Repository: ScandEval/ScandEval (language: python)

Issue:
[FEATURE REQUEST] Support for peft and adapters 
### 🚀 The feature, motivation and pitch

The peft (https://huggingface.co/docs/peft) and adapters (https://huggingface.co/docs/hub/adapters) libraries are add-ons for transformers and popular for adapting LLMs with various PEFT methods. It would be convenient if there was support for evaluating peft models and/or adapter models in ScandEval. 

### Alternatives

_No response_

### Additional context

_No response_


Discussion / hints:

Fix patch:
diff --git a/CHANGELOG.md b/CHANGELOG.md
index bfa97cb3b..6e710f74a 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -10,6 +10,8 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 ### Added
 - Added the new Faroese reading comprehension dataset FoQA. This is now the default
   Faroese reading comprehension benchmark, as there was none previously.
+- Now supports evaluation of models with adapters. This requires that the model
+  repository has an `adapter_config.json` file, but no additional setup is needed.
 
 ### Fixed
 - If a model does not use attention mask then we now do not supply it. This caused
@@ -21,6 +23,8 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
   determine if the model exists, without needing an OpenAI API key.
 - If a model has set its EOS token ID to multiple tokens and hasn't set the padding
   token ID, we use the first EOS token ID as the padding token ID.
+- Fixed a bug related to the loading of some encoder models by updating `accelerate` to
+  `>=0.34.2` and `transformers` to `>=4.45.0`.
 
 
 ## [v13.0.0] - 2024-07-31
diff --git a/README.md b/README.md
index bdfd59b43..1842a9fd3 100644
--- a/README.md
+++ b/README.md
@@ -10,7 +10,7 @@ ______________________________________________________________________
 [![Second paper](https://img.shields.io/badge/arXiv-2406.13469-b31b1b.svg)](https://arxiv.org/abs/2406.13469)
 [![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-68%25-yellow.svg)](https://github.com/ScandEval/ScandEval/tree/main/tests)
+[![Code Coverage](https://img.shields.io/badge/Coverage-65%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/makefile b/makefile
index 678916a78..0e83a177a 100644
--- a/makefile
+++ b/makefile
@@ -88,6 +88,8 @@ format:  ## Format the code
 type-check:  ## Run type checking
 	@poetry run mypy . --install-types --non-interactive --ignore-missing-imports --show-error-codes --check-untyped-defs
 
+check: lint format type-check  ## Run all checks
+
 setup-environment-variables:
 	@poetry run python src/scripts/fix_dot_env_file.py
 
diff --git a/poetry.lock b/poetry.lock
index 73f21ac66..d56503e14 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -13,34 +13,34 @@ files = [
 
 [[package]]
 name = "accelerate"
-version = "0.33.0"
+version = "1.0.1"
 description = "Accelerate"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "accelerate-0.33.0-py3-none-any.whl", hash = "sha256:0a7f33d60ba09afabd028d4f0856dd19c5a734b7a596d637d9dd6e3d0eadbaf3"},
-    {file = "accelerate-0.33.0.tar.gz", hash = "sha256:11ba481ed6ea09191775df55ce464aeeba67a024bd0261a44b77b30fb439e26a"},
+    {file = "accelerate-1.0.1-py3-none-any.whl", hash = "sha256:c6aa0c7b8a797cb150471e90e3ca36ac41f5d4b40512cdd6f058b8bf25589467"},
+    {file = "accelerate-1.0.1.tar.gz", hash = "sha256:e8f95fc2db14915dc0a9182edfcf3068e5ddb2fa310b583717ad44e5c442399c"},
 ]
 
 [package.dependencies]
 huggingface-hub = ">=0.21.0"
-numpy = ">=1.17,<2.0.0"
+numpy = ">=1.17,<3.0.0"
 packaging = ">=20.0"
 psutil = "*"
 pyyaml = "*"
-safetensors = ">=0.3.1"
+safetensors = ">=0.4.3"
 torch = ">=1.10.0"
 
 [package.extras]
-deepspeed = ["deepspeed (<=0.14.0)"]
-dev = ["bitsandbytes", "black (>=23.1,<24.0)", "datasets", "diffusers", "evaluate", "hf-doc-builder (>=0.3.0)", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "rich", "ruff (>=0.2.1,<0.3.0)", "scikit-learn", "scipy", "timm", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
-quality = ["black (>=23.1,<24.0)", "hf-doc-builder (>=0.3.0)", "ruff (>=0.2.1,<0.3.0)"]
+deepspeed = ["deepspeed"]
+dev = ["bitsandbytes", "black (>=23.1,<24.0)", "datasets", "diffusers", "evaluate", "hf-doc-builder (>=0.3.0)", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "rich", "ruff (>=0.6.4,<0.7.0)", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+quality = ["black (>=23.1,<24.0)", "hf-doc-builder (>=0.3.0)", "ruff (>=0.6.4,<0.7.0)"]
 rich = ["rich"]
 sagemaker = ["sagemaker"]
-test-dev = ["bitsandbytes", "datasets", "diffusers", "evaluate", "scikit-learn", "scipy", "timm", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+test-dev = ["bitsandbytes", "datasets", "diffusers", "evaluate", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
 test-prod = ["parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist"]
 test-trackers = ["comet-ml", "dvclive", "tensorboard", "wandb"]
-testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
 
 [[package]]
 name = "aiofiles"
@@ -508,35 +508,6 @@ files = [
     {file = "cloudpickle-3.0.0.tar.gz", hash = "sha256:996d9a482c6fb4f33c1a35335cf8afd065d2a56e973270364840712d9131a882"},
 ]
 
