diff --git a/conda/environment.yml b/conda/environment.yml
index 7f6deeaac..553bd011b 100644
--- a/conda/environment.yml
+++ b/conda/environment.yml
@@ -5,5 +5,3 @@ channels:
 dependencies:
   - python=3.12                 # note: at the time of writing, xformer (< vllm) has a broken wheel for 3.13. https://github.com/facebookresearch/xformers/issues/740#issuecomment-2753869337
   - uv
-variables:
-  VLLM_USE_V1: 0                 # need this to make outlines work
diff --git a/conda/install.sh b/conda/install.sh
index 6df570e7d..266154d54 100755
--- a/conda/install.sh
+++ b/conda/install.sh
@@ -4,8 +4,7 @@ CONDA=""
 if which mamba > /dev/null
 then
     CONDA=$(which mamba)
-fi
-if which conda > /dev/null
+elif which conda > /dev/null
 then
     CONDA=$(which conda)
 fi
@@ -53,7 +52,7 @@ while getopts "yh" OPTNAME ; do
     esac
 done
 
-if $CONDA env list | grep -q mellea
+if ($CONDA activate mellea)
 then
     echo "An existing mellea environment was found."
     $CONDA env remove $CONDA_OPTIONS -n mellea
diff --git a/docs/kv_smash/kvcache.py b/docs/kv_smash/kvcache.py
index 40a472ac8..83f226cf1 100644
--- a/docs/kv_smash/kvcache.py
+++ b/docs/kv_smash/kvcache.py
@@ -6,12 +6,14 @@
 # ///
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizer
+from transformers.generation import GenerateDecoderOnlyOutput
 
 from mellea.backends.kv_block_helpers import DynamicCache, merge_dynamic_caches
 
 model_id = "ibm-granite/granite-4.0-tiny-preview"
 device = torch.device("mps")
-model = AutoModelForCausalLM.from_pretrained(model_id).to(device)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+# model = model.to(device=device) # this part does not pass mypy; possible misconfiguration
 tokenizer: PreTrainedTokenizer = AutoTokenizer.from_pretrained(model_id)
 
 
@@ -19,8 +21,8 @@ def cache(toks) -> DynamicCache:
     dc = DynamicCache()
     with torch.no_grad():
         rv = model(
-            toks["input_ids"].to(device),
-            attention_mask=toks["attention_mask"].to(device),
+            toks["input_ids"].to(model.device),
+            attention_mask=toks["attention_mask"].to(model.device),
             past_key_values=dc,
         ).past_key_values
     return rv
@@ -42,15 +44,18 @@ def merge(strs: list[str]):
 merged_toks, merged_masks, merged_dcs = merge(strs)
 merged_dcs.crop(-1)
 
+# GenerateDecoderOnlyOutput | GenerateEncoderDecoderOutput | GenerateBeamDecoderOnlyOutput | GenerateBeamEncoderDecoderOutput | LongTensor
 result = model.generate(
-    merged_toks.to(device),
-    attention_mask=merged_masks.to(device),
+    merged_toks.to(model.device),
+    attention_mask=merged_masks.to(model.device),
     past_key_values=merged_dcs,
     use_cache=True,
     return_dict_in_generate=True,
     output_scores=True,
 )
 
+assert isinstance(result, GenerateDecoderOnlyOutput)
+
 result_decoded = tokenizer.decode(
     result.sequences[0, merged_toks.shape[1] :], skip_special_tokens=True
 )
diff --git a/mellea/backends/huggingface.py b/mellea/backends/huggingface.py
index 3915678ad..4a5439194 100644
--- a/mellea/backends/huggingface.py
+++ b/mellea/backends/huggingface.py
@@ -15,12 +15,14 @@
 from typing import Any, overload
 
 import granite_common
-import outlines
-import outlines_core
+import llguidance
+import llguidance.hf
+import llguidance.torch
 import peft
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from transformers.cache_utils import DynamicCache
+from transformers.generation.logits_process import LogitsProcessorList
 from transformers.generation.streamers import AsyncTextIteratorStreamer
 from transformers.generation.utils import GenerateDecoderOnlyOutput
 from transformers.modeling_utils import PreTrainedModel
@@ -67,8 +69,6 @@
 )
 from .utils import to_chat, to_tool_calls
 
