diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 1aac22f6c3..f025bae5be 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -57,18 +57,17 @@ repos:
     hooks:
     -   id: uv-lock
 
--   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.16.1
+-   repo: local
     hooks:
     -   id: mypy
+        name: mypy
         additional_dependencies:
-          - uv==0.6.2
-          - mypy
-          - pytest
-          - rich
-          - types-requests
-          - pydantic
+          - uv==0.7.8
+        entry: uv run --group dev --group type_checking mypy
+        language: python
+        types: [python]
         pass_filenames: false
+        require_serial: true
 
 # - repo: https://github.com/tcort/markdown-link-check
 #   rev: v3.11.2
diff --git a/pyproject.toml b/pyproject.toml
index f5c0a39c57..1093a4c82b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -72,6 +72,12 @@ dev = [
     "black",
     "ruff",
     "mypy",
+    "pre-commit",
+    "ruamel.yaml", # needed for openapi generator
+]
+# Type checking dependencies - includes type stubs and optional runtime dependencies
+# needed for complete mypy coverage across all optional features
+type_checking = [
     "types-requests",
     "types-setuptools",
     "types-jsonschema",
@@ -79,8 +85,25 @@ dev = [
     "types-psutil",
     "types-tqdm",
     "boto3-stubs[s3]",
-    "pre-commit",
-    "ruamel.yaml", # needed for openapi generator
+    "streamlit",
+    "streamlit-option-menu",
+    "pandas",
+    "anthropic",
+    "databricks-sdk",
+    "fairscale",
+    "torchtune",
+    "trl",
+    "peft",
+    "datasets",
+    "together",
+    "nest-asyncio",
+    "pymongo",
+    "torchvision",
+    "sqlite-vec",
+    "faiss-cpu",
+    "lm-format-enforcer",
+    "mcp",
+    "ollama",
 ]
 # These are the dependencies required for running unit tests.
 unit = [
@@ -322,7 +345,17 @@ exclude = [
 
 [[tool.mypy.overrides]]
 # packages that lack typing annotations, do not have stubs, or are unavailable.
-module = ["yaml", "fire"]
+module = [
+    "yaml",
+    "fire",
+    "torchtune.*",
+    "fairscale.*",
+    "torchvision.*",
+    "datasets",
+    "nest_asyncio",
+    "streamlit_option_menu",
+    "lmformatenforcer.*",
+]
 ignore_missing_imports = true
 
 [tool.pydantic-mypy]
diff --git a/src/llama_stack/providers/inline/post_training/huggingface/recipes/finetune_single_device.py b/src/llama_stack/providers/inline/post_training/huggingface/recipes/finetune_single_device.py
index d9ee3d2a87..39b83a3fd0 100644
--- a/src/llama_stack/providers/inline/post_training/huggingface/recipes/finetune_single_device.py
+++ b/src/llama_stack/providers/inline/post_training/huggingface/recipes/finetune_single_device.py
@@ -14,7 +14,6 @@
 from datasets import Dataset
 from peft import LoraConfig
 from transformers import (
-    AutoModelForCausalLM,
     AutoTokenizer,
 )
 from trl import SFTConfig, SFTTrainer
@@ -32,6 +31,7 @@
 
 from ..config import HuggingFacePostTrainingConfig
 from ..utils import (
+    HFAutoModel,
     calculate_training_steps,
     create_checkpoints,
     get_memory_stats,
@@ -338,7 +338,7 @@ def setup_training_args(
 
     def save_model(
         self,
-        model_obj: AutoModelForCausalLM,
+        model_obj: HFAutoModel,
         trainer: SFTTrainer,
         peft_config: LoraConfig | None,
         output_dir_path: Path,
@@ -350,14 +350,22 @@ def save_model(
             peft_config: Optional LoRA configuration
             output_dir_path: Path to save the model
         """
+        from typing import cast
+
         logger.info("Saving final model")
         model_obj.config.use_cache = True
 