-[[package]]
-name = "cmake"
-version = "3.30.1"
-description = "CMake is an open-source, cross-platform family of tools designed to build, test and package software"
-optional = true
-python-versions = ">=3.7"
-files = [
-    {file = "cmake-3.30.1-py3-none-macosx_10_10_x86_64.macosx_11_0_arm64.macosx_11_0_universal2.whl", hash = "sha256:d56c1d96c4f8277bebbef768ad008a15d8b20b5946c87d888b85251d00b7509d"},
-    {file = "cmake-3.30.1-py3-none-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:15796c4ca5f32207d315a402604785e3288a9ca8bcf3a59427af31c21a09df50"},
-    {file = "cmake-3.30.1-py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:c94ce1df31a0e9244e1ec00b1efc2c4df2cbb9450d640087bacb46dc99a90abd"},
-    {file = "cmake-3.30.1-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e8ec762c5364a4d33cbc395c435a0afbf706cc623f55d7c51166d6c48e745dfd"},
-    {file = "cmake-3.30.1-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:065d68e35f6fa7973982f2d725ee8662b7e94cb5fd6856787608be7d62f64e30"},
-    {file = "cmake-3.30.1-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:88c561e29af6a21fb4dc80f9438767af8ba5081d2c58cfc2a16298076d731539"},
-    {file = "cmake-3.30.1-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:77577bdc99c6597da9674d788f23421c0417c598b411d6b8ada64d0c70ff32a5"},
-    {file = "cmake-3.30.1-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7743a2ba38edf56701ad3a40fac09ea3bba0538c6843fbc29cfccdbfc567873c"},
-    {file = "cmake-3.30.1-py3-none-musllinux_1_1_aarch64.whl", hash = "sha256:b6cd7e8b854e4bd366632317ea3a8d7554fd5de8f5056ba13fab78576b31a2f8"},
-    {file = "cmake-3.30.1-py3-none-musllinux_1_1_i686.whl", hash = "sha256:21c9db134fb859bbf163431f13c38c10bbcbc9a93287f6df61a305fe80c030b1"},
-    {file = "cmake-3.30.1-py3-none-musllinux_1_1_ppc64le.whl", hash = "sha256:6d86335029ca716bad3c6fbcb83eb14acb0f70daa961cdf229a349057c7f1df4"},
-    {file = "cmake-3.30.1-py3-none-musllinux_1_1_s390x.whl", hash = "sha256:212a6061ea724dfe89225005303f9f5ec804f46338338e9061381c22aca990ae"},
-    {file = "cmake-3.30.1-py3-none-musllinux_1_1_x86_64.whl", hash = "sha256:3f6b8f12be57e8246f553ff1b081d2d02dc0b6194565e92ff08eb7159eceef24"},
-    {file = "cmake-3.30.1-py3-none-win32.whl", hash = "sha256:8b15804f28dd3c22798c93e38be4d328e2aca00cc852a5afd72ca2332e28a021"},
-    {file = "cmake-3.30.1-py3-none-win_amd64.whl", hash = "sha256:b512dfdbfe99d608aa22a152dac614fa00456b6adc2a24f4e586ab781b1c573a"},
-    {file = "cmake-3.30.1-py3-none-win_arm64.whl", hash = "sha256:5b2556b2e999169121a7720f4e597848391d174dde05de9dfeec43c29565c97f"},
-    {file = "cmake-3.30.1.tar.gz", hash = "sha256:51f01ce429a55acbfe1f1baf507f0fe6916243a9f3e5868a928d073ae4b18ef9"},
-]
-
-[package.extras]
-test = ["coverage (>=4.2)", "pytest (>=3.0.3)", "pytest-cov (>=2.4.0)"]
-
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -548,6 +519,26 @@ files = [
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
 
+[[package]]
+name = "compressed-tensors"
+version = "0.6.0"
+description = "Library for utilization of compressed safetensors of neural network models"
+optional = true
+python-versions = "*"
+files = [
+    {file = "compressed-tensors-0.6.0.tar.gz", hash = "sha256:639ca97afc852602be0d3666b236ad6a96880de45af87851f515047eff700927"},
+    {file = "compressed_tensors-0.6.0-py3-none-any.whl", hash = "sha256:1be9c466e38b992b1d462e577f7e1b2bfad5d1aa0e25e9c95ab1ee458b9e92a2"},
+]
+
+[package.dependencies]
+pydantic = ">=2.0"
+torch = ">=1.7.0"
+transformers = "*"
+
+[package.extras]
+accelerate = ["accelerate"]
+dev = ["black (==22.12.0)", "flake8 (>=3.8.3)", "isort (==5.8.0)", "nbconvert (>=7.16.3)", "pytest (>=6.0.0)", "wheel (>=0.36.2)"]
+
 [[package]]
 name = "contourpy"
 version = "1.2.1"
@@ -815,6 +806,17 @@ idna = ["idna (>=3.6)"]
 trio = ["trio (>=0.23)"]
 wmi = ["wmi (>=1.5.1)"]
 
+[[package]]
+name = "einops"
+version = "0.8.0"
+description = "A new flavour of deep learning operations"
+optional = true
+python-versions = ">=3.8"
+files = [
+    {file = "einops-0.8.0-py3-none-any.whl", hash = "sha256:9572fb63046264a862693b0a87088af3bdc8c068fde03de63453cbbde245465f"},
+    {file = "einops-0.8.0.tar.gz", hash = "sha256:63486517fed345712a8385c100cb279108d9d47e6ae59099b07657e983deae85"},
+]
+
 [[package]]
 name = "email-validator"
 version = "2.2.0"
@@ -1221,6 +1223,22 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask-expr", "dask[dataframe,
 test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
 tqdm = ["tqdm"]
 
+[[package]]
+name = "gguf"
+version = "0.10.0"
+description = "Read and write ML models in GGUF for GGML"
+optional = true
+python-versions = ">=3.8"
+files = [
+    {file = "gguf-0.10.0-py3-none-any.whl", hash = "sha256:706089fba756a06913227841b4a6c8398360fa991569fd974e663a92b224e33f"},
+    {file = "gguf-0.10.0.tar.gz", hash = "sha256:52a30ef26328b419ffc47d9269fc580c238edf1c8a19b5ea143c323e04a038c1"},
+]
+
+[package.dependencies]
+numpy = ">=1.17"
+pyyaml = ">=5.1"
+tqdm = ">=4.27"
+
 [[package]]
 name = "gradio"
 version = "4.39.0"
@@ -1446,6 +1464,25 @@ files = [
     {file = "idna-3.7.tar.gz", hash = "sha256:028ff3aadf0609c1fd278d8ea3089299412a7a8b9bd005dd08b9f8285bcb5cfc"},
 ]
 
+[[package]]
+name = "importlib-metadata"
+version = "8.4.0"
+description = "Read metadata from Python packages"
+optional = true
+python-versions = ">=3.8"
+files = [
+    {file = "importlib_metadata-8.4.0-py3-none-any.whl", hash = "sha256:66f342cc6ac9818fc6ff340576acd24d65ba0b3efabb2b4ac08b598965a4a2f1"},
+    {file = "importlib_metadata-8.4.0.tar.gz", hash = "sha256:9a547d3bc3608b025f93d403fdd1aae741c24fbb8314df4b155675742ce303c5"},
+]
+
+[package.dependencies]
+zipp = ">=0.5"
+
+[package.extras]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+perf = ["ipython"]
+test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
+
 [[package]]
 name = "importlib-resources"
 version = "6.4.0"
@@ -1472,20 +1509,6 @@ files = [
     {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
 ]
 
-[[package]]
-name = "intel-openmp"
-version = "2021.4.0"
-description = "Intel OpenMP* Runtime Library"
-optional = false
-python-versions = "*"
-files = [
-    {file = "intel_openmp-2021.4.0-py2.py3-none-macosx_10_15_x86_64.macosx_11_0_x86_64.whl", hash = "sha256:41c01e266a7fdb631a7609191709322da2bbf24b252ba763f125dd651bcc7675"},
-    {file = "intel_openmp-2021.4.0-py2.py3-none-manylinux1_i686.whl", hash = "sha256:3b921236a38384e2016f0f3d65af6732cf2c12918087128a9163225451e776f2"},
-    {file = "intel_openmp-2021.4.0-py2.py3-none-manylinux1_x86_64.whl", hash = "sha256:e2240ab8d01472fed04f3544a878cda5da16c26232b7ea1b59132dbfb48b186e"},
-    {file = "intel_openmp-2021.4.0-py2.py3-none-win32.whl", hash = "sha256:6e863d8fd3d7e8ef389d52cf97a50fe2afe1a19247e8c0d168ce021546f96fc9"},
-    {file = "intel_openmp-2021.4.0-py2.py3-none-win_amd64.whl", hash = "sha256:eef4c8bcc8acefd7f5cd3b9384dbf73d59e2c99fc56545712ded913f43c4a94f"},
-]
-
 [[package]]
 name = "interegular"
 version = "0.3.3"
@@ -1991,13 +2014,13 @@ files = [
 
