diff --git a/libs/embed_package/Makefile b/libs/embed_package/Makefile
index dd2fcfd8..f60ab9dc 100644
--- a/libs/embed_package/Makefile
+++ b/libs/embed_package/Makefile
@@ -32,13 +32,12 @@ lint format: PYTHON_FILES=.
 lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/infinity_emb --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
 
 lint lint_diff:
-	poetry run ruff .
-	[ "$(PYTHON_FILES)" = "" ] || poetry run black $(PYTHON_FILES) --check
+	poetry run ruff check .
+	
 	[ "$(PYTHON_FILES)" = "" ] || poetry run mypy $(PYTHON_FILES)
 
 format format_diff:
-	[ "$(PYTHON_FILES)" = "" ] || poetry run black $(PYTHON_FILES)
-	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff --select I --fix $(PYTHON_FILES)
+	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES)
 
 poetry_check:
 	poetry check
diff --git a/libs/embed_package/poetry.lock b/libs/embed_package/poetry.lock
index 7acc4c87..46d7e125 100644
--- a/libs/embed_package/poetry.lock
+++ b/libs/embed_package/poetry.lock
@@ -463,83 +463,73 @@ cron = ["capturer (>=2.4)"]
 
 [[package]]
 name = "coverage"
-version = "7.6.1"
+version = "7.6.3"
 description = "Code coverage measurement for Python"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
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 ]
 
 [package.dependencies]
@@ -1642,13 +1637,13 @@ sympy = "*"
 
 [[package]]
 name = "optimum"
-version = "1.22.0"
+version = "1.23.1"
 description = "Optimum Library is an extension of the Hugging Face Transformers library, providing a framework to integrate third-party libraries from Hardware Partners and interface with their specific functionality."
 optional = false
 python-versions = ">=3.7.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -1659,14 +1654,17 @@ datasets = [
 ]
 evaluate = {version = "*", optional = true, markers = "extra == \"onnxruntime\""}
 huggingface-hub = ">=0.8.0"
-numpy = "<2.0"
+numpy = "*"
 onnx = {version = "*", optional = true, markers = "extra == \"onnxruntime\""}
 onnxruntime = {version = ">=1.11.0", optional = true, markers = "extra == \"onnxruntime\""}
 packaging = "*"
 protobuf = {version = ">=3.20.1", optional = true, markers = "extra == \"onnxruntime\""}
 sympy = "*"
 torch = ">=1.11"
-transformers = {version = ">=4.29,<4.45.0", extras = ["sentencepiece"]}
+transformers = [
+    {version = ">=4.29", extras = ["sentencepiece"]},
+    {version = "<4.46.0", optional = true, markers = "extra == \"onnxruntime\""},
+]
 
 [package.extras]
 amd = ["optimum-amd"]
@@ -1674,8 +1672,8 @@ benchmark = ["evaluate (>=0.2.0)", "optuna", "scikit-learn", "seqeval", "torchvi
 dev = ["Pillow", "accelerate", "black (>=23.1,<24.0)", "diffusers (>=0.17.0)", "einops", "invisible-watermark", "parameterized", "pytest (<=8.0.0)", "pytest-xdist", "requests", "rjieba", "ruff (==0.1.5)", "sacremoses", "scikit-learn", "timm", "torchaudio", "torchvision"]
 diffusers = ["diffusers"]
 doc-build = ["accelerate"]
-exporters = ["onnx", "onnxruntime", "timm"]
-exporters-gpu = ["onnx", "onnxruntime-gpu", "timm"]
+exporters = ["onnx", "onnxruntime", "timm", "transformers (<4.46.0)"]
+exporters-gpu = ["onnx", "onnxruntime-gpu", "timm", "transformers (<4.46.0)"]
 exporters-tf = ["datasets (<=2.16)", "h5py", "numpy (<1.24.0)", "onnx", "onnxruntime", "tensorflow (>=2.4,<=2.12.1)", "tf2onnx", "timm", "transformers[sentencepiece] (>=4.26,<4.38)"]
 furiosa = ["optimum-furiosa"]
 graphcore = ["optimum-graphcore"]
@@ -1686,8 +1684,8 @@ neural-compressor = ["optimum-intel[neural-compressor] (>=1.18.0)"]
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 nncf = ["optimum-intel[nncf] (>=1.18.0)"]
-onnxruntime = ["datasets (>=1.2.1)", "evaluate", "onnx", "onnxruntime (>=1.11.0)", "protobuf (>=3.20.1)"]
-onnxruntime-gpu = ["accelerate", "datasets (>=1.2.1)", "evaluate", "onnx", "onnxruntime-gpu (>=1.11.0)", "protobuf (>=3.20.1)"]
+onnxruntime = ["datasets (>=1.2.1)", "evaluate", "onnx", "onnxruntime (>=1.11.0)", "protobuf (>=3.20.1)", "transformers (<4.46.0)"]
+onnxruntime-gpu = ["accelerate", "datasets (>=1.2.1)", "evaluate", "onnx", "onnxruntime-gpu (>=1.11.0)", "protobuf (>=3.20.1)", "transformers (<4.46.0)"]
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 quality = ["black (>=23.1,<24.0)", "ruff (==0.1.5)"]
 quanto = ["optimum-quanto (>=0.2.4)"]
@@ -2027,13 +2025,13 @@ dev = ["build", "flake8", "mypy", "pytest", "twine"]
 
 [[package]]
 name = "pytest"
-version = "7.4.4"
+version = "8.3.3"
 description = "pytest: simple powerful testing with Python"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2041,11 +2039,11 @@ colorama = {version = "*", markers = "sys_platform == \"win32\""}
 exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""}
 iniconfig = "*"
 packaging = "*"
-pluggy = ">=0.12,<2.0"
-tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
+pluggy = ">=1.5,<2"
+tomli = {version = ">=1", markers = "python_version < \"3.11\""}
 
 [package.extras]
-testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
+dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
 [[package]]
 name = "python-dateutil"
@@ -2691,111 +2689,111 @@ torchvision = "*"
 
 [[package]]
 name = "tokenizers"
-version = "0.19.1"
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 python-versions = ">=3.7"
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 [package.dependencies]
@@ -2931,13 +2929,13 @@ telegram = ["requests"]
 
 [[package]]
 name = "transformers"
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+version = "4.45.2"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
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 python-versions = ">=3.8.0"
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-tokenizers = ">=0.19,<0.20"
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 tqdm = ">=4.27"
 
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-deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.21.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "optuna", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
-dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
-dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.19,<0.20)", "urllib3 (<2.0.0)"]
-dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+deepspeed = ["accelerate (>=0.26.0)", "deepspeed (>=0.9.3)"]
+deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.26.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk (<=3.8.1)", "optuna", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
+dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "librosa", "nltk (<=3.8.1)", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.20,<0.21)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "librosa", "nltk (<=3.8.1)", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.20,<0.21)", "urllib3 (<2.0.0)"]
+dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "libcst", "librosa", "nltk (<=3.8.1)", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.20,<0.21)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
 flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"]
 flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 ftfy = ["ftfy"]
@@ -2976,7 +2974,7 @@ natten = ["natten (>=0.14.6,<0.15.0)"]
 onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
 onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
 optuna = ["optuna"]
-quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "ruff (==0.5.1)", "urllib3 (<2.0.0)"]
+quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "libcst", "rich", "ruff (==0.5.1)", "urllib3 (<2.0.0)"]
 ray = ["ray[tune] (>=2.7.0)"]
 retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
 ruff = ["ruff (==0.5.1)"]
@@ -2986,16 +2984,17 @@ serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
 sigopt = ["sigopt"]
 sklearn = ["scikit-learn"]
 speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk (<=3.8.1)", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
 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)"]
 
@@ -3404,4 +3403,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4"
-content-hash = "afe3d1133505841ee51c12b5ecf65e4aba579adf37fc60bbecc9060b05918caa"
+content-hash = "d6a3aa547ea0b12776d6e61b9e54484cd1de9287aa4abe3fa893ce321184343f"
diff --git a/libs/embed_package/pyproject.toml b/libs/embed_package/pyproject.toml
index 3a6bfe57..6d031b05 100644
--- a/libs/embed_package/pyproject.toml
+++ b/libs/embed_package/pyproject.toml
@@ -18,9 +18,9 @@ python = ">=3.9,<4"
 # infinity_emb = {path = "../infinity_emb", extras = ["optimum","vision","torch","audio"]}
 infinity_emb = {version = "0.0.58", extras = ["optimum","vision","torch","audio"]}
 [tool.poetry.group.test.dependencies]
-pytest = "^7.0.0"
-coverage = {extras = ["toml"], version = "^7.3.2"}
-mypy = "^1.5.1"
+pytest = "^8.0.0"
+coverage = {extras = ["toml"], version = "^7.6.3"}
+mypy = "^1.12.0"
 
 [tool.poetry.group.codespell.dependencies]
 codespell = "^2.2.0"
diff --git a/libs/infinity_emb/Makefile b/libs/infinity_emb/Makefile
index 68c574a0..1502a507 100644
--- a/libs/infinity_emb/Makefile
+++ b/libs/infinity_emb/Makefile
@@ -30,17 +30,15 @@ openapi:
 
 # Define a variable for Python and notebook files.
 PYTHON_FILES=.
-lint format: PYTHON_FILES=.
+lint: PYTHON_FILES=./infinity_emb
 lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/infinity_emb --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
 
 lint lint_diff:
-	poetry run ruff .
-	[ "$(PYTHON_FILES)" = "" ] || poetry run black $(PYTHON_FILES) --check
+	poetry run ruff check .
 	[ "$(PYTHON_FILES)" = "" ] || poetry run mypy $(PYTHON_FILES)
 
 format format_diff:
-	[ "$(PYTHON_FILES)" = "" ] || poetry run black $(PYTHON_FILES)
-	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff --select I --fix $(PYTHON_FILES)
+	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES)
 
 template_docker:
 	jinja2 Dockerfile.jinja2 Docker.template.yaml --format=yaml -s amd > Dockerfile.amd_auto
diff --git a/libs/infinity_emb/infinity_emb/args.py b/libs/infinity_emb/infinity_emb/args.py
index 8c7f4ddc..c7046000 100644
--- a/libs/infinity_emb/infinity_emb/args.py
+++ b/libs/infinity_emb/infinity_emb/args.py
@@ -74,13 +74,9 @@ def __post_init__(self):
         if not isinstance(self.dtype, Dtype):
             object.__setattr__(self, "dtype", Dtype[self.dtype])
         if not isinstance(self.pooling_method, PoolingMethod):
-            object.__setattr__(
-                self, "pooling_method", PoolingMethod[self.pooling_method]
-            )
+            object.__setattr__(self, "pooling_method", PoolingMethod[self.pooling_method])
         if not isinstance(self.embedding_dtype, EmbeddingDtype):
-            object.__setattr__(
-                self, "embedding_dtype", EmbeddingDtype[self.embedding_dtype]
-            )
+            object.__setattr__(self, "embedding_dtype", EmbeddingDtype[self.embedding_dtype])
         if not self.served_model_name:
             object.__setattr__(
                 self,
diff --git a/libs/infinity_emb/infinity_emb/engine.py b/libs/infinity_emb/infinity_emb/engine.py
index 9d876aa3..34bc2807 100644
--- a/libs/infinity_emb/infinity_emb/engine.py
+++ b/libs/infinity_emb/infinity_emb/engine.py
@@ -52,9 +52,7 @@ def __init__(
 
         self.running = False
         self._running_sepamore: Optional[Semaphore] = None
-        self._model, self._min_inference_t, self._max_inference_t = select_model(
-            self._engine_args
-        )
+        self._model, self._min_inference_t, self._max_inference_t = select_model(self._engine_args)
 
     @classmethod
     def from_args(
@@ -130,9 +128,7 @@ def capabilities(self) -> set[ModelCapabilites]:
     def engine_args(self) -> EngineArgs:
         return self._engine_args
 
-    async def embed(
-        self, sentences: list[str]
-    ) -> tuple[list["EmbeddingReturnType"], int]:
+    async def embed(self, sentences: list[str]) -> tuple[list["EmbeddingReturnType"], int]:
         """embed multiple sentences
 
         Kwargs:
@@ -279,9 +275,7 @@ def __init__(self, engines: Iterable["AsyncEmbeddingEngine"]):
             set(engine.engine_args.served_model_name for engine in engines)
         ):
             raise ValueError("Engines must have unique model names")
-        self.engines_dict = {
-            engine.engine_args.served_model_name: engine for engine in engines
-        }
+        self.engines_dict = {engine.engine_args.served_model_name: engine for engine in engines}
 
     @classmethod
     def from_args(cls, engine_args_array: Iterable[EngineArgs]) -> "AsyncEngineArray":
@@ -358,9 +352,7 @@ async def rerank(
             list[float]: list of scores
             int: token usage
         """
-        return await self[model].rerank(
-            query=query, docs=docs, raw_scores=raw_scores, top_n=top_n
-        )
+        return await self[model].rerank(query=query, docs=docs, raw_scores=raw_scores, top_n=top_n)
 
     async def classify(
         self, *, model: str, sentences: list[str], raw_scores: bool = False
diff --git a/libs/infinity_emb/infinity_emb/env.py b/libs/infinity_emb/infinity_emb/env.py
index a22c0f88..ba379a0b 100644
--- a/libs/infinity_emb/infinity_emb/env.py
+++ b/libs/infinity_emb/infinity_emb/env.py
@@ -61,9 +61,7 @@ def _optional_infinity_var(self, name: str, default: str = ""):
         self._debug(f"{name}=`{value}`")
         return value
 
