Repository: ScandEval/ScandEval (language: python)

Issue:
Use vLLM
### Added
- 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
- 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.

### Fixed
- If a model is already pre-quantized then we will not attempt to quantize it as well.

This closes #116 
Use vLLM
### Added
- 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
- 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.

### Fixed
- If a model is already pre-quantized then we will not attempt to quantize it as well.

This closes #116 
NaN validation loss when benchmarking
The models `vesteinn/ScandiBERT` and `mideind/IceBERT-mC4-is` get NaN validation loss when benchmarking.

We need to check if this is due to the framework or the models themselves. If it's the models then an `InvalidBenchmark` should be thrown instead of getting ~0% scores on everything.


Discussion / hints:


This seems to be mixed precision causing this. When disabling `fp16` it stops occuring. We need to figure out what the solution is in this case. Either we would explicitly look for NaN validation loss, in which case it should restart the evaluation without `fp16`, or alternatively `fp16` should never be enabled by default, and instead by enabled if we're running out of memory.

@peterbjorgensen @AJDERS @KennethEnevoldsen Thoughts?
Can we reliably detect when the error happens and then flag models where it occurs? (and then disable fp16 for those models)

Ideally these would then be added as known issues to the models.
@KennethEnevoldsen I would guess that we would be able to catch the NaN loss if it occurs early. Maybe one way could be having a check within the `compute_metrics` function, and throw an error if the val loss is NaN. The benchmarking script could catch that error and restart the benchmark with `fp16` set. 

But this assumes that these models all exhibit NaN loss in the first evaluation (step 30). I noticed that `xlm-roberta-base` also has weird behaviour now - I'd have to check if that's due to NaN loss too. 
Hmm right. if we do 32 as default, but switch to fp16 if there is insufficient memory though that would make it notably slower (apparently up to 8 times). I would probably just set a custom flag for the specific models and raise a warning if you try to call them with fp16.


Fix patch:
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 7a2bb5ba9..cf5638d66 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
@@ -52,9 +55,14 @@ 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.
+- When timeout errors occur when loading a model then we will try again at most 5 times
+  now, where previously we would attempt to re-load it indefinitely.
 
 ### Fixed
 - Removed `text2text-generation` temporarily from the tags defining generative models,
@@ -77,10 +85,16 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
   the model's maximum context length was really small (128). This has been fixed now.
 - Now sets `ignore_mismatched_sizes` when loading models if the model cannot be loaded
   otherwise. This previously caused some issues when loading certain models.
+- Fixed bug where some encoder models did not work properly when loaded in with FP16
+  mixed precision due to overflow. We now load in models with BF16 as these have a
+  larger range, but fall back to FP16 if BF16 is not available. If both lead to
+  overflow then we attempt again with full FP32, and lastly throw an informative error
+  and block evaluation if the overflow persists.
 - When few-shot evaluating models on NER tasks, we are now more lenient towards the
   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"},
+    {file = "black-23.12.1-py3-none-any.whl", hash = "sha256:78baad24af0f033958cad29731e27363183e140962595def56423e626f4bee3e"},
+    {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)"]
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+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)"]
 metrics-tests = ["Werkzeug (>=1.0.1)", "accelerate", "bert-score (>=0.3.6)", "jiwer", "langdetect", "mauve-text", "nltk", "requests-file (>=1.5.1)", "rouge-score", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "spacy (>=3.0.0)", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "typer (<0.5.0)"]
-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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+tests = ["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)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy (<2.0.0)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch (>=2.0.0)", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
 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"
+python-versions = ">=3.10"
 files = [
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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]"]
 eapp = ["absl-py", "etils[epy]", "simple_parsing"]
-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
+python-versions = ">=3.8"
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+starlette = ">=0.29.0,<0.33.0"
+typing-extensions = ">=4.8.0"
+
+[package.extras]
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+
 [[package]]
 name = "filelock"
 version = "3.13.1"
@@ -800,59 +859,59 @@ testing = ["black[jupyter] (==23.7.0)", "clu", "clu (<=0.0.9)", "einops", "gymna
 
 [[package]]
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+version = "4.47.0"
 description = "Tools to manipulate font files"
 optional = false
 python-versions = ">=3.8"
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 [[package]]
@@ -972,15 +1047,74 @@ smb = ["smbprotocol"]
 ssh = ["paramiko"]
 tqdm = ["tqdm"]
 
