Repository: ScandEval/ScandEval (language: python)

Issue:
Investigate whether vLLM can speed up evaluation
Try experimenting with vLLM for few-shot evaluating generative models, to see if we can speed up the evaluation a bit.


Discussion / hints:

Fix patch:
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 7a2bb5ba9..d38ade865 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -41,6 +41,9 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
   dataset). This is especially useful with paid models like OpenAI models. The value of
   this flag is stored in the benchmark results, so this will be visible on
   leaderboards.
+- Now uses vLLM as the underlying engine for few-shot evaluating generative models,
+  which drastically improves the evaluation speed, as well as requiring less GPU
+  memory.
 
 ### Changed
 - Now compatible with`transformers >= 4.36.2`, and this is required now as they have
@@ -52,6 +55,9 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
   previously.
 - Now doesn't force ASCII characters in the NER task for generative models, making the
   target JSON dictionary more consistent with the input text.
+- If a model is stored in the Safetensors format on Hugging Face Hub, then we read out
+  the number of parameters directly from those files. This results in more accurate
+  parameter counts as opposed to loading in the model in 4-bit and counting manually.
 - Samples with excessively short or long texts have been removed.
 - Adjusted number of few-shot examples in datasets to ensure that the resulting prompt
   is at most ~3000 tokens long.
@@ -81,6 +87,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
   generated model output. Instead of taking the output as-is, we are now extracting the
   first dictionary (enclosed in curly brackets), as well as replacing all single
   apostrophes (') with double ones (").
+- If a model is already pre-quantized then we will not attempt to quantize it as well.
 
 
 ## [v8.2.1] - 2023-12-20
diff --git a/README.md b/README.md
index e91e4db4e..2dac2eb30 100644
--- a/README.md
+++ b/README.md
@@ -9,7 +9,7 @@ ______________________________________________________________________
 [![Paper](https://img.shields.io/badge/arXiv-2304.00906-b31b1b.svg)](https://arxiv.org/abs/2304.00906)
 [![License](https://img.shields.io/github/license/ScandEval/ScandEval)](https://github.com/ScandEval/ScandEval/blob/main/LICENSE)
 [![LastCommit](https://img.shields.io/github/last-commit/ScandEval/ScandEval)](https://github.com/ScandEval/ScandEval/commits/main)
-[![Code Coverage](https://img.shields.io/badge/Coverage-73%25-yellow.svg)](https://github.com/ScandEval/ScandEval/tree/main/tests)
+[![Code Coverage](https://img.shields.io/badge/Coverage-74%25-yellow.svg)](https://github.com/ScandEval/ScandEval/tree/main/tests)
 [![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-2.0-4baaaa.svg)](https://github.com/ScandEval/ScandEval/blob/main/CODE_OF_CONDUCT.md)
 
 
diff --git a/poetry.lock b/poetry.lock
index 90e3e886e..a07339373 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -137,6 +137,26 @@ yarl = ">=1.0,<2.0"
 [package.extras]
 speedups = ["Brotli", "aiodns", "brotlicffi"]
 
+[[package]]
+name = "aioprometheus"
+version = "23.12.0"
+description = "A Prometheus Python client library for asyncio-based applications"
+optional = false
+python-versions = ">=3.8.0"
+files = [
+    {file = "aioprometheus-23.12.0-py3-none-any.whl", hash = "sha256:b1a77259131153ef820b494e76439b278434eaf2a5e25dc0b8bf3d835f455960"},
+]
+
+[package.dependencies]
+orjson = "*"
+quantile-python = ">=1.1"
+starlette = {version = ">=0.14.2", optional = true, markers = "extra == \"starlette\""}
+
+[package.extras]
+aiohttp = ["aiohttp (>=3.3.2)"]
+quart = ["quart (>=0.15.1)"]
+starlette = ["starlette (>=0.14.2)"]
+
 [[package]]
 name = "aiosignal"
 version = "1.3.1"
@@ -152,16 +172,27 @@ files = [
 frozenlist = ">=1.1.0"
 
 [[package]]
-name = "annotated-types"
-version = "0.6.0"
-description = "Reusable constraint types to use with typing.Annotated"
+name = "anyio"
+version = "4.2.0"
+description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"},
-    {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"},
+    {file = "anyio-4.2.0-py3-none-any.whl", hash = "sha256:745843b39e829e108e518c489b31dc757de7d2131d53fac32bd8df268227bfee"},
+    {file = "anyio-4.2.0.tar.gz", hash = "sha256:e1875bb4b4e2de1669f4bc7869b6d3f54231cdced71605e6e64c9be77e3be50f"},
 ]
 
+[package.dependencies]
+exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
+idna = ">=2.8"
+sniffio = ">=1.1"
+typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
+
+[package.extras]
+doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+trio = ["trio (>=0.23)"]
+
 [[package]]
 name = "async-timeout"
 version = "4.0.3"
@@ -175,21 +206,22 @@ files = [
 
 [[package]]
 name = "attrs"
-version = "23.1.0"
+version = "23.2.0"
 description = "Classes Without Boilerplate"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "attrs-23.1.0-py3-none-any.whl", hash = "sha256:1f28b4522cdc2fb4256ac1a020c78acf9cba2c6b461ccd2c126f3aa8e8335d04"},
-    {file = "attrs-23.1.0.tar.gz", hash = "sha256:6279836d581513a26f1bf235f9acd333bc9115683f14f7e8fae46c98fc50e015"},
+    {file = "attrs-23.2.0-py3-none-any.whl", hash = "sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1"},
+    {file = "attrs-23.2.0.tar.gz", hash = "sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30"},
 ]
 
 [package.extras]
 cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
-dev = ["attrs[docs,tests]", "pre-commit"]
+dev = ["attrs[tests]", "pre-commit"]
 docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
 tests = ["attrs[tests-no-zope]", "zope-interface"]
-tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
+tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
 
 [[package]]
 name = "bert-score"
@@ -214,40 +246,47 @@ transformers = ">=3.0.0"
 
 [[package]]
 name = "bitsandbytes"
-version = "0.41.2.post2"
+version = "0.42.0"
 description = "k-bit optimizers and matrix multiplication routines."
 optional = false
 python-versions = "*"
 files = [
-    {file = "bitsandbytes-0.41.2.post2-py3-none-any.whl", hash = "sha256:98e5e1979aea3d481ed06181c689f3a154d7f5dc1af770c5173485bc54cf7b72"},
-    {file = "bitsandbytes-0.41.2.post2.tar.gz", hash = "sha256:d374da4700651f36a285ed53e012ee527736109614e3f5c0249985d41027136d"},
+    {file = "bitsandbytes-0.42.0-py3-none-any.whl", hash = "sha256:63798680912cc63bb77b535a2d0860af024e290a52e157f777ad2a52e2585967"},
+    {file = "bitsandbytes-0.42.0.tar.gz", hash = "sha256:fc1505f184f0d275766f2a6c663f1a43b734c1409b5c5a406f3a6073d9f329fd"},
 ]
 