-assert outlines, "outlines needs to be present to make outlines_core work"
-
 """A configuration type for the unhappy path: Tokenizer * Model * torch device string
 
 Huggingface backends can initialize themselves from a model string if the transformers `Auto*` classes can be used. Therefore, a TransformersTorchConfig usually isn't required. However, sometimes a model needs special care to instantiate properly, or a custom device type needs to bse used. Instead of trying to do a lot of partial magic, we basically have two modaliites: either the constructor can figure out everything from the model_id, or the user has to provide an entire config.
@@ -88,6 +88,65 @@ class HFAloraCacheInfo:
     q_end: int = -1
 
 
+# modified from VLLM v0.9.2 code base
+# https://github.com/vllm-project/vllm/blob/v0.9.2/vllm/model_executor/guided_decoding/guidance_logits_processors.py
+class _GuidanceLogitsProcessor:
+    def __init__(self, grammar: str, ll_tokenizer: llguidance.LLTokenizer) -> None:
+        self.grammar = grammar
+        self.vocab_size: int = ll_tokenizer.vocab_size
+        self.ll_tokenizer: llguidance.LLTokenizer = ll_tokenizer
+        self.ll_matchers: list[llguidance.LLMatcher] = []
+        self.bitmasks: list[torch.Tensor] = []
+        self.new_sampling: bool = False
+        self.batch_size: int = -1
+
+    def __call__(
+        self, batch_input_ids: torch.Tensor, batch_scores: torch.Tensor
+    ) -> torch.Tensor:
+        i_batch, _ = batch_input_ids.shape
+        s_batch, _ = batch_scores.shape
+        assert i_batch == s_batch
+
+        # s_batch, s_vocab = batch_scores.shape
+        # assert s_vocab == self.vocab_size
+        #
+        # NOTE: somehow, this does not hold. s_vocab is not same as either of
+        # * self._tokenizer._tokenizer.get_vocab_size(with_added_tokens=True) == self.vocab_size == ll_tokenizer.vocab_size
+        # * self._tokenizer._tokenizer.get_vocab_size(with_added_tokens=False)
+
+        if self.batch_size != i_batch:
+            self.batch_size = i_batch
+            self.bitmasks = [
+                llguidance.torch.allocate_token_bitmask(1, self.vocab_size)  # type: ignore[attr-defined]
+                for _ in range(self.batch_size)
+            ]
+
+            self.ll_matchers = [
+                llguidance.LLMatcher(self.ll_tokenizer, self.grammar)
+                for _ in range(self.batch_size)
+            ]
+
+        for input_ids, scores, ll_matcher, bitmask in zip(
+            batch_input_ids, batch_scores, self.ll_matchers, self.bitmasks
+        ):
+            if self.new_sampling and len(input_ids) > 0:
+                ll_matcher.consume_token(  # type: ignore[attr-defined]
+                    input_ids.tolist()[-1]
+                )
+                err = ll_matcher.get_error()  # type: ignore[attr-defined]
+                if err:
+                    FancyLogger.get_logger().warning("Error in LLMatcher: %s", err)
+
+            llguidance.torch.fill_next_token_bitmask(ll_matcher, bitmask, 0)
+            llguidance.torch.apply_token_bitmask_inplace(
+                scores, bitmask.to(scores.device)
+            )  # type: ignore[attr-defined]
+
+        self.new_sampling = True
+
+        return batch_scores
+
+
 class LocalHFBackend(FormatterBackend, AdapterMixin):
     """The LocalHFBackend uses Huggingface's transformers library for inference, and uses a Formatter to convert `Component`s into prompts. This backend also supports Activated LoRAs (ALoras)](https://arxiv.org/pdf/2504.12397).
 