+[[package]]
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+
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 typing-extensions = ">=3.7.4.3"
 
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 description = "File identification library for Python"
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 python-versions = ">=3.8"
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 [package.extras]
@@ -1058,13 +1191,13 @@ files = [
 
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 [package.dependencies]
@@ -1082,44 +1215,45 @@ scipy = [
 
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 html5 = ["html5lib"]
 htmlsoup = ["BeautifulSoup4"]
-source = ["Cython (>=0.29.35)"]
+source = ["Cython (==0.29.37)"]
 
 [[package]]
 name = "markdown-it-py"
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 ]
 
 [package.extras]
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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"
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 description = "Orbax Checkpoint"
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 [package.dependencies]
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 numpy = "*"
 protobuf = "*"
 pyyaml = "*"
-tensorstore = ">=0.1.35"
+tensorstore = ">=0.1.51"
 typing_extensions = "*"
 
 [package.extras]
 testing = ["flax", "pytest", "pytest-xdist"]
 
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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
+python-versions = ">=3.8"
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 [[package]]
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+
 [[package]]
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 version = "1.12"
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 [package.dependencies]
@@ -4012,13 +4423,13 @@ tutorials = ["matplotlib", "pandas", "tabulate"]
 
 [[package]]
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+version = "4.9.0"
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 version = "20.25.0"
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+    {file = "yarl-1.9.4-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4aa9741085f635934f3a2583e16fcf62ba835719a8b2b28fb2917bb0537c1dfa"},
+    {file = "yarl-1.9.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:206a55215e6d05dbc6c98ce598a59e6fbd0c493e2de4ea6cc2f4934d5a18d130"},
+    {file = "yarl-1.9.4-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:07574b007ee20e5c375a8fe4a0789fad26db905f9813be0f9fef5a68080de559"},
+    {file = "yarl-1.9.4-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:5a2e2433eb9344a163aced6a5f6c9222c0786e5a9e9cac2c89f0b28433f56e23"},
+    {file = "yarl-1.9.4-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:6ad6d10ed9b67a382b45f29ea028f92d25bc0bc1daf6c5b801b90b5aa70fb9ec"},
+    {file = "yarl-1.9.4-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:6fe79f998a4052d79e1c30eeb7d6c1c1056ad33300f682465e1b4e9b5a188b78"},
+    {file = "yarl-1.9.4-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:a825ec844298c791fd28ed14ed1bffc56a98d15b8c58a20e0e08c1f5f2bea1be"},
+    {file = "yarl-1.9.4-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:8619d6915b3b0b34420cf9b2bb6d81ef59d984cb0fde7544e9ece32b4b3043c3"},
+    {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..2c5167a95 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
@@ -29,16 +32,12 @@
 from .protocols import GenerativeModel, Tokenizer
 from .scores import log_scores
 from .speed_benchmark import benchmark_speed
-from .types import SCORE_DICT
+from .types import Labels, Predictions, ScoreDict
 from .utils import GENERATIVE_MODEL_TASKS, enforce_reproducibility, model_is_generative
 
 logger = logging.getLogger(__package__)
 
 
-Predictions = Type[np.ndarray]
-Labels = Type[np.ndarray]
-
-
 class BenchmarkDataset(ABC):
     """Abstract benchmarking dataset class.
 
@@ -82,7 +81,7 @@ def __init__(
         logging_level = logging.DEBUG if self.benchmark_config.verbose else logging.INFO
         logger.setLevel(logging_level)
 
-    def benchmark(self, model_id: str) -> tuple[SCORE_DICT, dict[str, bool | int]]:
+    def benchmark(self, model_id: str) -> tuple[ScoreDict, dict[str, bool | int]]:
         """Benchmark a model.
 
         Args:
@@ -128,7 +127,9 @@ 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 +210,15 @@ 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 +228,21 @@ 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 +251,9 @@ 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 +262,7 @@ 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..1b873f111 100644
--- a/src/scandeval/benchmarker.py
+++ b/src/scandeval/benchmarker.py
@@ -15,7 +15,7 @@
 from .dataset_factory import DatasetFactory
 from .enums import Device, Framework
 from .exceptions import InvalidBenchmark
-from .types import SCORE_DICT
+from .types import ScoreDict
 from .utils import get_huggingface_model_lists
 