+[package.dependencies]
+scipy = "*"
+
 [[package]]
 name = "black"
-version = "23.11.0"
+version = "23.12.1"
 description = "The uncompromising code formatter."
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "black-23.12.1-cp39-cp39-win_amd64.whl", hash = "sha256:dd15245c8b68fe2b6bd0f32c1556509d11bb33aec9b5d0866dd8e2ed3dba09c2"},
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+    {file = "black-23.12.1.tar.gz", hash = "sha256:4ce3ef14ebe8d9509188014d96af1c456a910d5b5cbf434a09fef7e024b3d0d5"},
 ]
 
 [package.dependencies]
@@ -261,7 +300,7 @@ typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
 colorama = ["colorama (>=0.4.3)"]
-d = ["aiohttp (>=3.7.4)"]
+d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"]
 jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
 uvloop = ["uvloop (>=0.15.2)"]
 
@@ -578,20 +617,21 @@ tests = ["pytest", "pytest-cov", "pytest-xdist"]
 
 [[package]]
 name = "datasets"
-version = "2.15.0"
+version = "2.16.1"
 description = "HuggingFace community-driven open-source library of datasets"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "datasets-2.15.0-py3-none-any.whl", hash = "sha256:6d658d23811393dfc982d026082e1650bdaaae28f6a86e651966cb072229a228"},
-    {file = "datasets-2.15.0.tar.gz", hash = "sha256:a26d059370bd7503bd60e9337977199a13117a83f72fb61eda7e66f0c4d50b2b"},
+    {file = "datasets-2.16.1-py3-none-any.whl", hash = "sha256:fafa300c78ff92d521473a3d47d60c2d3e0d6046212cc03ceb6caf6550737257"},
+    {file = "datasets-2.16.1.tar.gz", hash = "sha256:ad3215e9b1984d1de4fda2123bc7319ccbdf1e17d0c3d5590d13debff308a080"},
 ]
 
 [package.dependencies]
 aiohttp = "*"
 dill = ">=0.3.0,<0.3.8"
+filelock = "*"
 fsspec = {version = ">=2023.1.0,<=2023.10.0", extras = ["http"]}
-huggingface-hub = ">=0.18.0"
+huggingface-hub = ">=0.19.4"
 multiprocess = "*"
 numpy = ">=1.17"
 packaging = "*"
@@ -607,15 +647,15 @@ xxhash = "*"
 apache-beam = ["apache-beam (>=2.26.0,<2.44.0)"]
 audio = ["librosa", "soundfile (>=0.12.1)"]
 benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"]
-dev = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0,<2.44.0)", "black (>=23.1,<24.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "pyyaml (>=5.3.1)", "rarfile (>=4.0)", "ruff (>=0.0.241)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy (<2.0.0)", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
+dev = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0,<2.44.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.1.5)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy (<2.0.0)", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch", "torch (>=2.0.0)", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
 docs = ["s3fs", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos", "torch", "transformers"]
 jax = ["jax (>=0.3.14)", "jaxlib (>=0.3.14)"]
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-quality = ["black (>=23.1,<24.0)", "pyyaml (>=5.3.1)", "ruff (>=0.0.241)"]
+quality = ["ruff (>=0.1.5)"]
 s3 = ["s3fs"]
 tensorflow = ["tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos"]
 tensorflow-gpu = ["tensorflow-gpu (>=2.2.0,!=2.6.0,!=2.6.1)"]
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 torch = ["torch"]
 vision = ["Pillow (>=6.2.1)"]
 
@@ -645,13 +685,13 @@ graph = ["objgraph (>=1.7.2)"]
 
 [[package]]
 name = "distlib"
-version = "0.3.7"
+version = "0.3.8"
 description = "Distribution utilities"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [[package]]
@@ -667,13 +707,13 @@ files = [
 
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 name = "etils"
-version = "1.5.2"
+version = "1.6.0"
 description = "Collection of common python utils"
 optional = false
-python-versions = ">=3.9"
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 ]
 
 [package.dependencies]
@@ -688,7 +728,7 @@ array-types = ["etils[enp]"]
 dev = ["chex", "dataclass_array", "optree", "pyink", "pylint (>=2.6.0)", "pytest", "pytest-subtests", "pytest-xdist", "torch"]
 docs = ["etils[all,dev]", "sphinx-apitree[ext]"]
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-ecolab = ["etils[enp]", "etils[epy]", "jupyter", "mediapy", "numpy", "packaging"]
+ecolab = ["etils[enp]", "etils[epy]", "etils[etree]", "jupyter", "mediapy", "numpy", "packaging", "protobuf"]
 edc = ["etils[epy]"]
 enp = ["etils[epy]", "numpy"]
 epath = ["etils[epy]", "fsspec", "importlib_resources", "typing_extensions", "zipp"]
@@ -752,6 +792,25 @@ files = [
 [package.extras]
 test = ["pytest (>=6)"]
 
+[[package]]
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+version = "0.108.0"
+description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
+optional = false
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+
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+
 [[package]]
 name = "filelock"
 version = "3.13.1"
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 [[package]]
@@ -972,15 +1047,74 @@ smb = ["smbprotocol"]
 ssh = ["paramiko"]
 tqdm = ["tqdm"]
 
+[[package]]
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+
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 html5 = ["html5lib"]
 htmlsoup = ["BeautifulSoup4"]
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 [[package]]
 name = "markdown-it-py"
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 version = "3.8.1"
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 [[package]]
@@ -2261,13 +2458,13 @@ test = ["dm-haiku (>=0.0.3)", "dm-tree (>=0.1.7)", "flax (==0.5.3)"]
 
 [[package]]
 name = "orbax-checkpoint"
-version = "0.4.4"
+version = "0.4.8"
 description = "Orbax Checkpoint"
 optional = false
 python-versions = ">=3.9"
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 [package.dependencies]
@@ -2280,12 +2477,71 @@ nest_asyncio = "*"
 numpy = "*"
 protobuf = "*"
 pyyaml = "*"
-tensorstore = ">=0.1.35"
+tensorstore = ">=0.1.51"
 typing_extensions = "*"
 
 [package.extras]
 testing = ["flax", "pytest", "pytest-xdist"]
 
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 docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-removed-in", "sphinxext-opengraph"]
+fpx = ["olefile"]
+mic = ["olefile"]
 tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
+typing = ["typing-extensions"]
+xmp = ["defusedxml"]
 
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-version = "4.0.0"
+version = "4.1.0"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
 optional = false
-python-versions = ">=3.7"
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@@ -2473,13 +2747,13 @@ testing = ["pytest", "pytest-benchmark"]
 
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 ]
 
 [package.extras]
@@ -2550,47 +2824,47 @@ test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
 