@@ -176,6 +235,14 @@ def __init__(
             case _:
                 self._tokenizer, self._model, self._device = custom_config
 
+        self._llguidance_tokenizer: llguidance.LLTokenizer = (
+            llguidance.hf.from_tokenizer(self._tokenizer)  # type:ignore
+        )
+        assert (
+            self._llguidance_tokenizer.vocab_size
+            == self._tokenizer._tokenizer.get_vocab_size(with_added_tokens=True)
+        ), "vocab size mismatch between llguidance and huggingface tokenizers ... wtf?"
+
         self._use_caches = use_caches
         self._cache = cache if cache is not None else SimpleLRUCache(3)
 
@@ -609,24 +676,15 @@ async def _generate_from_context_with_kv_cache(
 
             format_kwargs = {}
             if _format:
-                # outlines.generate.json always parses the resulting json into a python dict.
-                # We however want to keep it as a json string for later storing it in ModelOutputThunk
-                schema: dict[str, Any] = _format.model_json_schema()  # type: ignore
-                schema_json: str = json.dumps(schema)
-                regex_str: str = outlines_core.fsm.json_schema.build_regex_from_schema(  # type: ignore
-                    schema_json
+                schema: dict[str, Any] = _format.model_json_schema()
+                grammar: str = llguidance.LLMatcher.grammar_from_json_schema(
+                    schema, defaults={"whitespace_flexible": False}
+                )
+                logits_processor = _GuidanceLogitsProcessor(
+                    grammar, self._llguidance_tokenizer
                 )
-
-                from outlines.models.transformers import TransformerTokenizer
-                from outlines.processors.structured import RegexLogitsProcessor
-                from transformers import LogitsProcessorList  # type: ignore
-
                 format_kwargs["logits_processor"] = LogitsProcessorList(
-                    [
-                        RegexLogitsProcessor(
-                            regex_str, tokenizer=TransformerTokenizer(self._tokenizer)
-                        )
-                    ]
+                    [logits_processor]
                 )
 
             streaming_kwargs = {}
@@ -776,24 +834,15 @@ async def _generate_from_context_standard(
 
             format_kwargs = {}
             if _format:
-                # outlines.generate.json always parses the resulting json into a python dict.
-                # We however want to keep it as a json string for later storing it in ModelOutputThunk
-                schema: dict[str, Any] = _format.model_json_schema()  # type: ignore
-                schema_json: str = json.dumps(schema)
-                regex_str: str = outlines_core.fsm.json_schema.build_regex_from_schema(  # type: ignore
-                    schema_json
+                schema: dict[str, Any] = _format.model_json_schema()
+                grammar: str = llguidance.LLMatcher.grammar_from_json_schema(
+                    schema, defaults={"whitespace_flexible": False}
+                )
+                logits_processor = _GuidanceLogitsProcessor(
+                    grammar, self._llguidance_tokenizer
                 )
-
-                from outlines.models.transformers import TransformerTokenizer
-                from outlines.processors.structured import RegexLogitsProcessor
-                from transformers import LogitsProcessorList  # type: ignore
-
                 format_kwargs["logits_processor"] = LogitsProcessorList(
-                    [
-                        RegexLogitsProcessor(
-                            regex_str, tokenizer=TransformerTokenizer(self._tokenizer)
-                        )
-                    ]
+                    [logits_processor]
                 )
 
             streaming_kwargs = {}
@@ -1043,24 +1092,15 @@ async def generate_from_raw(
 
             format_kwargs = {}
             if format:
-                # outlines.generate.json always parses the resulting json into a python dict.
-                # We however want to keep it as a json string for later storing it in ModelOutputThunk
-                schema: dict[str, Any] = format.model_json_schema()  # type: ignore
-                schema_json: str = json.dumps(schema)
-                regex_str: str = outlines_core.fsm.json_schema.build_regex_from_schema(  # type: ignore
-                    schema_json
+                schema: dict[str, Any] = format.model_json_schema()
+                grammar: str = llguidance.LLMatcher.grammar_from_json_schema(
+                    schema, defaults={"whitespace_flexible": False}
+                )
+                logits_processor = _GuidanceLogitsProcessor(
+                    grammar, self._llguidance_tokenizer
                 )
-
-                from outlines.models.transformers import TransformerTokenizer
-                from outlines.processors.structured import RegexLogitsProcessor
-                from transformers import LogitsProcessorList  # type: ignore
-
                 format_kwargs["logits_processor"] = LogitsProcessorList(
-                    [
-                        RegexLogitsProcessor(
-                            regex_str, tokenizer=TransformerTokenizer(self._tokenizer)
-                        )
-                    ]
+                    [logits_processor]
                 )
 