 logger = logging.getLogger(__package__)
@@ -28,7 +28,7 @@ class BenchmarkResult(BaseModel):
     task: str
     dataset_languages: list[str]
     model: str
-    results: SCORE_DICT
+    results: ScoreDict
     num_model_parameters: int
     max_sequence_length: int
     vocabulary_size: int
@@ -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/enums.py b/src/scandeval/enums.py
index f79f261f5..562913170 100644
--- a/src/scandeval/enums.py
+++ b/src/scandeval/enums.py
@@ -65,3 +65,20 @@ class ModelType(AutoStrEnum):
     HF = auto()
     LOCAL = auto()
     OPENAI = auto()
+
+
+class DataType(AutoStrEnum):
+    """The data type of the model weights.
+
+    Attributes:
+        FP32:
+            32-bit floating point.
+        FP16:
+            16-bit floating point.
+        BF16:
+            16-bit bfloat.
+    """
+
+    FP32 = auto()
+    FP16 = auto()
+    BF16 = auto()
diff --git a/src/scandeval/exceptions.py b/src/scandeval/exceptions.py
index 58d859b35..9083a86e3 100644
--- a/src/scandeval/exceptions.py
+++ b/src/scandeval/exceptions.py
@@ -43,3 +43,41 @@ def __init__(self, message: str = "There is currently no internet connection."):
         """
         self.message = message
         super().__init__(self.message)
+
+
+class NaNValueInModelOutput(Exception):
+    """There is a NaN value in the model output."""
+
+    def __init__(self, message: str = "There is a NaN value in the model output."):
+        """Initialize the exception.
+
+        Args:
+            message:
+                The message to display.
+        """
+        self.message = message
+        super().__init__(self.message)
+
+
+class FlashAttentionNotInstalled(Exception):
+    """The `flash-attn` package has not been installed."""
+
+    def __init__(
+        self,
+        message: str = (
+            "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`."
+        ),
+    ):
+        """Initialize the exception.
+
+        Args:
+            message:
+                The message to display.
+        """
+        self.message = message
+        super().__init__(self.message)
diff --git a/src/scandeval/finetuning.py b/src/scandeval/finetuning.py
index 7e36e9d4b..9ff4f4957 100644
--- a/src/scandeval/finetuning.py
+++ b/src/scandeval/finetuning.py
@@ -22,10 +22,10 @@
 )
 from transformers.trainer import OptimizerNames
 
-from scandeval.exceptions import InvalidBenchmark
-
 from .callbacks import NeverLeaveProgressCallback
 from .config import BenchmarkConfig, DatasetConfig, ModelConfig
+from .enums import DataType
+from .exceptions import InvalidBenchmark, NaNValueInModelOutput
 from .model_loading import load_model
 from .protocols import Tokenizer
 from .utils import block_terminal_output, clear_memory, enforce_reproducibility
@@ -100,6 +100,14 @@ def finetune(
     """
     scores: dict[str, list[dict[str, float]]] = defaultdict(list)
 
+    using_cuda = benchmark_config.device == torch.device("cuda")
+    if using_cuda and torch.cuda.is_bf16_supported():
+        dtype = DataType.BF16
+    elif using_cuda:
+        dtype = DataType.FP16
+    else:
+        dtype = DataType.FP32
+
     bs: int = benchmark_config.batch_size
     for idx in itr:
         # Set variable that tracks whether we need to initialize new models in
@@ -134,6 +142,7 @@ def finetune(
                     benchmark_config=benchmark_config,
                     model_config=model_config,
                     iteration_idx=idx,
+                    dtype=dtype,
                     batch_size=bs,
                 )
 
@@ -167,6 +176,23 @@ def finetune(
 
                 break
 
+            # NaN values can appear in the model output when using mixed precision, as
+            # the hidden states get overflowed. In this case we try to disable mixed
+            # precision and try again.
+            except NaNValueInModelOutput:
+                if dtype != DataType.FP32:
+                    dtype = DataType.FP32
+                    model_already_initialized = False
+                    logger.debug(
+                        "NaN value detected in model outputs while using mixed "
+                        "precision. Retrying with full fp32 precision."
+                    )
+                else:
+                    raise InvalidBenchmark(
+                        "NaN value detected in model outputs, even with mixed "
+                        "precision disabled."
+                    )
+
             except Exception as e:
                 if "CUDA" not in str(e) and "out of memory" not in str(e):
                     raise InvalidBenchmark(str(e))
@@ -326,6 +352,13 @@ def no_logging(logs: dict[str, float]) -> None:
 
         return scores
 
+    except NaNValueInModelOutput as e:
+        del trainer
+        del model
+        del tokenizer
+        clear_memory()
+        raise e
+
     except (RuntimeError, ValueError, IndexError) as e:
         raise InvalidBenchmark(str(e))
 