 [[package]]
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-version = "14.0.1"
+version = "14.0.2"
 description = "Python library for Apache Arrow"
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 python-versions = ">=3.8"
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 [[package]]
@@ -3215,30 +3490,138 @@ nltk = "*"
 numpy = "*"
 six = ">=1.14.0"
 
+[[package]]
+name = "rpds-py"
+version = "0.16.2"
+description = "Python bindings to Rust's persistent data structures (rpds)"
+optional = false
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 [[package]]
@@ -3536,13 +3919,13 @@ scikit-learn = ">=0.21.3"
 
 [[package]]
 name = "setuptools"
-version = "69.0.2"
+version = "69.0.3"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.extras]
@@ -3561,6 +3944,34 @@ files = [
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+description = "Sniff out which async library your code is running under"
+optional = false
+python-versions = ">=3.7"
+files = [
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+optional = false
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+
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+full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart", "pyyaml"]
+
 [[package]]
 name = "sympy"
 version = "1.12"
@@ -3591,28 +4002,28 @@ widechars = ["wcwidth"]
 
 [[package]]
 name = "tensorstore"
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 description = "Read and write large, multi-dimensional arrays"
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 python-versions = ">=3.9"
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 ]
 
 [package.dependencies]
@@ -4012,13 +4423,13 @@ tutorials = ["matplotlib", "pandas", "tabulate"]
 
 [[package]]
 name = "typing-extensions"
-version = "4.8.0"
+version = "4.9.0"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
@@ -4037,6 +4448,76 @@ brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
 
+[[package]]
+name = "uvicorn"
+version = "0.25.0"
+description = "The lightning-fast ASGI server."
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.dependencies]
+click = ">=7.0"
+colorama = {version = ">=0.4", optional = true, markers = "sys_platform == \"win32\" and extra == \"standard\""}
+h11 = ">=0.8"
+httptools = {version = ">=0.5.0", optional = true, markers = "extra == \"standard\""}
+python-dotenv = {version = ">=0.13", optional = true, markers = "extra == \"standard\""}
+pyyaml = {version = ">=5.1", optional = true, markers = "extra == \"standard\""}
+typing-extensions = {version = ">=4.0", markers = "python_version < \"3.11\""}
+uvloop = {version = ">=0.14.0,<0.15.0 || >0.15.0,<0.15.1 || >0.15.1", optional = true, markers = "(sys_platform != \"win32\" and sys_platform != \"cygwin\") and platform_python_implementation != \"PyPy\" and extra == \"standard\""}
+watchfiles = {version = ">=0.13", optional = true, markers = "extra == \"standard\""}
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+    {file = "yarl-1.9.4-cp38-cp38-win32.whl", hash = "sha256:686a0c2f85f83463272ddffd4deb5e591c98aac1897d65e92319f729c320eece"},
+    {file = "yarl-1.9.4-cp38-cp38-win_amd64.whl", hash = "sha256:a00862fb23195b6b8322f7d781b0dc1d82cb3bcac346d1e38689370cc1cc398b"},
+    {file = "yarl-1.9.4-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:604f31d97fa493083ea21bd9b92c419012531c4e17ea6da0f65cacdcf5d0bd27"},
+    {file = "yarl-1.9.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:8a854227cf581330ffa2c4824d96e52ee621dd571078a252c25e3a3b3d94a1b1"},
+    {file = "yarl-1.9.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:ba6f52cbc7809cd8d74604cce9c14868306ae4aa0282016b641c661f981a6e91"},
+    {file = "yarl-1.9.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a6327976c7c2f4ee6816eff196e25385ccc02cb81427952414a64811037bbc8b"},
+    {file = "yarl-1.9.4-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8397a3817d7dcdd14bb266283cd1d6fc7264a48c186b986f32e86d86d35fbac5"},
+    {file = "yarl-1.9.4-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e0381b4ce23ff92f8170080c97678040fc5b08da85e9e292292aba67fdac6c34"},
+    {file = "yarl-1.9.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:23d32a2594cb5d565d358a92e151315d1b2268bc10f4610d098f96b147370136"},
+    {file = "yarl-1.9.4-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ddb2a5c08a4eaaba605340fdee8fc08e406c56617566d9643ad8bf6852778fc7"},
+    {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:26a1dc6285e03f3cc9e839a2da83bcbf31dcb0d004c72d0730e755b33466c30e"},
+    {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:18580f672e44ce1238b82f7fb87d727c4a131f3a9d33a5e0e82b793362bf18b4"},
+    {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:29e0f83f37610f173eb7e7b5562dd71467993495e568e708d99e9d1944f561ec"},
+    {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:1f23e4fe1e8794f74b6027d7cf19dc25f8b63af1483d91d595d4a07eca1fb26c"},
+    {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:db8e58b9d79200c76956cefd14d5c90af54416ff5353c5bfd7cbe58818e26ef0"},
+    {file = "yarl-1.9.4-cp39-cp39-win32.whl", hash = "sha256:c7224cab95645c7ab53791022ae77a4509472613e839dab722a72abe5a684575"},
+    {file = "yarl-1.9.4-cp39-cp39-win_amd64.whl", hash = "sha256:824d6c50492add5da9374875ce72db7a0733b29c2394890aef23d533106e2b15"},
+    {file = "yarl-1.9.4-py3-none-any.whl", hash = "sha256:928cecb0ef9d5a7946eb6ff58417ad2fe9375762382f1bf5c55e61645f2c43ad"},
+    {file = "yarl-1.9.4.tar.gz", hash = "sha256:566db86717cf8080b99b58b083b773a908ae40f06681e87e589a976faf8246bf"},
 ]
 
 [package.dependencies]
@@ -4295,4 +4994,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<4.0"
-content-hash = "c94d7d85b2353afa559b6f82ca5e19563e7cc6b71384204d8fadfb47e5afaa77"
+content-hash = "94202acdcb197f5a8659ae6b5ccc6637ccfaa27010309dd6dd00e5bc96cc7719"
diff --git a/pyproject.toml b/pyproject.toml
index df66448c5..5d260a863 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -36,13 +36,13 @@ levenshtein = ">=0.21.1,<1.0.0"
 einops = ">=0.6.0,<1.0.0"
 bitsandbytes = {markers="sys_platform != 'darwin' or platform_machine != 'arm64'", version = ">=0.40.0.post4,<1.0.0"}
 requests = "^2.31.0"
-pydantic = "^2.3.0"
 torch = "^2.1.1"
 transformers = "^4.36.2"
 accelerate = ">=0.23.0,<1.0.0"
 rouge-score = ">=0.1.2,<1.0.0"
 bert-score = ">=0.3.13,<1.0.0"
 evaluate = ">=0.4.1,<1.0.0"
+vllm = ">=0.2.7,<1.0.0"
 demjson3 = "^3.0.6"
 