             outputs = await asyncio.to_thread(
diff --git a/mellea/backends/vllm.py b/mellea/backends/vllm.py
index a01900dd5..eb25bd3fc 100644
--- a/mellea/backends/vllm.py
+++ b/mellea/backends/vllm.py
@@ -18,8 +18,6 @@
 
 try:
     import msgspec
-    import outlines
-    import outlines_core
     import torch
     import vllm
     from transformers import AutoTokenizer
@@ -55,8 +53,6 @@
 )
 from .utils import to_chat, to_tool_calls
 
-assert outlines, "outlines needs to be present to make outlines_core work"
-
 format: None = None  # typing this variable in order to shadow the global format function and ensure mypy checks for errors
 
 
@@ -91,14 +87,6 @@ def __init__(
             formatter (Formatter): A mechanism for turning `stdlib` stuff into strings. Experimental Span-based models should use `mellea.backends.span.*` backends.
             model_options (Optional[dict]): Default model options.
         """
-        if os.environ.get("VLLM_USE_V1", -1) != "0":
-            FancyLogger.get_logger().error(
-                "Mellea LocalVLLMBackend doesn't support VLLM V1. Must `export VLLM_USE_V1=0`."
-            )
-            raise ValueError(
-                "Mellea LocalVLLMBackend doesn't support VLLM V1. Must `export VLLM_USE_V1=0`."
-            )
-
         formatter = (
             formatter if formatter is not None else TemplateFormatter(model_id=model_id)
         )
@@ -213,23 +201,20 @@ def __init__(
 
         # Keep track of the event loop the engine was instantiated in.
         self._event_loop = get_current_event_loop()
+        # we store the engine args because we have to reset the engine with a different event loop. See _model .
+        self.engine_args = engine_args
 
         self._tokenizer: PreTrainedTokenizerBase = AutoTokenizer.from_pretrained(
             self._hf_model_id
         )  # type:ignore
 
-        # See the notes in outlines.models.vllm.adapt_tokenizer for why this is needed.
-        # Note: there is a module named outlines.models.vllm and a function named outlines.models.vllm.vllm .
-        # However, outlines.models import outlines.models.vllm.vllm as vllm,
-        # thus the module outlines.models.vllm becomes inaccessible,
-        # hence the use of importlib to get the module.
-        self._tokenizer_for_outlines: PreTrainedTokenizerBase = importlib.import_module(
-            "outlines.models.vllm"
-        ).adapt_tokenizer(self._tokenizer)
-
     @property
     def _model(self) -> vllm.AsyncLLMEngine:
         """Use model when making generation requests."""
+        # 2026/01/06 Masa: Temporarily canceling the mechanism below.
+        # After vllm 0.11.0, start/shutdown_background_loop is gone.
+        # 2026/01/07 Masa: Rewrote it to reinstantiate the engine.
+
         el = get_current_event_loop()
 
         # vLLM attaches itself to the event loop that is running when instantiated /
@@ -239,8 +224,13 @@ def _model(self) -> vllm.AsyncLLMEngine:
         # Most of the time, this should be a no-op. The event loop will only change
         # if switching between async and sync calls.
         if el != self._event_loop:
-            self._underlying_model.shutdown_background_loop()
-            self._underlying_model.start_background_loop()
+            FancyLogger.get_logger().warning("restarting the vllm event loop")
+            # self._underlying_model.shutdown_background_loop()
+            # self._underlying_model.start_background_loop()
+            self._underlying_model.shutdown()
+            self._underlying_model = vllm.AsyncLLMEngine.from_engine_args(
+                vllm.AsyncEngineArgs(model=self._hf_model_id, **self.engine_args)
+            )
             self._event_loop = el
 
         return self._underlying_model
@@ -328,22 +318,10 @@ async def _generate_from_context_standard(
             )
 