-    def _optional_infinity_var_multiple(
-        self, name: str, default: list[str]
-    ) -> list[str]:
+    def _optional_infinity_var_multiple(self, name: str, default: list[str]) -> list[str]:
         self.__pre_fetch_env_manager()
         name = self.to_name(name)
         value = os.getenv(name)
@@ -133,9 +131,7 @@ def vector_disk_cache(self):
     @cached_property
     def lengths_via_tokenize(self):
         return self._to_bool_multiple(
-            self._optional_infinity_var_multiple(
-                "lengths_via_tokenize", default=["false"]
-            )
+            self._optional_infinity_var_multiple("lengths_via_tokenize", default=["false"])
         )
 
     @cached_property
@@ -152,9 +148,7 @@ def bettertransformer(self):
 
     @cached_property
     def preload_only(self):
-        return self._to_bool(
-            self._optional_infinity_var("preload_only", default="false")
-        )
+        return self._to_bool(self._optional_infinity_var("preload_only", default="false"))
 
     @cached_property
     def calibration_dataset_url(self):
@@ -198,9 +192,7 @@ def queue_size(self) -> int:
 
     @cached_property
     def permissive_cors(self):
-        return self._to_bool(
-            self._optional_infinity_var("permissive_cors", default="false")
-        )
+        return self._to_bool(self._optional_infinity_var("permissive_cors", default="false"))
 
     @cached_property
     def url_prefix(self):
@@ -223,9 +215,7 @@ def host(self):
     @cached_property
     def redirect_slash(self):
         route = self._optional_infinity_var("redirect_slash", default="/docs")
-        assert not route or route.startswith(
-            "/"
-        ), "INFINITY_REDIRECT_SLASH must start with /"
+        assert not route or route.startswith("/"), "INFINITY_REDIRECT_SLASH must start with /"
         return route
 
     @cached_property
@@ -233,9 +223,7 @@ def log_level(self):
         return self._optional_infinity_var("log_level", default="info")
 
     def _typed_multiple(self, name: str, cls: type["EnumTypeLike"]) -> list["str"]:
-        result = self._optional_infinity_var_multiple(
-            name, default=[cls.default_value()]
-        )
+        result = self._optional_infinity_var_multiple(name, default=[cls.default_value()])
         assert all(cls(v) for v in result)
         return result
 
diff --git a/libs/infinity_emb/infinity_emb/fastapi_schemas/data_uri.py b/libs/infinity_emb/infinity_emb/fastapi_schemas/data_uri.py
index 68cb3d06..16bfe2db 100644
--- a/libs/infinity_emb/infinity_emb/fastapi_schemas/data_uri.py
+++ b/libs/infinity_emb/infinity_emb/fastapi_schemas/data_uri.py
@@ -192,9 +192,7 @@ def __get_pydantic_core_schema__(cls, source_type: Any, handler: Any) -> Any:
         from pydantic_core import core_schema
 
         # return core_schema.no_info_after_validator_function(cls, handler(str))
-        return core_schema.no_info_after_validator_function(
-            cls.validate, core_schema.str_schema()
-        )
+        return core_schema.no_info_after_validator_function(cls.validate, core_schema.str_schema())
 
     @classmethod
     def validate(
@@ -214,9 +212,7 @@ def validate(
         return m
 
     @classmethod
-    def __get_pydantic_json_schema__(
-        cls, schema: MutableMapping[str, Any], handler: Any
-    ) -> Any:
+    def __get_pydantic_json_schema__(cls, schema: MutableMapping[str, Any], handler: Any) -> Any:
         json_schema = handler(schema)
         json_schema.update(
             pattern=DATA_URI_REGEX,
diff --git a/libs/infinity_emb/infinity_emb/fastapi_schemas/pymodels.py b/libs/infinity_emb/infinity_emb/fastapi_schemas/pymodels.py
index cd031bf1..f94b79be 100644
--- a/libs/infinity_emb/infinity_emb/fastapi_schemas/pymodels.py
+++ b/libs/infinity_emb/infinity_emb/fastapi_schemas/pymodels.py
@@ -197,9 +197,10 @@ def to_embeddings_response(
                 raise ValueError(
                     f"model {engine_args.served_model_name} does not support base64 encoding, as it uses uint8-bitpacking with {engine_args.embedding_dtype}"
                 )
-            embeddings = [base64.b64encode(np.frombuffer(emb.astype(np.float32), dtype=np.float32)) for emb in embeddings]  # type: ignore
-        elif isinstance(embeddings, np.ndarray):
-            embeddings = embeddings.tolist()
+            embeddings = [
+                base64.b64encode(np.frombuffer(emb.astype(np.float32), dtype=np.float32))  # type: ignore
+                for emb in embeddings
+            ]  # type: ignore
         else:
             embeddings = [e.tolist() for e in embeddings]
         return dict(
diff --git a/libs/infinity_emb/infinity_emb/inference/batch_handler.py b/libs/infinity_emb/infinity_emb/inference/batch_handler.py
index 1c5a962b..61288a86 100644
--- a/libs/infinity_emb/infinity_emb/inference/batch_handler.py
+++ b/libs/infinity_emb/infinity_emb/inference/batch_handler.py
@@ -121,9 +121,7 @@ def __init__(
                 " Consider increasing queue size"
             )
 
-    async def embed(
-        self, sentences: list[str]
-    ) -> tuple[list["EmbeddingReturnType"], int]:
+    async def embed(self, sentences: list[str]) -> tuple[list["EmbeddingReturnType"], int]:
         """Schedule a sentence to be embedded. Awaits until embedded.
 
         Args:
@@ -286,9 +284,7 @@ async def audio_embed(
         embeddings, usage = await self._schedule(items)
         return embeddings, usage
 
-    async def _schedule(
-        self, list_queueitem: Sequence[AbstractSingle]
-    ) -> tuple[list[Any], int]:
+    async def _schedule(self, list_queueitem: Sequence[AbstractSingle]) -> tuple[list[Any], int]:
         """adds list of items to the queue and awaits until these are completed."""
         prios, usage = await self._get_prios_usage(list_queueitem)
         new_prioqueue: list[PrioritizedQueueItem] = []
@@ -331,9 +327,7 @@ def overload_status(self) -> OverloadStatus:
             results_absolute=len(self._result_store),
         )
 
-    async def _get_prios_usage(
-        self, items: Sequence[AbstractSingle]
-    ) -> tuple[list[int], int]:
+    async def _get_prios_usage(self, items: Sequence[AbstractSingle]) -> tuple[list[int], int]:
         """get priorities and usage
 
         Args:
@@ -465,8 +459,7 @@ def _preprocess_batch(self):
                 # - or if many items are queued anyhow, so that a good batch
                 #   may be popped already.
                 if not self._feature_queue.empty() and (
-                    self._feature_queue.full()
-                    or (len(self._queue_prio) < self._max_batch_size * 4)
+                    self._feature_queue.full() or (len(self._queue_prio) < self._max_batch_size * 4)
                 ):
                     # add some stochastic delay
                     time.sleep(self._batch_delay)
@@ -550,8 +543,7 @@ def _postprocess_batch(self):
 
                 if (
                     self._postprocess_queue.empty()
-                    and self._last_inference
-                    < time.perf_counter() + self._batch_delay * 2
+                    and self._last_inference < time.perf_counter() + self._batch_delay * 2
                 ):
                     # 5 ms, assuming this is below
                     # 3-50ms for inference on avg.
diff --git a/libs/infinity_emb/infinity_emb/inference/caching_layer.py b/libs/infinity_emb/infinity_emb/inference/caching_layer.py
index b71ac964..0d48814f 100644
--- a/libs/infinity_emb/infinity_emb/inference/caching_layer.py
+++ b/libs/infinity_emb/infinity_emb/inference/caching_layer.py
@@ -4,6 +4,7 @@
 This file contains the experimental code for retrieving and storing result of embeddings to diskcache
 which may reduce latency.
 """
+
 import asyncio
 import queue
 import threading
diff --git a/libs/infinity_emb/infinity_emb/inference/select_model.py b/libs/infinity_emb/infinity_emb/inference/select_model.py
index ea901f8c..134a737e 100644
--- a/libs/infinity_emb/infinity_emb/inference/select_model.py
+++ b/libs/infinity_emb/infinity_emb/inference/select_model.py
@@ -22,9 +22,7 @@
 
 def get_engine_type_from_config(
     engine_args: EngineArgs,
-) -> Union[
-    EmbedderEngine, RerankEngine, PredictEngine, ImageEmbedEngine, AudioEmbedEngine
-]:
+) -> Union[EmbedderEngine, RerankEngine, PredictEngine, ImageEmbedEngine, AudioEmbedEngine]:
     """resolved the class of inference engine path from config.json of the repo."""
     if engine_args.engine in [InferenceEngine.debugengine]:
         return EmbedderEngine.from_inference_engine(engine_args.engine)
@@ -44,9 +42,7 @@ def get_engine_type_from_config(
     with open(config_path, "r") as f:
         config = json.load(f)
 
-    if any(
-        "SequenceClassification" in arch for arch in config.get("architectures", [])
-    ):
+    if any("SequenceClassification" in arch for arch in config.get("architectures", [])):
         id2label = config.get("id2label", {"0": "dummy"})
         if len(id2label) < 2:
             return RerankEngine.from_inference_engine(engine_args.engine)
@@ -81,9 +77,7 @@ def select_model(
         # size one, warm up warm start timings.
         loaded_engine.warmup(batch_size=engine_args.batch_size, n_tokens=1)
         # size one token
-        min_inference_t = min(
-            loaded_engine.warmup(batch_size=1, n_tokens=1)[1] for _ in range(10)
-        )
+        min_inference_t = min(loaded_engine.warmup(batch_size=1, n_tokens=1)[1] for _ in range(10))
         loaded_engine.warmup(batch_size=engine_args.batch_size, n_tokens=1)
         emb_per_sec_short, max_inference_t, log_msg = loaded_engine.warmup(
             batch_size=engine_args.batch_size, n_tokens=1
diff --git a/libs/infinity_emb/infinity_emb/infinity_server.py b/libs/infinity_emb/infinity_emb/infinity_server.py
index 044e44f4..10ed64d4 100644
--- a/libs/infinity_emb/infinity_emb/infinity_server.py
+++ b/libs/infinity_emb/infinity_emb/infinity_server.py
@@ -112,10 +112,7 @@ async def kill_later(seconds: int):
                 await asyncio.sleep(seconds)
                 os.kill(os.getpid(), signal.SIGINT)
 
-            logger.info(
-                f"Preloaded configuration successfully. {engine_args_list} "
-                " -> exit ."
-            )
+            logger.info(f"Preloaded configuration successfully. {engine_args_list} " " -> exit .")
             asyncio.create_task(kill_later(3))
 
         yield
@@ -235,7 +232,7 @@ def _resolve_engine(model: str) -> "AsyncEmbeddingEngine":
         return engine
 
     def _resolve_mixed_input(
-        inputs: Union[DataURIorURL, list[DataURIorURL]]
+        inputs: Union[DataURIorURL, list[DataURIorURL]],
     ) -> list[Union[str, bytes]]:
         if hasattr(inputs, "host"):
             # if it is a single url
@@ -245,7 +242,8 @@ def _resolve_mixed_input(
         else:
             # is list, resolve to bytes or url
             urls_or_bytes: list[Union[str, bytes]] = [  # type: ignore
-                str(d) if hasattr(d, "host") else d.data for d in inputs  # type: ignore
+                str(d) if hasattr(d, "host") else d.data
+                for d in inputs  # type: ignore
             ]
         return urls_or_bytes
 
@@ -650,10 +648,7 @@ def typer_option_resolve(*args):
             if hasattr(args[0], "default") and hasattr(args[0], "envvar")
             else args[0]
         )
-    return (
-        a.default if (hasattr(a, "default") and hasattr(a, "envvar")) else a
-        for a in args
-    )
+    return (a.default if (hasattr(a, "default") and hasattr(a, "envvar")) else a for a in args)
 
 
 # CLI
@@ -694,9 +689,7 @@ def v1(
     ):
         """Infinity API ♾️  cli v1 - deprecated, consider use cli v2 via `infinity_emb v2`."""
         if api_key:
-            raise ValueError(
-                "api_key is not supported in `v1`. Please migrate to `v2`."
-            )
+            raise ValueError("api_key is not supported in `v1`. Please migrate to `v2`.")
         if not (
             embedding_dtype == EmbeddingDtype.float32
             or embedding_dtype == EmbeddingDtype.default_value()
@@ -820,12 +813,8 @@ def v2(
             **_construct("preload_only"),
             help="If true, only downloads models and verifies setup, then exit. Recommended for pre-caching the download in a Dockerfile.",
         ),
-        host: str = typer.Option(
-            **_construct("host"), help="host for the FastAPI uvicorn server"
-        ),
-        port: int = typer.Option(
-            **_construct("port"), help="port for the FastAPI uvicorn server"
-        ),
+        host: str = typer.Option(**_construct("host"), help="host for the FastAPI uvicorn server"),
+        port: int = typer.Option(**_construct("port"), help="port for the FastAPI uvicorn server"),
         url_prefix: str = typer.Option(
             **_construct("url_prefix"),
             callback=validate_url,
@@ -834,7 +823,9 @@ def v2(
         redirect_slash: str = typer.Option(
             **_construct("redirect_slash"), help="where to redirect `/` requests to."
         ),
-        log_level: UVICORN_LOG_LEVELS = typer.Option(**_construct("log_level"), help="console log level."),  # type: ignore
+        log_level: UVICORN_LOG_LEVELS = typer.Option(
+            **_construct("log_level"), help="console log level."
+        ),  # type: ignore
         permissive_cors: bool = typer.Option(
             **_construct("permissive_cors"), help="whether to allow permissive cors."
         ),
diff --git a/libs/infinity_emb/infinity_emb/primitives.py b/libs/infinity_emb/infinity_emb/primitives.py
index fa0d8e3a..66bcf512 100644
--- a/libs/infinity_emb/infinity_emb/primitives.py
+++ b/libs/infinity_emb/infinity_emb/primitives.py
@@ -76,9 +76,7 @@ def names_enum(cls) -> enum.Enum:
 
         Allows for type hinting of the enum names.
         """
-        return enum.Enum(
-            cls.__name__ + "__names", {k: k for k in cls.__members__.keys()}
-        )
+        return enum.Enum(cls.__name__ + "__names", {k: k for k in cls.__members__.keys()})
 
     @staticmethod
     def default_value() -> str:
@@ -372,9 +370,7 @@ async def get_result(self) -> EmbeddingReturnType:
         return self.embedding
 
 
-QueueItemInner = Union[
-    EmbeddingInner, ReRankInner, PredictInner, ImageInner, AudioInner
-]
+QueueItemInner = Union[EmbeddingInner, ReRankInner, PredictInner, ImageInner, AudioInner]
 
 _type_to_inner_item_map = {
     EmbeddingSingle: EmbeddingInner,
diff --git a/libs/infinity_emb/infinity_emb/sync_engine.py b/libs/infinity_emb/infinity_emb/sync_engine.py
index ec1b1852..ecafcc02 100644
--- a/libs/infinity_emb/infinity_emb/sync_engine.py
+++ b/libs/infinity_emb/infinity_emb/sync_engine.py
@@ -93,7 +93,7 @@ def async_run(
         self,
         async_function: Callable[..., Awaitable[T]],
         *funcion_args,
-        **function_kwargs
+        **function_kwargs,
     ) -> Future[T]:
         """run an async function in the background event loop.
 