 [tool.poetry.group.dev.dependencies]
diff --git a/src/scandeval/benchmark_dataset.py b/src/scandeval/benchmark_dataset.py
index 7ad0977c3..e2c707a5c 100644
--- a/src/scandeval/benchmark_dataset.py
+++ b/src/scandeval/benchmark_dataset.py
@@ -13,7 +13,10 @@
 from datasets.arrow_dataset import Dataset
 from datasets.dataset_dict import DatasetDict
 from datasets.load import load_dataset
-from huggingface_hub.utils._errors import HfHubHTTPError
+from huggingface_hub import HfApi
+from huggingface_hub.hf_api import ModelInfo
+from huggingface_hub.utils import HfHubHTTPError, HFValidationError
+from requests import RequestException
 from tqdm.auto import tqdm
 from transformers import PretrainedConfig, Trainer
 from transformers.modeling_utils import ModelOutput, PreTrainedModel
@@ -128,7 +131,9 @@ def benchmark(self, model_id: str) -> tuple[SCORE_DICT, dict[str, bool | int]]:
             else:
                 model_config.task = "fill-mask"
 
-        metadata_dict = self._get_metadata(model=model, tokenizer=tokenizer)
+        metadata_dict = self._get_metadata(
+            model_id=model_id, model=model, tokenizer=tokenizer
+        )
 
         # Set variable with number of iterations
         num_iter = 2 if hasattr(sys, "_called_from_test") else 10
@@ -209,12 +214,15 @@ def benchmark(self, model_id: str) -> tuple[SCORE_DICT, dict[str, bool | int]]:
 
     def _get_metadata(
         self,
+        model_id: str,
         model: PreTrainedModel | GenerativeModel,
         tokenizer: Tokenizer,
     ) -> dict[str, int]:
         """Get metadata about the model.
 
         Args:
+            model_id:
+                The Hugging Face model ID.
             model:
                 The model to get metadata about.
             tokenizer:
@@ -224,7 +232,21 @@ def _get_metadata(
             A dictionary containing metadata about the model, with the keys being the
             metadata names and the values being the metadata values.
         """
-        if hasattr(model.config, "num_params"):
+        api = HfApi()
+        try:
+            repo_info = api.repo_info(repo_id=model_id, repo_type="model")
+            assert isinstance(repo_info, ModelInfo)
+        except (RequestException, HFValidationError):
+            repo_info = None
+
+        if (
+            repo_info is not None
+            and hasattr(repo_info, "safetensors")
+            and repo_info.safetensors is not None
+            and "total" in repo_info.safetensors
+        ):
+            num_params = repo_info.safetensors["total"]
+        elif hasattr(model.config, "num_params"):
             num_params = model.config.num_params
         elif isinstance(model, PreTrainedModel):
             num_params = sum(p.numel() for p in model.parameters())
@@ -233,7 +255,9 @@ def _get_metadata(
 
         if hasattr(model.config, "model_max_length"):
             max_seq_length = getattr(model.config, "model_max_length")
-        elif hasattr(tokenizer, "model_max_length"):
+        elif hasattr(
+            tokenizer, "model_max_length"
+        ) and tokenizer.model_max_length < int(1e30):
             max_seq_length = getattr(tokenizer, "model_max_length")
         else:
             max_seq_length = -1
@@ -242,7 +266,7 @@ def _get_metadata(
         # length from the maximum length to allow it to keep generating. But for the
         # model metadata we want to know the maximum length, so we add the generation
         # length back on here
-        if model_is_generative(model=model):
+        if max_seq_length >= 0 and model_is_generative(model=model):
             max_seq_length += self.dataset_config.max_generated_tokens
 
             # If the model is an OpenAI chat model then we add on 7 extra tokens, as
diff --git a/src/scandeval/benchmarker.py b/src/scandeval/benchmarker.py
index 96f557fd6..8c6bdfff7 100644
--- a/src/scandeval/benchmarker.py
+++ b/src/scandeval/benchmarker.py
@@ -63,6 +63,17 @@ def from_dict(cls, config: dict) -> "BenchmarkResult":
 
         return cls(**config)
 
+    def append_to_results(self, results_path: Path) -> None:
+        """Append the benchmark result to the results file.
+
+        Args:
+            results_path:
+                The path to the results file.
+        """
+        json_str = json.dumps(self.dict())
+        with results_path.open("a") as f:
+            f.write("\n" + json_str)
+
 
 class Benchmarker:
     """Benchmarking all the Scandinavian language models.
@@ -300,8 +311,7 @@ def benchmark(
 
                 # Save the benchmark results
                 if self.benchmark_config.save_results:
-                    with self.results_path.open("a") as f:
-                        f.write("\n" + record.model_dump_json())
+                    record.append_to_results(results_path=self.results_path)
 
             self.clear_model_cache()
 
diff --git a/src/scandeval/finetuning.py b/src/scandeval/finetuning.py
index 7e36e9d4b..16f60571c 100644
--- a/src/scandeval/finetuning.py
+++ b/src/scandeval/finetuning.py
@@ -22,10 +22,9 @@
 )
 from transformers.trainer import OptimizerNames
 
-from scandeval.exceptions import InvalidBenchmark
-
 from .callbacks import NeverLeaveProgressCallback
 from .config import BenchmarkConfig, DatasetConfig, ModelConfig
+from .exceptions import InvalidBenchmark
 from .model_loading import load_model
 from .protocols import Tokenizer
 from .utils import block_terminal_output, clear_memory, enforce_reproducibility
diff --git a/src/scandeval/generation.py b/src/scandeval/generation.py
index 7241274b6..2fad07c60 100644
--- a/src/scandeval/generation.py
+++ b/src/scandeval/generation.py
@@ -25,6 +25,7 @@
 from .openai_models import OpenAIModel
 from .protocols import GenerativeModel, Tokenizer
 from .utils import clear_memory
+from .vllm_models import VLLMModel
 
 logger = logging.getLogger(__package__)
 
@@ -236,9 +237,13 @@ def generate_single_iteration(
             ]
         )
 
-        batch_size = (
-            1 if isinstance(model, OpenAIModel) else benchmark_config.batch_size
-        )
+        if isinstance(model, OpenAIModel):
+            batch_size = 1
+        elif isinstance(model, VLLMModel):
+            batch_size = len(torch_dataset)
+        else:
+            batch_size = benchmark_config.batch_size
+
         dataloader = DataLoader(
             dataset=torch_dataset,
             batch_size=batch_size,
@@ -246,9 +251,14 @@ def generate_single_iteration(
             num_workers=4,
             collate_fn=data_collator,
         )
+        itr = (
+            dataloader
+            if isinstance(model, VLLMModel)
+            else tqdm(dataloader, leave=False)
+        )
 
         # Generate the completions for the non-cached examples
-        for batch_idx, batch in enumerate(tqdm(dataloader, leave=False)):
+        for batch_idx, batch in enumerate(itr):
             model_output, extracted_labels = generate_batch(
                 batch=batch,
                 batch_idx=batch_idx,
@@ -408,11 +418,13 @@ def generate_batch(
         warnings.simplefilter("ignore", category=UserWarning)
         inputs = batch["input_ids"].to(model.device)
         stopping_criteria.clear()
-        model_output: ModelOutput = model.generate(
+
+        model_output = model.generate(
             inputs=inputs,
             generation_config=generation_config,
             stopping_criteria=[stopping_criteria],
         )
+        assert isinstance(model_output, ModelOutput)
 
     # Hugging Face models include the input in the generated sequence, so we
     # need to remove it in that case
diff --git a/src/scandeval/model_setups/hf.py b/src/scandeval/model_setups/hf.py
index 509d6db21..a6ab31782 100644
--- a/src/scandeval/model_setups/hf.py
+++ b/src/scandeval/model_setups/hf.py
@@ -33,6 +33,7 @@
     internet_connection_available,
     model_is_generative,
 )
+from ..vllm_models import VLLMModel
 from .utils import align_model_and_tokenizer, setup_model_for_question_answering
 
 logger = logging.getLogger(__package__)
@@ -63,7 +64,7 @@ def model_exists(self, model_id: str) -> bool:
         """Check if a model ID denotes an OpenAI model.
 
