feat(starter)!: simplify starter distro; litellm model registry changes (#2916)

.github/workflows/integration-tests.yml

@@ -117,17 +117,13 @@ jobs:
 
           EXCLUDE_TESTS="builtin_tool or safety_with_image or code_interpreter or test_rag"
           if [ "${{ matrix.provider }}" == "ollama" ]; then
-            export ENABLE_OLLAMA="ollama"
             export OLLAMA_URL="http://0.0.0.0:11434"
-            export OLLAMA_INFERENCE_MODEL="llama3.2:3b-instruct-fp16"
+            export TEXT_MODEL=ollama/llama3.2:3b-instruct-fp16
-            export TEXT_MODEL=ollama/$OLLAMA_INFERENCE_MODEL
+            export SAFETY_MODEL="ollama/llama-guard3:1b"
-            export SAFETY_MODEL="llama-guard3:1b"
+            EXTRA_PARAMS="--safety-shield=llama-guard"
-            EXTRA_PARAMS="--safety-shield=$SAFETY_MODEL"
           else
-            export ENABLE_VLLM="vllm"
             export VLLM_URL="http://localhost:8000/v1"
-            export VLLM_INFERENCE_MODEL="meta-llama/Llama-3.2-1B-Instruct"
+            export TEXT_MODEL=vllm/meta-llama/Llama-3.2-1B-Instruct
-            export TEXT_MODEL=vllm/$VLLM_INFERENCE_MODEL
             # TODO: remove the not(test_inference_store_tool_calls) once we can get the tool called consistently
             EXTRA_PARAMS=
             EXCLUDE_TESTS="${EXCLUDE_TESTS} or test_inference_store_tool_calls"

docs/quick_start.ipynb

@@ -249,12 +249,6 @@
       ],
       "source": [
         "from llama_stack_client import Agent, AgentEventLogger, RAGDocument, LlamaStackClient\n",
-        "import os\n",
-        "\n",
-        "os.environ[\"ENABLE_OLLAMA\"] = \"ollama\"\n",
-        "os.environ[\"OLLAMA_INFERENCE_MODEL\"] = \"llama3.2:3b\"\n",
-        "os.environ[\"OLLAMA_EMBEDDING_MODEL\"] = \"all-minilm:l6-v2\"\n",
-        "os.environ[\"OLLAMA_EMBEDDING_DIMENSION\"] = \"384\"\n",
         "\n",
         "vector_db_id = \"my_demo_vector_db\"\n",
         "client = LlamaStackClient(base_url=\"http://0.0.0.0:8321\")\n",

docs/source/distributions/self_hosted_distro/nvidia.md

@@ -40,16 +40,16 @@ The following environment variables can be configured:
 
 The following models are available by default:
 
-- `meta/llama3-8b-instruct (aliases: meta-llama/Llama-3-8B-Instruct)`
+- `meta/llama3-8b-instruct `
-- `meta/llama3-70b-instruct (aliases: meta-llama/Llama-3-70B-Instruct)`
+- `meta/llama3-70b-instruct `
-- `meta/llama-3.1-8b-instruct (aliases: meta-llama/Llama-3.1-8B-Instruct)`
+- `meta/llama-3.1-8b-instruct `
-- `meta/llama-3.1-70b-instruct (aliases: meta-llama/Llama-3.1-70B-Instruct)`
+- `meta/llama-3.1-70b-instruct `
-- `meta/llama-3.1-405b-instruct (aliases: meta-llama/Llama-3.1-405B-Instruct-FP8)`
+- `meta/llama-3.1-405b-instruct `
-- `meta/llama-3.2-1b-instruct (aliases: meta-llama/Llama-3.2-1B-Instruct)`
+- `meta/llama-3.2-1b-instruct `
-- `meta/llama-3.2-3b-instruct (aliases: meta-llama/Llama-3.2-3B-Instruct)`
+- `meta/llama-3.2-3b-instruct `
-- `meta/llama-3.2-11b-vision-instruct (aliases: meta-llama/Llama-3.2-11B-Vision-Instruct)`
+- `meta/llama-3.2-11b-vision-instruct `
-- `meta/llama-3.2-90b-vision-instruct (aliases: meta-llama/Llama-3.2-90B-Vision-Instruct)`
+- `meta/llama-3.2-90b-vision-instruct `
-- `meta/llama-3.3-70b-instruct (aliases: meta-llama/Llama-3.3-70B-Instruct)`
+- `meta/llama-3.3-70b-instruct `
 - `nvidia/llama-3.2-nv-embedqa-1b-v2 `
 - `nvidia/nv-embedqa-e5-v5 `
 - `nvidia/nv-embedqa-mistral-7b-v2 `

docs/source/distributions/self_hosted_distro/starter.md

@@ -158,7 +158,7 @@ export ENABLE_PGVECTOR=__disabled__
 The starter distribution uses several patterns for provider IDs:
 
 1. **Direct provider IDs**: `faiss`, `ollama`, `vllm`
-2. **Environment-based provider IDs**: `${env.ENABLE_SQLITE_VEC+sqlite-vec}`
+2. **Environment-based provider IDs**: `${env.ENABLE_SQLITE_VEC:+sqlite-vec}`
 3. **Model-based provider IDs**: `${env.OLLAMA_INFERENCE_MODEL:__disabled__}`
 
 When using the `+` pattern (like `${env.ENABLE_SQLITE_VEC+sqlite-vec}`), the provider is enabled by default and can be disabled by setting the environment variable to `__disabled__`.

docs/source/getting_started/detailed_tutorial.md

@@ -59,7 +59,7 @@ Now let's build and run the Llama Stack config for Ollama.
 We use `starter` as template. By default all providers are disabled, this requires enable ollama by passing environment variables.
 
 ```bash
-ENABLE_OLLAMA=ollama OLLAMA_INFERENCE_MODEL="llama3.2:3b" llama stack build --template starter --image-type venv --run
+llama stack build --template starter --image-type venv --run
 ```
 :::
 :::{tab-item} Using `conda`
@@ -70,7 +70,7 @@ which defines the providers and their settings.
 Now let's build and run the Llama Stack config for Ollama.
 
 ```bash
-ENABLE_OLLAMA=ollama INFERENCE_MODEL="llama3.2:3b" llama stack build --template starter --image-type conda --run
+llama stack build --template starter --image-type conda --run
 ```
 :::
 :::{tab-item} Using a Container
@@ -80,8 +80,6 @@ component that works with different inference providers out of the box. For this
 configurations, please check out [this guide](../distributions/building_distro.md).
 First lets setup some environment variables and create a local directory to mount into the container’s file system.
 ```bash
-export INFERENCE_MODEL="llama3.2:3b"
-export ENABLE_OLLAMA=ollama
 export LLAMA_STACK_PORT=8321
 mkdir -p ~/.llama
 ```
@@ -94,7 +92,6 @@ docker run -it \
   -v ~/.llama:/root/.llama \
   llamastack/distribution-starter \
   --port $LLAMA_STACK_PORT \
-  --env INFERENCE_MODEL=$INFERENCE_MODEL \
   --env OLLAMA_URL=http://host.docker.internal:11434
 ```
 Note to start the container with Podman, you can do the same but replace `docker` at the start of the command with
@@ -116,7 +113,6 @@ docker run -it \
   --network=host \
   llamastack/distribution-starter \
   --port $LLAMA_STACK_PORT \
-  --env INFERENCE_MODEL=$INFERENCE_MODEL \
   --env OLLAMA_URL=http://localhost:11434
 ```
 :::

docs/source/getting_started/quickstart.md

@@ -19,7 +19,7 @@ ollama run llama3.2:3b --keepalive 60m
 #### Step 2: Run the Llama Stack server
 We will use `uv` to run the Llama Stack server.
 ```bash
-ENABLE_OLLAMA=ollama OLLAMA_INFERENCE_MODEL=llama3.2:3b uv run --with llama-stack llama stack build --template starter --image-type venv --run
+uv run --with llama-stack llama stack build --template starter --image-type venv --run
 ```
 #### Step 3: Run the demo
 Now open up a new terminal and copy the following script into a file named `demo_script.py`.

docs/source/providers/inference/remote_anthropic.md

@@ -13,7 +13,7 @@ Anthropic inference provider for accessing Claude models and Anthropic's AI serv
 ## Sample Configuration
 
 ```yaml
-api_key: ${env.ANTHROPIC_API_KEY}
+api_key: ${env.ANTHROPIC_API_KEY:=}
 