 [[package]]
 name = "lm-format-enforcer"
-version = "0.10.3"
+version = "0.10.6"
 description = "Enforce the output format (JSON Schema, Regex etc) of a language model"
 optional = true
 python-versions = "<4.0,>=3.8"
 files = [
-    {file = "lm_format_enforcer-0.10.3-py3-none-any.whl", hash = "sha256:e71177ec42b9319496040c43488a0921798b411c549c568e99e5c0e0f80f2e96"},
-    {file = "lm_format_enforcer-0.10.3.tar.gz", hash = "sha256:673ebee1bc9efa43418d178a62be912ffdd7c728665b9a8a01bdf60826b4c563"},
+    {file = "lm_format_enforcer-0.10.6-py3-none-any.whl", hash = "sha256:cad5a0cfbc9708332b57586a43d2945612fbcf40f9098f3ba95740d1a467a896"},
+    {file = "lm_format_enforcer-0.10.6.tar.gz", hash = "sha256:ca2f061e9cf22fbe9f5ada2bc774c2bd369e8649b806fb5d9080eee37449f4a6"},
 ]
 
 [package.dependencies]
@@ -2259,35 +2282,51 @@ files = [
 
 [[package]]
 name = "matplotlib"
-version = "3.9.1"
+version = "3.9.2"
 description = "Python plotting package"
 optional = true
 python-versions = ">=3.9"
 files = [
-    {file = "matplotlib-3.9.1-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:7ccd6270066feb9a9d8e0705aa027f1ff39f354c72a87efe8fa07632f30fc6bb"},
-    {file = "matplotlib-3.9.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:591d3a88903a30a6d23b040c1e44d1afdd0d778758d07110eb7596f811f31842"},
-    {file = "matplotlib-3.9.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:dd2a59ff4b83d33bca3b5ec58203cc65985367812cb8c257f3e101632be86d92"},
-    {file = "matplotlib-3.9.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0fc001516ffcf1a221beb51198b194d9230199d6842c540108e4ce109ac05cc0"},
-    {file = "matplotlib-3.9.1-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:83c6a792f1465d174c86d06f3ae85a8fe36e6f5964633ae8106312ec0921fdf5"},
-    {file = "matplotlib-3.9.1-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:b3fce58971b465e01b5c538f9d44915640c20ec5ff31346e963c9e1cd66fa812"},
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+
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+doc = ["furo", "ipython", "sphinx", "sphinx-copybutton", "sphinx-design"]
+test = ["attrs", "msgpack", "mypy", "pyright", "pytest", "pyyaml", "tomli", "tomli-w"]
+toml = ["tomli", "tomli-w"]
+yaml = ["pyyaml"]
+
 [[package]]
 name = "multidict"
 version = "6.0.5"
@@ -2696,33 +2794,6 @@ doc = ["myst-nb (>=1.0)", "numpydoc (>=1.7)", "pillow (>=9.4)", "pydata-sphinx-t
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 test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
 
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-python-versions = "*"
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-
 [[package]]
 name = "nltk"
 version = "3.8.1"
@@ -2884,12 +2955,13 @@ files = [
 
 [[package]]
 name = "nvidia-cudnn-cu12"
-version = "8.9.2.26"
+version = "9.1.0.70"
 description = "cuDNN runtime libraries"
 optional = false
 python-versions = ">=3"
 files = [
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 [package.dependencies]
@@ -3016,6 +3088,30 @@ typing-extensions = ">=4.11,<5"
 [package.extras]
 datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
 
+[[package]]
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+version = "4.10.0.84"
+description = "Wrapper package for OpenCV python bindings."
+optional = true
+python-versions = ">=3.6"
+files = [
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+]
+
+[package.dependencies]
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+    {version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
+    {version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\" and python_version < \"3.11\""},
+    {version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
+]
+
 [[package]]
 name = "opt-einsum"
 version = "3.3.0"
@@ -3266,6 +3362,49 @@ sql-other = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-d
 test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"]
 xml = ["lxml (>=4.9.2)"]
 
+[[package]]
+name = "partial-json-parser"
+version = "0.2.1.1.post4"
+description = "Parse partial JSON generated by LLM"
+optional = true
+python-versions = ">=3.6"
+files = [
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+]
+
+[package.extras]
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+
+[[package]]
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+version = "0.13.2"
+description = "Parameter-Efficient Fine-Tuning (PEFT)"
+optional = false
+python-versions = ">=3.8.0"
+files = [
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+
+[package.dependencies]
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+huggingface-hub = ">=0.17.0"
+numpy = ">=1.17"
+packaging = ">=20.0"
+psutil = "*"
+pyyaml = "*"
+safetensors = "*"
+torch = ">=1.13.0"
+tqdm = "*"
+transformers = "*"
+
+[package.extras]
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+docs-specific = ["black", "hf-doc-builder"]
+quality = ["black", "hf-doc-builder", "ruff (>=0.6.1,<0.7.0)"]
+test = ["black", "datasets", "diffusers (<0.21.0)", "hf-doc-builder", "parameterized", "pytest", "pytest-cov", "pytest-xdist", "ruff (>=0.6.1,<0.7.0)", "scipy"]
+
 [[package]]
 name = "pillow"
 version = "10.4.0"
@@ -3609,18 +3748,18 @@ files = [
 
 [[package]]
 name = "pydantic"
-version = "2.8.2"
+version = "2.9.2"
 description = "Data validation using Python type hints"
 optional = false
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 [package.dependencies]
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+annotated-types = ">=0.6.0"
+pydantic-core = "2.23.4"
 typing-extensions = [
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     {version = ">=4.6.1", markers = "python_version < \"3.13\""},
@@ -3628,103 +3767,104 @@ typing-extensions = [
 
 [package.extras]
 email = ["email-validator (>=2.0.0)"]
+timezone = ["tzdata"]
 
 [[package]]
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 [[package]]
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 [package.dependencies]
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 description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
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 [package.dependencies]
 filelock = "*"
 fsspec = "*"
 jinja2 = "*"
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 networkx = "*"
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-nvidia-cudnn-cu12 = {version = "8.9.2.26", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
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 nvidia-cufft-cu12 = {version = "11.0.2.54", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
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 nvidia-cusolver-cu12 = {version = "11.4.5.107", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
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 nvidia-nccl-cu12 = {version = "2.20.5", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 sympy = "*"
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+triton = {version = "3.0.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.13\""}
 typing-extensions = ">=4.8.0"
 
 [package.extras]
 opt-einsum = ["opt-einsum (>=3.3)"]
-optree = ["optree (>=0.9.1)"]
+optree = ["optree (>=0.11.0)"]
 
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 description = "image and video datasets and models for torch deep learning"
 optional = true
 python-versions = ">=3.8"
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 numpy = "*"
 pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0"
-torch = "2.3.1"
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 [package.extras]
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 scipy = ["scipy"]
 
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@@ -5351,13 +5487,13 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
 