         Args:
-            model_id :
+            model_id:
                 The model ID.
 
         Returns:
@@ -240,94 +241,129 @@ def load_model(
             model_cache_dir=model_config.model_cache_dir,
         )
 
-        model_kwargs = dict(
-            config=config,
-            from_flax=from_flax,
-            ignore_mismatched_sizes=ignore_mismatched_sizes,
-            revision=model_config.revision,
-            token=self.benchmark_config.token,
-            cache_dir=model_config.model_cache_dir,
-            trust_remote_code=self.benchmark_config.trust_remote_code,
-            quantization_config=bnb_config,
-            torch_dtype=self._get_torch_dtype(config=config),
-            use_flash_attention_2=use_flash_attention,
+        use_vllm = (
+            model_config.task in GENERATIVE_MODEL_TASKS
+            and self.benchmark_config.device == torch.device("cuda")
         )
 
-        while True:
+        if use_vllm:
             try:
-                # Get the model class associated with the supertask
-                if model_config.task in ["text-generation", "conversational"]:
-                    model_cls_supertask = "causal-l-m"
-                elif model_config.task == "text2text-generation":
-                    model_cls_supertask = "seq-2-seq-l-m"
-                else:
-                    model_cls_supertask = supertask
-                model_cls_or_none: Type[PreTrainedModel] | None = get_class_by_name(
-                    class_name=f"auto-model-for-{model_cls_supertask}",
-                    module_name="transformers",
+                model = VLLMModel(
+                    model_config=model_config,
+                    hf_model_config=config,
+                    model_cache_dir=model_config.model_cache_dir,
+                )
+            except ValueError as e:
+                oom_error = (
+                    "larger than the maximum number of tokens that can be stored "
+                    "in KV cache"
+                )
+                use_vllm = oom_error in str(e)
+                logger.info(
+                    "Failed to benchmark with vLLM - trying with the Hugging Face "
+                    "implementation instead."
                 )
 
-                # If the model class could not be found then raise an error
-                if not model_cls_or_none:
-                    raise InvalidBenchmark(
-                        f"The supertask {supertask!r} does not correspond to a "
-                        "Hugging Face AutoModel type (such as "
-                        "`AutoModelForSequenceClassification`)."
+        if not use_vllm:
+            model_kwargs = dict(
+                config=config,
+                from_flax=from_flax,
+                ignore_mismatched_sizes=ignore_mismatched_sizes,
+                revision=model_config.revision,
+                token=self.benchmark_config.token,
+                cache_dir=model_config.model_cache_dir,
+                trust_remote_code=self.benchmark_config.trust_remote_code,
+                quantization_config=bnb_config,
+                torch_dtype=self._get_torch_dtype(config=config),
+                use_flash_attention_2=use_flash_attention,
+            )
+            while True:
+                try:
+                    # Get the model class associated with the supertask
+                    if model_config.task in ["text-generation", "conversational"]:
+                        model_cls_supertask = "causal-l-m"
+                    elif model_config.task == "text2text-generation":
+                        model_cls_supertask = "seq-2-seq-l-m"
+                    else:
+                        model_cls_supertask = supertask
+                    model_cls_or_none: Type[PreTrainedModel] | None = get_class_by_name(
+                        class_name=f"auto-model-for-{model_cls_supertask}",
+                        module_name="transformers",
                     )
 
-                # If the model is a DeBERTaV2 model then we ensure that
-                # `pooler_hidden_size` is the same size as `hidden_size`
-                if config.model_type == "deberta-v2":
-                    config.pooler_hidden_size = config.hidden_size
-
-                with warnings.catch_warnings(), HiddenPrints():
-                    warnings.filterwarnings("ignore", category=UserWarning)
-                    warnings.filterwarnings("ignore", category=FutureWarning)
-                    try:
-                        model_or_tuple = model_cls_or_none.from_pretrained(
-                            model_config.model_id, **model_kwargs
+                    # If the model class could not be found then raise an error
+                    if not model_cls_or_none:
+                        raise InvalidBenchmark(
+                            f"The supertask {supertask!r} does not correspond to a "
+                            "Hugging Face AutoModel type (such as "
+                            "`AutoModelForSequenceClassification`)."
                         )
-                    except ImportError as e:
-                        if "flash attention" in str(e).lower():
-                            raise InvalidBenchmark(
-                                "The model you are trying to load requires Flash "
-                                "Attention. To use Flash Attention, please install "
-                                "the `flash-attn` package, which can be done by "
-                                "running `pip install -U wheel && "
-                                "FLASH_ATTENTION_SKIP_CUDA_BUILD=TRUE pip install "
-                                "flash-attn --no-build-isolation`."
+
+                    # If the model is a DeBERTaV2 model then we ensure that
+                    # `pooler_hidden_size` is the same size as `hidden_size`
+                    if config.model_type == "deberta-v2":
+                        config.pooler_hidden_size = config.hidden_size
+
+                    with warnings.catch_warnings(), HiddenPrints():
+                        warnings.filterwarnings("ignore", category=UserWarning)
+                        warnings.filterwarnings("ignore", category=FutureWarning)
+                        try:
+                            model_or_tuple = model_cls_or_none.from_pretrained(
+                                model_config.model_id, **model_kwargs
                             )
-                    except (KeyError, RuntimeError) as e:
-                        if not model_kwargs["ignore_mismatched_sizes"]:
-                            logger.debug(
-                                f"{type(e).__name__} occurred during the loading "
-                                f"of the {model_id!r} model. Retrying with "
-                                "`ignore_mismatched_sizes` set to True."
+                        except ImportError as e:
+                            if "flash attention" in str(e).lower():
+                                raise InvalidBenchmark(
+                                    "The model you are trying to load requires Flash "
+                                    "Attention. To use Flash Attention, please install "
+                                    "the `flash-attn` package, which can be done by "
+                                    "running `pip install -U wheel && "
+                                    "FLASH_ATTENTION_SKIP_CUDA_BUILD=TRUE pip install "
+                                    "flash-attn --no-build-isolation`."
+                                )
+                        except (KeyError, RuntimeError) as e:
+                            if not model_kwargs["ignore_mismatched_sizes"]:
+                                logger.debug(
+                                    f"{type(e).__name__} occurred during the loading "
+                                    f"of the {model_id!r} model. Retrying with "
+                                    "`ignore_mismatched_sizes` set to True."
+                                )
+                                model_kwargs["ignore_mismatched_sizes"] = True
+                                continue
+                            else:
+                                raise InvalidBenchmark(str(e))
+                        except (TimeoutError, RequestError):
+                            logger.info(
+                                f"Couldn't load the model {model_id!r}. Retrying."
                             )
-                            model_kwargs["ignore_mismatched_sizes"] = True
+                            sleep(5)
                             continue
-                        else:
-                            raise InvalidBenchmark(str(e))
-                    except (TimeoutError, RequestError):
-                        logger.info(f"Couldn't load the model {model_id!r}. Retrying.")
-                        sleep(5)
+                        except ValueError as e:
+                            if "already quantized" in str(e):
+                                model_kwargs["quantization_config"] = None
+                                model_or_tuple = model_cls_or_none.from_pretrained(
+                                    model_config.model_id, **model_kwargs
+                                )
+                            else:
+                                raise e
+
+                    if isinstance(model_or_tuple, tuple):
+                        model = model_or_tuple[0]
+                    else:
+                        model = model_or_tuple
+                    break
+
+                except (OSError, ValueError) as e:
+                    # If `from_flax` is False but only Flax models are available then
+                    # try again with `from_flax` set to True
+                    if (
+                        not from_flax
+                        and "Use `from_flax=True` to load this model" in str(e)
+                    ):
+                        from_flax = True
                         continue
-                if isinstance(model_or_tuple, tuple):
-                    model = model_or_tuple[0]
-                else:
-                    model = model_or_tuple
-                break
-
-            except (OSError, ValueError) as e:
-                # If `from_flax` is False but only Flax models are available then try
-                # again with `from_flax` set to True
-                if not from_flax and "Use `from_flax=True` to load this model" in str(
-                    e
-                ):
-                    from_flax = True
-                    continue
 
