Add centml as remote inference provider

distributions/dependencies.json

@@ -587,6 +587,38 @@
     "sentence-transformers --no-deps",
     "torch torchvision --index-url https://download.pytorch.org/whl/cpu"
   ],
+  "centml": [
+    "aiosqlite",
+    "autoevals",
+    "blobfile",
+    "chardet",
+    "chromadb-client",
+    "datasets",
+    "faiss-cpu",
+    "fastapi",
+    "fire",
+    "httpx",
+    "matplotlib",
+    "nltk",
+    "numpy",
+    "openai",
+    "opentelemetry-exporter-otlp-proto-http",
+    "opentelemetry-sdk",
+    "pandas",
+    "pillow",
+    "psycopg2-binary",
+    "pypdf",
+    "redis",
+    "requests",
+    "scikit-learn",
+    "scipy",
+    "sentencepiece",
+    "tqdm",
+    "transformers",
+    "uvicorn",
+    "sentence-transformers --no-deps",
+    "torch --index-url https://download.pytorch.org/whl/cpu"
+  ],
   "vllm-gpu": [
     "aiosqlite",
     "autoevals",

llama_stack/providers/registry/inference.py

@@ -34,20 +34,19 @@ def available_providers() -> List[ProviderSpec]:
             provider_type="inline::meta-reference",
             pip_packages=META_REFERENCE_DEPS,
             module="llama_stack.providers.inline.inference.meta_reference",
-            config_class="llama_stack.providers.inline.inference.meta_reference.MetaReferenceInferenceConfig",
+            config_class=
+            "llama_stack.providers.inline.inference.meta_reference.MetaReferenceInferenceConfig",
         ),
         InlineProviderSpec(
             api=Api.inference,
             provider_type="inline::meta-reference-quantized",
-            pip_packages=(
-                META_REFERENCE_DEPS
-                + [
-                    "fbgemm-gpu",
-                    "torchao==0.5.0",
-                ]
-            ),
+            pip_packages=(META_REFERENCE_DEPS + [
+                "fbgemm-gpu",
+                "torchao==0.5.0",
+            ]),
             module="llama_stack.providers.inline.inference.meta_reference",
-            config_class="llama_stack.providers.inline.inference.meta_reference.MetaReferenceQuantizedInferenceConfig",
+            config_class=
+            "llama_stack.providers.inline.inference.meta_reference.MetaReferenceQuantizedInferenceConfig",
         ),
         InlineProviderSpec(
             api=Api.inference,
@@ -56,7 +55,8 @@ def available_providers() -> List[ProviderSpec]:
                 "vllm",
             ],
             module="llama_stack.providers.inline.inference.vllm",
-            config_class="llama_stack.providers.inline.inference.vllm.VLLMConfig",
+            config_class=
+            "llama_stack.providers.inline.inference.vllm.VLLMConfig",
         ),
         InlineProviderSpec(
             api=Api.inference,
@@ -74,7 +74,8 @@ def available_providers() -> List[ProviderSpec]:
                 adapter_type="sample",
                 pip_packages=[],
                 module="llama_stack.providers.remote.inference.sample",
-                config_class="llama_stack.providers.remote.inference.sample.SampleConfig",
+                config_class=
+                "llama_stack.providers.remote.inference.sample.SampleConfig",
             ),
         ),
         remote_provider_spec(
@@ -85,7 +86,8 @@ def available_providers() -> List[ProviderSpec]:
                     "cerebras_cloud_sdk",
                 ],
                 module="llama_stack.providers.remote.inference.cerebras",