 [[package]]
 name = "transformers"
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 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 [package.dependencies]
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 regex = "!=2019.12.17"
 requests = "*"
 safetensors = ">=0.4.1"
-tokenizers = ">=0.19,<0.20"
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 tqdm = ">=4.27"
 
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 flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 ftfy = ["ftfy"]
@@ -5394,42 +5530,47 @@ natten = ["natten (>=0.14.6,<0.15.0)"]
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 ray = ["ray[tune] (>=2.7.0)"]
 retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
-ruff = ["ruff (==0.4.4)"]
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 sagemaker = ["sagemaker (>=2.31.0)"]
 sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
 serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
 sigopt = ["sigopt"]
 sklearn = ["scikit-learn"]
 speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
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 tf = ["keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+tiktoken = ["blobfile", "tiktoken"]
 timm = ["timm (<=0.9.16)"]
-tokenizers = ["tokenizers (>=0.19,<0.20)"]
-torch = ["accelerate (>=0.21.0)", "torch"]
+tokenizers = ["tokenizers (>=0.20,<0.21)"]
+torch = ["accelerate (>=0.26.0)", "torch"]
 torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
 torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
-torchhub = ["filelock", "huggingface-hub (>=0.23.2,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"]
+torchhub = ["filelock", "huggingface-hub (>=0.23.2,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.20,<0.21)", "torch", "tqdm (>=4.27)"]
 video = ["av (==9.2.0)", "decord (==0.6.0)"]
 vision = ["Pillow (>=10.0.1,<=15.0)"]
 
 [[package]]
 name = "triton"
-version = "2.3.1"
+version = "3.0.0"
 description = "A language and compiler for custom Deep Learning operations"
 optional = false
 python-versions = "*"
 files = [
-    {file = "triton-2.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3c84595cbe5e546b1b290d2a58b1494df5a2ef066dd890655e5b8a8a92205c33"},
-    {file = "triton-2.3.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c9d64ae33bcb3a7a18081e3a746e8cf87ca8623ca13d2c362413ce7a486f893e"},
-    {file = "triton-2.3.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:eaf80e8761a9e3498aa92e7bf83a085b31959c61f5e8ac14eedd018df6fccd10"},
-    {file = "triton-2.3.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b13bf35a2b659af7159bf78e92798dc62d877aa991de723937329e2d382f1991"},
-    {file = "triton-2.3.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:63381e35ded3304704ea867ffde3b7cfc42c16a55b3062d41e017ef510433d66"},
-    {file = "triton-2.3.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1d968264523c7a07911c8fb51b4e0d1b920204dae71491b1fe7b01b62a31e124"},
+    {file = "triton-3.0.0-1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e1efef76935b2febc365bfadf74bcb65a6f959a9872e5bddf44cc9e0adce1e1a"},
+    {file = "triton-3.0.0-1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5ce8520437c602fb633f1324cc3871c47bee3b67acf9756c1a66309b60e3216c"},
+    {file = "triton-3.0.0-1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:34e509deb77f1c067d8640725ef00c5cbfcb2052a1a3cb6a6d343841f92624eb"},
+    {file = "triton-3.0.0-1-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:bcbf3b1c48af6a28011a5c40a5b3b9b5330530c3827716b5fbf6d7adcc1e53e9"},
+    {file = "triton-3.0.0-1-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:6e5727202f7078c56f91ff13ad0c1abab14a0e7f2c87e91b12b6f64f3e8ae609"},
+    {file = "triton-3.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:39b052da883351fdf6be3d93cedae6db3b8e3988d3b09ed221bccecfa9612230"},
+    {file = "triton-3.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cd34f19a8582af96e6291d4afce25dac08cb2a5d218c599163761e8e0827208e"},
+    {file = "triton-3.0.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0d5e10de8c011adeb7c878c6ce0dd6073b14367749e34467f1cff2bde1b78253"},
+    {file = "triton-3.0.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e8903767951bf86ec960b4fe4e21bc970055afc65e9d57e916d79ae3c93665e3"},
+    {file = "triton-3.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:41004fb1ae9a53fcb3e970745feb87f0e3c94c6ce1ba86e95fa3b8537894bef7"},
 ]
 
 [package.dependencies]
@@ -5437,8 +5578,8 @@ filelock = "*"
 
 [package.extras]
 build = ["cmake (>=3.20)", "lit"]
-tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)", "torch"]
-tutorials = ["matplotlib", "pandas", "tabulate", "torch"]
+tests = ["autopep8", "flake8", "isort", "llnl-hatchet", "numpy", "pytest", "scipy (>=1.7.1)"]
+tutorials = ["matplotlib", "pandas", "tabulate"]
 
 [[package]]
 name = "typer"
@@ -5588,66 +5729,59 @@ test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess
 
 [[package]]
 name = "vllm"
-version = "0.5.3.post1"
+version = "0.6.3.post1"
 description = "A high-throughput and memory-efficient inference and serving engine for LLMs"
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "vllm-0.5.3.post1-cp38-abi3-manylinux1_x86_64.whl", hash = "sha256:bc29055476f527ad11c75d8938a5b40241e6817d546106c4a0234685ebe8b728"},
-    {file = "vllm-0.5.3.post1.tar.gz", hash = "sha256:2fd5c0d1b428f332963380b87196b2c331dc92b29dfa3efc32c4022cf0ac0c73"},
+    {file = "vllm-0.6.3.post1-cp38-abi3-manylinux1_x86_64.whl", hash = "sha256:691f10edb9869eb8b85bebfe2c0fb3c6a6b2cf2aefad7cdb2ab97688a57ca60e"},
+    {file = "vllm-0.6.3.post1.tar.gz", hash = "sha256:0aae6ddd5348f86bf20e4f323c09e77d5ad2638d77f0d69323c5a63a40f8c143"},
 ]
 
 [package.dependencies]
 aiohttp = "*"
-cmake = ">=3.21"
-fastapi = "*"
+compressed-tensors = "0.6.0"
+einops = "*"
+fastapi = {version = ">=0.107.0,<0.113.dev0 || >0.114.0", markers = "python_version >= \"3.9\""}
 filelock = ">=3.10.4"
-lm-format-enforcer = "0.10.3"
-ninja = "*"
+gguf = "0.10.0"
+importlib-metadata = "*"
+lm-format-enforcer = "0.10.6"
+mistral-common = {version = ">=1.4.4", extras = ["opencv"]}
+msgspec = "*"
 numpy = "<2.0.0"
 nvidia-ml-py = "*"
-openai = "*"
+openai = ">=1.40.0"
 outlines = ">=0.0.43,<0.1"
+partial-json-parser = "*"
 pillow = "*"
 prometheus-client = ">=0.18.0"
 prometheus-fastapi-instrumentator = ">=7.0.0"
+protobuf = "*"
 psutil = "*"
 py-cpuinfo = "*"
-pydantic = ">=2.0"
+pydantic = ">=2.9"
+pyyaml = "*"
 pyzmq = "*"
 ray = ">=2.9"
-requests = "*"
+requests = ">=2.26.0"
 sentencepiece = "*"
+setuptools = {version = ">=74.1.1", markers = "python_version > \"3.11\""}
+six = {version = ">=1.16.0", markers = "python_version > \"3.11\""}
 tiktoken = ">=0.6.0"
 tokenizers = ">=0.19.1"
-torch = "2.3.1"
-torchvision = "0.18.1"
+torch = "2.4.0"
+torchvision = "0.19"
 tqdm = "*"
-transformers = ">=4.42.4"
-typing-extensions = "*"
+transformers = ">=4.45.2"
+typing-extensions = ">=4.10"
 uvicorn = {version = "*", extras = ["standard"]}
-vllm-flash-attn = "2.5.9.post1"
-xformers = "0.0.27"
+xformers = {version = "0.0.27.post2", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 
 [package.extras]
+audio = ["librosa", "soundfile"]
 tensorizer = ["tensorizer (>=2.9.0)"]
 