-                self._handle_loading_exception(exception=e, model_id=model_id)
+                    self._handle_loading_exception(exception=e, model_id=model_id)
 
         if supertask == "question-answering":
             model = setup_model_for_question_answering(model=model)
@@ -337,6 +373,9 @@ def load_model(
             warnings.filterwarnings("ignore", category=FutureWarning)
             tokenizer = self._load_tokenizer(model=model, model_id=model_id)
 
+        if use_vllm:
+            model.tokenizer = tokenizer
+
         model, tokenizer = align_model_and_tokenizer(
             model=model,
             tokenizer=tokenizer,
diff --git a/src/scandeval/protocols.py b/src/scandeval/protocols.py
index ff9771f57..e82643add 100644
--- a/src/scandeval/protocols.py
+++ b/src/scandeval/protocols.py
@@ -9,6 +9,7 @@
     PretrainedConfig,
     PreTrainedModel,
 )
+from transformers.utils import ModelOutput
 
 from .config import BenchmarkConfig, DatasetConfig, ModelConfig
 
@@ -150,10 +151,10 @@ def device(self) -> torch.device:
 
     def generate(
         self,
-        inputs: torch.LongTensor,
+        inputs: torch.Tensor,
         generation_config: GenerationConfig | None = None,
         **generation_kwargs,
-    ) -> torch.LongTensor:
+    ) -> ModelOutput | torch.Tensor:
         """Generate text.
 
         Args:
diff --git a/src/scandeval/utils.py b/src/scandeval/utils.py
index 4f62ca267..1fc7397f4 100644
--- a/src/scandeval/utils.py
+++ b/src/scandeval/utils.py
@@ -17,6 +17,7 @@
 import pkg_resources
 import requests
 import torch
+import vllm
 from datasets.utils import disable_progress_bar
 from huggingface_hub import HfApi, ModelFilter
 from huggingface_hub.hf_api import ModelInfo
@@ -153,6 +154,15 @@ def block_terminal_output():
     logging.getLogger("openai").setLevel(logging.ERROR)
     logging.getLogger("torch.distributed.distributed_c10d").setLevel(logging.ERROR)
     logging.getLogger("torch.nn.parallel.distributed").setLevel(logging.ERROR)
+    logging.getLogger("vllm.engine.llm_engine").setLevel(logging.ERROR)
+
+    def init_vllm_logger(name: str):
+        """Dummy function to initialise vLLM loggers with the ERROR level."""
+        vllm_logger = logging.getLogger(name)
+        vllm_logger.setLevel(logging.ERROR)
+        return vllm_logger
+
+    vllm.logger.init_logger = init_vllm_logger
 