-                config_class="llama_stack.providers.remote.inference.cerebras.CerebrasImplConfig",
+                config_class=
+                "llama_stack.providers.remote.inference.cerebras.CerebrasImplConfig",
             ),
         ),
         remote_provider_spec(
@@ -93,7 +95,8 @@ def available_providers() -> List[ProviderSpec]:
             adapter=AdapterSpec(
                 adapter_type="ollama",
                 pip_packages=["ollama", "aiohttp"],
-                config_class="llama_stack.providers.remote.inference.ollama.OllamaImplConfig",
+                config_class=
+                "llama_stack.providers.remote.inference.ollama.OllamaImplConfig",
                 module="llama_stack.providers.remote.inference.ollama",
             ),
         ),
@@ -103,7 +106,8 @@ def available_providers() -> List[ProviderSpec]:
                 adapter_type="vllm",
                 pip_packages=["openai"],
                 module="llama_stack.providers.remote.inference.vllm",
-                config_class="llama_stack.providers.remote.inference.vllm.VLLMInferenceAdapterConfig",
+                config_class=
+                "llama_stack.providers.remote.inference.vllm.VLLMInferenceAdapterConfig",
             ),
         ),
         remote_provider_spec(
@@ -112,7 +116,8 @@ def available_providers() -> List[ProviderSpec]:
                 adapter_type="tgi",
                 pip_packages=["huggingface_hub", "aiohttp"],
                 module="llama_stack.providers.remote.inference.tgi",
-                config_class="llama_stack.providers.remote.inference.tgi.TGIImplConfig",
+                config_class=
+                "llama_stack.providers.remote.inference.tgi.TGIImplConfig",
             ),
         ),
         remote_provider_spec(
@@ -121,7 +126,8 @@ def available_providers() -> List[ProviderSpec]:
                 adapter_type="hf::serverless",
                 pip_packages=["huggingface_hub", "aiohttp"],
                 module="llama_stack.providers.remote.inference.tgi",
-                config_class="llama_stack.providers.remote.inference.tgi.InferenceAPIImplConfig",
+                config_class=
+                "llama_stack.providers.remote.inference.tgi.InferenceAPIImplConfig",
             ),
         ),
         remote_provider_spec(
@@ -130,7 +136,8 @@ def available_providers() -> List[ProviderSpec]:
                 adapter_type="hf::endpoint",
                 pip_packages=["huggingface_hub", "aiohttp"],
                 module="llama_stack.providers.remote.inference.tgi",
-                config_class="llama_stack.providers.remote.inference.tgi.InferenceEndpointImplConfig",
+                config_class=
+                "llama_stack.providers.remote.inference.tgi.InferenceEndpointImplConfig",
             ),
         ),
         remote_provider_spec(
@@ -141,8 +148,10 @@ def available_providers() -> List[ProviderSpec]:
                     "fireworks-ai",
                 ],
                 module="llama_stack.providers.remote.inference.fireworks",
-                config_class="llama_stack.providers.remote.inference.fireworks.FireworksImplConfig",
-                provider_data_validator="llama_stack.providers.remote.inference.fireworks.FireworksProviderDataValidator",
+                config_class=
+                "llama_stack.providers.remote.inference.fireworks.FireworksImplConfig",