-[[package]]
-name = "vllm-flash-attn"
-version = "2.5.9.post1"
-description = "Forward-only flash-attn"
-optional = true
-python-versions = ">=3.8"
-files = [
-    {file = "vllm_flash_attn-2.5.9.post1-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:d2f8227e5232f119f9150143f736d7e336b02723ee85746437c18b5385b170c8"},
-    {file = "vllm_flash_attn-2.5.9.post1-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:e6e1ba5b0d6d9aca9b5aa0a7f3cf902c88fd06eb1a5f0e20d866fd0105244309"},
-    {file = "vllm_flash_attn-2.5.9.post1-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:71e3e0fe8d90407044737a41a9632e12b4fa2fd9c9ca4b9907b653b0e4eb4462"},
-    {file = "vllm_flash_attn-2.5.9.post1-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:46314c7ec0c5f917c22f05eb904a7ab78cde3d45d846ee76187a286e4c9e60f9"},
-]
-
-[package.dependencies]
-torch = "2.3.1"
-
 [[package]]
 name = "watchfiles"
 version = "0.22.0"
@@ -5816,27 +5950,27 @@ files = [
 
 [[package]]
 name = "xformers"
-version = "0.0.27"
+version = "0.0.27.post2"
 description = "XFormers: A collection of composable Transformer building blocks."
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "xformers-0.0.27-cp310-cp310-manylinux2014_x86_64.whl", hash = "sha256:d0efa09336da8bbff5335ab29c46f7baff03eb1fabd2d6daad4df5fd0f3ba396"},
-    {file = "xformers-0.0.27-cp310-cp310-win_amd64.whl", hash = "sha256:bef8100b1c53cdb5f884414b7daea0cec83966d977a70cbb7947786e473a4a1b"},
-    {file = "xformers-0.0.27-cp311-cp311-manylinux2014_x86_64.whl", hash = "sha256:253a647e40d5381dca6f556893d64e642cf839dda8bac46c3bea07292ca3f8fc"},
-    {file = "xformers-0.0.27-cp311-cp311-win_amd64.whl", hash = "sha256:93a41717c6edbfda4ca55a583d6d0a3e0726262ffa7f7f26ab439d84a7c482b9"},
-    {file = "xformers-0.0.27-cp312-cp312-manylinux2014_x86_64.whl", hash = "sha256:dc55cc75bebd522ce77672f5e49205d02e4d6f6eff6b3ee6ebc1de42aec66b49"},
-    {file = "xformers-0.0.27-cp312-cp312-win_amd64.whl", hash = "sha256:eb2ca11ee4f189f96ac10698e81aeec7dfe157c4180bb6a88e37ad50b14c7d7d"},
-    {file = "xformers-0.0.27-cp38-cp38-manylinux2014_x86_64.whl", hash = "sha256:b92348c98a96b01fa1b385f07914394363cd3c008d93978049da0e2212520daa"},
-    {file = "xformers-0.0.27-cp38-cp38-win_amd64.whl", hash = "sha256:d79457fd3d3b655527596e8de16787928b38ef21007bcd9fdb193c348a9516a4"},
-    {file = "xformers-0.0.27-cp39-cp39-manylinux2014_x86_64.whl", hash = "sha256:f88ccc7613a72be9cf75e2f7c0cf6bb6ec244bdcce9fe8b3448af48a685c7ab5"},
-    {file = "xformers-0.0.27-cp39-cp39-win_amd64.whl", hash = "sha256:67ab5341c7dbfeb96450778a1cd358710c4e71566dd1115246c766d01f2d5417"},
-    {file = "xformers-0.0.27.tar.gz", hash = "sha256:7bf9db7330fbe7b9b633d720db6f2284f939ec1564e4916bae57aafdc265d43d"},
+    {file = "xformers-0.0.27.post2-cp310-cp310-manylinux2014_x86_64.whl", hash = "sha256:7d4bd4876559fdc3cccbcaea717d3a28ca7f2fb09ab84cac3d859449ba247830"},
+    {file = "xformers-0.0.27.post2-cp310-cp310-win_amd64.whl", hash = "sha256:cd476c23fea18aa7298753c031c4827a544d919ee542cec771c01bc824d0127d"},
+    {file = "xformers-0.0.27.post2-cp311-cp311-manylinux2014_x86_64.whl", hash = "sha256:373a1c5d65dab89ea058d17a37c07e5da5ff3b9bebf61425e26a0a5293b0d4a9"},
+    {file = "xformers-0.0.27.post2-cp311-cp311-win_amd64.whl", hash = "sha256:7a26febb550b642c7da9864b6a46ccb89461571d27cfef54f7a252f03060d07c"},
+    {file = "xformers-0.0.27.post2-cp312-cp312-manylinux2014_x86_64.whl", hash = "sha256:3500f5ff3614aa18762fddc945e4057e7225e23f35986c6666cc49d6cb1c8ee7"},
+    {file = "xformers-0.0.27.post2-cp312-cp312-win_amd64.whl", hash = "sha256:0454ba745babf43500320b1d171ef4e44dec38f279fab9df1710ca5ce44a749c"},
+    {file = "xformers-0.0.27.post2-cp38-cp38-manylinux2014_x86_64.whl", hash = "sha256:ab39ba95a7529c412c3d81e7817e2b42c31980b72e1937055fd4ba4503a34b3d"},
+    {file = "xformers-0.0.27.post2-cp38-cp38-win_amd64.whl", hash = "sha256:5a83aa75a7208c359b2755af318e97e70855dbae6cdf998227f6dee220a56203"},
+    {file = "xformers-0.0.27.post2-cp39-cp39-manylinux2014_x86_64.whl", hash = "sha256:f9fca3af140a8649678e946d55e32c8be59917c4b7d0e96437c057a4bbe30e11"},
+    {file = "xformers-0.0.27.post2-cp39-cp39-win_amd64.whl", hash = "sha256:d555ef6d3722941cd785cf4b7c6039b06e8a0ca1360ff661200cf043393ca5a2"},
+    {file = "xformers-0.0.27.post2.tar.gz", hash = "sha256:5c3bcefabf29532cac7eb7556fb684be209698955b004aac069742e49373bdb3"},
 ]
 
 [package.dependencies]
 numpy = "*"
-torch = "2.3.1"
+torch = "2.4.0"
 