             if _format is not None:
-                # outlines.generate.json always parses the resulting json into a python dict.
-                # We however want to keep it as a json string for later storing it in ModelOutputThunk
-                schema: dict[str, Any] = _format.model_json_schema()  # type: ignore
-                schema_json: str = json.dumps(schema)
-                regex_str: str = outlines_core.fsm.json_schema.build_regex_from_schema(  # type: ignore
-                    schema_json  # type: ignore
-                )  # type: ignore
-
-                from outlines.processors import RegexLogitsProcessor  # type: ignore
-
-                logits_processor = RegexLogitsProcessor(
-                    regex_str,
-                    tokenizer=self._tokenizer_for_outlines,  # type: ignore
-                )
-                sampling_params.logits_processors = (
-                    [logits_processor] if logits_processor is not None else []
+                sampling_params.structured_outputs = (
+                    vllm.sampling_params.StructuredOutputsParams(
+                        json=_format.model_json_schema()
+                    )
                 )
 
             # stream = model_options.get(ModelOption.STREAM, False)
@@ -491,20 +469,10 @@ async def generate_from_raw(
             )
 
             if format is not None:
-                schema: dict[str, Any] = format.model_json_schema()  # type: ignore
-                schema_json: str = json.dumps(schema)
-                regex_str: str = outlines_core.fsm.json_schema.build_regex_from_schema(  # type: ignore
-                    schema_json  # type: ignore
-                )  # type: ignore
-
-                from outlines.processors import RegexLogitsProcessor  # type: ignore
-
-                logits_processor = RegexLogitsProcessor(
-                    regex_str,
-                    tokenizer=self._tokenizer_for_outlines,  # type: ignore
-                )
-                sampling_params.logits_processors = (
-                    [logits_processor] if logits_processor is not None else []
+                sampling_params.structured_outputs = (
+                    vllm.sampling_params.StructuredOutputsParams(
+                        json=format.model_json_schema()
+                    )
                 )
 
             async def generate(prompt, request_id):
diff --git a/mellea/core/backend.py b/mellea/core/backend.py
index 8f505b00e..7f0c88e68 100644
--- a/mellea/core/backend.py
+++ b/mellea/core/backend.py
@@ -12,13 +12,19 @@
 from .base import C, CBlock, Component, Context, ModelOutputThunk
 from .utils import FancyLogger
 
-# Necessary to define a type that supports `None` so that the BaseModelSubclass
-# can have a default value. Otherwise, Python complains about typed-components
-# since types with default values must come after those without default values in
-# function signatures (which is incompatible with our function parameter formatting).
-pydantic_model_or_none = pydantic.BaseModel | None
+# Necessary to define a type variable that has a default value.
+# This is because VSCode's pyright static type checker instantiates
+# all type parameters, order them in a specific order, and checks
+# that a type parameter with a default is not followed by a type parameter without a default.
+# This was originally specified in PEP 696 (Type Defaults for Type Parameters) and
+# is now part of official spec https://typing.python.org/en/latest/spec/generics.html#type-parameter-defaults .
+# For example, in mellea/stdlib/functional.py ,
+#    def act(action: Component[S], ... format: type[BaseModelSubclass] | None = None, ...)
+# gets instantiated as act[S, BaseModelSubclass].
+# S is TypeVar("S", default=Any, covariant=True) which has a default.
+#
 BaseModelSubclass = typing_extensions.TypeVar(
-    "BaseModelSubclass", bound=pydantic_model_or_none, default=None
+    "BaseModelSubclass", bound=pydantic.BaseModel, default=pydantic.BaseModel
 )  # must be a subclass of BaseModel
 
 
diff --git a/mellea/stdlib/components/react.py b/mellea/stdlib/components/react.py
index 23eb87a57..55af32a9a 100644
--- a/mellea/stdlib/components/react.py
+++ b/mellea/stdlib/components/react.py
@@ -24,7 +24,7 @@ def _mellea_finalize_tool(answer: str) -> str:
     return answer
 
 
-class ReactInitiator(Component[str], Generic[BaseModelSubclass]):
+class ReactInitiator(Component[str]):
     """`ReactInitiator` is used at the start of the ReACT loop to prime the model."""
 