@@ -122,7 +122,7 @@ def async_run(
         self,
         async_function: Callable[..., Awaitable[T]],
         *funcion_args,
-        **function_kwargs
+        **function_kwargs,
     ) -> Future[T]:
         """run an async function in the background event loop.
 
@@ -134,9 +134,7 @@ def async_run(
         Returns:
             concurrent.futures.Future returning the result of async_function.
         """
-        return self.__asynlifemixin.async_run(
-            async_function, *funcion_args, **function_kwargs
-        )
+        return self.__asynlifemixin.async_run(async_function, *funcion_args, **function_kwargs)
 
 
 @add_start_docstrings(AsyncEngineArray.__doc__)
@@ -177,9 +175,7 @@ def stop(self):
     @add_start_docstrings(AsyncEngineArray.embed.__doc__)
     def embed(self, *, model: str, sentences: list[str]):
         """sync interface of AsyncEngineArray"""
-        return self.async_run(
-            self.async_engine_array.embed, model=model, sentences=sentences
-        )
+        return self.async_run(self.async_engine_array.embed, model=model, sentences=sentences)
 
     @add_start_docstrings(AsyncEngineArray.rerank.__doc__)
     def rerank(
@@ -189,7 +185,7 @@ def rerank(
         query: str,
         docs: list[str],
         raw_scores: bool = False,
-        top_n: Optional[int] = None
+        top_n: Optional[int] = None,
     ):
         """sync interface of AsyncEngineArray"""
         return self.async_run(
@@ -214,16 +210,12 @@ def classify(self, *, model: str, sentences: list[str], raw_scores: bool = False
     @add_start_docstrings(AsyncEngineArray.image_embed.__doc__)
     def image_embed(self, *, model: str, images: list[Union[str, ImageClassType]]):
         """sync interface of AsyncEngineArray"""
-        return self.async_run(
-            self.async_engine_array.image_embed, model=model, images=images
-        )
+        return self.async_run(self.async_engine_array.image_embed, model=model, images=images)
 
     @add_start_docstrings(AsyncEngineArray.audio_embed.__doc__)
     def audio_embed(self, *, model: str, audios: list[npt.NDArray]):
         """sync interface of AsyncEngineArray"""
-        return self.async_run(
-            self.async_engine_array.audio_embed, model=model, audios=audios
-        )
+        return self.async_run(self.async_engine_array.audio_embed, model=model, audios=audios)
 
     def __del__(self):
         self.stop()
diff --git a/libs/infinity_emb/infinity_emb/telemetry.py b/libs/infinity_emb/infinity_emb/telemetry.py
index 5112b14b..354927db 100644
--- a/libs/infinity_emb/infinity_emb/telemetry.py
+++ b/libs/infinity_emb/infinity_emb/telemetry.py
@@ -28,24 +28,19 @@
 def telemetry_log_info():
     if TELEMETRY_ENABLED:
         logger.info(
-            "Anonymized telemetry can be disabled "
-            "via environment variable `DO_NOT_TRACK=1`."
+            "Anonymized telemetry can be disabled " "via environment variable `DO_NOT_TRACK=1`."
         )
     else:
-        return logger.info(
-            "DO_NOT_TRACK=1 registered. Anonymized usage statistics are disabled."
-        )
+        return logger.info("DO_NOT_TRACK=1 registered. Anonymized usage statistics are disabled.")
 
 
 @dataclass
 class ProductTelemetryEvent:
     @abstractmethod
-    def render(self) -> dict[str, Any]:
-        ...
+    def render(self) -> dict[str, Any]: ...
 
     @abstractmethod
-    def name(self) -> str:
-        ...
+    def name(self) -> str: ...
 
 
 @cache
@@ -84,9 +79,7 @@ def get_system_properties():
             device_property = torch.cuda.get_device_properties(0)
             gpu_count = torch.cuda.device_count()
             gpu_type = str(device_property.name)
-            gpu_memory_per_device_mb = (
-                int(device_property.total_memory) * 1000000 / 1024**2
-            )
+            gpu_memory_per_device_mb = int(device_property.total_memory) * 1000000 / 1024**2
 
     return {
         "gpu_count": gpu_count,
diff --git a/libs/infinity_emb/infinity_emb/transformer/abstract.py b/libs/infinity_emb/infinity_emb/transformer/abstract.py
index c1ee4fe5..846f12b1 100644
--- a/libs/infinity_emb/infinity_emb/transformer/abstract.py
+++ b/libs/infinity_emb/infinity_emb/transformer/abstract.py
@@ -81,9 +81,7 @@ def encode_pre(self, sentences: list[Union[str, Any]]) -> INPUT_FEATURE:
         """takes care of the tokenization and feature preparation"""
 
     @abstractmethod
-    def encode_post(
-        self, embedding: OUT_FEATURES, skip_quanitzation=True
-    ) -> EmbeddingReturnType:
+    def encode_post(self, embedding: OUT_FEATURES, skip_quanitzation=True) -> EmbeddingReturnType:
         """runs post encoding such as normalization"""
 
     def warmup(self, *, batch_size: int = 64, n_tokens=1) -> tuple[float, float, str]:
@@ -104,18 +102,14 @@ def embedding_dtype(self) -> EmbeddingDtype:
         return self.engine_args.embedding_dtype
 
     @abstractmethod  # Decorator to define an abstract method
-    def encode_pre(
-        self, sentences_or_images: list[Union[str, "ImageClass"]]
-    ) -> INPUT_FEATURE:
+    def encode_pre(self, sentences_or_images: list[Union[str, "ImageClass"]]) -> INPUT_FEATURE:
         """
         takes a list of sentences, or a list of images.
         Images could be url or numpy arrays/pil
         """
 
     @abstractmethod
-    def encode_post(
-        self, embedding: OUT_FEATURES, skip_quanitzation=True
-    ) -> EmbeddingReturnType:
+    def encode_post(self, embedding: OUT_FEATURES, skip_quanitzation=True) -> EmbeddingReturnType:
         """runs post encoding such as normalization"""
 
     def warmup(self, *, batch_size: int = 64, n_tokens=1) -> tuple[float, float, str]:
@@ -127,7 +121,8 @@ def warmup(self, *, batch_size: int = 64, n_tokens=1) -> tuple[float, float, str
             for img in sample_image
         ] + [
             EmbeddingInner(
-                content=EmbeddingSingle(sentence=s), future=None  # type: ignore
+                content=EmbeddingSingle(sentence=s),
+                future=None,  # type: ignore
             )
             for s in sample_text
         ]
@@ -149,33 +144,33 @@ def sampling_rate(self) -> int:
         raise NotImplementedError
 
     @abstractmethod  # Decorator to define an abstract method
-    def encode_pre(
-        self, sentences_or_audios: list[Union[str, AudioInputType]]
-    ) -> INPUT_FEATURE:
+    def encode_pre(self, sentences_or_audios: list[Union[str, AudioInputType]]) -> INPUT_FEATURE:
         """
         takes a list of sentences, or a list of audios.
         Audios could be raw byte array of the wave file
         """
 
     @abstractmethod
-    def encode_post(
-        self, embedding: OUT_FEATURES, skip_quanitzation=True
-    ) -> EmbeddingReturnType:
+    def encode_post(self, embedding: OUT_FEATURES, skip_quanitzation=True) -> EmbeddingReturnType:
         """runs post encoding such as normalization"""
 
     def warmup(self, *, batch_size: int = 64, n_tokens=1) -> tuple[float, float, str]:
         sample_text = ["warm " * n_tokens] * max(1, batch_size // 2)
         # sample_audios = [sf.SoundFile()] * max(1, batch_size // 2)  # type: ignore
-        inp: list[Union[AudioInner, EmbeddingInner]] = [
-            # TODO: warmup for audio
-            # AudioInner(content=AudioSingle(audio=audio), future=None)  # type: ignore
-            # for audio in sample_audios
-        ] + [
-            EmbeddingInner(
-                content=EmbeddingSingle(sentence=s), future=None  # type: ignore
-            )
-            for s in sample_text
-        ]
+        inp: list[Union[AudioInner, EmbeddingInner]] = (
+            [
+                # TODO: warmup for audio
+                # AudioInner(content=AudioSingle(audio=audio), future=None)  # type: ignore
+                # for audio in sample_audios
+            ]
+            + [
+                EmbeddingInner(
+                    content=EmbeddingSingle(sentence=s),
+                    future=None,  # type: ignore
+                )
+                for s in sample_text
+            ]
+        )
         random.shuffle(inp)
 
         return run_warmup(self, inp)
@@ -217,7 +212,8 @@ def warmup(self, *, batch_size: int = 64, n_tokens=1) -> tuple[float, float, str
         sample = ["warm " * n_tokens] * batch_size
         inp = [
             ReRankInner(
-                content=ReRankSingle(query=s, document=s), future=None  # type: ignore
+                content=ReRankSingle(query=s, document=s),
+                future=None,  # type: ignore
             )
             for s in sample
         ]
diff --git a/libs/infinity_emb/infinity_emb/transformer/acceleration.py b/libs/infinity_emb/infinity_emb/transformer/acceleration.py
index cd1bfd14..e643f741 100644
--- a/libs/infinity_emb/infinity_emb/transformer/acceleration.py
+++ b/libs/infinity_emb/infinity_emb/transformer/acceleration.py
@@ -28,9 +28,7 @@
     from infinity_emb.args import EngineArgs
 
 
-def to_bettertransformer(
-    model: "PreTrainedModel", engine_args: "EngineArgs", logger: "Logger"
-):
+def to_bettertransformer(model: "PreTrainedModel", engine_args: "EngineArgs", logger: "Logger"):
     if not engine_args.bettertransformer:
         return model
 
diff --git a/libs/infinity_emb/infinity_emb/transformer/audio/torch.py b/libs/infinity_emb/infinity_emb/transformer/audio/torch.py
index 311cc8fd..7f5dbe26 100644
--- a/libs/infinity_emb/infinity_emb/transformer/audio/torch.py
+++ b/libs/infinity_emb/infinity_emb/transformer/audio/torch.py
@@ -46,9 +46,7 @@ def __init__(self, *, engine_args: EngineArgs):
         self.engine_args = engine_args
 
         if engine_args.compile:
-            self.model.vision_model = torch.compile(
-                self.model.vision_model, dynamic=True
-            )
+            self.model.vision_model = torch.compile(self.model.vision_model, dynamic=True)
             self.model.text_model = torch.compile(self.model.text_model, dynamic=True)
 
         assert hasattr(
@@ -136,8 +134,7 @@ def encode_post(self, out_features) -> list[float]:
         audio_embeds = self._normalize_cpu(audio_embeds)
 
         embeddings = list(
-            next(audio_embeds if is_audio else text_embeds)
-            for is_audio in type_is_audio
+            next(audio_embeds if is_audio else text_embeds) for is_audio in type_is_audio
         )
 
         return embeddings
diff --git a/libs/infinity_emb/infinity_emb/transformer/audio/utils.py b/libs/infinity_emb/infinity_emb/transformer/audio/utils.py
index 6dac01d6..4893df2a 100644
--- a/libs/infinity_emb/infinity_emb/transformer/audio/utils.py
+++ b/libs/infinity_emb/infinity_emb/transformer/audio/utils.py
@@ -31,9 +31,7 @@ async def resolve_audio(
             #
             audio_bytes = io.BytesIO(downloaded)
         except Exception as e:
-            raise AudioCorruption(
-                f"Error downloading audio from {audio}. \nError msg: {str(e)}"
-            )
+            raise AudioCorruption(f"Error downloading audio from {audio}. \nError msg: {str(e)}")
 
     try:
         data, rate = sf.read(audio_bytes)
@@ -57,10 +55,7 @@ async def resolve_audios(
     async with aiohttp.ClientSession(trust_env=True) as session:
         try:
             resolved_audios = await asyncio.gather(
-                *[
-                    resolve_audio(audio, allowed_sampling_rate, session)
-                    for audio in audio_urls
-                ]
+                *[resolve_audio(audio, allowed_sampling_rate, session) for audio in audio_urls]
             )
         except Exception as e:
             raise AudioCorruption(f"Failed to resolve audio: {e}")
diff --git a/libs/infinity_emb/infinity_emb/transformer/crossencoder/optimum.py b/libs/infinity_emb/infinity_emb/transformer/crossencoder/optimum.py
index 54a3c50c..1326b4ec 100644
--- a/libs/infinity_emb/infinity_emb/transformer/crossencoder/optimum.py
+++ b/libs/infinity_emb/infinity_emb/transformer/crossencoder/optimum.py
@@ -84,9 +84,7 @@ def encode_post(self, out_features: np.ndarray) -> list[float]:
 
     def tokenize_lengths(self, sentences: list[str]) -> list[int]:
         if hasattr(self._infinity_tokenizer, "encode_batch"):
-            tks = self._infinity_tokenizer.encode_batch(
-                sentences, padding=False, truncation=True
-            )
+            tks = self._infinity_tokenizer.encode_batch(sentences, padding=False, truncation=True)
         else:
             tks = self._infinity_tokenizer(sentences, padding=False, truncation=True)
 
diff --git a/libs/infinity_emb/infinity_emb/transformer/embedder/ct2.py b/libs/infinity_emb/infinity_emb/transformer/embedder/ct2.py
index c115d1bc..7ae7f1e3 100644
--- a/libs/infinity_emb/infinity_emb/transformer/embedder/ct2.py
+++ b/libs/infinity_emb/infinity_emb/transformer/embedder/ct2.py
@@ -85,9 +85,7 @@ def __init__(
     def device(self):
         if self._prefered_device is not None:
             return torch.device(self._prefered_device)
-        return (
-            torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-        )
+        return torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
 
 
 class CT2Transformer(Module):
@@ -112,9 +110,7 @@ def __init__(
         CHECK_CTRANSLATE2.mark_required()
         super().__init__()
 