 [[package]]
 name = "xxhash"
@@ -6084,4 +6218,4 @@ openai = ["bert-score", "levenshtein", "openai", "rouge-score", "tiktoken"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<4.0"
-content-hash = "e8634110e2ce1cbffb63c9ca26e9445b371d1f193bd063d276e8db814b783809"
+content-hash = "2241668e6e5a863202c677fae7147d5a0bbe80950fd6766557c78a15cbf88a06"
diff --git a/pyproject.toml b/pyproject.toml
index ee83b20bb..3cda22451 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,8 +19,8 @@ python = ">=3.10,<4.0"
 torch = ">=2.3.0"
 pandas = ">=2.2.0"
 numpy = ">=1.23.0,<2.0.0"
-transformers = ">=4.43.3"
-accelerate = ">=0.26.0"
+transformers = ">=4.45.0"
+accelerate = ">=0.34.2"
 evaluate = ">=0.4.1"
 datasets = ">=2.15.0"
 click = ">=8.1.3"
@@ -41,7 +41,7 @@ rouge-score = { version = ">=0.1.2", optional = true }
 bert-score = { version = ">=0.3.13", optional = true }
 demjson3 = { version = ">=3.0.6", optional = true }
 bitsandbytes = { markers = "sys_platform != 'darwin' or platform_machine != 'arm64'", version = ">=0.43.1", optional = true }
-vllm = { markers = "sys_platform != 'darwin'", version = ">=0.5.3", optional = true }
+vllm = { markers = "sys_platform != 'darwin'", version = ">=0.6.3", optional = true }
 outlines = { version = ">=0.0.44", optional = true }
 
 # Needed for loading JAX based encoder models
@@ -66,6 +66,7 @@ readme-coverage-badger = ">=0.1.2"
 ruff = ">=0.3.2"
 mypy = ">=1.9.0"
 nbstripout = ">=0.7.1"
+peft = ">=0.13.2"
 
 [tool.poetry.extras]
 jax = [
diff --git a/src/scandeval/benchmark_dataset.py b/src/scandeval/benchmark_dataset.py
index 57fd1dcd6..4a904f751 100644
--- a/src/scandeval/benchmark_dataset.py
+++ b/src/scandeval/benchmark_dataset.py
@@ -280,6 +280,24 @@ def _get_metadata(
             and "total" in repo_info.safetensors
         ):
             num_params = repo_info.safetensors["total"]
+        elif (
+            repo_info is not None
+            and hasattr(repo_info, "card_data")
+            and repo_info.card_data is not None
+            and "base_model" in repo_info.card_data
+            and repo_info.card_data["base_model"] is not None
+            and len(repo_info.card_data["base_model"]) > 0
+            and (
+                base_repo_info := api.repo_info(
+                    repo_info.card_data["base_model"][-1], repo_type="model"
+                )
+            )
+            is not None
+            and hasattr(base_repo_info, "safetensors")
+            and base_repo_info.safetensors is not None
+            and "total" in base_repo_info.safetensors
+        ):
+            num_params = base_repo_info.safetensors["total"]
         elif (
             hasattr(model.config, "num_params") and model.config.num_params is not None
         ):
diff --git a/src/scandeval/config.py b/src/scandeval/config.py
index a1815b537..21250c9d9 100644
--- a/src/scandeval/config.py
+++ b/src/scandeval/config.py
@@ -292,6 +292,9 @@ class ModelConfig:
             The type of the model.
         model_cache_dir:
             The directory to cache the model in.
+        adapter_base_model_id:
+            The model ID of the base model if the model is an adapter model. Can be None
+            if the model is not an adapter model.
     """
 
     model_id: str
@@ -301,6 +304,7 @@ class ModelConfig:
     languages: list[Language]
     model_type: "ModelType | str"
     model_cache_dir: str
+    adapter_base_model_id: str | None
 
     def __hash__(self) -> int:
         """Return a hash of the model configuration."""
diff --git a/src/scandeval/human_evaluation.py b/src/scandeval/human_evaluation.py
index 5b5675749..e2737457f 100644
--- a/src/scandeval/human_evaluation.py
+++ b/src/scandeval/human_evaluation.py
@@ -286,6 +286,7 @@ def update_dataset(
             model_cache_dir=create_model_cache_dir(
                 cache_dir=benchmark_config.cache_dir, model_id=model_id
             ),
+            adapter_base_model_id=None,
         )
 
         self.benchmark_dataset = dataset_factory.build_dataset(dataset=dataset_config)
diff --git a/src/scandeval/model_setups/fresh.py b/src/scandeval/model_setups/fresh.py
index c28d6e70d..c49efb17a 100644
--- a/src/scandeval/model_setups/fresh.py
+++ b/src/scandeval/model_setups/fresh.py
@@ -85,6 +85,7 @@ def get_model_config(self, model_id: str) -> ModelConfig:
             model_cache_dir=create_model_cache_dir(
                 cache_dir=self.benchmark_config.cache_dir, model_id=model_id
             ),
+            adapter_base_model_id=None,
         )
 
     def load_model(
diff --git a/src/scandeval/model_setups/hf.py b/src/scandeval/model_setups/hf.py
index 31aa875f4..034cb0736 100644
--- a/src/scandeval/model_setups/hf.py
+++ b/src/scandeval/model_setups/hf.py
@@ -8,9 +8,9 @@
 from typing import TYPE_CHECKING, Type
 
 import torch
-from huggingface_hub import HfApi
+from huggingface_hub import HfApi, HfFileSystem
 from huggingface_hub import whoami as hf_whoami
-from huggingface_hub.hf_api import RepositoryNotFoundError
+from huggingface_hub.hf_api import ModelInfo, RepositoryNotFoundError
 from huggingface_hub.utils import (
     GatedRepoError,
     HFValidationError,
@@ -48,11 +48,15 @@
 from .utils import align_model_and_tokenizer, setup_model_for_question_answering
 
 if TYPE_CHECKING:
+    from peft.config import PeftConfig
     from transformers import PretrainedConfig, PreTrainedModel
 
     from ..config import BenchmarkConfig, DatasetConfig
     from ..protocols import GenerativeModel, Tokenizer
 
+if importlib.util.find_spec("peft") is not None:
+    from peft.config import PeftConfig
+
 
 logger = logging.getLogger(__package__)
 
@@ -138,7 +142,7 @@ def get_model_config(self, model_id: str) -> ModelConfig:
         Returns:
             The model configuration.
         """
-        # Extract the revision from the model ID, if it is specified
+        # Extract the revision from the model IDs, if they are specified
         if "@" in model_id:
             model_id_without_revision, revision = model_id.split("@", 1)
         else:
@@ -156,6 +160,7 @@ def get_model_config(self, model_id: str) -> ModelConfig:
         # Attempt to fetch model data from the Hugging Face Hub
         try:
             api: HfApi = HfApi()
+            fs = HfFileSystem()
 
             # Fetch the model metadata
             models = api.list_models(
@@ -164,7 +169,7 @@ def get_model_config(self, model_id: str) -> ModelConfig:
 
             # Filter the models to only keep the one with the specified model ID
             models = [
-                model for model in models if model.modelId == model_id_without_revision
+                model for model in models if model.id == model_id_without_revision
             ]
 
             # Check that the model exists. If it does not then raise an error
@@ -173,7 +178,8 @@ def get_model_config(self, model_id: str) -> ModelConfig:
                     f"The model {model_id} does not exist on the Hugging Face Hub."
                 )
 
-            tags: list[str] = models[0].tags
+            model: ModelInfo = models[0]
+            tags: list[str] = model.tags or list()
 
             framework = Framework.PYTORCH
             if "pytorch" in tags:
@@ -185,15 +191,51 @@ def get_model_config(self, model_id: str) -> ModelConfig:
             elif "tf" in tags or "tensorflow" in tags or "keras" in tags:
                 raise InvalidModel("TensorFlow/Keras models are not supported.")
 