 ```
 

docs/source/providers/inference/remote_cerebras.md

@@ -15,7 +15,7 @@ Cerebras inference provider for running models on Cerebras Cloud platform.
 
 ```yaml
 base_url: https://api.cerebras.ai
-api_key: ${env.CEREBRAS_API_KEY}
+api_key: ${env.CEREBRAS_API_KEY:=}
 
 ```
 

docs/source/providers/inference/remote_databricks.md

@@ -14,8 +14,8 @@ Databricks inference provider for running models on Databricks' unified analytic
 ## Sample Configuration
 
 ```yaml
-url: ${env.DATABRICKS_URL}
+url: ${env.DATABRICKS_URL:=}
-api_token: ${env.DATABRICKS_API_TOKEN}
+api_token: ${env.DATABRICKS_API_TOKEN:=}
 
 ```
 

docs/source/providers/inference/remote_fireworks.md

@@ -16,7 +16,7 @@ Fireworks AI inference provider for Llama models and other AI models on the Fire
 
 ```yaml
 url: https://api.fireworks.ai/inference/v1
-api_key: ${env.FIREWORKS_API_KEY}
+api_key: ${env.FIREWORKS_API_KEY:=}
 
 ```
 

docs/source/providers/inference/remote_gemini.md

@@ -13,7 +13,7 @@ Google Gemini inference provider for accessing Gemini models and Google's AI ser
 ## Sample Configuration
 
 ```yaml
-api_key: ${env.GEMINI_API_KEY}
+api_key: ${env.GEMINI_API_KEY:=}
 
 ```
 

docs/source/providers/inference/remote_groq.md

@@ -15,7 +15,7 @@ Groq inference provider for ultra-fast inference using Groq's LPU technology.
 
 ```yaml
 url: https://api.groq.com
-api_key: ${env.GROQ_API_KEY}
+api_key: ${env.GROQ_API_KEY:=}
 
 ```
 

docs/source/providers/inference/remote_openai.md

@@ -13,7 +13,7 @@ OpenAI inference provider for accessing GPT models and other OpenAI services.
 ## Sample Configuration
 
 ```yaml
-api_key: ${env.OPENAI_API_KEY}
+api_key: ${env.OPENAI_API_KEY:=}
 
 ```
 

docs/source/providers/inference/remote_sambanova-openai-compat.md

@@ -15,7 +15,7 @@ SambaNova OpenAI-compatible provider for using SambaNova models with OpenAI API
 
 ```yaml
 openai_compat_api_base: https://api.sambanova.ai/v1
-api_key: ${env.SAMBANOVA_API_KEY}
+api_key: ${env.SAMBANOVA_API_KEY:=}
 
 ```
 

docs/source/providers/inference/remote_sambanova.md

@@ -15,7 +15,7 @@ SambaNova inference provider for running models on SambaNova's dataflow architec
 
 ```yaml
 url: https://api.sambanova.ai/v1
-api_key: ${env.SAMBANOVA_API_KEY}
+api_key: ${env.SAMBANOVA_API_KEY:=}
 
 ```
 

docs/source/providers/inference/remote_tgi.md

@@ -13,7 +13,7 @@ Text Generation Inference (TGI) provider for HuggingFace model serving.
 ## Sample Configuration
 
 ```yaml
-url: ${env.TGI_URL}
+url: ${env.TGI_URL:=}
 
 ```
 

docs/source/providers/inference/remote_together.md

@@ -16,7 +16,7 @@ Together AI inference provider for open-source models and collaborative AI devel
 
 ```yaml
 url: https://api.together.xyz/v1
-api_key: ${env.TOGETHER_API_KEY}
+api_key: ${env.TOGETHER_API_KEY:=}
 
 ```
 

docs/source/providers/safety/remote_sambanova.md

@@ -15,7 +15,7 @@ SambaNova's safety provider for content moderation and safety filtering.
 
 ```yaml
 url: https://api.sambanova.ai/v1
-api_key: ${env.SAMBANOVA_API_KEY}
+api_key: ${env.SAMBANOVA_API_KEY:=}
 
 ```
 

llama_stack/distribution/routing_tables/models.py

@@ -25,7 +25,8 @@ class ModelsRoutingTable(CommonRoutingTableImpl, Models):
     async def refresh(self) -> None:
         for provider_id, provider in self.impls_by_provider_id.items():
             refresh = await provider.should_refresh_models()
-            if not (refresh or provider_id in self.listed_providers):
+            refresh = refresh or provider_id not in self.listed_providers
+            if not refresh:
                 continue
 
             try:
@@ -138,6 +139,9 @@ class ModelsRoutingTable(CommonRoutingTableImpl, Models):
                 # avoid overwriting a non-provider-registered model entry
                 continue
 
+            if model.identifier == model.provider_resource_id:
+                model.identifier = f"{provider_id}/{model.provider_resource_id}"
+
             logger.debug(f"registering model {model.identifier} ({model.provider_resource_id})")
             await self.register_object(
                 ModelWithOwner(

llama_stack/distribution/server/server.py

@@ -611,11 +611,8 @@ def extract_path_params(route: str) -> list[str]:
 
 def remove_disabled_providers(obj):
     if isinstance(obj, dict):
-        if (
+        keys = ["provider_id", "shield_id", "provider_model_id", "model_id"]
-            obj.get("provider_id") == "__disabled__"
+        if any(k in obj and obj[k] in ("__disabled__", "", None) for k in keys):
-            or obj.get("shield_id") == "__disabled__"
-            or obj.get("provider_model_id") == "__disabled__"
-        ):
             return None
         return {k: v for k, v in ((k, remove_disabled_providers(v)) for k, v in obj.items()) if v is not None}
     elif isinstance(obj, list):

llama_stack/distribution/stack.py

@@ -105,23 +105,10 @@ async def register_resources(run_config: StackRunConfig, impls: dict[Api, Any]):
         method = getattr(impls[api], register_method)
         for obj in objects:
             logger.debug(f"registering {rsrc.capitalize()} {obj} for provider {obj.provider_id}")
-            # Do not register models on disabled providers
-            if hasattr(obj, "provider_id") and obj.provider_id is not None and obj.provider_id == "__disabled__":
-                logger.debug(f"Skipping {rsrc.capitalize()} registration for disabled provider.")
-                continue
-            # In complex templates, like our starter template, we may have dynamic model ids
-            # given by environment variables. This allows those environment variables to have
-            # a default value of __disabled__ to skip registration of the model if not set.
-            if (
-                hasattr(obj, "provider_model_id")
-                and obj.provider_model_id is not None
-                and "__disabled__" in obj.provider_model_id
-            ):
-                logger.debug(f"Skipping {rsrc.capitalize()} registration for disabled model.")
-                continue
 
-            if hasattr(obj, "shield_id") and obj.shield_id is not None and obj.shield_id == "__disabled__":
+            # Do not register models on disabled providers
-                logger.debug(f"Skipping {rsrc.capitalize()} registration for disabled shield.")
+            if hasattr(obj, "provider_id") and (not obj.provider_id or obj.provider_id == "__disabled__"):
+                logger.debug(f"Skipping {rsrc.capitalize()} registration for disabled provider.")
                 continue
 
             # we want to maintain the type information in arguments to method.
@@ -331,8 +318,10 @@ async def construct_stack(
 
     await register_resources(run_config, impls)
 
+    await refresh_registry_once(impls)
+
     global REGISTRY_REFRESH_TASK
-    REGISTRY_REFRESH_TASK = asyncio.create_task(refresh_registry(impls))
+    REGISTRY_REFRESH_TASK = asyncio.create_task(refresh_registry_task(impls))
 
     def cb(task):
         import traceback
@@ -368,12 +357,18 @@ async def shutdown_stack(impls: dict[Api, Any]):
         REGISTRY_REFRESH_TASK.cancel()
 
 
-async def refresh_registry(impls: dict[Api, Any]):
+async def refresh_registry_once(impls: dict[Api, Any]):
+    logger.info("refreshing registry")
     routing_tables = [v for v in impls.values() if isinstance(v, CommonRoutingTableImpl)]
-    while True:
     for routing_table in routing_tables:
         await routing_table.refresh()
 