@@ -334,6 +367,7 @@ def get_training_args(
     benchmark_config: BenchmarkConfig,
     model_config: ModelConfig,
     iteration_idx: int,
+    dtype: DataType,
     batch_size: int | None = None,
 ) -> TrainingArguments:
     """Get the training arguments for the current iteration.
@@ -346,6 +380,8 @@ def get_training_args(
         iteration_idx:
             The index of the current iteration. This is only used to generate a
             unique random seed for the current iteration.
+        dtype:
+            The data type to use for the model weights.
         batch_size:
             The batch size to use for the current iteration, or None if the batch size
             in the benchmark config should be used.
@@ -392,7 +428,8 @@ def get_training_args(
             load_best_model_at_end=True,
             optim=optimizer,
             seed=seed,
-            fp16=benchmark_config.device == torch.device("cuda"),
+            fp16=dtype == DataType.FP16,
+            bf16=dtype == DataType.BF16,
             disable_tqdm=not benchmark_config.progress_bar,
             ddp_find_unused_parameters=False,
         )
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..d99bde2a8 100644
--- a/src/scandeval/model_setups/hf.py
+++ b/src/scandeval/model_setups/hf.py
@@ -21,7 +21,12 @@
 
 from ..config import BenchmarkConfig, DatasetConfig, ModelConfig
 from ..enums import Framework, ModelType
-from ..exceptions import HuggingFaceHubDown, InvalidBenchmark, NoInternetConnection
+from ..exceptions import (
+    FlashAttentionNotInstalled,
+    HuggingFaceHubDown,
+    InvalidBenchmark,
+    NoInternetConnection,
+)
 from ..languages import get_all_languages
 from ..protocols import GenerativeModel, Tokenizer
 from ..utils import (
@@ -33,6 +38,7 @@
     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 +69,7 @@ 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:
@@ -227,6 +233,8 @@ def load_model(
             else None
         )
 
+        # We hardcode that we're using flash attention for models with "mistral" in
+        # their name, as this is currently one of the only ones that support it
         use_flash_attention = (
             self.benchmark_config.use_flash_attention or "mistral" in model_id.lower()
         )
@@ -240,94 +248,128 @@ 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:
+                use_vllm = False
+                logger.info(
+                    "Failed to benchmark with vLLM - trying with the Hugging Face "
+                    "implementation instead."
                 )
+                logger.debug(f"The error was: {e!r}")
+
+        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,
+            )
 
-                # 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`)."
-                    )
+            # These are used when a timeout occurs
+            attempts_left = 5
 
-                # 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
+            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",
+                    )
 
-                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 FlashAttentionNotInstalled()
+                        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):
+                            attempts_left -= 1
+                            if attempts_left == 0:
+                                raise InvalidBenchmark(
+                                    "The model could not be loaded after 5 attempts."
+                                )
+                            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 +379,9 @@ 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/model_setups/utils.py b/src/scandeval/model_setups/utils.py
index 151f1e023..a8dccba99 100644
--- a/src/scandeval/model_setups/utils.py
+++ b/src/scandeval/model_setups/utils.py
@@ -50,7 +50,6 @@ def setup_model_for_question_answering(model: PreTrainedModel) -> PreTrainedMode
         PreTrainedModel:
             The setup model.
     """
-
     # Get the models' token type embedding children, if they exist
     children = get_children_of_module(name="model", module=model)
 
@@ -154,11 +153,14 @@ def align_model_and_tokenizer(
     # Manually check that this model max length is valid for the model, and adjust
     # otherwise
     initial_max_length = tokenizer.model_max_length
+    first_non_special_token = next(
+        i for i in range(initial_max_length) if i not in tokenizer.all_special_ids
+    )
     for max_length in range(initial_max_length, 0, -1):
         tokenizer.model_max_length = max_length
-        dummy_inputs = torch.zeros(
-            1,
-            max_length,
+        dummy_inputs = torch.full(
+            size=(1, max_length),
+            fill_value=first_non_special_token,
             dtype=torch.long,
             device=model.device,
         )
diff --git a/src/scandeval/named_entity_recognition.py b/src/scandeval/named_entity_recognition.py
index f7365773d..c74540c47 100644
--- a/src/scandeval/named_entity_recognition.py
+++ b/src/scandeval/named_entity_recognition.py
@@ -21,7 +21,12 @@
 from .exceptions import InvalidBenchmark
 from .generation import extract_raw_predictions
 from .protocols import GenerativeModel, Tokenizer
-from .utils import GENERATIVE_MODEL_TASKS, model_is_generative
+from .types import Labels, Predictions
+from .utils import (
+    GENERATIVE_MODEL_TASKS,
+    model_is_generative,
+    raise_if_model_output_contains_nan_values,
+)
 
 logger = logging.getLogger(__package__)
 