-        logger.warning(
-            "deprecated: ct2 inference is deprecated and will be removed in the future."
-        )
+        logger.warning("deprecated: ct2 inference is deprecated and will be removed in the future.")
 
         self.tokenizer = transformer.tokenizer
         self._tokenize = transformer.tokenize
@@ -130,9 +126,7 @@ def __init__(
         )
 
         if not os.path.exists(os.path.join(self.ct2_model_dir, "model.bin")) or force:
-            if os.path.exists(self.ct2_model_dir) and not os.listdir(
-                self.ct2_model_dir
-            ):
+            if os.path.exists(self.ct2_model_dir) and not os.listdir(self.ct2_model_dir):
                 force = True
             converter = ctranslate2.converters.TransformersConverter(model_dir)
             converter.convert(self.ct2_model_dir, force=force, vmap=vmap)
@@ -177,9 +171,9 @@ def forward(self, features):
         if device.type == "cpu":
             last_hidden_state = np.array(last_hidden_state)
 
-        features["token_embeddings"] = torch.as_tensor(
-            last_hidden_state, device=device
-        ).to(torch.float32)
+        features["token_embeddings"] = torch.as_tensor(last_hidden_state, device=device).to(
+            torch.float32
+        )
 
         return features
 
diff --git a/libs/infinity_emb/infinity_emb/transformer/embedder/neuron.py b/libs/infinity_emb/infinity_emb/transformer/embedder/neuron.py
index 8b558e56..240e27ae 100644
--- a/libs/infinity_emb/infinity_emb/transformer/embedder/neuron.py
+++ b/libs/infinity_emb/infinity_emb/transformer/embedder/neuron.py
@@ -81,9 +81,7 @@ def __init__(self, *, engine_args: EngineArgs):
         CHECK_OPTIMUM_NEURON.mark_required()
 
         self.pooling = (
-            mean_pooling
-            if engine_args.pooling_method == PoolingMethod.mean
-            else cls_token_pooling
+            mean_pooling if engine_args.pooling_method == PoolingMethod.mean else cls_token_pooling
         )
 
         self.tokenizer = AutoTokenizer.from_pretrained(
@@ -157,8 +155,6 @@ def tokenize_lengths(self, sentences: list[str]) -> list[int]:
                 truncation="longest_first",
             )
         else:
-            tks = self._infinity_tokenizer(
-                sentences, padding=False, truncation="longest_first"
-            )
+            tks = self._infinity_tokenizer(sentences, padding=False, truncation="longest_first")
 
         return [len(t) for t in tks["input_ids"]]
diff --git a/libs/infinity_emb/infinity_emb/transformer/embedder/optimum.py b/libs/infinity_emb/infinity_emb/transformer/embedder/optimum.py
index 615a9e67..e32639de 100644
--- a/libs/infinity_emb/infinity_emb/transformer/embedder/optimum.py
+++ b/libs/infinity_emb/infinity_emb/transformer/embedder/optimum.py
@@ -46,9 +46,7 @@ def __init__(self, *, engine_args: EngineArgs):
         )
 
         self.pooling = (
-            mean_pooling
-            if engine_args.pooling_method == PoolingMethod.mean
-            else cls_token_pooling
+            mean_pooling if engine_args.pooling_method == PoolingMethod.mean else cls_token_pooling
         )
 
         self.model = optimize_model(
@@ -112,8 +110,6 @@ def tokenize_lengths(self, sentences: list[str]) -> list[int]:
                 truncation="longest_first",
             )
         else:
-            tks = self._infinity_tokenizer(
-                sentences, padding=False, truncation="longest_first"
-            )
+            tks = self._infinity_tokenizer(sentences, padding=False, truncation="longest_first")
 
         return [len(t) for t in tks["input_ids"]]
diff --git a/libs/infinity_emb/infinity_emb/transformer/quantization/interface.py b/libs/infinity_emb/infinity_emb/transformer/quantization/interface.py
index 7b257c50..2c5c46d5 100644
--- a/libs/infinity_emb/infinity_emb/transformer/quantization/interface.py
+++ b/libs/infinity_emb/infinity_emb/transformer/quantization/interface.py
@@ -65,9 +65,7 @@ def quant_interface(model: Any, dtype: Dtype = Dtype.int8, device: Device = Devi
         logger.info("using dtype=fp8")
         swap_linear_with_float8_linear(model, Float8DynamicLinear)
     else:
-        raise ValueError(
-            f"Quantization is not supported on {device} with dtype {dtype}."
-        )
+        raise ValueError(f"Quantization is not supported on {device} with dtype {dtype}.")
     return model
 
 
@@ -115,14 +113,10 @@ def _encode(model, dataset, batch_size=8):
         )
     dataset = _get_text_calibration_dataset()
 
-    logger.info(
-        f"Creating calibration dataset for model using {len(dataset)} sentences."
-    )
+    logger.info(f"Creating calibration dataset for model using {len(dataset)} sentences.")
 
     calibration_embeddings = np.concatenate(list(_encode(model, dataset)))
-    assert (
-        percentile > 50 and percentile <= 100
-    ), "percentile should be between 50 and 100"
+    assert percentile > 50 and percentile <= 100, "percentile should be between 50 and 100"
     return np.percentile(calibration_embeddings, [100 - percentile, percentile], axis=0)
 
 
diff --git a/libs/infinity_emb/infinity_emb/transformer/quantization/quant.py b/libs/infinity_emb/infinity_emb/transformer/quantization/quant.py
index adee2f07..9574dd5a 100644
--- a/libs/infinity_emb/infinity_emb/transformer/quantization/quant.py
+++ b/libs/infinity_emb/infinity_emb/transformer/quantization/quant.py
@@ -102,9 +102,9 @@ def get_group_qparams(w, n_bit=4, groupsize=128):
     max_int = 2**n_bit - 1
     scales = (max_val - min_val).clamp(min=1e-6) / max_int
     zeros = min_val + scales * (2 ** (n_bit - 1))
-    return scales.to(torch.bfloat16).reshape(w.shape[0], -1), zeros.to(
-        torch.bfloat16
-    ).reshape(w.shape[0], -1)
+    return scales.to(torch.bfloat16).reshape(w.shape[0], -1), zeros.to(torch.bfloat16).reshape(
+        w.shape[0], -1
+    )
 
 
 def pack_scales_and_zeros(scales, zeros):
@@ -166,9 +166,7 @@ def group_quantize_tensor(w, n_bit=4, groupsize=128):
     return w_int32, scales_and_zeros
 
 
-def group_dequantize_tensor_from_qparams(
-    w_int32, scales, zeros, n_bit=4, groupsize=128
-):
+def group_dequantize_tensor_from_qparams(w_int32, scales, zeros, n_bit=4, groupsize=128):
     assert groupsize > 1
     # needed for GPTQ single column dequantize
     if groupsize > w_int32.shape[-1] and scales.shape[-1] == 1:
@@ -180,17 +178,13 @@ def group_dequantize_tensor_from_qparams(
     scales = scales.reshape(-1, 1)
     zeros = zeros.reshape(-1, 1)
 
-    w_dq = (
-        w_int32_grouped.sub(2 ** (n_bit - 1)).mul(scales).add(zeros).reshape_as(w_int32)
-    )
+    w_dq = w_int32_grouped.sub(2 ** (n_bit - 1)).mul(scales).add(zeros).reshape_as(w_int32)
     return w_dq
 
 
 def group_dequantize_tensor(w_int32, scales_and_zeros, n_bit=4, groupsize=128):
     scales, zeros = unpack_scales_and_zeros(scales_and_zeros)
-    return group_dequantize_tensor_from_qparams(
-        w_int32, scales, zeros, n_bit, groupsize
-    )
+    return group_dequantize_tensor_from_qparams(w_int32, scales, zeros, n_bit, groupsize)
 
 
 class QuantHandler(abc.ABC):
@@ -384,9 +378,7 @@ def create_quantized_state_dict(self):
                         mod.weight.float(), -128, 127, torch.int8
                     )
                     cur_state_dict[f"{fqn}.weight"] = int8_weight.to("cpu")
-                    cur_state_dict[f"{fqn}.scales"] = scales.to(mod.weight.dtype).to(
-                        "cpu"
-                    )
+                    cur_state_dict[f"{fqn}.scales"] = scales.to(mod.weight.dtype).to("cpu")
                     if mod.bias is not None:
                         cur_state_dict[f"{fqn}.bias"] = mod.bias.to("cpu")
 
@@ -416,17 +408,15 @@ def __init__(
         super().__init__()
         self.in_features = in_features
         self.out_features = out_features
-        self.register_buffer(
-            "weight", torch.empty((out_features, in_features), dtype=torch.int8)
-        )
+        self.register_buffer("weight", torch.empty((out_features, in_features), dtype=torch.int8))
         self.register_buffer("scales", torch.ones(out_features, dtype=torch.bfloat16))
         # initialize bias to zero, in case the original model has no bias
         self.register_buffer("bias", torch.zeros((out_features), dtype=torch.bfloat16))
 
     def forward(self, input: Tensor) -> Tensor:
-        return F.linear(
-            input, self.weight.to(dtype=input.dtype)
-        ) * self.scales + self.bias.to(dtype=input.dtype)
+        return F.linear(input, self.weight.to(dtype=input.dtype)) * self.scales + self.bias.to(
+            dtype=input.dtype
+        )
 
 
 ##### weight only int4 per channel groupwise quantized code ######
@@ -436,18 +426,14 @@ def prepare_int4_weight_and_scales_and_zeros(weight_bf16, groupsize, inner_k_til
     weight_int32, scales_and_zeros = group_quantize_tensor(
         weight_bf16, n_bit=4, groupsize=groupsize
     )
-    weight_int4pack = torch.ops.aten._convert_weight_to_int4pack(
-        weight_int32, inner_k_tiles
-    )
+    weight_int4pack = torch.ops.aten._convert_weight_to_int4pack(weight_int32, inner_k_tiles)
     return weight_int4pack, scales_and_zeros
 
 
 def linear_forward_int4(x, weight_int4pack, scales_and_zeros, out_features, groupsize):
     origin_x_size = x.size()
     x = x.reshape(-1, origin_x_size[-1])
-    c = torch.ops.aten._weight_int4pack_mm(
-        x, weight_int4pack, groupsize, scales_and_zeros
-    )
+    c = torch.ops.aten._weight_int4pack_mm(x, weight_int4pack, groupsize, scales_and_zeros)
     new_shape = origin_x_size[:-1] + (out_features,)
     c = c.reshape(new_shape)
     return c
@@ -513,17 +499,13 @@ def create_quantized_state_dict(self):
                     logger.info(f"linear: {fqn}, in={in_features}, out={out_features}")
 
                     weight = mod.weight.data
-                    if not _check_linear_int4_k(
-                        in_features, self.groupsize, self.inner_k_tiles
-                    ):
+                    if not _check_linear_int4_k(in_features, self.groupsize, self.inner_k_tiles):
                         if self.padding:
                             logger.info(
                                 f"warning: {fqn} is padded to satisfy in_features % 1024 == 0"
                             )
                             padded_in_features = find_multiple(in_features, 1024)
-                            weight = F.pad(
-                                weight, pad=(0, padded_in_features - in_features)
-                            )
+                            weight = F.pad(weight, pad=(0, padded_in_features - in_features))
                         else:
                             logger.info(
                                 f"warning: {fqn} is skipped, int4 requires that in_features is 32, 64, or is divisible by 1024, "
@@ -537,16 +519,12 @@ def create_quantized_state_dict(self):
                         weight.to(torch.bfloat16), self.groupsize, self.inner_k_tiles
                     )
                     cur_state_dict[f"{fqn}.weight"] = weight_int4pack.to("cpu")
-                    cur_state_dict[f"{fqn}.scales_and_zeros"] = scales_and_zeros.to(
-                        "cpu"
-                    )
+                    cur_state_dict[f"{fqn}.scales_and_zeros"] = scales_and_zeros.to("cpu")
 
             return cur_state_dict
 
     def convert_for_runtime(self, use_cuda):
-        replace_linear_int4(
-            self.mod, self.groupsize, self.inner_k_tiles, self.padding, use_cuda
-        )
+        replace_linear_int4(self.mod, self.groupsize, self.inner_k_tiles, self.padding, use_cuda)
         return self.mod
 