         if peft_config:
             logger.info("Merging LoRA weights with base model")
-            model_obj = trainer.model.merge_and_unload()
+            # TRL's merge_and_unload returns a HuggingFace model
+            # Both cast() and type: ignore are needed here:
+            # - cast() tells mypy the return type is HFAutoModel for downstream code
+            # - type: ignore suppresses errors on the merge_and_unload() call itself,
+            #   which mypy can't type-check due to TRL library's incomplete type stubs
+            model_obj = cast(HFAutoModel, trainer.model.merge_and_unload())  # type: ignore[union-attr,operator]
         else:
-            model_obj = trainer.model
+            # trainer.model is the trained HuggingFace model
+            model_obj = cast(HFAutoModel, trainer.model)
 
         save_path = output_dir_path / "merged_model"
         logger.info(f"Saving model to {save_path}")
@@ -411,7 +419,7 @@ async def _run_training(
         # Initialize trainer
         logger.info("Initializing SFTTrainer")
         trainer = SFTTrainer(
-            model=model_obj,
+            model=model_obj,  # type: ignore[arg-type]
             train_dataset=train_dataset,
             eval_dataset=eval_dataset,
             peft_config=peft_config,
diff --git a/src/llama_stack/providers/inline/post_training/huggingface/recipes/finetune_single_device_dpo.py b/src/llama_stack/providers/inline/post_training/huggingface/recipes/finetune_single_device_dpo.py
index b39a24c666..11d707df92 100644
--- a/src/llama_stack/providers/inline/post_training/huggingface/recipes/finetune_single_device_dpo.py
+++ b/src/llama_stack/providers/inline/post_training/huggingface/recipes/finetune_single_device_dpo.py
@@ -309,7 +309,7 @@ def setup_training_args(
             save_total_limit=provider_config.save_total_limit,
             # DPO specific parameters
             beta=dpo_config.beta,
-            loss_type=provider_config.dpo_loss_type,
+            loss_type=provider_config.dpo_loss_type,  # type: ignore[arg-type]
         )
 
     def save_model(
@@ -381,13 +381,16 @@ async def _run_training(
 
         # Initialize DPO trainer
         logger.info("Initializing DPOTrainer")
+        # TRL library has incomplete type stubs - use Any to bypass
+        from typing import Any, cast
+
         trainer = DPOTrainer(
-            model=model_obj,
-            ref_model=ref_model,
+            model=cast(Any, model_obj),  # HFAutoModel satisfies PreTrainedModel protocol
+            ref_model=cast(Any, ref_model),
             args=training_args,
             train_dataset=train_dataset,
             eval_dataset=eval_dataset,
-            processing_class=tokenizer,
+            processing_class=cast(Any, tokenizer),  # AutoTokenizer satisfies interface
         )
 
         try:
diff --git a/src/llama_stack/providers/inline/post_training/huggingface/utils.py b/src/llama_stack/providers/inline/post_training/huggingface/utils.py
index f229c87dd0..a930602d05 100644
--- a/src/llama_stack/providers/inline/post_training/huggingface/utils.py
+++ b/src/llama_stack/providers/inline/post_training/huggingface/utils.py
@@ -9,13 +9,31 @@
 import sys
 from datetime import UTC, datetime
 from pathlib import Path
-from typing import Any
+from typing import TYPE_CHECKING, Any, Protocol
 
 import psutil
 import torch
 from datasets import Dataset
 from transformers import AutoConfig, AutoModelForCausalLM
 
+if TYPE_CHECKING:
+    from transformers import PretrainedConfig
+
+
+class HFAutoModel(Protocol):
+    """Protocol describing HuggingFace AutoModel interface.
+
+    This protocol defines the common interface for HuggingFace AutoModelForCausalLM
+    and similar models, providing type safety without requiring type stubs.
+    """
+
+    config: PretrainedConfig
+    device: torch.device
+
+    def to(self, device: torch.device) -> "HFAutoModel": ...
+    def save_pretrained(self, save_directory: str | Path) -> None: ...
+
+
 from llama_stack.apis.datasetio import DatasetIO
 from llama_stack.apis.post_training import Checkpoint, TrainingConfig
 from llama_stack.log import get_logger
@@ -132,7 +150,7 @@ def load_model(
     model: str,
     device: torch.device,
     provider_config: HuggingFacePostTrainingConfig,
-) -> AutoModelForCausalLM:
+) -> HFAutoModel:
     """Load and initialize the model for training.
     Args:
         model: The model identifier to load
@@ -143,6 +161,8 @@ def load_model(
     Raises:
         RuntimeError: If model loading fails
     """
+    from typing import cast
+
     logger.info("Loading the base model")
     try:
         model_config = AutoConfig.from_pretrained(model, **provider_config.model_specific_config)
@@ -154,9 +174,10 @@ def load_model(
             **provider_config.model_specific_config,
         )
         # Always move model to specified device
-        model_obj = model_obj.to(device)
+        model_obj = model_obj.to(device)  # type: ignore[arg-type]
         logger.info(f"Model loaded and moved to device: {model_obj.device}")
-        return model_obj
+        # Cast to HFAutoModel protocol - transformers models satisfy this interface
+        return cast(HFAutoModel, model_obj)
     except Exception as e:
         raise RuntimeError(f"Failed to load model: {str(e)}") from e
 