@@ -63,7 +68,7 @@ def _process_data(self, dataset_dict: DatasetDict) -> DatasetDict:
 
     def _compute_metrics(
         self,
-        model_outputs_and_labels: tuple[np.ndarray, np.ndarray],
+        model_outputs_and_labels: tuple[Predictions, Labels],
         id2label: list[str],
     ) -> dict[str, float]:
         """Compute the metrics needed for evaluation.
@@ -81,6 +86,8 @@ def _compute_metrics(
         """
         model_outputs, labels = model_outputs_and_labels
 
+        raise_if_model_output_contains_nan_values(model_output=model_outputs)
+
         predictions: list[list[str]]
         if not isinstance(model_outputs[0][0], str):
             raw_predictions: list[list[int]] = np.argmax(
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/question_answering.py b/src/scandeval/question_answering.py
index 2da4c1abd..6380708cd 100644
--- a/src/scandeval/question_answering.py
+++ b/src/scandeval/question_answering.py
@@ -16,7 +16,8 @@
 from .generation import extract_raw_predictions
 from .protocols import GenerativeModel, Tokenizer
 from .question_answering_trainer import QuestionAnsweringTrainer
-from .utils import get_special_token_metadata
+from .types import Labels, Predictions
+from .utils import get_special_token_metadata, raise_if_model_output_contains_nan_values
 
 logger = logging.getLogger(__package__)
 
@@ -133,7 +134,7 @@ def _load_data_collator(
 
     def _compute_metrics(
         self,
-        model_outputs_and_labels: tuple[np.ndarray, np.ndarray],
+        model_outputs_and_labels: tuple[Predictions, Labels],
         id2label: list[str],
     ) -> dict[str, float]:
         """Compute the metrics needed for evaluation.
@@ -151,6 +152,8 @@ def _compute_metrics(
         """
         model_outputs, labels = model_outputs_and_labels
 
+        raise_if_model_output_contains_nan_values(model_output=model_outputs)
+
         model_output_dtype = np.asarray(model_outputs).dtype
         if model_output_dtype in [np.float16, np.float32, np.float64]:
             predictions = np.asarray(model_outputs).argmax(axis=-1)
@@ -173,6 +176,7 @@ def _compute_metrics(
                 if isinstance(scores, list):
                     scores = sum(scores) / len(scores)
                 results[cfg.name] = scores
+
         return results
 
     def _extract_few_shot_examples(
diff --git a/src/scandeval/scores.py b/src/scandeval/scores.py
index 210edda3a..2db05e01c 100644
--- a/src/scandeval/scores.py
+++ b/src/scandeval/scores.py
@@ -6,7 +6,7 @@
 import numpy as np
 
 from .config import MetricConfig
-from .types import SCORE_DICT
+from .types import ScoreDict
 
 logger = logging.getLogger(__package__)
 
@@ -16,7 +16,7 @@ def log_scores(
     metric_configs: list[MetricConfig],
     scores: dict[str, list[dict[str, float]]],
     model_id: str,
-) -> SCORE_DICT:
+) -> ScoreDict:
     """Log the scores.
 
     Args:
diff --git a/src/scandeval/sequence_classification.py b/src/scandeval/sequence_classification.py
index 76f8592a6..73932f107 100644
--- a/src/scandeval/sequence_classification.py
+++ b/src/scandeval/sequence_classification.py
@@ -20,7 +20,12 @@
 from .exceptions import InvalidBenchmark
 from .generation import extract_raw_predictions
 from .protocols import GenerativeModel, Tokenizer
-from .utils import GENERATIVE_MODEL_TASKS, get_special_token_metadata
+from .types import Labels, Predictions
+from .utils import (
+    GENERATIVE_MODEL_TASKS,
+    get_special_token_metadata,
+    raise_if_model_output_contains_nan_values,
+)
 
 logger = logging.getLogger(__package__)
 