     def __init__(self, goal: str, tools: list[AbstractMelleaTool] | None):
diff --git a/pyproject.toml b/pyproject.toml
index b3d4cac9e..0690b555d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -57,21 +57,10 @@ m = "cli.m:cli"
 # uv pip install -e ".[hf, watsonx]"
 # if you want to install all dependencies, use uv sync --all-extras
 
-
-# note on outlines versions:
-# outlines>=1.2.0 requires outlines-core==0.2.11
-# outlines<=1.1.* requires outlines-core==0.1.26
-# vllm==0.10.0    requires outlines-core==0.2.10
-# vllm==0.9.*     requires outlines-core==0.1.26
-#
-# thus the following version combination allows installing vllm and outlines
-# (main library) at the same time.
-
 hf = [
     "accelerate>=1.9.0",
     "datasets>=4.0.0",
-    "outlines-core==0.1.26",
-    "outlines",                 # intentionally un-versioned, expecting a minor update. coutlines-core version should be enough to specify it
+    "llguidance",
     "peft>=0.18.1",  # Native aLoRA support added in PEFT 0.18.0
     "transformers>=4.53.2,<5",
     "trl==0.19.1",
@@ -79,12 +68,7 @@ hf = [
 ]
 
 vllm = [
-    "transformers<4.54.0",
-    # see https://github.com/vllm-project/vllm-ascend/issues/2046
-    "numpy<2.0.0",              # patching incorrect dependencies in vllm and outlines.
-    # see https://github.com/vllm-project/vllm/issues/5587
-    "outlines-core==0.1.26",
-    "vllm>=0.9.1",
+    "vllm>=0.13.0; sys_platform != 'darwin'",
 ]
 
 litellm = [
@@ -240,14 +224,14 @@ markers = [
     "huggingface: Tests requiring HuggingFace backend (local, heavy)",
     "vllm: Tests requiring vLLM backend (local, GPU required)",
     "litellm: Tests requiring LiteLLM backend",
-    
+
     # Capability markers
     "requires_api_key: Tests requiring external API keys",
     "requires_gpu: Tests requiring GPU",
     "requires_heavy_ram: Tests requiring 48GB+ RAM",
     "qualitative: Non-deterministic quality tests",
     "slow: Tests taking >5 minutes (e.g., dataset loading)",
-    
+
     # Composite markers
     "llm: Tests that make LLM calls (needs at least Ollama)",
 ]
@@ -268,15 +252,15 @@ filterwarnings = [
     # Show each deprecation warning only once per session
     "once::DeprecationWarning",
     "once::PendingDeprecationWarning",
-    
+
     # Deduplicate noisy Pydantic serialization warnings
     "once:.*PydanticSerializationUnexpectedValue.*:UserWarning",
-    
+
     # Ignore SWIG C/C++ binding warnings from vLLM (Python 3.12 issue)
     "ignore:builtin type SwigPyPacked has no __module__ attribute:DeprecationWarning",
     "ignore:builtin type SwigPyObject has no __module__ attribute:DeprecationWarning",
     "ignore:builtin type swigvarlink has no __module__ attribute:DeprecationWarning",
-    
+
     # Keep Watsonx deprecation visible (important for migration)
     "default:.*Watsonx Backend is deprecated.*:DeprecationWarning",
 ]
diff --git a/test/backends/test_vllm.py b/test/backends/test_vllm.py
index 23e0feac5..f6573fb9c 100644
--- a/test/backends/test_vllm.py
+++ b/test/backends/test_vllm.py
@@ -36,9 +36,6 @@
 @pytest.fixture(scope="module")
 def backend():
     """Shared vllm backend for all tests in this module."""
-    if os.environ.get("VLLM_USE_V1", -1) != "0":
-        pytest.skip("skipping vllm tests; tests require `export VLLM_USE_V1=0`")
-
     backend = LocalVLLMBackend(
         model_id=model_ids.QWEN3_0_6B,
         # formatter=TemplateFormatter(model_id="ibm-granite/granite-4.0-tiny-preview"),
diff --git a/test/backends/test_vllm_tools.py b/test/backends/test_vllm_tools.py
index 4f02bb1e3..db2d2d7d2 100644
--- a/test/backends/test_vllm_tools.py
+++ b/test/backends/test_vllm_tools.py
@@ -33,8 +33,6 @@
 @pytest.fixture(scope="module")
 def backend():
     """Shared vllm backend for all tests in this module."""
-    if os.environ.get("VLLM_USE_V1", -1) != "0":
-        pytest.skip("skipping vllm tests; tests require `export VLLM_USE_V1=0`")
 