+
+async def refresh_registry_task(impls: dict[Api, Any]):
+    logger.info("starting registry refresh task")
+    while True:
+        await refresh_registry_once(impls)
+
         await asyncio.sleep(REGISTRY_REFRESH_INTERVAL_SECONDS)
 
 

llama_stack/providers/datatypes.py

@@ -43,6 +43,9 @@ class ModelsProtocolPrivate(Protocol):
        -> Provider uses provider-model-id for inference
     """
 
+    # this should be called `on_model_register` or something like that.
+    # the provider should _not_ be able to change the object in this
+    # callback
     async def register_model(self, model: Model) -> Model: ...
 
     async def unregister_model(self, model_id: str) -> None: ...

llama_stack/providers/inline/safety/llama_guard/llama_guard.py

@@ -146,9 +146,9 @@ class LlamaGuardSafetyImpl(Safety, ShieldsProtocolPrivate):
         pass
 
     async def register_shield(self, shield: Shield) -> None:
-        # Allow any model to be registered as a shield
+        model_id = shield.provider_resource_id
-        # The model will be validated during runtime when making inference calls
+        if not model_id:
-        pass
+            raise ValueError("Llama Guard shield must have a model id")
 
     async def run_shield(
         self,

llama_stack/providers/remote/inference/anthropic/anthropic.py

@@ -15,6 +15,7 @@ class AnthropicInferenceAdapter(LiteLLMOpenAIMixin):
         LiteLLMOpenAIMixin.__init__(
             self,
             MODEL_ENTRIES,
+            litellm_provider_name="anthropic",
             api_key_from_config=config.api_key,
             provider_data_api_key_field="anthropic_api_key",
         )

llama_stack/providers/remote/inference/anthropic/config.py

@@ -26,7 +26,7 @@ class AnthropicConfig(BaseModel):
     )
 
     @classmethod
-    def sample_run_config(cls, api_key: str = "${env.ANTHROPIC_API_KEY}", **kwargs) -> dict[str, Any]:
+    def sample_run_config(cls, api_key: str = "${env.ANTHROPIC_API_KEY:=}", **kwargs) -> dict[str, Any]:
         return {
             "api_key": api_key,
         }

llama_stack/providers/remote/inference/anthropic/models.py

@@ -10,9 +10,9 @@ from llama_stack.providers.utils.inference.model_registry import (
 )
 
 LLM_MODEL_IDS = [
-    "anthropic/claude-3-5-sonnet-latest",
+    "claude-3-5-sonnet-latest",
-    "anthropic/claude-3-7-sonnet-latest",
+    "claude-3-7-sonnet-latest",
-    "anthropic/claude-3-5-haiku-latest",
+    "claude-3-5-haiku-latest",
 ]
 
 SAFETY_MODELS_ENTRIES = []
@@ -21,17 +21,17 @@ MODEL_ENTRIES = (
     [ProviderModelEntry(provider_model_id=m) for m in LLM_MODEL_IDS]
     + [
         ProviderModelEntry(
-            provider_model_id="anthropic/voyage-3",
+            provider_model_id="voyage-3",
             model_type=ModelType.embedding,
             metadata={"embedding_dimension": 1024, "context_length": 32000},
         ),
         ProviderModelEntry(
-            provider_model_id="anthropic/voyage-3-lite",
+            provider_model_id="voyage-3-lite",
             model_type=ModelType.embedding,
             metadata={"embedding_dimension": 512, "context_length": 32000},
         ),
         ProviderModelEntry(
-            provider_model_id="anthropic/voyage-code-3",
+            provider_model_id="voyage-code-3",
             model_type=ModelType.embedding,
             metadata={"embedding_dimension": 1024, "context_length": 32000},
         ),

llama_stack/providers/remote/inference/bedrock/bedrock.py

@@ -63,18 +63,20 @@ class BedrockInferenceAdapter(
     def __init__(self, config: BedrockConfig) -> None:
         ModelRegistryHelper.__init__(self, MODEL_ENTRIES)
         self._config = config
-
+        self._client = None
-        self._client = create_bedrock_client(config)
 
     @property
     def client(self) -> BaseClient:
+        if self._client is None:
+            self._client = create_bedrock_client(self._config)
         return self._client
 
     async def initialize(self) -> None:
         pass
 
     async def shutdown(self) -> None:
-        self.client.close()
+        if self._client is not None:
+            self._client.close()
 
     async def completion(
         self,

llama_stack/providers/remote/inference/cerebras/cerebras.py

@@ -65,6 +65,7 @@ class CerebrasInferenceAdapter(
         )
         self.config = config
 
+        # TODO: make this use provider data, etc. like other providers
         self.client = AsyncCerebras(
             base_url=self.config.base_url,
             api_key=self.config.api_key.get_secret_value(),

llama_stack/providers/remote/inference/cerebras/config.py

@@ -26,7 +26,7 @@ class CerebrasImplConfig(BaseModel):
     )
 
     @classmethod
-    def sample_run_config(cls, api_key: str = "${env.CEREBRAS_API_KEY}", **kwargs) -> dict[str, Any]:
+    def sample_run_config(cls, api_key: str = "${env.CEREBRAS_API_KEY:=}", **kwargs) -> dict[str, Any]:
         return {
             "base_url": DEFAULT_BASE_URL,
             "api_key": api_key,

llama_stack/providers/remote/inference/databricks/config.py

@@ -25,8 +25,8 @@ class DatabricksImplConfig(BaseModel):
     @classmethod
     def sample_run_config(
         cls,
-        url: str = "${env.DATABRICKS_URL}",
+        url: str = "${env.DATABRICKS_URL:=}",
-        api_token: str = "${env.DATABRICKS_API_TOKEN}",
+        api_token: str = "${env.DATABRICKS_API_TOKEN:=}",
         **kwargs: Any,
     ) -> dict[str, Any]:
         return {

llama_stack/providers/remote/inference/fireworks/config.py

@@ -24,7 +24,7 @@ class FireworksImplConfig(RemoteInferenceProviderConfig):
     )
 
     @classmethod
-    def sample_run_config(cls, api_key: str = "${env.FIREWORKS_API_KEY}", **kwargs) -> dict[str, Any]:
+    def sample_run_config(cls, api_key: str = "${env.FIREWORKS_API_KEY:=}", **kwargs) -> dict[str, Any]:
         return {
             "url": "https://api.fireworks.ai/inference/v1",
             "api_key": api_key,

llama_stack/providers/remote/inference/gemini/config.py

@@ -26,7 +26,7 @@ class GeminiConfig(BaseModel):
     )
 
     @classmethod
-    def sample_run_config(cls, api_key: str = "${env.GEMINI_API_KEY}", **kwargs) -> dict[str, Any]:
+    def sample_run_config(cls, api_key: str = "${env.GEMINI_API_KEY:=}", **kwargs) -> dict[str, Any]:
         return {
             "api_key": api_key,
         }

llama_stack/providers/remote/inference/gemini/gemini.py

@@ -15,6 +15,7 @@ class GeminiInferenceAdapter(LiteLLMOpenAIMixin):
         LiteLLMOpenAIMixin.__init__(
             self,
             MODEL_ENTRIES,
+            litellm_provider_name="gemini",
             api_key_from_config=config.api_key,
             provider_data_api_key_field="gemini_api_key",
         )

llama_stack/providers/remote/inference/gemini/models.py

@@ -10,11 +10,11 @@ from llama_stack.providers.utils.inference.model_registry import (
 )
 
 LLM_MODEL_IDS = [
-    "gemini/gemini-1.5-flash",
+    "gemini-1.5-flash",
-    "gemini/gemini-1.5-pro",
+    "gemini-1.5-pro",
-    "gemini/gemini-2.0-flash",
+    "gemini-2.0-flash",
-    "gemini/gemini-2.5-flash",
+    "gemini-2.5-flash",
-    "gemini/gemini-2.5-pro",
+    "gemini-2.5-pro",
 ]
 
 SAFETY_MODELS_ENTRIES = []
@@ -23,7 +23,7 @@ MODEL_ENTRIES = (
     [ProviderModelEntry(provider_model_id=m) for m in LLM_MODEL_IDS]
     + [
         ProviderModelEntry(
-            provider_model_id="gemini/text-embedding-004",
+            provider_model_id="text-embedding-004",
             model_type=ModelType.embedding,
             metadata={"embedding_dimension": 768, "context_length": 2048},
         ),

llama_stack/providers/remote/inference/groq/config.py

@@ -32,7 +32,7 @@ class GroqConfig(BaseModel):
     )
 