@@ -125,7 +130,7 @@ def _load_data_collator(
 
     def _compute_metrics(
         self,
-        model_outputs_and_labels: tuple[np.ndarray, np.ndarray],
+        model_outputs_and_labels: tuple[Predictions, Labels],
         id2label: list[str],
     ) -> dict[str, float]:
         """Compute the metrics needed for evaluation.
@@ -143,6 +148,8 @@ def _compute_metrics(
         """
         model_outputs, labels = model_outputs_and_labels
 
+        raise_if_model_output_contains_nan_values(model_output=model_outputs)
+
         model_output_dtype = np.asarray(model_outputs).dtype
         if model_output_dtype in [np.float16, np.float32, np.float64]:
             predictions = np.asarray(model_outputs).argmax(axis=-1)
@@ -181,6 +188,7 @@ def _compute_metrics(
                 if isinstance(scores, list):
                     scores = sum(scores) / len(scores)
                 results[cfg.name] = scores
+
         return results
 
     def _extract_few_shot_examples(
diff --git a/src/scandeval/text_to_text.py b/src/scandeval/text_to_text.py
index 0be4ef0ef..653766a77 100644
--- a/src/scandeval/text_to_text.py
+++ b/src/scandeval/text_to_text.py
@@ -13,6 +13,7 @@
 from .benchmark_dataset import BenchmarkDataset, Labels, Predictions
 from .generation import extract_raw_predictions
 from .protocols import GenerativeModel, Tokenizer
+from .utils import raise_if_model_output_contains_nan_values
 
 logger = logging.getLogger(__package__)
 
@@ -95,6 +96,8 @@ def _compute_metrics(
         """
         model_outputs, labels = model_outputs_and_labels
 
+        raise_if_model_output_contains_nan_values(model_output=model_outputs)
+
         model_output_dtype = np.asarray(model_outputs).dtype
         output_is_prob = model_output_dtype in [np.float16, np.float32, np.float64]
         if output_is_prob:
@@ -118,6 +121,7 @@ def _compute_metrics(
                 if isinstance(scores, list):
                     scores = sum(scores) / len(scores)
                 results[cfg.name] = scores
+
         return results
 
     def _extract_few_shot_examples(
diff --git a/src/scandeval/types.py b/src/scandeval/types.py
index 92a7756c9..987a02aca 100644
--- a/src/scandeval/types.py
+++ b/src/scandeval/types.py
@@ -2,7 +2,11 @@
 
 from typing import Any, TypeGuard
 
-SCORE_DICT = dict[str, dict[str, float] | dict[str, list[dict[str, float]]]]
+import numpy as np
+
+ScoreDict = dict[str, dict[str, float] | dict[str, list[dict[str, float]]]]
+Predictions = np.ndarray | list[str] | list[list[str]]
+Labels = np.ndarray | list[str] | list[list[str]]
 
 
 def is_list_of_int(x: Any) -> TypeGuard[list[int]]:
diff --git a/src/scandeval/utils.py b/src/scandeval/utils.py
index 4f62ca267..5d7b11c9b 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
@@ -26,8 +27,10 @@
 
 from .config import Language
 from .enums import Framework
+from .exceptions import NaNValueInModelOutput
 from .languages import DA, NB, NN, NO, SV, get_all_languages
 from .protocols import GenerativeModel, Tokenizer
+from .types import Predictions
 
 logger = logging.getLogger(__package__)
 