diff --git a/src/llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py b/src/llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py
index 634cfe4572..c648cdc46e 100644
--- a/src/llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py
+++ b/src/llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py
@@ -193,7 +193,7 @@ async def setup(self) -> None:
         log.info("Optimizer is initialized.")
 
         self._loss_fn = CEWithChunkedOutputLoss()
-        self._model.set_num_output_chunks(self._loss_fn.num_output_chunks)
+        self._model.set_num_output_chunks(self._loss_fn.num_output_chunks)  # type: ignore[operator]
         log.info("Loss is initialized.")
 
         assert isinstance(self.training_config.data_config, DataConfig), "DataConfig must be initialized"
@@ -284,7 +284,7 @@ async def _setup_model(
         if self._is_dora:
             for m in model.modules():
                 if hasattr(m, "initialize_dora_magnitude"):
-                    m.initialize_dora_magnitude()
+                    m.initialize_dora_magnitude()  # type: ignore[operator]
         if lora_weights_state_dict:
             lora_missing, lora_unexpected = model.load_state_dict(lora_weights_state_dict, strict=False)
         else:
@@ -353,7 +353,7 @@ async def fetch_rows(dataset_id: str):
             dataset_type=self._data_format.value,
         )
 
-        sampler = DistributedSampler(
+        sampler: DistributedSampler = DistributedSampler(
             ds,
             num_replicas=1,
             rank=0,
@@ -389,7 +389,7 @@ async def _setup_lr_scheduler(
             num_training_steps=num_training_steps,
             last_epoch=last_epoch,
         )
-        return lr_scheduler
+        return lr_scheduler  # type: ignore[no-any-return]
 
     async def save_checkpoint(self, epoch: int) -> str:
         ckpt_dict = {}
@@ -447,7 +447,7 @@ async def _loss_step(self, batch: dict[str, torch.Tensor]) -> torch.Tensor:
         # free logits otherwise it peaks backward memory
         del logits
 
-        return loss
+        return loss  # type: ignore[no-any-return]
 
     async def train(self) -> tuple[dict[str, Any], list[Checkpoint]]:
         """
diff --git a/src/llama_stack/providers/inline/vector_io/faiss/faiss.py b/src/llama_stack/providers/inline/vector_io/faiss/faiss.py
index 9d8e282b03..b01eb1b5c5 100644
--- a/src/llama_stack/providers/inline/vector_io/faiss/faiss.py
+++ b/src/llama_stack/providers/inline/vector_io/faiss/faiss.py
@@ -10,7 +10,7 @@
 import json
 from typing import Any
 
-import faiss
+import faiss  # type: ignore[import-untyped]
 import numpy as np
 from numpy.typing import NDArray
 
diff --git a/src/llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py b/src/llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py
index accf5cead0..9cf7d8f44c 100644
--- a/src/llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py
+++ b/src/llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py
@@ -11,7 +11,7 @@
 from typing import Any
 
 import numpy as np
-import sqlite_vec
+import sqlite_vec  # type: ignore[import-untyped]
 from numpy.typing import NDArray
 