 
@@ -568,8 +546,7 @@ def __init__(self, mod, groupsize=128, inner_k_tiles=8, padding=True):
         ]
         # skip unless padding=True or its correctly sized
         self.skip_layer_func = lambda linear_weight: not (
-            _check_linear_int4_k(linear_weight.shape[-1], groupsize, inner_k_tiles)
-            or padding
+            _check_linear_int4_k(linear_weight.shape[-1], groupsize, inner_k_tiles) or padding
         )
 
         # we need to do the padding here, both for q and the qparams if necessary
@@ -584,18 +561,14 @@ def make_names_and_values_dict_func(q, qparams):
             scales_and_zeros = pack_scales_and_zeros(*qparams)
             # how many new groups we need for padded weight
             delta_groups = new_k // groupsize - scales_and_zeros.shape[0]
-            final_s_and_z = F.pad(
-                scales_and_zeros, pad=(0, 0, 0, 0, 0, delta_groups), value=1
-            )
+            final_s_and_z = F.pad(scales_and_zeros, pad=(0, 0, 0, 0, 0, delta_groups), value=1)
             return {"weight": final_q, "scales_and_zeros": final_s_and_z}
 
         self.make_names_and_values_dict_func = make_names_and_values_dict_func
         super().__init__()
 
     def convert_for_runtime(self, use_cuda):
-        replace_linear_int4(
-            self.mod, self.groupsize, self.inner_k_tiles, self.padding, use_cuda
-        )
+        replace_linear_int4(self.mod, self.groupsize, self.inner_k_tiles, self.padding, use_cuda)
         return self.mod
 
 
@@ -653,9 +626,7 @@ def __init__(
             )
         self.register_buffer(
             "scales_and_zeros",
-            torch.empty(
-                (in_features // groupsize, out_features, 2), dtype=torch.bfloat16
-            ),
+            torch.empty((in_features // groupsize, out_features, 2), dtype=torch.bfloat16),
         )
 
     def forward(self, input: Tensor) -> Tensor:
@@ -721,9 +692,7 @@ def quantize(
 
         dir_name = checkpoint_path.parent
         base_name = checkpoint_path.name
-        new_base_name = base_name.replace(
-            ".pth", f"{label}int4.g{groupsize}.{device}.pth"
-        )
+        new_base_name = base_name.replace(".pth", f"{label}int4.g{groupsize}.{device}.pth")
 
     elif mode == "int4-gptq":
         from sentencepiece import SentencePieceProcessor  # type: ignore[import-untyped]
@@ -759,9 +728,7 @@ def quantize(
     if checkpoint_path:
         quantize_path = dir_name / new_base_name
         logger.info(f"Writing quantized weights to {quantize_path}")
-        quantize_path.unlink(
-            missing_ok=True
-        )  # remove existing file if one already there
+        quantize_path.unlink(missing_ok=True)  # remove existing file if one already there
         torch.save(quantized_state_dict, quantize_path)
     logger.info(f"Quantization complete took {time.time() - t0:.02f} seconds")
     return quant_handler, quantized_state_dict
diff --git a/libs/infinity_emb/infinity_emb/transformer/utils_optimum.py b/libs/infinity_emb/infinity_emb/transformer/utils_optimum.py
index 23891c4f..aa5bc712 100644
--- a/libs/infinity_emb/infinity_emb/transformer/utils_optimum.py
+++ b/libs/infinity_emb/infinity_emb/transformer/utils_optimum.py
@@ -30,9 +30,7 @@
 def mean_pooling(last_hidden_states: np.ndarray, attention_mask: np.ndarray):
     input_mask_expanded = (np.expand_dims(attention_mask, axis=-1)).astype(float)
 
-    sum_embeddings = np.sum(
-        last_hidden_states.astype(float) * input_mask_expanded, axis=1
-    )
+    sum_embeddings = np.sum(last_hidden_states.astype(float) * input_mask_expanded, axis=1)
     mask_sum = np.maximum(np.sum(input_mask_expanded, axis=1), 1e-9)
 
     return sum_embeddings / mask_sum
@@ -91,10 +89,7 @@ def optimize_model(
     """
     CHECK_ONNXRUNTIME.mark_required()
     path_folder = (
-        Path(HUGGINGFACE_HUB_CACHE)
-        / "infinity_onnx"
-        / execution_provider
-        / model_name_or_path
+        Path(HUGGINGFACE_HUB_CACHE) / "infinity_onnx" / execution_provider / model_name_or_path
     )
     OPTIMIZED_SUFFIX = "_optimized.onnx"
     files_optimized = list(path_folder.glob(f"**/*{OPTIMIZED_SUFFIX}"))
@@ -165,9 +160,7 @@ def optimize_model(
             file_name=Path(file_name).name.replace(".onnx", OPTIMIZED_SUFFIX),
         )
     except Exception as e:
-        logger.warning(
-            f"Optimization failed with {e}. Going to use the unoptimized model."
-        )
+        logger.warning(f"Optimization failed with {e}. Going to use the unoptimized model.")
         model = unoptimized_model
 
     return model
@@ -186,9 +179,7 @@ def _list_all_repo_files(
         return list(
             map(
                 Path,
-                HfApi().list_repo_files(
-                    model_name_or_path, revision=revision, token=token
-                ),
+                HfApi().list_repo_files(model_name_or_path, revision=revision, token=token),
             )
         )
     else:
@@ -223,6 +214,4 @@ def get_onnx_files(
     elif len(onnx_files) == 1:
         return onnx_files[0]
     else:
-        raise ValueError(
-            f"No onnx files found for {model_name_or_path} and revision {revision}"
-        )
+        raise ValueError(f"No onnx files found for {model_name_or_path} and revision {revision}")
diff --git a/libs/infinity_emb/infinity_emb/transformer/vision/torch_vision.py b/libs/infinity_emb/infinity_emb/transformer/vision/torch_vision.py
index 658f78f8..886d8930 100644
--- a/libs/infinity_emb/infinity_emb/transformer/vision/torch_vision.py
+++ b/libs/infinity_emb/infinity_emb/transformer/vision/torch_vision.py
@@ -114,12 +114,8 @@ def __init__(self, *, engine_args: "EngineArgs"):
             if self.is_colipali:
                 self.model = torch.compile(self.model, dynamic=True)
             else:
-                self.model.vision_model = torch.compile(
-                    self.model.vision_model, dynamic=True
-                )
-                self.model.text_model = torch.compile(
-                    self.model.text_model, dynamic=True
-                )
+                self.model.vision_model = torch.compile(self.model.vision_model, dynamic=True)
+                self.model.text_model = torch.compile(self.model.text_model, dynamic=True)
 
         self.max_length = None
         if hasattr(self.model.config, "max_length"):
@@ -172,9 +168,7 @@ def encode_pre(self, sentences_or_images: list[Union[str, "ImageClass"]]):
 
         return (preprocessed, type_is_img)
 
-    def _normalize_cpu(
-        self, tensor: Optional["Tensor"], normalize: bool
-    ) -> Iterable["Tensor"]:
+    def _normalize_cpu(self, tensor: Optional["Tensor"], normalize: bool) -> Iterable["Tensor"]:
         if tensor is None:
             return iter([])
         tensor = tensor.to(torch.float32)
@@ -221,9 +215,7 @@ def encode_post(self, out_features) -> list[float]:
         text_embeds = self._normalize_cpu(text_embeds, normalize=not self.is_colipali)
         image_embeds = self._normalize_cpu(image_embeds, normalize=not self.is_colipali)
 
-        embeddings = list(
-            next(image_embeds if is_img else text_embeds) for is_img in type_is_img
-        )
+        embeddings = list(next(image_embeds if is_img else text_embeds) for is_img in type_is_img)
         return embeddings
 
     def tokenize_lengths(self, text_list: list[str]) -> list[int]:
diff --git a/libs/infinity_emb/infinity_emb/transformer/vision/utils.py b/libs/infinity_emb/infinity_emb/transformer/vision/utils.py
index f8efa24f..16b80c37 100644
--- a/libs/infinity_emb/infinity_emb/transformer/vision/utils.py
+++ b/libs/infinity_emb/infinity_emb/transformer/vision/utils.py
@@ -24,17 +24,13 @@ def resolve_from_img_obj(img_obj: "ImageClassType") -> ImageSingle:
     return ImageSingle(image=img_obj)
 
 
-async def resolve_from_img_url(
-    img_url: str, session: "aiohttp.ClientSession"
-) -> ImageSingle:
+async def resolve_from_img_url(img_url: str, session: "aiohttp.ClientSession") -> ImageSingle:
     """Resolve an image from an URL."""
     try:
         # requests.get(img_url, stream=True).raw
         downloaded_img = await (await session.get(img_url)).read()
     except Exception as e:
-        raise ImageCorruption(
-            f"error opening an image in your request image from url: {e}"
-        )
+        raise ImageCorruption(f"error opening an image in your request image from url: {e}")
 
     try:
         img = Image.open(io.BytesIO(downloaded_img))
@@ -70,13 +66,11 @@ async def resolve_image(
     elif isinstance(img, str):
         return await resolve_from_img_url(img, session=session)
     else:
-        raise ValueError(
-            f"Invalid image type: {img} is neither str nor ImageClassType object"
-        )
+        raise ValueError(f"Invalid image type: {img} is neither str nor ImageClassType object")
 
 
 async def resolve_images(
-    images: list[Union[str, "ImageClassType", bytes]]
+    images: list[Union[str, "ImageClassType", bytes]],
 ) -> list[ImageSingle]:
     """Resolve images from URLs or ImageClassType Objects using multithreading."""
     # TODO: improve parallel requests, safety, error handling
@@ -87,12 +81,8 @@ async def resolve_images(
 
     try:
         async with aiohttp.ClientSession(trust_env=True) as session:
-            resolved_imgs = await asyncio.gather(
-                *[resolve_image(img, session) for img in images]
-            )
+            resolved_imgs = await asyncio.gather(*[resolve_image(img, session) for img in images])
     except Exception as e:
-        raise ImageCorruption(
-            f"Failed to resolve image: {images}.\nError msg: {str(e)}"
-        )
+        raise ImageCorruption(f"Failed to resolve image: {images}.\nError msg: {str(e)}")
 
     return resolved_imgs
diff --git a/libs/infinity_emb/poetry.lock b/libs/infinity_emb/poetry.lock
index 35b56e19..96177593 100644
--- a/libs/infinity_emb/poetry.lock
+++ b/libs/infinity_emb/poetry.lock
@@ -2,13 +2,13 @@
 
 [[package]]
 name = "accelerate"
-version = "1.0.0"
+version = "1.0.1"
 description = "Accelerate"
 optional = true
 python-versions = ">=3.8.0"
 files = [
-    {file = "accelerate-1.0.0-py3-none-any.whl", hash = "sha256:6fc65cd7a43c41530e3cf8ffb1fbe3ac6e416ec3f4c9a55ddf407d374b887b68"},
-    {file = "accelerate-1.0.0.tar.gz", hash = "sha256:e39c99327d37c4583b4e6b70946915072b56f892836942eca22e1d25b71d6d4b"},
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 [package.dependencies]
@@ -44,102 +44,102 @@ files = [
 
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 ]
 
 [package.dependencies]
@@ -181,13 +181,13 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.6.0"
+version = "4.6.2.post1"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
@@ -198,7 +198,7 @@ typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
 doc = ["Sphinx (>=7.4,<8.0)", "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 (>=0.21.0b1)"]
+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", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"]
 trio = ["trio (>=0.26.1)"]
 
 [[package]]
@@ -293,33 +293,33 @@ lxml = ["lxml"]
 
 [[package]]
 name = "black"
-version = "23.12.1"
+version = "24.10.0"
 description = "The uncompromising code formatter."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
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 ]
 
 [package.dependencies]
@@ -333,7 +333,7 @@ typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
 colorama = ["colorama (>=0.4.3)"]
-d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"]
+d = ["aiohttp (>=3.10)"]
 jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
 uvloop = ["uvloop (>=0.15.2)"]
 
@@ -429,18 +429,118 @@ pycparser = "*"
 
 [[package]]
 name = "charset-normalizer"
-version = "2.1.1"
+version = "3.4.0"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
-python-versions = ">=3.6.0"
+python-versions = ">=3.7.0"
 files = [
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 ]
 
-[package.extras]
-unicode-backport = ["unicodedata2"]
-
 [[package]]
 name = "click"
 version = "8.1.7"
@@ -502,13 +602,13 @@ cron = ["capturer (>=2.4)"]
 
 [[package]]
 name = "colpali-engine"
-version = "0.3.1"
+version = "0.3.2"
 description = "The code used to train and run inference with the ColPali architecture."
 optional = true
 python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
@@ -521,89 +621,80 @@ torch = ">=2.2.0"
 transformers = ">=4.45.0"
 
 [package.extras]
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+all = ["accelerate (>=0.34.0)", "bitsandbytes", "configure (>=5.0.0)", "datasets (>=2.19.1)", "mteb (>=1.12.22,<2.0.0)", "pytest (>=8.0.0)", "ruff (>=0.4.0)", "typer (>=0.12.3,<1.0.0)"]
 dev = ["pytest (>=8.0.0)", "ruff (>=0.4.0)"]
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+interpretability = ["einops (>=0.8.0,<1.0.0)", "matplotlib (>=3.9.0,<4.0.0)", "seaborn (>=0.13.2,<1.0.0)"]
+train = ["accelerate (>=0.34.0)", "bitsandbytes", "configure (>=5.0.0)", "datasets (>=2.19.1)", "mteb (>=1.12.22,<2.0.0)", "typer (>=0.12.3,<1.0.0)"]
 