     @classmethod
-    def sample_run_config(cls, api_key: str = "${env.GROQ_API_KEY}", **kwargs) -> dict[str, Any]:
+    def sample_run_config(cls, api_key: str = "${env.GROQ_API_KEY:=}", **kwargs) -> dict[str, Any]:
         return {
             "url": "https://api.groq.com",
             "api_key": api_key,

llama_stack/providers/remote/inference/groq/groq.py

@@ -34,6 +34,7 @@ class GroqInferenceAdapter(LiteLLMOpenAIMixin):
         LiteLLMOpenAIMixin.__init__(
             self,
             model_entries=MODEL_ENTRIES,
+            litellm_provider_name="groq",
             api_key_from_config=config.api_key,
             provider_data_api_key_field="groq_api_key",
         )
@@ -96,7 +97,7 @@ class GroqInferenceAdapter(LiteLLMOpenAIMixin):
             tool_choice = "required"
 
         params = await prepare_openai_completion_params(
-            model=model_obj.provider_resource_id.replace("groq/", ""),
+            model=model_obj.provider_resource_id,
             messages=messages,
             frequency_penalty=frequency_penalty,
             function_call=function_call,

llama_stack/providers/remote/inference/groq/models.py

@@ -14,19 +14,19 @@ SAFETY_MODELS_ENTRIES = []
 
 MODEL_ENTRIES = [
     build_hf_repo_model_entry(
-        "groq/llama3-8b-8192",
+        "llama3-8b-8192",
         CoreModelId.llama3_1_8b_instruct.value,
     ),
     build_model_entry(
-        "groq/llama-3.1-8b-instant",
+        "llama-3.1-8b-instant",
         CoreModelId.llama3_1_8b_instruct.value,
     ),
     build_hf_repo_model_entry(
-        "groq/llama3-70b-8192",
+        "llama3-70b-8192",
         CoreModelId.llama3_70b_instruct.value,
     ),
     build_hf_repo_model_entry(
-        "groq/llama-3.3-70b-versatile",
+        "llama-3.3-70b-versatile",
         CoreModelId.llama3_3_70b_instruct.value,
     ),
     # Groq only contains a preview version for llama-3.2-3b
@@ -34,23 +34,15 @@ MODEL_ENTRIES = [
     # to pass the test fixture
     # TODO(aidand): Replace this with a stable model once Groq supports it
     build_hf_repo_model_entry(
-        "groq/llama-3.2-3b-preview",
+        "llama-3.2-3b-preview",
         CoreModelId.llama3_2_3b_instruct.value,
     ),
     build_hf_repo_model_entry(
-        "groq/llama-4-scout-17b-16e-instruct",
+        "meta-llama/llama-4-scout-17b-16e-instruct",
         CoreModelId.llama4_scout_17b_16e_instruct.value,
     ),
     build_hf_repo_model_entry(
-        "groq/meta-llama/llama-4-scout-17b-16e-instruct",
+        "meta-llama/llama-4-maverick-17b-128e-instruct",
-        CoreModelId.llama4_scout_17b_16e_instruct.value,
-    ),
-    build_hf_repo_model_entry(
-        "groq/llama-4-maverick-17b-128e-instruct",
-        CoreModelId.llama4_maverick_17b_128e_instruct.value,
-    ),
-    build_hf_repo_model_entry(
-        "groq/meta-llama/llama-4-maverick-17b-128e-instruct",
         CoreModelId.llama4_maverick_17b_128e_instruct.value,
     ),
 ] + SAFETY_MODELS_ENTRIES

llama_stack/providers/remote/inference/llama_openai_compat/llama.py

@@ -32,6 +32,7 @@ class LlamaCompatInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
         LiteLLMOpenAIMixin.__init__(
             self,
             model_entries=MODEL_ENTRIES,
+            litellm_provider_name="llama",
             api_key_from_config=config.api_key,
             provider_data_api_key_field="llama_api_key",
             openai_compat_api_base=config.openai_compat_api_base,

llama_stack/providers/remote/inference/ollama/ollama.py

@@ -166,7 +166,7 @@ class OllamaInferenceAdapter(
         ]
         for m in response.models:
             # kill embedding models since we don't know dimensions for them
-            if m.details.family in ["bert"]:
+            if "bert" in m.details.family:
                 continue
             models.append(
                 Model(
@@ -420,9 +420,6 @@ class OllamaInferenceAdapter(
         except ValueError:
             pass  # Ignore statically unknown model, will check live listing
 
-        if model.provider_resource_id is None:
-            raise ValueError("Model provider_resource_id cannot be None")
-
         if model.model_type == ModelType.embedding:
             response = await self.client.list()
             if model.provider_resource_id not in [m.model for m in response.models]:
@@ -433,9 +430,9 @@ class OllamaInferenceAdapter(
         #  - models not currently running are run by the ollama server as needed
         response = await self.client.list()
         available_models = [m.model for m in response.models]
-        provider_resource_id = self.register_helper.get_provider_model_id(model.provider_resource_id)
+
-        if provider_resource_id is None:
         provider_resource_id = model.provider_resource_id
+        assert provider_resource_id is not None  # mypy
         if provider_resource_id not in available_models:
             available_models_latest = [m.model.split(":latest")[0] for m in response.models]
             if provider_resource_id in available_models_latest:
@@ -443,7 +440,9 @@ class OllamaInferenceAdapter(
                     f"Imprecise provider resource id was used but 'latest' is available in Ollama - using '{model.provider_resource_id}:latest'"
                 )
                 return model
-            raise UnsupportedModelError(model.provider_resource_id, available_models)
+            raise UnsupportedModelError(provider_resource_id, available_models)
+
+        # mutating this should be considered an anti-pattern
         model.provider_resource_id = provider_resource_id
 
         return model

llama_stack/providers/remote/inference/openai/config.py

@@ -26,7 +26,7 @@ class OpenAIConfig(BaseModel):
     )
 
     @classmethod
-    def sample_run_config(cls, api_key: str = "${env.OPENAI_API_KEY}", **kwargs) -> dict[str, Any]:
+    def sample_run_config(cls, api_key: str = "${env.OPENAI_API_KEY:=}", **kwargs) -> dict[str, Any]:
         return {
             "api_key": api_key,
         }

llama_stack/providers/remote/inference/openai/openai.py

@@ -45,6 +45,7 @@ class OpenAIInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
         LiteLLMOpenAIMixin.__init__(
             self,
             MODEL_ENTRIES,
+            litellm_provider_name="openai",
             api_key_from_config=config.api_key,
             provider_data_api_key_field="openai_api_key",
         )

llama_stack/providers/remote/inference/sambanova/config.py

@@ -30,7 +30,7 @@ class SambaNovaImplConfig(BaseModel):
     )
 
     @classmethod
-    def sample_run_config(cls, api_key: str = "${env.SAMBANOVA_API_KEY}", **kwargs) -> dict[str, Any]:
+    def sample_run_config(cls, api_key: str = "${env.SAMBANOVA_API_KEY:=}", **kwargs) -> dict[str, Any]:
         return {
             "url": "https://api.sambanova.ai/v1",
             "api_key": api_key,

llama_stack/providers/remote/inference/sambanova/models.py

@@ -9,49 +9,20 @@ from llama_stack.providers.utils.inference.model_registry import (
     build_hf_repo_model_entry,
 )
 
-SAFETY_MODELS_ENTRIES = [
+SAFETY_MODELS_ENTRIES = []
-    build_hf_repo_model_entry(
-        "sambanova/Meta-Llama-Guard-3-8B",
-        CoreModelId.llama_guard_3_8b.value,
-    ),
-]
 