     # Disable the tokeniser progress bars
     disable_progress_bar()
@@ -516,6 +526,10 @@ def model_is_generative(model: PreTrainedModel | GenerativeModel) -> bool:
     Returns:
         Whether the model is generative or not.
     """
+    known_generative_models = ["VLLMModel", "OpenAIModel"]
+    if any(model.__class__.__name__ == name for name in known_generative_models):
+        return True
+
     try:
         with warnings.catch_warnings():
             warnings.simplefilter("ignore", category=UserWarning)
diff --git a/src/scandeval/vllm_models.py b/src/scandeval/vllm_models.py
new file mode 100644
index 000000000..3f0c26691
--- /dev/null
+++ b/src/scandeval/vllm_models.py
@@ -0,0 +1,242 @@
+"""A wrapper for vLLM models."""
+
+import warnings
+from pathlib import Path
+from types import MethodType
+
+import torch
+from tqdm import tqdm
+from transformers import GenerationConfig, PretrainedConfig, PreTrainedTokenizer
+from transformers.utils import ModelOutput
+from vllm import LLM, RequestOutput, SamplingParams
+from vllm.model_executor.parallel_utils.parallel_state import destroy_model_parallel
+
+from .config import ModelConfig
+from .utils import HiddenPrints, clear_memory
+
+
+class VLLMModel:
+    """A wrapper for vLLM models."""
+
+    def __init__(
+        self,
+        model_config: ModelConfig,
+        hf_model_config: PretrainedConfig,
+        model_cache_dir: str | Path,
+        tokenizer: PreTrainedTokenizer | None = None,
+    ) -> None:
+        """Initialize a vLLM model.
+
+        Args:
+            model_config:
+                A model configuration.
+            hf_model_config:
+                A Hugging Face model configuration.
+            model_cache_dir:
+                The directory to cache the model in.
+            tokenizer:
+                A Hugging Face tokenizer. If None, the tokenizer will need to be
+                loaded separately.
+        """
+        self.model_config = model_config
+        self.config = hf_model_config
+        self.device = torch.device("cuda")
+        self.tokenizer = tokenizer
+        with warnings.catch_warnings(), HiddenPrints():
+            warnings.simplefilter("ignore", category=UserWarning)
+
+            # This is required to be able to re-initialize the model, in case we
+            # have already initialized it once
+            destroy_model_parallel()
+            clear_memory()
+
+            self._model = LLM(
+                model=model_config.model_id,
+                gpu_memory_utilization=0.9,
+                max_model_len=10_000,
+                download_dir=str(model_cache_dir),
+                trust_remote_code=True,
+            )
+            self._model._run_engine = MethodType(
+                _run_engine_with_fixed_progress_bars, self._model
+            )
+
+    def generate(
+        self,
+        inputs: torch.Tensor,
+        generation_config: GenerationConfig | None = None,
+        **generation_kwargs,
+    ) -> torch.Tensor | torch.LongTensor | ModelOutput:
+        """Generate sequences using the model.
+
+        Args:
+            inputs:
+                The input batch of sequences to generate from.
+            generation_config:
+                The generation config to use for generation.
+            **generation_kwargs:
+                Additional generation kwargs to pass to the model.
+
+        Returns:
+            The generated sequences.
+        """
+        if self.tokenizer is None:
+            raise ValueError("Tokenizer must be loaded to generate sequences.")
+
+        if generation_config is None:
+            generation_config = GenerationConfig(**generation_kwargs)
+        else:
+            for key, value in generation_kwargs.items():
+                setattr(generation_config, key, value)
+
+        # Define which tokens to use as stopping criteria. We want to use the padding
+        # token, end-of-sentence token, and a double newline (since these separate the
+        # few-shot examples in the input)
+        stop_tokens: list[str] = ["\n\n"]
+        if self.tokenizer.pad_token_id is not None:
+            stop_tokens.append(self.tokenizer.pad_token)
+        if self.tokenizer.eos_token_id is not None:
+            stop_tokens.append(self.tokenizer.eos_token)
+            if self.tokenizer.pad_token_id is None:
+                self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
+                self.tokenizer.pad_token = self.tokenizer.eos_token
+        if (
+            self.tokenizer.bos_token_id is not None
+            and self.tokenizer.pad_token_id is None
+        ):
+            self.tokenizer.pad_token_id = self.tokenizer.bos_token_id
+            self.tokenizer.pad_token = self.tokenizer.bos_token
+        assert self.tokenizer.pad_token_id is not None
+
+        # Define the parameters used for vLLM generation
+        max_tokens: int = generation_config.max_new_tokens or 1
+        sampling_params = SamplingParams(
+            max_tokens=max_tokens,
+            temperature=generation_config.temperature,
+            top_p=generation_config.top_p,
+            n=generation_config.num_return_sequences,
+            repetition_penalty=generation_config.repetition_penalty,
+            frequency_penalty=generation_config.repetition_penalty - 1.0,
+            stop=stop_tokens,
+            logprobs=100,
+        )
+
+        # The inputs are tokenised, so we decode them to get the original text, which
+        # is the input to the vLLM model
+        prompts = self.tokenizer.batch_decode(
+            sequences=inputs, skip_special_tokens=True
+        )
+
+        # Generate sequences using vLLM
+        input_is_a_test = len(prompts) == 1 and len(prompts[0]) == 1
+        raw_outputs = self._model.generate(
+            prompts=prompts,
+            use_tqdm=(not input_is_a_test),
+            sampling_params=sampling_params,
+        )
+
+        # Collect the generated sequences into a single tensor of shape
+        # (batch_size, generated_sequence_length)
+        output = torch.nn.utils.rnn.pad_sequence(
+            sequences=[
+                torch.LongTensor(output.outputs[0].token_ids).to(self.device)
+                for output in raw_outputs
+            ],
+            batch_first=True,
+            padding_value=float(self.tokenizer.pad_token_id),
+        )
+
+        if generation_config.return_dict_in_generate:
+            # Add logprobs scores to the output
+            if generation_config.output_scores:
+                # Create a list with placeholder logprobs for every token generated.
+                # Each tensor in the list will be of shape (batch_size, vocab_size)
+                batch_size = len(raw_outputs)
+                vocab_size = len(self.tokenizer.get_vocab())
+                max_seq_len = max(
+                    len(raw_output.outputs[0].logprobs) for raw_output in raw_outputs
+                )
+                scores = [
+                    torch.full(size=(batch_size, vocab_size), fill_value=-1e9)
+                    for _ in range(max_seq_len)
+                ]
+
+                # Fill in the logprobs for each generated token. The logprobs from the
+                # vLLM output only contain the logprobs for the top-k tokens, so we
+                # only fill in these and leave the rest as -1e9, corresponding to ~0%
+                # probability
+                for sample_idx, raw_output in enumerate(raw_outputs):
+                    assert raw_output.outputs[0].logprobs is not None
+                    seq_len = len(raw_output.outputs[0].logprobs)
+                    for gen_token_idx in range(seq_len):
+                        logprobs_dict = raw_output.outputs[0].logprobs[gen_token_idx]
+                        for token_idx, logprob in logprobs_dict.items():
+                            scores[gen_token_idx][sample_idx, token_idx] = logprob
+
+                output = ModelOutput(dict(sequences=output, scores=tuple(scores)))
+            else:
+                output = ModelOutput(dict(sequences=output))
+
+        return output
+
+    def __call__(
+        self,
+        inputs: torch.Tensor,
+        generation_config: GenerationConfig | None = None,
+        **generation_kwargs,
+    ) -> torch.Tensor | torch.LongTensor | ModelOutput:
+        """Generate sequences using the model.
+
+        Args:
+            inputs:
+                The input batch of sequences to generate from.
+            generation_config:
+                The generation config to use for generation.
+            **generation_kwargs:
+                Additional generation kwargs to pass to the model.
+
+        Returns:
+            The generated sequences.
+        """
+        return self.generate(
+            inputs=inputs,
+            generation_config=generation_config,
+            **generation_kwargs,
+        )
+
+    def to(self, _: torch.device) -> None:
+        """Dummy method to make the model compatible with the benchmarking script."""
+        pass
+
+    def eval(self) -> None:
+        """Dummy method to make the model compatible with the benchmarking script."""
+        pass
+
+    def children(self) -> list:
+        """Dummy method to make the model compatible with the benchmarking script."""
+        return []
+
+
+def _run_engine_with_fixed_progress_bars(self, use_tqdm: bool) -> list[RequestOutput]:
+    if use_tqdm:
+        num_requests = self.llm_engine.get_num_unfinished_requests()
+        pbar = tqdm(total=num_requests, leave=False)
+
+    # Run the engine.
+    outputs: list[RequestOutput] = list()
+    while self.llm_engine.has_unfinished_requests():
+        step_outputs = self.llm_engine.step()
+        for output in step_outputs:
+            if output.finished:
+                outputs.append(output)
+                if use_tqdm:
+                    pbar.update(1)
+
+    if use_tqdm:
+        pbar.close()
+
+    # Sort the outputs by request ID. This is necessary because some requests may be
+    # finished earlier than its previous requests.
+    outputs = sorted(outputs, key=lambda x: int(x.request_id))
+
+    return outputs


Test patch:
diff --git a/tests/conftest.py b/tests/conftest.py
index 625e8cec7..1f9f2b922 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -59,7 +59,7 @@ def benchmark_config(
         token=auth,
         openai_api_key=None,
         progress_bar=False,
-        save_results=False,
+        save_results=True,
         device=torch.device("cpu"),
         verbose=False,
         trust_remote_code=True,
diff --git a/tests/test_benchmarker.py b/tests/test_benchmarker.py
index bc8ee125b..c5ff199e3 100644
--- a/tests/test_benchmarker.py
+++ b/tests/test_benchmarker.py
@@ -1,6 +1,8 @@
 """Unit tests for the `benchmarker` module."""
 