 [[package]]
 name = "coverage"
-version = "7.6.1"
+version = "7.6.3"
 description = "Code coverage measurement for Python"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
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 [package.dependencies]
@@ -614,38 +705,38 @@ toml = ["tomli"]
 
 [[package]]
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 description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
 optional = false
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 ]
 
 [package.dependencies]
@@ -658,7 +749,7 @@ nox = ["nox"]
 pep8test = ["check-sdist", "click", "mypy", "ruff"]
 sdist = ["build"]
 ssh = ["bcrypt (>=3.1.5)"]
-test = ["certifi", "cryptography-vectors (==43.0.1)", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
+test = ["certifi", "cryptography-vectors (==43.0.3)", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
 test-randomorder = ["pytest-randomly"]
 
 [[package]]
@@ -835,18 +926,18 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "fastapi"
-version = "0.115.0"
+version = "0.115.2"
 description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0 || >2.0.0,<2.0.1 || >2.0.1,<2.1.0 || >2.1.0,<3.0.0"
-starlette = ">=0.37.2,<0.39.0"
+starlette = ">=0.37.2,<0.41.0"
 typing-extensions = ">=4.8.0"
 
 [package.extras]
@@ -1133,51 +1224,58 @@ trio = ["trio (>=0.22.0,<1.0)"]
 
 [[package]]
 name = "httptools"
-version = "0.6.1"
+version = "0.6.4"
 description = "A collection of framework independent HTTP protocol utils."
 optional = true
 python-versions = ">=3.8.0"
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 ]
 
 [package.extras]
-test = ["Cython (>=0.29.24,<0.30.0)"]
+test = ["Cython (>=0.29.24)"]
 
 [[package]]
 name = "httpx"
@@ -1206,13 +1304,13 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.25.1"
+version = "0.26.0"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
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 ]
 
 [package.dependencies]
@@ -2266,13 +2369,13 @@ sympy = "*"
 
 [[package]]
 name = "openai"
-version = "1.51.2"
+version = "1.52.0"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.7.1"
 files = [
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 ]
 
 [package.dependencies]
@@ -2885,32 +2988,33 @@ files = [
 
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 name = "psutil"
-version = "6.0.0"
+version = "6.1.0"
 description = "Cross-platform lib for process and system monitoring in Python."
 optional = true
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
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 ]
 
 [package.extras]
-test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
+dev = ["black", "check-manifest", "coverage", "packaging", "pylint", "pyperf", "pypinfo", "pytest-cov", "requests", "rstcheck", "ruff", "sphinx", "sphinx_rtd_theme", "toml-sort", "twine", "virtualenv", "wheel"]
+test = ["pytest", "pytest-xdist", "setuptools"]
 
 [[package]]
 name = "pyarrow"
@@ -3132,13 +3236,13 @@ extra = ["pygments (>=2.12)"]
 
 [[package]]
 name = "pyparsing"
-version = "3.1.4"
+version = "3.2.0"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
 optional = false
-python-versions = ">=3.6.8"
+python-versions = ">=3.9"
 files = [
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 ]
 
 [package.extras]
@@ -3160,13 +3264,13 @@ dev = ["build", "flake8", "mypy", "pytest", "twine"]
 
 [[package]]
 name = "pytest"
-version = "7.4.4"
+version = "8.3.3"
 description = "pytest: simple powerful testing with Python"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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+    {file = "pytest-8.3.3.tar.gz", hash = "sha256:70b98107bd648308a7952b06e6ca9a50bc660be218d53c257cc1fc94fda10181"},
 ]
 
 [package.dependencies]
@@ -3174,11 +3278,11 @@ colorama = {version = "*", markers = "sys_platform == \"win32\""}
 exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""}
 iniconfig = "*"
 packaging = "*"
-pluggy = ">=0.12,<2.0"
-tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
+pluggy = ">=1.5,<2"
+tomli = {version = ">=1", markers = "python_version < \"3.11\""}
 
 [package.extras]
-testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
+dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
 [[package]]
 name = "pytest-mock"
@@ -3417,20 +3521,20 @@ files = [
 
 [[package]]
 name = "requests"
-version = "2.28.1"
+version = "2.32.3"
 description = "Python HTTP for Humans."
 optional = false
-python-versions = ">=3.7, <4"
+python-versions = ">=3.8"
 files = [
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+    {file = "requests-2.32.3.tar.gz", hash = "sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760"},
 ]
 
 [package.dependencies]
 certifi = ">=2017.4.17"
-charset-normalizer = ">=2,<3"
+charset-normalizer = ">=2,<4"
 idna = ">=2.5,<4"
-urllib3 = ">=1.21.1,<1.27"
+urllib3 = ">=1.21.1,<3"
 
 [package.extras]
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
@@ -3457,28 +3561,29 @@ jupyter = ["ipywidgets (>=7.5.1,<9)"]
 
 [[package]]
 name = "ruff"
-version = "0.2.2"
+version = "0.7.0"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 files = [
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 [[package]]
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 scipy = "*"
 torch = ">=1.11.0"
 tqdm = "*"
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 train = ["accelerate (>=0.20.3)", "datasets"]
 
 [[package]]
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 [package.dependencies]
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 [package.dependencies]
@@ -4274,13 +4382,13 @@ vision = ["Pillow (>=10.0.1,<=15.0)"]
 
 [[package]]
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-version = "0.26.2"
+version = "0.27.0"
 description = "A friendly Python library for async concurrency and I/O"
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 python-versions = ">=3.8"
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 [package.dependencies]
@@ -4321,28 +4429,21 @@ tutorials = ["matplotlib", "pandas", "tabulate"]
 
 [[package]]
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+version = "0.12.5"
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 typing-extensions = ">=3.7.4.3"
 
-[package.extras]
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-
 [[package]]
 name = "types-cffi"
 version = "1.16.0.20240331"
@@ -4436,13 +4537,13 @@ types-urllib3 = "<1.27"
 
 [[package]]
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 python-versions = ">=3.8"
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 [[package]]
@@ -4491,29 +4592,30 @@ files = [
 
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 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
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 docs = ["Sphinx (>=4.1.2,<4.2.0)", "sphinx-rtd-theme (>=0.5.2,<0.6.0)", "sphinxcontrib-asyncio (>=0.3.0,<0.4.0)"]
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 [[package]]
 name = "verspec"
@@ -4955,103 +5064,93 @@ files = [
 
 [[package]]
 name = "yarl"
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 description = "Yet another URL library"
 optional = true
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+    {file = "yarl-1.15.5.tar.gz", hash = "sha256:8249147ee81c1cf4d1dc6f26ba28a1b9d92751529f83c308ad02164bb93abd0d"},
 ]
 
 [package.dependencies]
@@ -5095,4 +5194,4 @@ vision = ["colpali-engine", "pillow", "timm", "torchvision"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4"
-content-hash = "88abd4ba04370e75955d60208cbaa746d0de66247d123cfba0d8a5a4e79371dd"
+content-hash = "49cc99edf0b7213d00cfa6606b9a1bfa62700c79a09529adb86d858ce242f21d"
diff --git a/libs/infinity_emb/pyproject.toml b/libs/infinity_emb/pyproject.toml
index 3ae7baf3..a34547ca 100644
--- a/libs/infinity_emb/pyproject.toml
+++ b/libs/infinity_emb/pyproject.toml
@@ -22,8 +22,8 @@ rich = {version = "^13", optional=true}
 fastapi = {version = ">=0.103.2", optional=true}
 orjson = {version = ">=3.9.8,!=3.10.0", optional=true} 
 prometheus-fastapi-instrumentator = {version = ">=6.1.0", optional=true}
-uvicorn = {version = "^0.23.2", optional=true, extras = ["standard"]}
-typer = {version = "^0.9.0", optional=true, extras = ["all"]}
+uvicorn = {version = "^0.32.0", optional=true, extras = ["standard"]}
+typer = {version = "^0.12.5", optional=true, extras = ["all"]}
 pydantic = {version = ">=2.4.0,<3", optional=true} 
 posthog = {version = "*", optional=true}
 # backend
@@ -53,15 +53,15 @@ soundfile = {version="^0.12.1", optional=true}
 infinity_emb = "infinity_emb.infinity_server:cli"
 
 [tool.poetry.group.test.dependencies]
-pytest = "^7.0.0"
+pytest = "^8.0.0"
 pytest-mock = "*"
 httpx  = "*"
 asgi_lifespan = "*"
 anyio = "*"
 trio = "*"
 coverage = {extras = ["toml"], version = "^7.3.2"}
-mypy = "^1.5.1"
-requests = "2.28.1"
+mypy = "^1.12.0"
+requests = "2.32.3"
 types-requests = "2.28.1"
 openai = "*" # 1.51.0 works
 jinja2 = "*" 
@@ -70,7 +70,7 @@ jinja2-cli = "*"
 # preferred dev dependencies
 torch = "2.4.1"
 prometheus-fastapi-instrumentator = "7.0.0"
-fastapi = "0.115.0"
+fastapi = "0.115.2"
 
 [tool.poetry.group.codespell.dependencies]
 codespell = "^2.2.0"
@@ -81,11 +81,11 @@ mike = "*"
 mkdocs-swagger-ui-tag = "*"
 
 [tool.poetry.group.lint.dependencies]
-ruff = "^0.2.0"
+ruff = "^0.7.0"
 types-toml = "^0.10.8.1"
 types-redis = "^4.3.21.6"
 types-pytz = "^2023.3.0.0"
-black = "^23.1.0"
+black = "^24.10.0"
 types-chardet = "^5.0.4.6"
 mypy-protobuf = "^3.0.0"
 
@@ -150,6 +150,9 @@ markers = [
     "performance: tests that measure performance (deselect with '-m \"not performance\"')",
 ]
 
+[tool.ruff]
+line-length = 100
+
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
diff --git a/libs/infinity_emb/tests/conftest.py b/libs/infinity_emb/tests/conftest.py
index 32166ecc..b030d2c0 100644
--- a/libs/infinity_emb/tests/conftest.py
+++ b/libs/infinity_emb/tests/conftest.py
@@ -66,17 +66,13 @@ def pytest_runtest_setup(item):
 
 
 @pytest.fixture(scope="session")
-def get_sts_bechmark_dataset() -> (
-    tuple[list[InputExample], list[InputExample], list[InputExample]]
-):
+def get_sts_bechmark_dataset() -> tuple[list[InputExample], list[InputExample], list[InputExample]]:
     sts_dataset_path = os.path.join(
         os.path.dirname(__file__), "data", "datasets", "stsbenchmark.tsv.gz"
     )
 
     if not os.path.exists(sts_dataset_path):
-        util.http_get(
-            "https://sbert.net/datasets/stsbenchmark.tsv.gz", sts_dataset_path
-        )
+        util.http_get("https://sbert.net/datasets/stsbenchmark.tsv.gz", sts_dataset_path)
 
     train_samples = []
     dev_samples = []
@@ -85,9 +81,7 @@ def get_sts_bechmark_dataset() -> (
         reader = csv.DictReader(fIn, delimiter="\t", quoting=csv.QUOTE_NONE)
         for row in reader:
             score = float(row["score"]) / 5.0  # Normalize score to range 0 ... 1
-            inp_example = InputExample(
-                texts=[row["sentence1"], row["sentence2"]], label=score
-            )
+            inp_example = InputExample(texts=[row["sentence1"], row["sentence2"]], label=score)
 
             if row["split"] == "dev":
                 dev_samples.append(inp_example)
diff --git a/libs/infinity_emb/tests/end_to_end/conftest.py b/libs/infinity_emb/tests/end_to_end/conftest.py
index 3ce61fc8..40e0c9cd 100644
--- a/libs/infinity_emb/tests/end_to_end/conftest.py
+++ b/libs/infinity_emb/tests/end_to_end/conftest.py
@@ -69,9 +69,7 @@ async def _post_batch(inputs):
         for r, e in zip(responses, encodings):
             cosine_sim = np.dot(r, e) / (np.linalg.norm(e) * np.linalg.norm(r))
             assert cosine_sim > 0.94
-        np.testing.assert_almost_equal(
-            np.array(responses), np.array(encodings), decimal=decimal
-        )
+        np.testing.assert_almost_equal(np.array(responses), np.array(encodings), decimal=decimal)
         assert time_api / time_st < 2.5
 
     @staticmethod
@@ -85,9 +83,7 @@ async def embedding_verify(client, model_base, prefix, model_name, decimal=3):
             response = await client.post(
                 f"{prefix}/embeddings", json=dict(input=inp, model=model_name)
             )
-            assert (
-                response.status_code == 200
-            ), f"{response.status_code}, {response.text}"
+            assert response.status_code == 200, f"{response.status_code}, {response.text}"
             rdata = response.json()
             assert "data" in rdata and isinstance(rdata["data"], list)
             assert all("embedding" in d for d in rdata["data"])
diff --git a/libs/infinity_emb/tests/end_to_end/test_api_with_dummymodel.py b/libs/infinity_emb/tests/end_to_end/test_api_with_dummymodel.py
index d6362958..73e68e2f 100644
--- a/libs/infinity_emb/tests/end_to_end/test_api_with_dummymodel.py
+++ b/libs/infinity_emb/tests/end_to_end/test_api_with_dummymodel.py
@@ -44,9 +44,7 @@
 