+                provider_data_validator=
+                "llama_stack.providers.remote.inference.fireworks.FireworksProviderDataValidator",
             ),
         ),
         remote_provider_spec(
@@ -153,8 +162,10 @@ def available_providers() -> List[ProviderSpec]:
                     "together",
                 ],
                 module="llama_stack.providers.remote.inference.together",
-                config_class="llama_stack.providers.remote.inference.together.TogetherImplConfig",
-                provider_data_validator="llama_stack.providers.remote.inference.together.TogetherProviderDataValidator",
+                config_class=
+                "llama_stack.providers.remote.inference.together.TogetherImplConfig",
+                provider_data_validator=
+                "llama_stack.providers.remote.inference.together.TogetherProviderDataValidator",
             ),
         ),
         remote_provider_spec(
@@ -163,7 +174,8 @@ def available_providers() -> List[ProviderSpec]:
                 adapter_type="bedrock",
                 pip_packages=["boto3"],
                 module="llama_stack.providers.remote.inference.bedrock",
-                config_class="llama_stack.providers.remote.inference.bedrock.BedrockConfig",
+                config_class=
+                "llama_stack.providers.remote.inference.bedrock.BedrockConfig",
             ),
         ),
         remote_provider_spec(
@@ -174,7 +186,8 @@ def available_providers() -> List[ProviderSpec]:
                     "openai",
                 ],
                 module="llama_stack.providers.remote.inference.databricks",
-                config_class="llama_stack.providers.remote.inference.databricks.DatabricksImplConfig",
+                config_class=
+                "llama_stack.providers.remote.inference.databricks.DatabricksImplConfig",
             ),
         ),
         remote_provider_spec(
@@ -185,7 +198,8 @@ def available_providers() -> List[ProviderSpec]:
                     "openai",
                 ],
                 module="llama_stack.providers.remote.inference.nvidia",
-                config_class="llama_stack.providers.remote.inference.nvidia.NVIDIAConfig",
+                config_class=
+                "llama_stack.providers.remote.inference.nvidia.NVIDIAConfig",
             ),
         ),
         remote_provider_spec(
@@ -245,7 +259,22 @@ def available_providers() -> List[ProviderSpec]:
                     "openai",
                 ],
                 module="llama_stack.providers.remote.inference.sambanova",
-                config_class="llama_stack.providers.remote.inference.sambanova.SambaNovaImplConfig",
+                config_class=
+                "llama_stack.providers.remote.inference.sambanova.SambaNovaImplConfig",
+            ),
+        ),
+        remote_provider_spec(
+            api=Api.inference,
+            adapter=AdapterSpec(
+                adapter_type="centml",
+                pip_packages=[
+                    "openai",
+                ],
+                module="llama_stack.providers.remote.inference.centml",
+                config_class=
+                "llama_stack.providers.remote.inference.centml.CentMLImplConfig",
+                provider_data_validator=
+                "llama_stack.providers.remote.inference.centml.CentMLProviderDataValidator",
             ),
         ),
         remote_provider_spec(