@@ -153,6 +156,16 @@ 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)
+    logging.getLogger("vllm.transformers_utils.tokenizer").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 +529,10 @@ 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)
@@ -529,3 +546,26 @@ def model_is_generative(model: PreTrainedModel | GenerativeModel) -> bool:
             return True
     except (NotImplementedError, TypeError):
         return False
+
+
+def raise_if_model_output_contains_nan_values(model_output: Predictions) -> None:
+    """Raise an exception if the model output contains NaN values.
+
+    Args:
+        model_output:
+            The model output to check.
+
+    Raises:
+        NaNValueInModelOutput:
+            If the model output contains NaN values.
+    """
+    if isinstance(model_output, np.ndarray):
+        if model_output.dtype == np.float32 and np.isnan(model_output).any():
+            raise NaNValueInModelOutput()
+    elif len(model_output) > 0:
+        if isinstance(model_output[0], str):
+            if any(x != x for x in model_output):
+                raise NaNValueInModelOutput()
+        elif len(model_output[0]) > 0:
+            if any(x != x for sublist in model_output for x in sublist):
+                raise NaNValueInModelOutput()
diff --git a/src/scandeval/vllm_models.py b/src/scandeval/vllm_models.py
new file mode 100644
index 000000000..f96ff7e96
--- /dev/null
+++ b/src/scandeval/vllm_models.py
@@ -0,0 +1,262 @@
+"""A wrapper for vLLM models."""
+
+import logging
+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
+
+logger = logging.getLogger(__package__)
+
+
+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()
+
+            max_model_len = 10_000
+            if hasattr(hf_model_config, "max_position_embeddings"):
+                max_model_len = min(10_000, hf_model_config.max_position_embeddings)
+            if hasattr(hf_model_config, "model_max_length"):
+                max_model_len = min(10_000, hf_model_config.model_max_length)
+            if hasattr(hf_model_config, "n_positions"):
+                max_model_len = min(10_000, hf_model_config.n_positions)
+
+            self._model = LLM(
+                model=model_config.model_id,
+                gpu_memory_utilization=0.9,
+                max_model_len=max_model_len,
+                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
+        )
+
+        # If any of the prompts are empty then we need to replace them with a BOS token
+        # so that the vLLM model can generate from them
+        if any(len(prompt) == 0 for prompt in prompts):
+            logger.debug("Found empty prompts, replacing with BOS token.")
+        prompts = [
+            prompt if len(prompt) > 0 else self.tokenizer.bos_token
+            for prompt in prompts
+        ]
+
+        # 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..3400f73d5 100644
--- a/tests/test_benchmarker.py
+++ b/tests/test_benchmarker.py
@@ -1,10 +1,12 @@
 """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
-from scandeval.types import SCORE_DICT
+from scandeval.types import ScoreDict
 
 
 class DataKwargs(TypedDict):
@@ -15,7 +17,7 @@ class DataKwargs(TypedDict):
     vocabulary_size: int
     dataset_languages: list[str]
     task: str
-    results: SCORE_DICT
+    results: ScoreDict
 
 
 DATA_KWARGS = DataKwargs(
@@ -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_scores.py b/tests/test_scores.py
index deef3e077..1f6bb2f3c 100644
--- a/tests/test_scores.py
+++ b/tests/test_scores.py
@@ -6,7 +6,7 @@
 import numpy as np
 import pytest
 from scandeval.scores import aggregate_scores, log_scores
-from scandeval.types import SCORE_DICT
+from scandeval.types import ScoreDict
 
 
 @pytest.fixture(scope="module")
@@ -104,7 +104,7 @@ class TestLogScores:
     """Unit tests for the `log_scores` function."""
 
     @pytest.fixture(scope="class")
-    def logged_scores(self, metric_config, scores) -> Generator[SCORE_DICT, None, None]:
+    def logged_scores(self, metric_config, scores) -> Generator[ScoreDict, None, None]:
         """Yields the logged scores."""
         yield log_scores(
             dataset_name="dataset",
diff --git a/tests/test_types.py b/tests/test_types.py
index ec4a2edc4..8eef73faa 100644
--- a/tests/test_types.py
+++ b/tests/test_types.py
@@ -1,5 +1,6 @@
 """Unit tests for the `types` module."""
 
+import re
 import typing
 from typing import Generator
 
@@ -10,16 +11,15 @@
 @pytest.fixture(scope="module")
 def module_variable_names() -> Generator[list[str], None, None]:
     """Yields the module variable names."""
-    yield [var for var in dir(types) if "_" not in var and not hasattr(typing, var)]
+    yield [
+        var
+        for var in dir(types)
+        if "_" not in var and not hasattr(typing, var) and var != "np"
+    ]
 
 
-def test_variable_names_are_upper_case(module_variable_names) -> None:
-    """Tests that all module variable names are upper case."""
+def test_type_variable_names_are_title_case(module_variable_names) -> None:
+    """Tests that all type variable names are title case."""
     for var in module_variable_names:
-        assert var.isupper()
-
-
-def test_variables_subclass_typing_types(module_variable_names) -> None:
-    """Test that all module variable names are attributes of the `typing` module."""
-    for var in module_variable_names:
-        assert hasattr(typing, type(var).__name__)
+        assert var[0].isupper()
+        assert re.search(r"[^A-Za-z]", var) is None
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."""