 
 MODEL_ENTRIES = [
     build_hf_repo_model_entry(
-        "sambanova/Meta-Llama-3.1-8B-Instruct",
+        "Meta-Llama-3.1-8B-Instruct",
         CoreModelId.llama3_1_8b_instruct.value,
     ),
     build_hf_repo_model_entry(
-        "sambanova/Meta-Llama-3.1-405B-Instruct",
+        "Meta-Llama-3.3-70B-Instruct",
-        CoreModelId.llama3_1_405b_instruct.value,
-    ),
-    build_hf_repo_model_entry(
-        "sambanova/Meta-Llama-3.2-1B-Instruct",
-        CoreModelId.llama3_2_1b_instruct.value,
-    ),
-    build_hf_repo_model_entry(
-        "sambanova/Meta-Llama-3.2-3B-Instruct",
-        CoreModelId.llama3_2_3b_instruct.value,
-    ),
-    build_hf_repo_model_entry(
-        "sambanova/Meta-Llama-3.3-70B-Instruct",
         CoreModelId.llama3_3_70b_instruct.value,
     ),
     build_hf_repo_model_entry(
-        "sambanova/Llama-3.2-11B-Vision-Instruct",
+        "Llama-4-Maverick-17B-128E-Instruct",
-        CoreModelId.llama3_2_11b_vision_instruct.value,
-    ),
-    build_hf_repo_model_entry(
-        "sambanova/Llama-3.2-90B-Vision-Instruct",
-        CoreModelId.llama3_2_90b_vision_instruct.value,
-    ),
-    build_hf_repo_model_entry(
-        "sambanova/Llama-4-Scout-17B-16E-Instruct",
-        CoreModelId.llama4_scout_17b_16e_instruct.value,
-    ),
-    build_hf_repo_model_entry(
-        "sambanova/Llama-4-Maverick-17B-128E-Instruct",
         CoreModelId.llama4_maverick_17b_128e_instruct.value,
     ),
 ] + SAFETY_MODELS_ENTRIES

llama_stack/providers/remote/inference/sambanova/sambanova.py

@@ -182,6 +182,7 @@ class SambaNovaInferenceAdapter(LiteLLMOpenAIMixin):
         LiteLLMOpenAIMixin.__init__(
             self,
             model_entries=MODEL_ENTRIES,
+            litellm_provider_name="sambanova",
             api_key_from_config=self.config.api_key.get_secret_value() if self.config.api_key else None,
             provider_data_api_key_field="sambanova_api_key",
         )

llama_stack/providers/remote/inference/tgi/config.py

@@ -19,7 +19,7 @@ class TGIImplConfig(BaseModel):
     @classmethod
     def sample_run_config(
         cls,
-        url: str = "${env.TGI_URL}",
+        url: str = "${env.TGI_URL:=}",
         **kwargs,
     ):
         return {

llama_stack/providers/remote/inference/tgi/tgi.py

@@ -305,6 +305,8 @@ class _HfAdapter(
 
 class TGIAdapter(_HfAdapter):
     async def initialize(self, config: TGIImplConfig) -> None:
+        if not config.url:
+            raise ValueError("You must provide a URL in run.yaml (or via the TGI_URL environment variable) to use TGI.")
         log.info(f"Initializing TGI client with url={config.url}")
         self.client = AsyncInferenceClient(
             model=config.url,

llama_stack/providers/remote/inference/together/config.py

@@ -27,5 +27,5 @@ class TogetherImplConfig(RemoteInferenceProviderConfig):
     def sample_run_config(cls, **kwargs) -> dict[str, Any]:
         return {
             "url": "https://api.together.xyz/v1",
-            "api_key": "${env.TOGETHER_API_KEY}",
+            "api_key": "${env.TOGETHER_API_KEY:=}",
         }

llama_stack/providers/remote/inference/together/models.py

@@ -69,15 +69,9 @@ MODEL_ENTRIES = [
     build_hf_repo_model_entry(
         "meta-llama/Llama-4-Scout-17B-16E-Instruct",
         CoreModelId.llama4_scout_17b_16e_instruct.value,
-        additional_aliases=[
-            "together/meta-llama/Llama-4-Scout-17B-16E-Instruct",
-        ],
     ),
     build_hf_repo_model_entry(
         "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
         CoreModelId.llama4_maverick_17b_128e_instruct.value,
-        additional_aliases=[
-            "together/meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
-        ],
     ),
 ] + SAFETY_MODELS_ENTRIES

llama_stack/providers/remote/inference/vllm/vllm.py

@@ -299,7 +299,10 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
         self.client = None
 
     async def initialize(self) -> None:
-        pass
+        if not self.config.url:
+            raise ValueError(
+                "You must provide a URL in run.yaml (or via the VLLM_URL environment variable) to use vLLM."
+            )
 
     async def should_refresh_models(self) -> bool:
         return self.config.refresh_models
@@ -337,9 +340,6 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
             HealthResponse: A dictionary containing the health status.
         """
         try:
-            if not self.config.url:
-                return HealthResponse(status=HealthStatus.ERROR, message="vLLM URL is not set")
-
             client = self._create_client() if self.client is None else self.client
             _ = [m async for m in client.models.list()]  # Ensure the client is initialized
             return HealthResponse(status=HealthStatus.OK)
@@ -355,11 +355,6 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
         if self.client is not None:
             return
 
-        if not self.config.url:
-            raise ValueError(
-                "You must provide a vLLM URL in the run.yaml file (or set the VLLM_URL environment variable)"
-            )
-
         log.info(f"Initializing vLLM client with base_url={self.config.url}")
         self.client = self._create_client()
 

llama_stack/providers/remote/safety/sambanova/config.py

@@ -30,7 +30,7 @@ class SambaNovaSafetyConfig(BaseModel):
     )
 
     @classmethod
-    def sample_run_config(cls, api_key: str = "${env.SAMBANOVA_API_KEY}", **kwargs) -> dict[str, Any]:
+    def sample_run_config(cls, api_key: str = "${env.SAMBANOVA_API_KEY:=}", **kwargs) -> dict[str, Any]:
         return {
             "url": "https://api.sambanova.ai/v1",
             "api_key": api_key,

llama_stack/providers/utils/inference/litellm_openai_mixin.py

@@ -68,11 +68,14 @@ class LiteLLMOpenAIMixin(
     def __init__(
         self,
         model_entries,
+        litellm_provider_name: str,
         api_key_from_config: str | None,
         provider_data_api_key_field: str,
         openai_compat_api_base: str | None = None,
     ):
         ModelRegistryHelper.__init__(self, model_entries)
+
+        self.litellm_provider_name = litellm_provider_name
         self.api_key_from_config = api_key_from_config
         self.provider_data_api_key_field = provider_data_api_key_field
         self.api_base = openai_compat_api_base
@@ -91,7 +94,11 @@ class LiteLLMOpenAIMixin(
     def get_litellm_model_name(self, model_id: str) -> str:
         # users may be using openai/ prefix in their model names. the openai/models.py did this by default.
         # model_id.startswith("openai/") is for backwards compatibility.
-        return "openai/" + model_id if self.is_openai_compat and not model_id.startswith("openai/") else model_id
+        return (
+            f"{self.litellm_provider_name}/{model_id}"
+            if self.is_openai_compat and not model_id.startswith(self.litellm_provider_name)
+            else model_id
+        )
 
     async def completion(
         self,

llama_stack/providers/utils/inference/model_registry.py

@@ -50,7 +50,8 @@ def build_hf_repo_model_entry(
     additional_aliases: list[str] | None = None,
 ) -> ProviderModelEntry:
     aliases = [
-        get_huggingface_repo(model_descriptor),
+        # NOTE: avoid HF aliases because they _cannot_ be unique across providers
+        # get_huggingface_repo(model_descriptor),
     ]
     if additional_aliases:
         aliases.extend(additional_aliases)
@@ -75,7 +76,9 @@ class ModelRegistryHelper(ModelsProtocolPrivate):
     __provider_id__: str
 
     def __init__(self, model_entries: list[ProviderModelEntry], allowed_models: list[str] | None = None):
+        self.model_entries = model_entries
         self.allowed_models = allowed_models
+
         self.alias_to_provider_id_map = {}
         self.provider_id_to_llama_model_map = {}
         for entry in model_entries:
@@ -98,7 +101,7 @@ class ModelRegistryHelper(ModelsProtocolPrivate):
                     continue
                 models.append(
                     Model(
-                        model_id=id,
+                        identifier=id,
                         provider_resource_id=entry.provider_model_id,
                         model_type=ModelType.llm,
                         metadata=entry.metadata,
@@ -185,8 +188,8 @@ class ModelRegistryHelper(ModelsProtocolPrivate):
         return model
 
