_dir / "model_config.yaml"))
+        config.fix_head_size = True
+        config.model_type = "gpt"
+        config.tie_embeddings = False
+        gpt = GPT(config)
+        gpt.to(gpt.device)
+        gpt.name_or_path = "EleutherAI/pythia-70m"
+
+        # model = LitGPT(config)
+        gpt.load_state_dict(torch.load(str(checkpoint_dir / "lit_model.pth")))
+        LM = WhittleLM(pretrained=gpt, dtype="float32")
+        res = LM.tok_decode([12110, 2534])
+        assert res == self.TEST_STRING
+
+    def test_batch_encode(self, checkpoint_dir) -> None:
+        config = Config.from_file(str(checkpoint_dir / "model_config.yaml"))
+        config.fix_head_size = True
+        config.model_type = "gpt"
+        config.tie_embeddings = False
+        gpt = GPT(config)
+        gpt.to(gpt.device)
+        gpt.name_or_path = "EleutherAI/pythia-70m"
+
+        # model = LitGPT(config)
+        gpt.load_state_dict(torch.load(str(checkpoint_dir / "lit_model.pth")))
+        LM = WhittleLM(pretrained=gpt, dtype="float32")
+        res = LM.tok_batch_encode([self.TEST_STRING, "bar foo"])[0].tolist()
+        assert res == [[12110, 2534], [2009, 17374]]
+
+    def test_model_generate(self, checkpoint_dir) -> None:
+        config = Config.from_file(str(checkpoint_dir / "model_config.yaml"))
+        config.fix_head_size = True
+        config.model_type = "gpt"
+        config.tie_embeddings = False
+        gpt = GPT(config)
+        gpt.to(gpt.device)
+        gpt.name_or_path = "EleutherAI/pythia-70m"
+
+        # model = LitGPT(config)
+        gpt.load_state_dict(torch.load(str(checkpoint_dir / "lit_model.pth")))
+        LM = WhittleLM(pretrained=gpt, dtype="float32")
+        context = LM.tok_batch_encode([self.TEST_STRING])[0]
+        res = LM._model_generate(context.to(gpt.device), max_length=10, stop=["\n\n"])
+        res = LM.tok_decode(res[0])
+        assert res == "foo bar\n<bazhang>!info bar"
+
+    def test_evaluate(self, checkpoint_dir, out_dir):
+        config = Config.from_file(str(checkpoint_dir / "model_config.yaml"))
+        config.fix_head_size = True
+        config.model_type = "gpt"
+        config.tie_embeddings = False
+        gpt = GPT(config)
+        gpt.to(gpt.device)
+        gpt.name_or_path = "EleutherAI/pythia-70m"
+
+        # model = LitGPT(config)
+        gpt.load_state_dict(torch.load(str(checkpoint_dir / "lit_model.pth")))
+        convert_and_evaluate(
+            gpt,
+            out_dir=out_dir,
+            device="cpu",
+            dtype=torch.float32,
+            limit=10,
+            tasks="logiqa",
+            batch_size=1,  # Test for non-positive integer
+        )
+        with open(str(out_dir / "results.json"), "r") as f:
+            results = json.load(f)
+        acc_api = results["results"]["logiqa"]["acc,none"]
+        stderr_api = results["results"]["logiqa"]["acc_stderr,none"]
+        import litgpt.eval.evaluate as module
+
+        module.convert_and_evaluate(
+            checkpoint_dir,
+            out_dir=out_dir,
+            device="cpu",
+            dtype=torch.float32,
+            limit=10,
+            tasks="logiqa",
+            force_conversion=True,
+            batch_size=1,  # Test for non-positive integer
+        )
+        with open(str(out_dir / "results.json"), "r") as f:
+            results = json.load(f)
+        acc_lit = results["results"]["logiqa"]["acc,none"]
+        stderr_lit = results["results"]["logiqa"]["acc_stderr,none"]
+        assert acc_api == acc_lit
+        assert stderr_api == stderr_lit