-            model_task: str | None = models[0].pipeline_tag
+            is_adapter = "adapter_config.json" in [
+                path.split("/")[-1]
+                for path in fs.ls(path=model_id, detail=False)
+                if isinstance(path, str)
+            ]
+            adapter_base_model_id: str | None = None
+            if is_adapter:
+                if importlib.util.find_spec("peft") is None:
+                    raise NeedsManualDependency(package="peft")
+                peft_config = PeftConfig.from_pretrained(model_id)
+                adapter_base_model_id = peft_config.base_model_name_or_path
+
+                # Add tags from the adapter base model
+                if adapter_base_model_id is not None:
+                    adapter_author, adapter_model_name = adapter_base_model_id.split(
+                        "/"
+                    )
+                    adapter_models = api.list_models(
+                        author=adapter_author,
+                        model_name=adapter_model_name,
+                        token=self.benchmark_config.token,
+                    )
+                    adapter_models = [
+                        model
+                        for model in adapter_models
+                        if model.id == adapter_base_model_id
+                    ]
+                    if len(adapter_models) == 0:
+                        raise InvalidModel(
+                            f"The adapter base model {adapter_base_model_id} does not "
+                            "exist on the Hugging Face Hub."
+                        )
+                    tags += adapter_models[0].tags or list()
+
+            model_task: str | None = model.pipeline_tag
             if model_task is None:
                 generative_tags = [
                     "trl",
                     "mistral",
                     "text-generation-inference",
                     "unsloth",
+                    "text-generation",
+                    "llama",
                 ]
-                if any(tag in models[0].tags for tag in generative_tags):
+                if any(tag in tags for tag in generative_tags):
                     model_task = "text-generation"
                 else:
                     model_task = "fill-mask"
@@ -202,7 +244,7 @@ def get_model_config(self, model_id: str) -> ModelConfig:
             language_codes = list(language_mapping.keys())
 
             model_config = ModelConfig(
-                model_id=models[0].modelId,
+                model_id=model_id,
                 framework=framework,
                 task=model_task,
                 languages=[
@@ -213,6 +255,7 @@ def get_model_config(self, model_id: str) -> ModelConfig:
                 model_cache_dir=create_model_cache_dir(
                     cache_dir=self.benchmark_config.cache_dir, model_id=model_id
                 ),
+                adapter_base_model_id=adapter_base_model_id,
             )
 
         # If fetching from the Hugging Face Hub failed then throw a reasonable
@@ -248,7 +291,7 @@ def load_model(
         ignore_mismatched_sizes = False
 
         config = self._load_hf_model_config(
-            model_id=model_id,
+            model_id=model_config.adapter_base_model_id or model_id,
             num_labels=dataset_config.num_labels,
             id2label=dataset_config.id2label,
             label2id=dataset_config.label2id,
@@ -312,6 +355,7 @@ def load_model(
                     hf_model_config=config,
                     model_cache_dir=model_config.model_cache_dir,
                     trust_remote_code=self.benchmark_config.trust_remote_code,
+                    adapter_base_model_id=model_config.adapter_base_model_id,
                 )
             except ValueError as e:
                 # If the model is too large to fit on the GPU then we simply throw an
@@ -331,7 +375,7 @@ def load_model(
                     f"implementation instead. The error raised was {e!r}"
                 )
 
-        if not use_vllm:
+        else:
             if self.benchmark_config.use_flash_attention is None:
                 flash_attention = model_config.task in GENERATIVE_MODEL_TASKS
             else:
@@ -467,7 +511,9 @@ def load_model(
             model = setup_model_for_question_answering(model=model)
 
         tokenizer = self._load_tokenizer(
-            model=model, model_id=model_id, generative_model=generative_model
+            model=model,
+            model_id=model_config.adapter_base_model_id or model_id,
+            generative_model=generative_model,
         )
 
         if use_vllm:
@@ -647,5 +693,5 @@ def _handle_loading_exception(exception: Exception, model_id: str) -> None:
             "please ensure that it has a framework registered. If it is a "
             "private model then enable the `--use-token` flag and make "
             "sure that you are logged in to the Hub via the "
-            "`huggingface-cli login` command."
+            f"`huggingface-cli login` command. The error raised was {exception!r}"
         )
diff --git a/src/scandeval/model_setups/local.py b/src/scandeval/model_setups/local.py
index d18a8ef81..4142cfddf 100644
--- a/src/scandeval/model_setups/local.py
+++ b/src/scandeval/model_setups/local.py
@@ -1,5 +1,6 @@
 """Model setup for local Hugging Face Hub models."""
 
+import importlib.util
 import logging
 from pathlib import Path
 from typing import TYPE_CHECKING
@@ -13,16 +14,20 @@
 
 from ..config import ModelConfig
 from ..enums import Framework, ModelType
-from ..exceptions import InvalidModel
+from ..exceptions import InvalidModel, NeedsManualDependency
 from ..utils import create_model_cache_dir
 from .hf import HFModelSetup
 
 if TYPE_CHECKING:
+    from peft.config import PeftConfig
     from transformers import PreTrainedModel
 
     from ..config import BenchmarkConfig, DatasetConfig
     from ..protocols import GenerativeModel, Tokenizer
 
+if importlib.util.find_spec("peft") is not None:
+    from peft.config import PeftConfig
+
 
 logger = logging.getLogger(__package__)
 
@@ -125,6 +130,16 @@ def get_model_config(self, model_id: str) -> ModelConfig:
         else:
             task = "unknown"
 
+        is_adapter = "adapter_config.json" in [
+            path.name for path in Path(model_id).glob("*.json")
+        ]
+        adapter_base_model_id: str | None = None
+        if is_adapter:
+            if importlib.util.find_spec("peft") is None:
+                raise NeedsManualDependency(package="peft")
+            peft_config = PeftConfig.from_pretrained(model_id)
+            adapter_base_model_id = peft_config.base_model_name_or_path
+
         model_config = ModelConfig(
             model_id=model_id,
             revision="main",
@@ -135,6 +150,7 @@ def get_model_config(self, model_id: str) -> ModelConfig:
             model_cache_dir=create_model_cache_dir(
                 cache_dir=self.benchmark_config.cache_dir, model_id=model_id
             ),
+            adapter_base_model_id=adapter_base_model_id,
         )
         return model_config
 
diff --git a/src/scandeval/model_setups/openai.py b/src/scandeval/model_setups/openai.py
index e840aebb6..c4244f85b 100644
--- a/src/scandeval/model_setups/openai.py
+++ b/src/scandeval/model_setups/openai.py
@@ -163,6 +163,7 @@ def get_model_config(self, model_id: str) -> ModelConfig:
             model_cache_dir=create_model_cache_dir(
                 cache_dir=self.benchmark_config.cache_dir, model_id=model_id
             ),
+            adapter_base_model_id=None,
         )
 
     def load_model(
diff --git a/src/scandeval/model_setups/utils.py b/src/scandeval/model_setups/utils.py
index 31286b8d7..cbbccc30b 100644
--- a/src/scandeval/model_setups/utils.py
+++ b/src/scandeval/model_setups/utils.py
@@ -135,11 +135,9 @@ def align_model_and_tokenizer(
 