     async def unregister_model(self, model_id: str) -> None:
-        # TODO: should we block unregistering base supported provider model IDs?
+        # model_id is the identifier, not the provider_resource_id
-        if model_id not in self.alias_to_provider_id_map:
+        # unfortunately, this ID can be of the form provider_id/model_id which
-            raise ValueError(f"Model id '{model_id}' is not registered.")
+        # we never registered. TODO: fix this by significantly rewriting
-
+        # registration and registry helper
-        del self.alias_to_provider_id_map[model_id]
+        pass

llama_stack/templates/ci-tests/build.yaml

@@ -7,21 +7,15 @@ distribution_spec:
     - provider_type: remote::ollama
     - provider_type: remote::vllm
     - provider_type: remote::tgi
-    - provider_type: remote::hf::serverless
-    - provider_type: remote::hf::endpoint
     - provider_type: remote::fireworks
     - provider_type: remote::together
     - provider_type: remote::bedrock
-    - provider_type: remote::databricks
     - provider_type: remote::nvidia
-    - provider_type: remote::runpod
     - provider_type: remote::openai
     - provider_type: remote::anthropic
     - provider_type: remote::gemini
     - provider_type: remote::groq
-    - provider_type: remote::llama-openai-compat
     - provider_type: remote::sambanova
-    - provider_type: remote::passthrough
     - provider_type: inline::sentence-transformers
     vector_io:
     - provider_type: inline::faiss

llama_stack/templates/nvidia/run.yaml

@@ -89,101 +89,51 @@ models:
   provider_id: nvidia
   provider_model_id: meta/llama3-8b-instruct
   model_type: llm
-- metadata: {}
-  model_id: meta-llama/Llama-3-8B-Instruct
-  provider_id: nvidia
-  provider_model_id: meta/llama3-8b-instruct
-  model_type: llm
 - metadata: {}
   model_id: meta/llama3-70b-instruct
   provider_id: nvidia
   provider_model_id: meta/llama3-70b-instruct
   model_type: llm
-- metadata: {}
-  model_id: meta-llama/Llama-3-70B-Instruct
-  provider_id: nvidia
-  provider_model_id: meta/llama3-70b-instruct
-  model_type: llm
 - metadata: {}
   model_id: meta/llama-3.1-8b-instruct
   provider_id: nvidia
   provider_model_id: meta/llama-3.1-8b-instruct
   model_type: llm
-- metadata: {}
-  model_id: meta-llama/Llama-3.1-8B-Instruct
-  provider_id: nvidia
-  provider_model_id: meta/llama-3.1-8b-instruct
-  model_type: llm
 - metadata: {}
   model_id: meta/llama-3.1-70b-instruct
   provider_id: nvidia
   provider_model_id: meta/llama-3.1-70b-instruct
   model_type: llm
-- metadata: {}
-  model_id: meta-llama/Llama-3.1-70B-Instruct
-  provider_id: nvidia
-  provider_model_id: meta/llama-3.1-70b-instruct
-  model_type: llm
 - metadata: {}
   model_id: meta/llama-3.1-405b-instruct
   provider_id: nvidia
   provider_model_id: meta/llama-3.1-405b-instruct
   model_type: llm
-- metadata: {}
-  model_id: meta-llama/Llama-3.1-405B-Instruct-FP8
-  provider_id: nvidia
-  provider_model_id: meta/llama-3.1-405b-instruct
-  model_type: llm
 - metadata: {}
   model_id: meta/llama-3.2-1b-instruct
   provider_id: nvidia
   provider_model_id: meta/llama-3.2-1b-instruct
   model_type: llm
-- metadata: {}
-  model_id: meta-llama/Llama-3.2-1B-Instruct
-  provider_id: nvidia
-  provider_model_id: meta/llama-3.2-1b-instruct
-  model_type: llm
 - metadata: {}
   model_id: meta/llama-3.2-3b-instruct
   provider_id: nvidia
   provider_model_id: meta/llama-3.2-3b-instruct
   model_type: llm
-- metadata: {}
-  model_id: meta-llama/Llama-3.2-3B-Instruct
-  provider_id: nvidia
-  provider_model_id: meta/llama-3.2-3b-instruct
-  model_type: llm
 - metadata: {}
   model_id: meta/llama-3.2-11b-vision-instruct
   provider_id: nvidia
   provider_model_id: meta/llama-3.2-11b-vision-instruct
   model_type: llm
-- metadata: {}
-  model_id: meta-llama/Llama-3.2-11B-Vision-Instruct
-  provider_id: nvidia
-  provider_model_id: meta/llama-3.2-11b-vision-instruct
-  model_type: llm
 - metadata: {}
   model_id: meta/llama-3.2-90b-vision-instruct
   provider_id: nvidia
   provider_model_id: meta/llama-3.2-90b-vision-instruct
   model_type: llm
-- metadata: {}
-  model_id: meta-llama/Llama-3.2-90B-Vision-Instruct
-  provider_id: nvidia
-  provider_model_id: meta/llama-3.2-90b-vision-instruct
-  model_type: llm
 - metadata: {}
   model_id: meta/llama-3.3-70b-instruct
   provider_id: nvidia
   provider_model_id: meta/llama-3.3-70b-instruct
   model_type: llm
-- metadata: {}
-  model_id: meta-llama/Llama-3.3-70B-Instruct
-  provider_id: nvidia
-  provider_model_id: meta/llama-3.3-70b-instruct
-  model_type: llm
 - metadata:
     embedding_dimension: 2048
     context_length: 8192

llama_stack/templates/open-benchmark/run.yaml

@@ -33,7 +33,7 @@ providers:
     provider_type: remote::together
     config:
       url: https://api.together.xyz/v1
-      api_key: ${env.TOGETHER_API_KEY}
+      api_key: ${env.TOGETHER_API_KEY:=}
   vector_io:
   - provider_id: sqlite-vec
     provider_type: inline::sqlite-vec

llama_stack/templates/starter/build.yaml

@@ -7,21 +7,15 @@ distribution_spec:
     - provider_type: remote::ollama
     - provider_type: remote::vllm
     - provider_type: remote::tgi
-    - provider_type: remote::hf::serverless
-    - provider_type: remote::hf::endpoint
     - provider_type: remote::fireworks
     - provider_type: remote::together
     - provider_type: remote::bedrock
-    - provider_type: remote::databricks
     - provider_type: remote::nvidia
-    - provider_type: remote::runpod
     - provider_type: remote::openai
     - provider_type: remote::anthropic
     - provider_type: remote::gemini
     - provider_type: remote::groq
-    - provider_type: remote::llama-openai-compat
     - provider_type: remote::sambanova
-    - provider_type: remote::passthrough
     - provider_type: inline::sentence-transformers
     vector_io:
     - provider_type: inline::faiss