 @pytest.fixture()
 async def client():
-    async with AsyncClient(app=app, base_url="http://test") as client, LifespanManager(
-        app
-    ):
+    async with AsyncClient(app=app, base_url="http://test") as client, LifespanManager(app):
         yield client
 
 
@@ -100,9 +98,7 @@ async def test_encoding_base_64(client, model_name):
     assert response_base64.status_code == 200
     embedding = response.json()["data"][0]["embedding"]
     embedding_base64 = response_base64.json()["data"][0]["embedding"]
-    embedding_base64 = np.frombuffer(
-        base64.b64decode(embedding_base64), dtype=np.float32
-    ).tolist()
+    embedding_base64 = np.frombuffer(base64.b64decode(embedding_base64), dtype=np.float32).tolist()
     assert embedding_base64 == embedding
 
 
@@ -113,9 +109,7 @@ async def test_embedding(client):
         ["This is a test sentence.", "This is another test sentence."],
     ]
     for inp in possible_inputs:
-        response = await client.post(
-            f"{PREFIX}/embeddings", json=dict(input=inp, model=MODEL_NAME)
-        )
+        response = await client.post(f"{PREFIX}/embeddings", json=dict(input=inp, model=MODEL_NAME))
         assert response.status_code == 200, f"{response.status_code}, {response.text}"
         rdata = response.json()
         assert "data" in rdata and isinstance(rdata["data"], list)
@@ -135,9 +129,7 @@ async def test_batch_embedding(client, get_sts_bechmark_dataset):
     sentences = sentences
 
     async def _post_batch(inputs):
-        return await client.post(
-            f"{PREFIX}/embeddings", json=dict(input=inputs, model=MODEL_NAME)
-        )
+        return await client.post(f"{PREFIX}/embeddings", json=dict(input=inputs, model=MODEL_NAME))
 
     _request_size = BATCH_SIZE // 2
     tasks = [
diff --git a/libs/infinity_emb/tests/end_to_end/test_authentication.py b/libs/infinity_emb/tests/end_to_end/test_authentication.py
index 23f87de9..6b0a58b5 100644
--- a/libs/infinity_emb/tests/end_to_end/test_authentication.py
+++ b/libs/infinity_emb/tests/end_to_end/test_authentication.py
@@ -32,9 +32,7 @@
 
 @pytest.fixture()
 async def client():
-    async with AsyncClient(app=app, base_url="http://test") as client, LifespanManager(
-        app
-    ):
+    async with AsyncClient(app=app, base_url="http://test") as client, LifespanManager(app):
         yield client
 
 
diff --git a/libs/infinity_emb/tests/end_to_end/test_ct2_sentence.py b/libs/infinity_emb/tests/end_to_end/test_ct2_sentence.py
index 6f85676e..99a31722 100644
--- a/libs/infinity_emb/tests/end_to_end/test_ct2_sentence.py
+++ b/libs/infinity_emb/tests/end_to_end/test_ct2_sentence.py
@@ -39,9 +39,9 @@ def model_base() -> SentenceTransformer:
 
 @pytest.fixture()
 async def client():
-    async with AsyncClient(
-        app=app, base_url="http://test", timeout=20
-    ) as client, LifespanManager(app):
+    async with AsyncClient(app=app, base_url="http://test", timeout=20) as client, LifespanManager(
+        app
+    ):
         yield client
 
 
@@ -52,9 +52,7 @@ def test_load_model(model_base):
     s = ["This is a test sentence."]
     e1 = model_base.encode(s)
     e2 = CT2SentenceTransformer(
-        engine_args=EngineArgs(
-            model_name_or_path=MODEL, device="cpu", bettertransformer=False
-        )
+        engine_args=EngineArgs(model_name_or_path=MODEL, device="cpu", bettertransformer=False)
     ).encode(s)
     np.testing.assert_almost_equal(e1, e2, decimal=6)
 
diff --git a/libs/infinity_emb/tests/end_to_end/test_openapi_client_compat.py b/libs/infinity_emb/tests/end_to_end/test_openapi_client_compat.py
index 97ec88d7..f007c54e 100644
--- a/libs/infinity_emb/tests/end_to_end/test_openapi_client_compat.py
+++ b/libs/infinity_emb/tests/end_to_end/test_openapi_client_compat.py
@@ -14,9 +14,7 @@
 from infinity_emb.args import EngineArgs
 
 PREFIX = ""
-MODEL: str = (
-    "michaelfeil/bge-small-en-v1.5"  #  pytest.DEFAULT_BERT_MODEL  # type: ignore
-)
+MODEL: str = "michaelfeil/bge-small-en-v1.5"  #  pytest.DEFAULT_BERT_MODEL  # type: ignore
 baseurl = "http://openaidemo"
 batch_size = 8
 
@@ -47,9 +45,7 @@ def model_base() -> SentenceTransformer:
 
 @pytest.fixture()
 async def client():
-    async with AsyncClient(
-        app=app, base_url=baseurl, timeout=20
-    ) as client, LifespanManager(app):
+    async with AsyncClient(app=app, base_url=baseurl, timeout=20) as client, LifespanManager(app):
         yield client
 
 
@@ -72,9 +68,7 @@ def url_to_base64(url, modality="image"):
 
 @pytest.mark.anyio
 async def test_openai(client: AsyncClient):
-    client_oai = AsyncOpenAI(
-        api_key="some_dummy_key", base_url=baseurl, http_client=client
-    )
+    client_oai = AsyncOpenAI(api_key="some_dummy_key", base_url=baseurl, http_client=client)
 
     async with client_oai:
         # test audio
@@ -156,9 +150,7 @@ async def test_openai(client: AsyncClient):
 
     # wrong key
     with pytest.raises(APIConnectionError):
-        client_oai = AsyncOpenAI(
-            api_key="some_wrong", base_url=baseurl, http_client=client
-        )
+        client_oai = AsyncOpenAI(api_key="some_wrong", base_url=baseurl, http_client=client)
         async with client_oai:
             await client_oai.embeddings.create(
                 model=pytest.DEFAULT_AUDIO_MODEL,
diff --git a/libs/infinity_emb/tests/end_to_end/test_optimum_embedding.py b/libs/infinity_emb/tests/end_to_end/test_optimum_embedding.py
index a93ffbb9..2b3c3f14 100644
--- a/libs/infinity_emb/tests/end_to_end/test_optimum_embedding.py
+++ b/libs/infinity_emb/tests/end_to_end/test_optimum_embedding.py
@@ -32,9 +32,9 @@ def model_base() -> SentenceTransformer:
 
 @pytest.fixture()
 async def client():
-    async with AsyncClient(
-        app=app, base_url="http://test", timeout=20
-    ) as client, LifespanManager(app):
+    async with AsyncClient(app=app, base_url="http://test", timeout=20) as client, LifespanManager(
+        app
+    ):
         yield client
 
 
@@ -50,9 +50,7 @@ async def test_model_route(client):
 
 @pytest.mark.anyio
 async def test_embedding(client, model_base, helpers):
-    await helpers.embedding_verify(
-        client, model_base, prefix=PREFIX, model_name=MODEL, decimal=2
-    )
+    await helpers.embedding_verify(client, model_base, prefix=PREFIX, model_name=MODEL, decimal=2)
 
 
 @pytest.mark.performance
diff --git a/libs/infinity_emb/tests/end_to_end/test_sentence_transformers.py b/libs/infinity_emb/tests/end_to_end/test_sentence_transformers.py
index 5170b347..0b44f2e3 100644
--- a/libs/infinity_emb/tests/end_to_end/test_sentence_transformers.py
+++ b/libs/infinity_emb/tests/end_to_end/test_sentence_transformers.py
@@ -32,9 +32,9 @@ def model_base() -> SentenceTransformer:
 
 @pytest.fixture()
 async def client():
-    async with AsyncClient(
-        app=app, base_url="http://test", timeout=20
-    ) as client, LifespanManager(app):
+    async with AsyncClient(app=app, base_url="http://test", timeout=20) as client, LifespanManager(
+        app
+    ):
         yield client
 
 
diff --git a/libs/infinity_emb/tests/end_to_end/test_torch_audio.py b/libs/infinity_emb/tests/end_to_end/test_torch_audio.py
index 5db6ccd6..ee7f7d99 100644
--- a/libs/infinity_emb/tests/end_to_end/test_torch_audio.py
+++ b/libs/infinity_emb/tests/end_to_end/test_torch_audio.py
@@ -30,9 +30,9 @@
 
 @pytest.fixture()
 async def client():
-    async with AsyncClient(
-        app=app, base_url="http://test", timeout=20
-    ) as client, LifespanManager(app):
+    async with AsyncClient(app=app, base_url="http://test", timeout=20) as client, LifespanManager(
+        app
+    ):
         yield client
 
 
diff --git a/libs/infinity_emb/tests/end_to_end/test_torch_classify.py b/libs/infinity_emb/tests/end_to_end/test_torch_classify.py
index 6f8ec274..a14b43d2 100644
--- a/libs/infinity_emb/tests/end_to_end/test_torch_classify.py
+++ b/libs/infinity_emb/tests/end_to_end/test_torch_classify.py
@@ -32,9 +32,9 @@ def model_base() -> pipeline:
 
 @pytest.fixture()
 async def client():
-    async with AsyncClient(
-        app=app, base_url="http://test", timeout=20
-    ) as client, LifespanManager(app):
+    async with AsyncClient(app=app, base_url="http://test", timeout=20) as client, LifespanManager(
+        app
+    ):
         yield client
 
 
diff --git a/libs/infinity_emb/tests/end_to_end/test_torch_reranker.py b/libs/infinity_emb/tests/end_to_end/test_torch_reranker.py
index 5e5efffd..c7b6c7bf 100644
--- a/libs/infinity_emb/tests/end_to_end/test_torch_reranker.py
+++ b/libs/infinity_emb/tests/end_to_end/test_torch_reranker.py
@@ -35,9 +35,9 @@ def model_base() -> pipeline:
 
 @pytest.fixture()
 async def client():
-    async with AsyncClient(
-        app=app, base_url="http://test", timeout=20
-    ) as client, LifespanManager(app):
+    async with AsyncClient(app=app, base_url="http://test", timeout=20) as client, LifespanManager(
+        app
+    ):
         yield client
 
 
@@ -87,9 +87,7 @@ async def test_reranker(client, model_base, helpers):
     rdata_results = rdata["results"]
     rdata_results = sorted(rdata_results, key=lambda x: x["index"], reverse=False)
 
-    predictions = [
-        model_base.predict({"text": query, "text_pair": doc}) for doc in documents
-    ]
+    predictions = [model_base.predict({"text": query, "text_pair": doc}) for doc in documents]
 
     assert len(rdata_results) == len(predictions)
     for i, pred in enumerate(predictions):
@@ -98,9 +96,7 @@ async def test_reranker(client, model_base, helpers):
 
 @pytest.mark.anyio
 async def test_reranker_top_n(client):
-    async def test_inner(
-        client, query: str, documents: list[str], return_docs: bool, top_n: int
-    ):
+    async def test_inner(client, query: str, documents: list[str], return_docs: bool, top_n: int):
         response = await client.post(
             f"{PREFIX}/rerank",
             json={
@@ -119,10 +115,7 @@ async def test_inner(
             "The Eiffel Tower is located in Paris, France"
         )
         if return_docs:
-            assert (
-                rdata_results[0]["document"]
-                == "The Eiffel Tower is located in Paris, France"
-            )
+            assert rdata_results[0]["document"] == "The Eiffel Tower is located in Paris, France"
 
         return True
 
diff --git a/libs/infinity_emb/tests/end_to_end/test_torch_vision.py b/libs/infinity_emb/tests/end_to_end/test_torch_vision.py
index fac78cb3..946fd65a 100644
--- a/libs/infinity_emb/tests/end_to_end/test_torch_vision.py
+++ b/libs/infinity_emb/tests/end_to_end/test_torch_vision.py
@@ -31,9 +31,9 @@
 