     # If we're not dealing with a generative model then we move it to CPU to avoid OOM
     # errors
-    if not generative_model:
-        model.to("cpu")
-        device = "cpu"
-    else:
-        device = model.device
+    device = model.device if generative_model else torch.device("cpu")
+    model_device = model.device
+    model.to(device)
 
     # Manually check that this model max length is valid for the model, and adjust
     # otherwise
@@ -209,4 +207,6 @@ def align_model_and_tokenizer(
         tokenizer.bos_token = tokenizer.eos_token
         tokenizer.bos_token_id = tokenizer.eos_token_id
 
+    model.to(model_device)
+
     return model, tokenizer
diff --git a/src/scandeval/utils.py b/src/scandeval/utils.py
index f6106425a..6af1b2dd4 100644
--- a/src/scandeval/utils.py
+++ b/src/scandeval/utils.py
@@ -181,6 +181,7 @@ def block_terminal_output():
     logging.getLogger("httpx").setLevel(logging.CRITICAL)
     logging.getLogger("ray._private.worker").setLevel(logging.CRITICAL)
     logging.getLogger("matplotlib.font_manager").setLevel(logging.CRITICAL)
+    logging.getLogger("accelerate").setLevel(logging.CRITICAL)
 
     # This suppresses vLLM logging
     os.environ["LOG_LEVEL"] = "CRITICAL"
diff --git a/src/scandeval/vllm_models.py b/src/scandeval/vllm_models.py
index 07a62b665..567c532ea 100644
--- a/src/scandeval/vllm_models.py
+++ b/src/scandeval/vllm_models.py
@@ -4,10 +4,12 @@
 import logging
 import math
 import sys
+from pathlib import Path
 from types import MethodType
 from typing import TYPE_CHECKING
 
 import torch
+from huggingface_hub import snapshot_download
 from tqdm import tqdm
 from transformers import GenerationConfig
 from transformers.utils import ModelOutput
@@ -16,10 +18,9 @@
 from .utils import clear_memory, get_end_of_chat_token_ids
 
 if TYPE_CHECKING:
-    from pathlib import Path
-
     from transformers import PretrainedConfig, PreTrainedTokenizerBase
     from vllm import LLM, RequestOutput
+    from vllm.lora.request import LoRARequest
 
     from .config import ModelConfig
 
@@ -28,6 +29,7 @@
 
 if importlib.util.find_spec("vllm") is not None:
     from vllm import LLM, SamplingParams
+    from vllm.lora.request import LoRARequest
 
     try:
         from vllm.model_executor.parallel_utils.parallel_state import (
@@ -50,6 +52,7 @@ def __init__(
         model_cache_dir: "str | Path",
         trust_remote_code: bool,
         tokenizer: "PreTrainedTokenizerBase | None" = None,
+        adapter_base_model_id: str | None = None,
     ) -> None:
         """Initialize a vLLM model.
 
@@ -65,6 +68,9 @@ def __init__(
             tokenizer:
                 A Hugging Face tokenizer. If None, the tokenizer will need to be
                 loaded separately.
+            adapter_base_model_id:
+                The base model ID of the adapter model, if the model is an adapter.
+                None otherwise.
         """
         self.model_config = model_config
         self.config = hf_model_config
@@ -72,6 +78,9 @@ def __init__(
         self.trust_remote_code = trust_remote_code
         self.device = torch.device("cuda")
         self.tokenizer = tokenizer
+        self.adapter_base_model_id = adapter_base_model_id
+        self.adapter_path: str | None = None
+        self.lora_request: "LoRARequest | None" = None
 
         self.max_model_len = 5_000
         potential_max_model_length_config_names = [
@@ -110,11 +119,17 @@ def __init__(
             )
             dtype = torch.float16
 
+        if self.adapter_base_model_id is not None:
+            download_dir = str(Path(self.model_cache_dir) / "base_model")
+        else:
+            download_dir = str(self.model_cache_dir)
+
         vllm_kwargs = dict(
-            model=self.model_config.model_id,
+            model=self.adapter_base_model_id or self.model_config.model_id,
+            tokenizer=self.adapter_base_model_id or self.model_config.model_id,
             gpu_memory_utilization=0.95,
             max_model_len=self.max_model_len,
-            download_dir=str(self.model_cache_dir),
+            download_dir=download_dir,
             trust_remote_code=self.trust_remote_code,
             revision=self.model_config.revision,
             seed=4242,
@@ -128,6 +143,8 @@ def __init__(
             # TEMP: Prefix caching isn't supported with sliding window in vLLM yet, so
             # we disable it for now
             enable_prefix_caching=False,
+            enable_lora=self.adapter_base_model_id is not None,
+            max_lora_rank=256,
         )
 
         self._model = self._initialise(vllm_kwargs=vllm_kwargs)
@@ -145,6 +162,15 @@ def _initialise(self, vllm_kwargs: dict) -> "LLM":
         clear_vllm()
         model = LLM(**vllm_kwargs)
         model._run_engine = MethodType(_run_engine_with_fixed_progress_bars, model)
+
+        if self.adapter_base_model_id is not None:
+            self.adapter_path = snapshot_download(
+                repo_id=self.model_config.model_id, cache_dir=Path(self.model_cache_dir)
+            )
+            self.lora_request = LoRARequest(
+                lora_name="adapter", lora_int_id=1, lora_path=self.adapter_path
+            )
+
         return model
 
     def __del__(self) -> None:
@@ -246,8 +272,9 @@ def generate(
         input_is_a_test = len(prompts) == 1 and len(set(prompts[0])) == 1
         raw_outputs = self._model.generate(
             prompts=prompts,
-            use_tqdm=(not input_is_a_test),
             sampling_params=sampling_params,
+            use_tqdm=(not input_is_a_test),
+            lora_request=self.lora_request,
         )
 
         # Collect the generated sequences into a single tensor of shape


Test patch:
diff --git a/tests/conftest.py b/tests/conftest.py
index d4f57c597..5178e6bfb 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -193,6 +193,7 @@ def model_config(language) -> Generator[ModelConfig, None, None]:
         languages=[language],
         model_type=ModelType.FRESH,
         model_cache_dir="cache_dir",
+        adapter_base_model_id=None,
     )
 
 
diff --git a/tests/test_model_loading.py b/tests/test_model_loading.py
index f28f53010..d5e1567f3 100644
--- a/tests/test_model_loading.py
+++ b/tests/test_model_loading.py
@@ -60,6 +60,7 @@ def test_load_non_existing_model(dataset_config, benchmark_config):
         languages=[DA],
         model_type="fresh",
         model_cache_dir="cache_dir",
+        adapter_base_model_id=None,
     )
     with pytest.raises(InvalidModel):
         load_model(