llama_stack/templates/starter/starter.py

@@ -7,20 +7,19 @@
 
 from typing import Any
 
-from llama_stack.apis.models import ModelType
 from llama_stack.distribution.datatypes import (
     BuildProvider,
-    ModelInput,
     Provider,
     ProviderSpec,
+    ShieldInput,
     ToolGroupInput,
 )
 from llama_stack.distribution.utils.dynamic import instantiate_class_type
+from llama_stack.providers.datatypes import RemoteProviderSpec
 from llama_stack.providers.inline.files.localfs.config import LocalfsFilesImplConfig
 from llama_stack.providers.inline.inference.sentence_transformers import (
     SentenceTransformersInferenceConfig,
 )
-from llama_stack.providers.inline.post_training.huggingface import HuggingFacePostTrainingConfig
 from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
 from llama_stack.providers.inline.vector_io.milvus.config import (
     MilvusVectorIOConfig,
@@ -29,117 +28,17 @@ from llama_stack.providers.inline.vector_io.sqlite_vec.config import (
     SQLiteVectorIOConfig,
 )
 from llama_stack.providers.registry.inference import available_providers
-from llama_stack.providers.remote.inference.anthropic.models import (
-    MODEL_ENTRIES as ANTHROPIC_MODEL_ENTRIES,
-)
-from llama_stack.providers.remote.inference.bedrock.models import (
-    MODEL_ENTRIES as BEDROCK_MODEL_ENTRIES,
-)
-from llama_stack.providers.remote.inference.cerebras.models import (
-    MODEL_ENTRIES as CEREBRAS_MODEL_ENTRIES,
-)
-from llama_stack.providers.remote.inference.databricks.databricks import (
-    MODEL_ENTRIES as DATABRICKS_MODEL_ENTRIES,
-)
-from llama_stack.providers.remote.inference.fireworks.models import (
-    MODEL_ENTRIES as FIREWORKS_MODEL_ENTRIES,
-)
-from llama_stack.providers.remote.inference.gemini.models import (
-    MODEL_ENTRIES as GEMINI_MODEL_ENTRIES,
-)
-from llama_stack.providers.remote.inference.groq.models import (
-    MODEL_ENTRIES as GROQ_MODEL_ENTRIES,
-)
-from llama_stack.providers.remote.inference.nvidia.models import (
-    MODEL_ENTRIES as NVIDIA_MODEL_ENTRIES,
-)
-from llama_stack.providers.remote.inference.openai.models import (
-    MODEL_ENTRIES as OPENAI_MODEL_ENTRIES,
-)
-from llama_stack.providers.remote.inference.runpod.runpod import (
-    MODEL_ENTRIES as RUNPOD_MODEL_ENTRIES,
-)
-from llama_stack.providers.remote.inference.sambanova.models import (
-    MODEL_ENTRIES as SAMBANOVA_MODEL_ENTRIES,
-)
-from llama_stack.providers.remote.inference.together.models import (
-    MODEL_ENTRIES as TOGETHER_MODEL_ENTRIES,
-)
 from llama_stack.providers.remote.vector_io.chroma.config import ChromaVectorIOConfig
 from llama_stack.providers.remote.vector_io.pgvector.config import (
     PGVectorVectorIOConfig,
 )
-from llama_stack.providers.utils.inference.model_registry import ProviderModelEntry
 from llama_stack.providers.utils.sqlstore.sqlstore import PostgresSqlStoreConfig
 from llama_stack.templates.template import (
     DistributionTemplate,
     RunConfigSettings,
-    get_model_registry,
-    get_shield_registry,
 )
 
 
-def _get_model_entries_for_provider(provider_type: str) -> list[ProviderModelEntry]:
-    """Get model entries for a specific provider type."""
-    model_entries_map = {
-        "openai": OPENAI_MODEL_ENTRIES,
-        "fireworks": FIREWORKS_MODEL_ENTRIES,
-        "together": TOGETHER_MODEL_ENTRIES,
-        "anthropic": ANTHROPIC_MODEL_ENTRIES,
-        "gemini": GEMINI_MODEL_ENTRIES,
-        "groq": GROQ_MODEL_ENTRIES,
-        "sambanova": SAMBANOVA_MODEL_ENTRIES,
-        "cerebras": CEREBRAS_MODEL_ENTRIES,
-        "bedrock": BEDROCK_MODEL_ENTRIES,
-        "databricks": DATABRICKS_MODEL_ENTRIES,
-        "nvidia": NVIDIA_MODEL_ENTRIES,
-        "runpod": RUNPOD_MODEL_ENTRIES,
-    }
-
-    # Special handling for providers with dynamic model entries
-    if provider_type == "ollama":
-        return [
-            ProviderModelEntry(
-                provider_model_id="${env.OLLAMA_INFERENCE_MODEL:=__disabled__}",
-                model_type=ModelType.llm,
-            ),
-            ProviderModelEntry(
-                provider_model_id="${env.SAFETY_MODEL:=__disabled__}",
-                model_type=ModelType.llm,
-            ),
-            ProviderModelEntry(
-                provider_model_id="${env.OLLAMA_EMBEDDING_MODEL:=__disabled__}",
-                model_type=ModelType.embedding,
-                metadata={
-                    "embedding_dimension": "${env.OLLAMA_EMBEDDING_DIMENSION:=384}",
-                },
-            ),
-        ]
-    elif provider_type == "vllm":
-        return [
-            ProviderModelEntry(
-                provider_model_id="${env.VLLM_INFERENCE_MODEL:=__disabled__}",
-                model_type=ModelType.llm,
-            ),
-        ]
-
-    return model_entries_map.get(provider_type, [])
-
-
-def _get_model_safety_entries_for_provider(provider_type: str) -> list[ProviderModelEntry]:
-    """Get model entries for a specific provider type."""
-    safety_model_entries_map = {
-        "ollama": [
-            ProviderModelEntry(
-                provider_model_id="${env.SAFETY_MODEL:=__disabled__}",
-                model_type=ModelType.llm,
-            ),
-        ],
-    }
-
-    return safety_model_entries_map.get(provider_type, [])
-
-
 def _get_config_for_provider(provider_spec: ProviderSpec) -> dict[str, Any]:
     """Get configuration for a provider using its adapter's config class."""
     config_class = instantiate_class_type(provider_spec.config_class)
@@ -150,40 +49,48 @@ def _get_config_for_provider(provider_spec: ProviderSpec) -> dict[str, Any]:
     return {}
 
 
-def get_remote_inference_providers() -> tuple[list[Provider], dict[str, list[ProviderModelEntry]]]:
+ENABLED_INFERENCE_PROVIDERS = [
-    all_providers = available_providers()
+    "ollama",
+    "vllm",
+    "tgi",
+    "fireworks",
+    "together",
+    "gemini",
+    "groq",
+    "sambanova",
+    "anthropic",
+    "openai",
+    "cerebras",
+    "nvidia",
+    "bedrock",
+]
 
-    # Filter out inline providers and watsonx - the starter distro only exposes remote providers
+INFERENCE_PROVIDER_IDS = {
+    "vllm": "${env.VLLM_URL:+vllm}",
+    "tgi": "${env.TGI_URL:+tgi}",
+    "cerebras": "${env.CEREBRAS_API_KEY:+cerebras}",
+    "nvidia": "${env.NVIDIA_API_KEY:+nvidia}",
+}
+
+
+def get_remote_inference_providers() -> list[Provider]:
+    # Filter out inline providers and some others - the starter distro only exposes remote providers
     remote_providers = [
         provider
-        for provider in all_providers
+        for provider in available_providers()
-        # TODO: re-add once the Python 3.13 issue is fixed
+        if isinstance(provider, RemoteProviderSpec) and provider.adapter.adapter_type in ENABLED_INFERENCE_PROVIDERS
-        # discussion: https://github.com/meta-llama/llama-stack/pull/2327#discussion_r2156883828
-        if hasattr(provider, "adapter") and provider.adapter.adapter_type != "watsonx"
     ]
 
-    providers = []
+    inference_providers = []
-    available_models = {}
-
     for provider_spec in remote_providers:
         provider_type = provider_spec.adapter.adapter_type
 
-        # Build the environment variable name for enabling this provider
+        if provider_type in INFERENCE_PROVIDER_IDS:
-        env_var = f"ENABLE_{provider_type.upper().replace('-', '_').replace('::', '_')}"
+            provider_id = INFERENCE_PROVIDER_IDS[provider_type]
-        model_entries = _get_model_entries_for_provider(provider_type)
+        else:
+            provider_id = provider_type.replace("-", "_").replace("::", "_")
         config = _get_config_for_provider(provider_spec)
-        providers.append(
-            (
-                f"${{env.{env_var}:=__disabled__}}",
-                provider_type,
-                model_entries,
-                config,
-            )
-        )
-        available_models[f"${{env.{env_var}:=__disabled__}}"] = model_entries
 