 from llama_stack.apis.common.errors import VectorStoreNotFoundError
diff --git a/src/llama_stack/providers/remote/inference/databricks/databricks.py b/src/llama_stack/providers/remote/inference/databricks/databricks.py
index 6b5783ec1e..8a8c5d4e32 100644
--- a/src/llama_stack/providers/remote/inference/databricks/databricks.py
+++ b/src/llama_stack/providers/remote/inference/databricks/databricks.py
@@ -32,8 +32,9 @@ def get_base_url(self) -> str:
         return f"{self.config.url}/serving-endpoints"
 
     async def list_provider_model_ids(self) -> Iterable[str]:
+        # Filter out None values from endpoint names
         return [
-            endpoint.name
+            endpoint.name  # type: ignore[misc]
             for endpoint in WorkspaceClient(
                 host=self.config.url, token=self.get_api_key()
             ).serving_endpoints.list()  # TODO: this is not async
diff --git a/src/llama_stack/providers/remote/inference/together/together.py b/src/llama_stack/providers/remote/inference/together/together.py
index 4caa4004d0..963b384a03 100644
--- a/src/llama_stack/providers/remote/inference/together/together.py
+++ b/src/llama_stack/providers/remote/inference/together/together.py
@@ -8,8 +8,8 @@
 from collections.abc import Iterable
 from typing import Any, cast
 
-from together import AsyncTogether
-from together.constants import BASE_URL
+from together import AsyncTogether  # type: ignore[import-untyped]
+from together.constants import BASE_URL  # type: ignore[import-untyped]
 
 from llama_stack.apis.inference import (
     OpenAIEmbeddingsRequestWithExtraBody,
diff --git a/src/llama_stack/testing/api_recorder.py b/src/llama_stack/testing/api_recorder.py
index e0c80d63cc..eb43019c90 100644
--- a/src/llama_stack/testing/api_recorder.py
+++ b/src/llama_stack/testing/api_recorder.py
@@ -599,7 +599,11 @@ def _combine_model_list_responses(endpoint: str, records: list[dict[str, Any]])
     if endpoint == "/api/tags":
         from ollama import ListResponse
 
-        body = ListResponse(models=ordered)
+        # Both cast(Any, ...) and type: ignore are needed here:
+        # - cast(Any, ...) attempts to bypass type checking on the argument
+        # - type: ignore is still needed because mypy checks the call site independently
+        #   and reports arg-type mismatch even after casting
+        body = ListResponse(models=cast(Any, ordered))  # type: ignore[arg-type]
     return {"request": canonical_req, "response": {"body": body, "is_streaming": False}}
 
 
diff --git a/uv.lock b/uv.lock
index a38a712ae0..21b1b3b55f 100644
--- a/uv.lock
+++ b/uv.lock
@@ -10,6 +10,25 @@ resolution-markers = [
     "python_full_version < '3.13' and sys_platform == 'darwin'",
 ]
 
+[[package]]
+name = "accelerate"
+version = "1.11.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "huggingface-hub" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "psutil" },
+    { name = "pyyaml" },
+    { name = "safetensors" },
+    { name = "torch", version = "2.8.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
+    { name = "torch", version = "2.8.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, 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 = [
+    { url = "https://files.pythonhosted.org/packages/77/85/85951bc0f9843e2c10baaa1b6657227056095de08f4d1eea7d8b423a6832/accelerate-1.11.0-py3-none-any.whl", hash = "sha256:a628fa6beb069b8e549460fc449135d5bd8d73e7a11fd09f0bc9fc4ace7f06f1", size = 375777, upload-time = "2025-10-20T14:42:23.256Z" },
+]
+
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.1"
@@ -148,6 +167,12 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/9b/38/75129688de5637eb5b383e5f2b1570a5cc3aecafa4de422da8eea4b90a6c/anthropic-0.69.0-py3-none-any.whl", hash = "sha256:1f73193040f33f11e27c2cd6ec25f24fe7c3f193dc1c5cde6b7a08b18a16bcc5", size = 337265, upload-time = "2025-09-29T16:53:43.686Z" },
 ]
 