-from typing import TypedDict
+import json
+from pathlib import Path
+from typing import Generator, TypedDict
 
 import pytest
 from scandeval.benchmarker import BenchmarkResult, model_has_been_benchmarked
@@ -28,6 +30,143 @@ class DataKwargs(TypedDict):
 )
 
 
+class TestBenchmarkResult:
+    """Tests related to the `BenchmarkResult` class."""
+
+    @pytest.fixture(scope="class")
+    def benchmark_result(self) -> Generator[BenchmarkResult, None, None]:
+        """Fixture for a `BenchmarkResult` object."""
+        yield BenchmarkResult(
+            dataset="dataset",
+            model="model",
+            few_shot=True,
+            validation_split=False,
+            **DATA_KWARGS,
+        )
+
+    @pytest.fixture(scope="class")
+    def results_path(self) -> Generator[Path, None, None]:
+        """Fixture for a `Path` object to a results file."""
+        results_path = Path(".scandeval_cache/test_results.jsonl")
+        results_path.parent.mkdir(parents=True, exist_ok=True)
+        yield results_path
+
+    def test_benchmark_result_parameters(self, benchmark_result):
+        """Test that the `BenchmarkResult` parameters are correct."""
+        assert benchmark_result.dataset == "dataset"
+        assert benchmark_result.model == "model"
+        assert benchmark_result.few_shot is True
+        assert benchmark_result.validation_split is False
+        assert benchmark_result.num_model_parameters == 100
+        assert benchmark_result.max_sequence_length == 100
+        assert benchmark_result.vocabulary_size == 100
+        assert benchmark_result.dataset_languages == ["da"]
+        assert benchmark_result.task == "task"
+        assert benchmark_result.results == dict()
+
+    @pytest.mark.parametrize(
+        argnames=["config", "expected"],
+        argvalues=[
+            (
+                dict(
+                    dataset="dataset",
+                    model="model",
+                    few_shot=True,
+                    validation_split=False,
+                    **DATA_KWARGS,
+                ),
+                BenchmarkResult(
+                    dataset="dataset",
+                    model="model",
+                    few_shot=True,
+                    validation_split=False,
+                    **DATA_KWARGS,
+                ),
+            ),
+            (
+                dict(
+                    dataset="dataset",
+                    model="model (few-shot)",
+                    validation_split=False,
+                    **DATA_KWARGS,
+                ),
+                BenchmarkResult(
+                    dataset="dataset",
+                    model="model",
+                    few_shot=True,
+                    validation_split=False,
+                    **DATA_KWARGS,
+                ),
+            ),
+            (
+                dict(
+                    dataset="dataset",
+                    model="model (val)",
+                    few_shot=True,
+                    **DATA_KWARGS,
+                ),
+                BenchmarkResult(
+                    dataset="dataset",
+                    model="model",
+                    few_shot=True,
+                    validation_split=True,
+                    **DATA_KWARGS,
+                ),
+            ),
+            (
+                dict(
+                    dataset="dataset",
+                    model="model (few-shot, val)",
+                    **DATA_KWARGS,
+                ),
+                BenchmarkResult(
+                    dataset="dataset",
+                    model="model",
+                    few_shot=True,
+                    validation_split=True,
+                    **DATA_KWARGS,
+                ),
+            ),
+        ],
+        ids=[
+            "normal case",
+            "few-shot model name",
+            "validation split model name",
+            "few-shot and validation split model name",
+        ],
+    )
+    def test_from_dict(self, config, expected):
+        """Test that `BenchmarkResult.from_dict` works as expected."""
+        assert BenchmarkResult.from_dict(config) == expected
+
+    def test_append_to_results(self, benchmark_result, results_path):
+        """Test that `BenchmarkResult.append_to_results` works as expected."""
+        results_path.unlink(missing_ok=True)
+        results_path.touch(exist_ok=True)
+
+        benchmark_result.append_to_results(results_path=results_path)
+        json_str = json.dumps(
+            dict(
+                dataset=benchmark_result.dataset,
+                task=benchmark_result.task,
+                dataset_languages=benchmark_result.dataset_languages,
+                model=benchmark_result.model,
+                results=benchmark_result.results,
+                num_model_parameters=benchmark_result.num_model_parameters,
+                max_sequence_length=benchmark_result.max_sequence_length,
+                vocabulary_size=benchmark_result.vocabulary_size,
+                few_shot=benchmark_result.few_shot,
+                validation_split=benchmark_result.validation_split,
+            )
+        )
+        assert results_path.read_text() == f"\n{json_str}"
+
+        benchmark_result.append_to_results(results_path=results_path)
+        assert results_path.read_text() == f"\n{json_str}\n{json_str}"
+
+        results_path.unlink(missing_ok=True)
+
+
 @pytest.mark.parametrize(
     argnames=[
         "model_id",
diff --git a/tests/test_config.py b/tests/test_config.py
index a7a5fb549..fc84a89e7 100644
--- a/tests/test_config.py
+++ b/tests/test_config.py
@@ -97,7 +97,7 @@ def test_attributes_correspond_to_arguments(
         assert benchmark_config.token is auth
         assert benchmark_config.openai_api_key is None
         assert benchmark_config.progress_bar is False
-        assert benchmark_config.save_results is False
+        assert benchmark_config.save_results is True
         assert benchmark_config.device == torch.device("cpu")
         assert benchmark_config.verbose is False
         assert benchmark_config.trust_remote_code is True
diff --git a/tests/test_model_cache.py b/tests/test_model_cache.py
new file mode 100644
index 000000000..05db4156a
--- /dev/null
+++ b/tests/test_model_cache.py
@@ -0,0 +1,1 @@
+"""Unit tests for the `model_cache` module."""
diff --git a/tests/test_vllm_models.py b/tests/test_vllm_models.py
new file mode 100644
index 000000000..03ee2b578
--- /dev/null
+++ b/tests/test_vllm_models.py
@@ -0,0 +1,1 @@
+"""Unit tests for the `vllms_models` module."""