-    inference_providers = []
-    for provider_id, provider_type, model_entries, config in providers:
         inference_providers.append(
             Provider(
                 provider_id=provider_id,
@@ -191,31 +98,13 @@ def get_remote_inference_providers() -> tuple[list[Provider], dict[str, list[Pro
                 config=config,
             )
         )
-        available_models[provider_id] = model_entries
+    return inference_providers
-    return inference_providers, available_models
-
-
-# build a list of shields for all possible providers
-def get_safety_models_for_providers(providers: list[Provider]) -> dict[str, list[ProviderModelEntry]]:
-    available_models = {}
-    for provider in providers:
-        provider_type = provider.provider_type.split("::")[1]
-        safety_model_entries = _get_model_safety_entries_for_provider(provider_type)
-        if len(safety_model_entries) == 0:
-            continue
-
-        env_var = f"ENABLE_{provider_type.upper().replace('-', '_').replace('::', '_')}"
-        provider_id = f"${{env.{env_var}:=__disabled__}}"
-
-        available_models[provider_id] = safety_model_entries
-
-    return available_models
 
 
 def get_distribution_template() -> DistributionTemplate:
-    remote_inference_providers, available_models = get_remote_inference_providers()
+    remote_inference_providers = get_remote_inference_providers()
     name = "starter"
-    # For build config, use BuildProvider with only provider_type and module
+
     providers = {
         "inference": [BuildProvider(provider_type=p.provider_type, module=p.module) for p in remote_inference_providers]
         + [BuildProvider(provider_type="inline::sentence-transformers")],
@@ -254,15 +143,10 @@ def get_distribution_template() -> DistributionTemplate:
         config=LocalfsFilesImplConfig.sample_run_config(f"~/.llama/distributions/{name}"),
     )
     embedding_provider = Provider(
-        provider_id="${env.ENABLE_SENTENCE_TRANSFORMERS:=sentence-transformers}",
+        provider_id="sentence-transformers",
         provider_type="inline::sentence-transformers",
         config=SentenceTransformersInferenceConfig.sample_run_config(),
     )
-    post_training_provider = Provider(
-        provider_id="huggingface",
-        provider_type="inline::huggingface",
-        config=HuggingFacePostTrainingConfig.sample_run_config(f"~/.llama/distributions/{name}"),
-    )
     default_tool_groups = [
         ToolGroupInput(
             toolgroup_id="builtin::websearch",
@@ -273,19 +157,14 @@ def get_distribution_template() -> DistributionTemplate:
             provider_id="rag-runtime",
         ),
     ]
-    embedding_model = ModelInput(
+    default_shields = [
-        model_id="all-MiniLM-L6-v2",
+        # if the
-        provider_id=embedding_provider.provider_id,
+        ShieldInput(
-        model_type=ModelType.embedding,
+            shield_id="llama-guard",
-        metadata={
+            provider_id="${env.SAFETY_MODEL:+llama-guard}",
-            "embedding_dimension": 384,
+            provider_shield_id="${env.SAFETY_MODEL:=}",
-        },
+        ),
-    )
+    ]
-
-    default_models, ids_conflict_in_models = get_model_registry(available_models)
-
-    available_safety_models = get_safety_models_for_providers(remote_inference_providers)
-    shields = get_shield_registry(available_safety_models, ids_conflict_in_models)
 
     return DistributionTemplate(
         name=name,
@@ -294,7 +173,6 @@ def get_distribution_template() -> DistributionTemplate:
         container_image=None,
         template_path=None,
         providers=providers,
-        available_models_by_provider=available_models,
         additional_pip_packages=PostgresSqlStoreConfig.pip_packages(),
         run_configs={
             "run.yaml": RunConfigSettings(
@@ -302,22 +180,22 @@ def get_distribution_template() -> DistributionTemplate:
                     "inference": remote_inference_providers + [embedding_provider],
                     "vector_io": [
                         Provider(
-                            provider_id="${env.ENABLE_FAISS:=faiss}",
+                            provider_id="faiss",
                             provider_type="inline::faiss",
                             config=FaissVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
                         ),
                         Provider(
-                            provider_id="${env.ENABLE_SQLITE_VEC:=__disabled__}",
+                            provider_id="sqlite-vec",
                             provider_type="inline::sqlite-vec",
                             config=SQLiteVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
                         ),
                         Provider(
-                            provider_id="${env.ENABLE_MILVUS:=__disabled__}",
+                            provider_id="${env.MILVUS_URL:+milvus}",
                             provider_type="inline::milvus",
                             config=MilvusVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
                         ),
                         Provider(
-                            provider_id="${env.ENABLE_CHROMADB:=__disabled__}",
+                            provider_id="${env.CHROMADB_URL:+chromadb}",
                             provider_type="remote::chromadb",
                             config=ChromaVectorIOConfig.sample_run_config(
                                 f"~/.llama/distributions/{name}/",
@@ -325,7 +203,7 @@ def get_distribution_template() -> DistributionTemplate:
                             ),
                         ),
                         Provider(
-                            provider_id="${env.ENABLE_PGVECTOR:=__disabled__}",
+                            provider_id="${env.PGVECTOR_DB:+pgvector}",
                             provider_type="remote::pgvector",
                             config=PGVectorVectorIOConfig.sample_run_config(
                                 f"~/.llama/distributions/{name}",
@@ -336,12 +214,10 @@ def get_distribution_template() -> DistributionTemplate:
                         ),
                     ],
                     "files": [files_provider],
-                    "post_training": [post_training_provider],
                 },
-                default_models=[embedding_model] + default_models,
+                default_models=[],
                 default_tool_groups=default_tool_groups,
-                # TODO: add a way to enable/disable shields on the fly
+                default_shields=default_shields,
-                default_shields=shields,
             ),
         },
         run_config_env_vars={
@@ -385,17 +261,5 @@ def get_distribution_template() -> DistributionTemplate:
                 "http://localhost:11434",
                 "Ollama URL",
             ),
-            "OLLAMA_INFERENCE_MODEL": (
-                "",
-                "Optional Ollama Inference Model to register on startup",
-            ),
-            "OLLAMA_EMBEDDING_MODEL": (
-                "",
-                "Optional Ollama Embedding Model to register on startup",
-            ),
-            "OLLAMA_EMBEDDING_DIMENSION": (
-                "384",
-                "Ollama Embedding Dimension",
-            ),
         },
     )

pyproject.toml

@@ -25,7 +25,7 @@ dependencies = [
     "fastapi>=0.115.0,<1.0",                  # server
     "fire",                                   # for MCP in LLS client
     "httpx",
-    "huggingface-hub>=0.30.0,<1.0",
+    "huggingface-hub>=0.34.0,<1.0",
     "jinja2>=3.1.6",
     "jsonschema",
     "llama-stack-client>=0.2.15",

requirements.txt

@@ -86,7 +86,7 @@ httpx==0.28.1
     #   llama-stack
     #   llama-stack-client
     #   openai
-huggingface-hub==0.33.0
+huggingface-hub==0.34.1
     # via llama-stack
 idna==3.10
     # via

scripts/install.sh

@@ -222,9 +222,7 @@ cmd=( run -d "${PLATFORM_OPTS[@]}" --name llama-stack \
       --network llama-net \
       -p "${PORT}:${PORT}" \
       "${SERVER_IMAGE}" --port "${PORT}" \
-      --env OLLAMA_INFERENCE_MODEL="${MODEL_ALIAS}" \
+      --env OLLAMA_URL="http://ollama-server:${OLLAMA_PORT}")
-      --env OLLAMA_URL="http://ollama-server:${OLLAMA_PORT}" \
-      --env ENABLE_OLLAMA=ollama)
 
 log "🦙 Starting Llama Stack..."
 if ! execute_with_log $ENGINE "${cmd[@]}"; then

tests/unit/distribution/routers/test_routing_tables.py

@@ -502,7 +502,7 @@ async def test_models_source_interaction_preserves_default(cached_disk_dist_regi
 
     # Find the user model and provider model
     user_model = next((m for m in models.data if m.identifier == "my-custom-alias"), None)
-    provider_model = next((m for m in models.data if m.identifier == "different-model"), None)
+    provider_model = next((m for m in models.data if m.identifier == "test_provider/different-model"), None)
 
     assert user_model is not None
     assert user_model.source == RegistryEntrySource.via_register_api
@@ -558,12 +558,12 @@ async def test_models_source_interaction_cleanup_provider_models(cached_disk_dis
 
     identifiers = {m.identifier for m in models.data}
     assert "test_provider/user-model" in identifiers  # User model preserved
-    assert "provider-model-new" in identifiers  # New provider model (uses provider's identifier)
+    assert "test_provider/provider-model-new" in identifiers  # New provider model (uses provider's identifier)
-    assert "provider-model-old" not in identifiers  # Old provider model removed
+    assert "test_provider/provider-model-old" not in identifiers  # Old provider model removed
 
     # Verify sources are correct
     user_model = next((m for m in models.data if m.identifier == "test_provider/user-model"), None)
-    provider_model = next((m for m in models.data if m.identifier == "provider-model-new"), None)
+    provider_model = next((m for m in models.data if m.identifier == "test_provider/provider-model-new"), None)
 
     assert user_model.source == RegistryEntrySource.via_register_api
     assert provider_model.source == RegistryEntrySource.listed_from_provider