+[[package]]
+name = "antlr4-python3-runtime"
+version = "4.9.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/3e/38/7859ff46355f76f8d19459005ca000b6e7012f2f1ca597746cbcd1fbfe5e/antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b", size = 117034, upload-time = "2021-11-06T17:52:23.524Z" }
+
 [[package]]
 name = "anyio"
 version = "4.9.0"
@@ -918,6 +943,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/12/b3/231ffd4ab1fc9d679809f356cebee130ac7daa00d6d6f3206dd4fd137e9e/distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2", size = 20277, upload-time = "2023-12-24T09:54:30.421Z" },
 ]
 
+[[package]]
+name = "dnspython"
+version = "2.8.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/8c/8b/57666417c0f90f08bcafa776861060426765fdb422eb10212086fb811d26/dnspython-2.8.0.tar.gz", hash = "sha256:181d3c6996452cb1189c4046c61599b84a5a86e099562ffde77d26984ff26d0f", size = 368251, upload-time = "2025-09-07T18:58:00.022Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ba/5a/18ad964b0086c6e62e2e7500f7edc89e3faa45033c71c1893d34eed2b2de/dnspython-2.8.0-py3-none-any.whl", hash = "sha256:01d9bbc4a2d76bf0db7c1f729812ded6d912bd318d3b1cf81d30c0f845dbf3af", size = 331094, upload-time = "2025-09-07T18:57:58.071Z" },
+]
+
 [[package]]
 name = "docstring-parser"
 version = "0.17.0"
@@ -963,6 +997,17 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/7b/8f/c4d9bafc34ad7ad5d8dc16dd1347ee0e507a52c3adb6bfa8887e1c6a26ba/executing-2.2.0-py2.py3-none-any.whl", hash = "sha256:11387150cad388d62750327a53d3339fad4888b39a6fe233c3afbb54ecffd3aa", size = 26702, upload-time = "2025-01-22T15:41:25.929Z" },
 ]
 
+[[package]]
+name = "fairscale"
+version = "0.4.13"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+    { name = "torch", version = "2.8.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
+    { name = "torch", version = "2.8.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c1/08/b3334d7b543ac10dcb129cef4f84723ab696725512f18d69ab3a784b0bf5/fairscale-0.4.13.tar.gz", hash = "sha256:1b797825c427f5dba92253fd0d8daa574e8bd651a2423497775fab1b30cfb768", size = 266261, upload-time = "2022-12-11T18:09:16.892Z" }
+
 [[package]]
 name = "faiss-cpu"
 version = "1.11.0.post1"
@@ -1386,6 +1431,38 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/69/b2/119f6e6dcbd96f9069ce9a2665e0146588dc9f88f29549711853645e736a/h2-4.3.0-py3-none-any.whl", hash = "sha256:c438f029a25f7945c69e0ccf0fb951dc3f73a5f6412981daee861431b70e2bdd", size = 61779, upload-time = "2025-08-23T18:12:17.779Z" },
 ]
 
+[[package]]
+name = "hf-transfer"
+version = "0.1.9"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/1a/eb/8fc64f40388c29ce8ce3b2b180a089d4d6b25b1d0d232d016704cb852104/hf_transfer-0.1.9.tar.gz", hash = "sha256:035572865dab29d17e783fbf1e84cf1cb24f3fcf8f1b17db1cfc7fdf139f02bf", size = 25201, upload-time = "2025-01-07T10:05:12.947Z" }
+wheels = [
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+    { name = "nest-asyncio" },
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+    { name = "pandas" },
+    { name = "pandas-stubs" },
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+    { name = "pymongo" },
+    { name = "sqlite-vec" },
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+    { name = "boto3-stubs", extras = ["s3"] },
+    { name = "databricks-sdk" },
+    { name = "datasets" },
+    { name = "fairscale" },
+    { name = "faiss-cpu" },
+    { name = "lm-format-enforcer" },
+    { name = "mcp" },
+    { name = "nest-asyncio" },
+    { name = "ollama" },
+    { name = "pandas" },
+    { name = "pandas-stubs" },
+    { name = "peft" },
+    { name = "pymongo" },
+    { name = "sqlite-vec" },
+    { name = "streamlit" },
+    { name = "streamlit-option-menu" },
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+    { name = "torchtune" },
+    { name = "torchvision", index = "https://download.pytorch.org/whl/cpu" },
+    { name = "trl" },
+    { name = "types-jsonschema" },
+    { name = "types-psutil" },
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