 @pytest.fixture()
 async def client():
-    async with AsyncClient(
-        app=app, base_url="http://test", timeout=20
-    ) as client, LifespanManager(app):
+    async with AsyncClient(app=app, base_url="http://test", timeout=20) as client, LifespanManager(
+        app
+    ):
         yield client
 
 
diff --git a/libs/infinity_emb/tests/script_live.py b/libs/infinity_emb/tests/script_live.py
index c59a87ad..d306466d 100644
--- a/libs/infinity_emb/tests/script_live.py
+++ b/libs/infinity_emb/tests/script_live.py
@@ -51,9 +51,7 @@ def remote(json_data: bytes, iters=1):
     model = None
 
     print("Measuring latency via requests")
-    latency_request = timeit.timeit(
-        "remote(json_d, iters=5)", number=2, globals=locals()
-    )
+    latency_request = timeit.timeit("remote(json_d, iters=5)", number=2, globals=locals())
     print(f"Request latency: {latency_request}")
 
     assert latency_st * 1.1 > latency_request
diff --git a/libs/infinity_emb/tests/unit_test/fastapi_schemas/test_data_uri.py b/libs/infinity_emb/tests/unit_test/fastapi_schemas/test_data_uri.py
index 5da2ebcd..9de947f4 100644
--- a/libs/infinity_emb/tests/unit_test/fastapi_schemas/test_data_uri.py
+++ b/libs/infinity_emb/tests/unit_test/fastapi_schemas/test_data_uri.py
@@ -2,9 +2,7 @@
 
 
 def test_data_uri_build():
-    DataURI(
-        "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAABEklEQVR42mNk"
-    )
+    DataURI("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAABEklEQVR42mNk")
 
 
 def test_data_uri_convert():
diff --git a/libs/infinity_emb/tests/unit_test/inference/test_batch_handler.py b/libs/infinity_emb/tests/unit_test/inference/test_batch_handler.py
index a4bc961c..dccd84a4 100644
--- a/libs/infinity_emb/tests/unit_test/inference/test_batch_handler.py
+++ b/libs/infinity_emb/tests/unit_test/inference/test_batch_handler.py
@@ -98,9 +98,7 @@ def method_st(_sentences):
         time.sleep(2)
         time_st = np.median([method_st(sentences) for _ in range(N_TIMINGS)])
         time.sleep(2)
-        time_st_patched = np.median(
-            [method_patched(sentences) for _ in range(N_TIMINGS)]
-        )
+        time_st_patched = np.median([method_patched(sentences) for _ in range(N_TIMINGS)])
 
         print(
             f"times are sentence-transformers: {time_st},"
@@ -116,8 +114,7 @@ def method_st(_sentences):
             f" SentenceTransformers.encode: {time_st_patched} > {time_st}"
         )
         assert time_batch_handler / time_st < LIMIT_SLOWDOWN, (
-            "batch_handler slower than Sentence Transformers"
-            f" {time_batch_handler}: > {time_st}"
+            "batch_handler slower than Sentence Transformers" f" {time_batch_handler}: > {time_st}"
         )
         assert time_batch_handler / time_st_patched < LIMIT_SLOWDOWN, (
             "raw batch_handler threaded queueing looses significant "
diff --git a/libs/infinity_emb/tests/unit_test/inference/test_models.py b/libs/infinity_emb/tests/unit_test/inference/test_models.py
index e3326798..0d91d4f3 100644
--- a/libs/infinity_emb/tests/unit_test/inference/test_models.py
+++ b/libs/infinity_emb/tests/unit_test/inference/test_models.py
@@ -42,9 +42,7 @@ def _pretrained_model_score(
                 bettertransformer=not torch.backends.mps.is_available(),
             )
         )
-    evaluator = EmbeddingSimilarityEvaluator.from_input_examples(
-        test_samples, name="sts-test"
-    )
+    evaluator = EmbeddingSimilarityEvaluator.from_input_examples(test_samples, name="sts-test")
 
     score = model.evaluate(evaluator)["sts-test_spearman_cosine"] * 100  # type: ignore
     print(model_name, "{:.2f} vs. exp: {:.2f}".format(score, expected_score))
diff --git a/libs/infinity_emb/tests/unit_test/inference/test_select_model.py b/libs/infinity_emb/tests/unit_test/inference/test_select_model.py
index 6145bb1e..ec75bbf4 100644
--- a/libs/infinity_emb/tests/unit_test/inference/test_select_model.py
+++ b/libs/infinity_emb/tests/unit_test/inference/test_select_model.py
@@ -5,9 +5,7 @@
 from infinity_emb.primitives import Device, InferenceEngine
 
 
-@pytest.mark.parametrize(
-    "engine", [e for e in InferenceEngine if e != InferenceEngine.neuron]
-)
+@pytest.mark.parametrize("engine", [e for e in InferenceEngine if e != InferenceEngine.neuron])
 def test_engine(engine):
     select_model(
         EngineArgs(
diff --git a/libs/infinity_emb/tests/unit_test/test_engine.py b/libs/infinity_emb/tests/unit_test/test_engine.py
index d318edc3..02a534db 100644
--- a/libs/infinity_emb/tests/unit_test/test_engine.py
+++ b/libs/infinity_emb/tests/unit_test/test_engine.py
@@ -23,9 +23,7 @@
 @pytest.mark.anyio
 async def test_async_api_debug():
     sentences = ["Embedded this is sentence via Infinity.", "Paris is in France."]
-    engine = AsyncEmbeddingEngine.from_args(
-        EngineArgs(engine=InferenceEngine.debugengine)
-    )
+    engine = AsyncEmbeddingEngine.from_args(EngineArgs(engine=InferenceEngine.debugengine))
     async with engine:
         embeddings, usage = await engine.embed(sentences)
         embeddings = np.array(embeddings)
@@ -89,8 +87,7 @@ async def test_engine_reranker_torch_opt(engine_name: InferenceEngine):
     async with engine:
         rankings_objects, usage = await engine.rerank(query=query, docs=documents)
     rankings = [
-        x.relevance_score
-        for x in sorted(rankings_objects, key=lambda x: x.index, reverse=False)
+        x.relevance_score for x in sorted(rankings_objects, key=lambda x: x.index, reverse=False)
     ]
     rankings_unpatched = model_unpatched.predict(query_docs)
 
@@ -176,9 +173,7 @@ async def test_async_api_torch_lengths_via_tokenize_usage():
 
 @pytest.mark.anyio
 async def test_torch_clip_embed():
-    image_urls = [
-        "http://images.cocodataset.org/val2017/000000039769.jpg"
-    ]  # a photo of two cats
+    image_urls = ["http://images.cocodataset.org/val2017/000000039769.jpg"]  # a photo of two cats
     sentences = [
         "a photo of two cats",
         "a photo of a cat",
@@ -193,9 +188,10 @@ async def test_torch_clip_embed():
         )
     )
     async with engine:
-        t1, t2 = asyncio.create_task(
-            engine.embed(sentences=sentences)
-        ), asyncio.create_task(engine.image_embed(images=image_urls))
+        t1, t2 = (
+            asyncio.create_task(engine.embed(sentences=sentences)),
+            asyncio.create_task(engine.image_embed(images=image_urls)),
+        )
         emb_text, usage_text = await t1
         emb_image, usage_image = await t2
         emb_text_np = np.array(emb_text)  # type: ignore
@@ -210,9 +206,7 @@ async def test_torch_clip_embed():
 
     # check if cat image and two cats are most similar
     for i in range(1, len(sentences)):
-        assert np.dot(emb_text_np[0], emb_image_np[0]) > np.dot(
-            emb_text_np[i], emb_image_np[0]
-        )
+        assert np.dot(emb_text_np[0], emb_image_np[0]) > np.dot(emb_text_np[i], emb_image_np[0])
 
 
 @pytest.mark.anyio
@@ -257,9 +251,10 @@ async def test_clip_embed_pil_image_input(image_sample):
         )
     )
     async with engine:
-        t1, t2 = asyncio.create_task(
-            engine.embed(sentences=sentences)
-        ), asyncio.create_task(engine.image_embed(images=images))
+        t1, t2 = (
+            asyncio.create_task(engine.embed(sentences=sentences)),
+            asyncio.create_task(engine.image_embed(images=images)),
+        )
         emb_text, usage_text = await t1
         emb_image, usage_image = await t2
         emb_text_np = np.array(emb_text)  # type: ignore
@@ -274,9 +269,7 @@ async def test_clip_embed_pil_image_input(image_sample):
 
     # check if cat image and two cats are most similar
     for i in range(1, len(sentences)):
-        assert np.dot(emb_text_np[0], emb_image_np[0]) > np.dot(
-            emb_text_np[i], emb_image_np[0]
-        )
+        assert np.dot(emb_text_np[0], emb_image_np[0]) > np.dot(emb_text_np[i], emb_image_np[0])
 
 
 @pytest.mark.anyio
diff --git a/libs/infinity_emb/tests/unit_test/transformer/classifier/test_torch_classifer.py b/libs/infinity_emb/tests/unit_test/transformer/classifier/test_torch_classifer.py
index 25e0feb6..814536d7 100644
--- a/libs/infinity_emb/tests/unit_test/transformer/classifier/test_torch_classifer.py
+++ b/libs/infinity_emb/tests/unit_test/transformer/classifier/test_torch_classifer.py
@@ -7,7 +7,10 @@
 
 def test_classifier(model_name: str = "SamLowe/roberta-base-go_emotions"):
     model = SentenceClassifier(
-        engine_args=EngineArgs(model_name_or_path=model_name, device="cuda" if torch.cuda.is_available() else "cpu")  # type: ignore
+        engine_args=EngineArgs(
+            model_name_or_path=model_name,
+            device="cuda" if torch.cuda.is_available() else "cpu",
+        )  # type: ignore
     )
     pipe = pipeline(model=model_name, task="text-classification")
 
diff --git a/libs/infinity_emb/tests/unit_test/transformer/crossencoder/test_optimum_crossencoder.py b/libs/infinity_emb/tests/unit_test/transformer/crossencoder/test_optimum_crossencoder.py
index 341505cb..c3635b54 100644
--- a/libs/infinity_emb/tests/unit_test/transformer/crossencoder/test_optimum_crossencoder.py
+++ b/libs/infinity_emb/tests/unit_test/transformer/crossencoder/test_optimum_crossencoder.py
@@ -55,6 +55,4 @@ def test_patched_crossencoder_vs_sentence_transformers():
 
     rankings_unpatched = model_unpatched.predict(query_docs)
 
-    np.testing.assert_allclose(
-        rankings_sigmoid, rankings_unpatched, rtol=0.04, atol=0.04
-    )
+    np.testing.assert_allclose(rankings_sigmoid, rankings_unpatched, rtol=0.04, atol=0.04)
diff --git a/libs/infinity_emb/tests/unit_test/transformer/crossencoder/test_torch_crossencoder.py b/libs/infinity_emb/tests/unit_test/transformer/crossencoder/test_torch_crossencoder.py
index 7fcc69bc..dff9065b 100644
--- a/libs/infinity_emb/tests/unit_test/transformer/crossencoder/test_torch_crossencoder.py
+++ b/libs/infinity_emb/tests/unit_test/transformer/crossencoder/test_torch_crossencoder.py
@@ -40,9 +40,7 @@ def test_patched_crossencoder_vs_sentence_transformers():
             model_name_or_path="mixedbread-ai/mxbai-rerank-xsmall-v1", compile=True
         )
     )
-    model_unpatched = CrossEncoder(
-        "mixedbread-ai/mxbai-rerank-xsmall-v1", trust_remote_code=True
-    )
+    model_unpatched = CrossEncoder("mixedbread-ai/mxbai-rerank-xsmall-v1", trust_remote_code=True)
 
     query = "Where is Paris?"
     documents = [
@@ -60,6 +58,4 @@ def test_patched_crossencoder_vs_sentence_transformers():
 
     rankings_unpatched = model_unpatched.predict(query_docs)
 
-    np.testing.assert_allclose(
-        rankings_sigmoid, rankings_unpatched, rtol=1e-2, atol=1e-2
-    )
+    np.testing.assert_allclose(rankings_sigmoid, rankings_unpatched, rtol=1e-2, atol=1e-2)
diff --git a/libs/infinity_emb/tests/unit_test/transformer/embedder/test_optimum.py b/libs/infinity_emb/tests/unit_test/transformer/embedder/test_optimum.py
index 369a79dc..c612d305 100644
--- a/libs/infinity_emb/tests/unit_test/transformer/embedder/test_optimum.py
+++ b/libs/infinity_emb/tests/unit_test/transformer/embedder/test_optimum.py
@@ -7,13 +7,9 @@
 
 def test_embedder_optimum(size="large"):
     model = OptimumEmbedder(
-        engine_args=EngineArgs(
-            model_name_or_path=f"Xenova/bge-{size}-en-v1.5", device="cpu"
-        )
-    )
-    st_model = SentenceTransformer(
-        model_name_or_path=f"BAAI/bge-{size}-en-v1.5", device="cpu"
+        engine_args=EngineArgs(model_name_or_path=f"Xenova/bge-{size}-en-v1.5", device="cpu")
     )
+    st_model = SentenceTransformer(model_name_or_path=f"BAAI/bge-{size}-en-v1.5", device="cpu")
 
     sentences = ["This is awesome.", "I am depressed."]
 
diff --git a/libs/infinity_emb/tests/unit_test/transformer/embedder/test_torch.py b/libs/infinity_emb/tests/unit_test/transformer/embedder/test_torch.py
index 51519c66..008caad8 100644
--- a/libs/infinity_emb/tests/unit_test/transformer/embedder/test_torch.py
+++ b/libs/infinity_emb/tests/unit_test/transformer/embedder/test_torch.py
@@ -22,14 +22,10 @@ def test_sentence_transformer_equals_fastembed(
     ),
     model_name="BAAI/bge-small-en-v1.5",
 ) -> None:
-    model_st = SentenceTransformerPatched(
-        engine_args=EngineArgs(model_name_or_path=model_name)
-    )
+    model_st = SentenceTransformerPatched(engine_args=EngineArgs(model_name_or_path=model_name))
     model_fastembed = TextEmbedding(model_name)
 
-    embedding_st = model_st.encode_post(
-        model_st.encode_core(model_st.encode_pre([text]))
-    )
+    embedding_st = model_st.encode_post(model_st.encode_core(model_st.encode_pre([text])))
     embedding_fast = np.array(list(model_fastembed.embed(documents=[text])))
 
     assert embedding_fast.shape == embedding_st.shape
diff --git a/libs/infinity_emb/tests/unit_test/transformer/quantization/test_interface.py b/libs/infinity_emb/tests/unit_test/transformer/quantization/test_interface.py
index 4321ed0d..36c66341 100644
--- a/libs/infinity_emb/tests/unit_test/transformer/quantization/test_interface.py
+++ b/libs/infinity_emb/tests/unit_test/transformer/quantization/test_interface.py
@@ -43,9 +43,7 @@ def test_quantize_bert(device: Device, dtype: Dtype):
     )
     tokens_encoded = {k: v.to(device.resolve()) for k, v in tokens_encoded.items()}
     with torch.no_grad():
-        out_default = model_unquantized.forward(**tokens_encoded)[
-            "last_hidden_state"
-        ].mean(dim=1)
+        out_default = model_unquantized.forward(**tokens_encoded)["last_hidden_state"].mean(dim=1)
         out_quant = model.forward(**tokens_encoded)["last_hidden_state"].mean(dim=1)
 
     assert torch.cosine_similarity(out_default, out_quant) > 0.95