     backend = LocalVLLMBackend(
         model_id=model_ids.MISTRALAI_MISTRAL_0_3_7B,
diff --git a/test/conftest.py b/test/conftest.py
index 98a5b7451..f2e434d03 100644
--- a/test/conftest.py
+++ b/test/conftest.py
@@ -329,7 +329,7 @@ def cleanup_vllm_backend(backend):
 
     import torch
 
-    backend._underlying_model.shutdown_background_loop()
+    backend._underlying_model.shutdown()
     del backend._underlying_model
     del backend
     gc.collect()
diff --git a/uv.lock b/uv.lock
index 7d6e644fb..8784ef359 100644
--- a/uv.lock
+++ b/uv.lock
@@ -37,12 +37,14 @@ version = "1.11.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "huggingface-hub" },
-    { name = "numpy" },
+    { name = "numpy", version = "1.26.4", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'darwin'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'darwin'" },
     { name = "packaging" },
     { name = "psutil" },
     { name = "pyyaml" },
     { name = "safetensors" },
-    { name = "torch" },
+    { name = "torch", version = "2.7.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'darwin'" },
+    { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'darwin'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/23/60/2757c4f03a8705dbf80b1268b03881927878dca5ed07d74f733fb6c219e0/accelerate-1.11.0.tar.gz", hash = "sha256:bb1caf2597b4cd632b917b5000c591d10730bb024a79746f1ee205bba80bd229", size = 393715, upload-time = "2025-10-20T14:42:25.025Z" }
 wheels = [
@@ -62,120 +64,160 @@ wheels = [
 name = "aiohttp"
 version = "3.13.2"
 source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version >= '3.14' and python_full_version < '4' and sys_platform == 'darwin'",
+    "python_full_version >= '4' and sys_platform == 'darwin'",
+    "python_full_version == '3.13.*' and sys_platform == 'darwin'",
+    "python_full_version == '3.12.*' and sys_platform == 'darwin'",
+    "python_full_version == '3.11.*' and sys_platform == 'darwin'",
+    "python_full_version < '3.11' and sys_platform == 'darwin'",
+]
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-    { name = "async-timeout", marker = "python_full_version < '3.11'" },
-    { name = "attrs" },
-    { name = "frozenlist" },
-    { name = "multidict" },
-    { name = "propcache" },
-    { name = "yarl" },
+    { name = "aiohappyeyeballs", marker = "sys_platform == 'darwin'" },
+    { name = "aiosignal", marker = "sys_platform == 'darwin'" },
+    { name = "async-timeout", marker = "python_full_version < '3.11' and sys_platform == 'darwin'" },
+    { name = "attrs", marker = "sys_platform == 'darwin'" },
+    { name = "frozenlist", marker = "sys_platform == 'darwin'" },
+    { name = "multidict", marker = "sys_platform == 'darwin'" },
+    { name = "propcache", marker = "sys_platform == 'darwin'" },
+    { name = "yarl", marker = "sys_platform == 'darwin'" },
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     { name = "types-tqdm" },
     { name = "uvicorn" },
-    { name = "vllm", marker = "extra == 'vllm'", specifier = ">=0.9.1" },
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 provides-extras = ["hf", "vllm", "litellm", "watsonx", "docling", "smolagents", "telemetry", "all"]
 
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-    { name = "pydantic-extra-types", extra = ["pycountry"] },
-    { name = "requests" },
-    { name = "tiktoken" },
-    { name = "typing-extensions" },
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+    { name = "pillow", marker = "sys_platform != 'darwin'" },
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+    { name = "pydantic-extra-types", extra = ["pycountry"], marker = "sys_platform != 'darwin'" },
+    { name = "requests", marker = "sys_platform != 'darwin'" },
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     { name = "sentencepiece", marker = "sys_platform == 'darwin'" },
-    { name = "transformers", marker = "sys_platform == 'darwin'" },
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