llama_stack/providers/remote/inference/centml/__init__.py

@@ -0,0 +1,31 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from pydantic import BaseModel
+
+from .config import CentMLImplConfig
+
+class CentMLProviderDataValidator(BaseModel):
+    centml_api_key: str
+
+async def get_adapter_impl(config: CentMLImplConfig, _deps):
+    """
+    Factory function to construct and initialize the CentML adapter.
+
+    :param config: Instance of CentMLImplConfig, containing `url`, `api_key`, etc.
+    :param _deps: Additional dependencies provided by llama-stack (unused here).
+    """
+    from .centml import CentMLInferenceAdapter
+
+    # Ensure the provided config is indeed a CentMLImplConfig
+    assert isinstance(
+        config, CentMLImplConfig
+    ), f"Unexpected config type: {type(config)}"
+
+    # Instantiate and initialize the adapter
+    adapter = CentMLInferenceAdapter(config)
+    await adapter.initialize()
+    return adapter

llama_stack/providers/remote/inference/centml/centml.py

@@ -0,0 +1,308 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import AsyncGenerator, List, Optional, Union
+
+from openai import OpenAI
+
+from llama_models.datatypes import CoreModelId
+from llama_models.llama3.api.chat_format import ChatFormat
+from llama_models.llama3.api.tokenizer import Tokenizer
+
+from llama_stack.apis.common.content_types import InterleavedContent
+from llama_stack.apis.inference import (
+    ChatCompletionRequest,
+    ChatCompletionResponse,
+    CompletionRequest,
+    EmbeddingsResponse,
+    Inference,
+    LogProbConfig,
+    Message,
+    ResponseFormat,
+    ResponseFormatType,
+    SamplingParams,
+    ToolChoice,
+    ToolDefinition,
+    ToolPromptFormat,
+)
+from llama_stack.distribution.request_headers import NeedsRequestProviderData
+from llama_stack.providers.utils.inference.model_registry import (
+    build_model_alias,
+    ModelRegistryHelper,
+)
+from llama_stack.providers.utils.inference.openai_compat import (
+    convert_message_to_openai_dict,
+    get_sampling_options,
+    process_chat_completion_response,
+    process_chat_completion_stream_response,
+    process_completion_response,
+    process_completion_stream_response,
+)
+from llama_stack.providers.utils.inference.prompt_adapter import (
+    chat_completion_request_to_prompt,
+    completion_request_to_prompt,
+    content_has_media,
+    interleaved_content_as_str,
+    request_has_media,
+)
+
+from .config import CentMLImplConfig
+
+# Example model aliases that map from CentML’s
+# published model identifiers to llama-stack's `CoreModelId`.
+MODEL_ALIASES = [
+    build_model_alias(
+        "meta-llama/Llama-3.3-70B-Instruct",
+        CoreModelId.llama3_3_70b_instruct.value,
+    ),
+    build_model_alias(
+        "meta-llama/Llama-3.1-405B-Instruct-FP8",
+        CoreModelId.llama3_1_405b_instruct.value,
+    ),
+]
+
+
+class CentMLInferenceAdapter(ModelRegistryHelper, Inference,
+                             NeedsRequestProviderData):
+    """
+    Adapter to use CentML's serverless inference endpoints,
+    which adhere to the OpenAI chat/completions API spec,
+    inside llama-stack.
+    """
+
+    def __init__(self, config: CentMLImplConfig) -> None:
+        super().__init__(MODEL_ALIASES)
+        self.config = config
+        self.formatter = ChatFormat(Tokenizer.get_instance())
+
+    async def initialize(self) -> None:
+        pass
+
+    async def shutdown(self) -> None:
+        pass
+
+    def _get_api_key(self) -> str:
+        """
+        Obtain the CentML API key either from the adapter config
+        or from the dynamic provider data in request headers.
+        """
+        if self.config.api_key is not None:
+            return self.config.api_key.get_secret_value()
+        else:
+            provider_data = self.get_request_provider_data()
+            if provider_data is None or not provider_data.centml_api_key:
+                raise ValueError(
+                    'Pass CentML API Key in the header X-LlamaStack-ProviderData as { "centml_api_key": "<your-api-key>" }'
+                )
+            return provider_data.centml_api_key
+
+    def _get_client(self) -> OpenAI:
+        """
+        Creates an OpenAI-compatible client pointing to CentML's base URL,
+        using the user's CentML API key.
+        """
+        api_key = self._get_api_key()
+        return OpenAI(api_key=api_key, base_url=self.config.url)
+
+    #
+    # COMPLETION (non-chat)
+    #
+
+    async def completion(
+        self,
+        model_id: str,
+        content: InterleavedContent,
+        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        response_format: Optional[ResponseFormat] = None,
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+    ) -> AsyncGenerator:
+        """
+        For "completion" style requests (non-chat).
+        """
+        model = await self.model_store.get_model(model_id)
+        request = CompletionRequest(
+            model=model.provider_resource_id,
+            content=content,
+            sampling_params=sampling_params,
+            response_format=response_format,
+            stream=stream,
+            logprobs=logprobs,
+        )
+        if stream:
+            return self._stream_completion(request)
+        else:
+            return await self._nonstream_completion(request)
+
+    async def _nonstream_completion(
+            self, request: CompletionRequest) -> ChatCompletionResponse:
+        params = await self._get_params(request)
+        # Using the older "completions" route for non-chat
+        response = self._get_client().completions.create(**params)
+        return process_completion_response(response, self.formatter)
+
+    async def _stream_completion(self,
+                                 request: CompletionRequest) -> AsyncGenerator:
+        params = await self._get_params(request)
+
+        async def _to_async_generator():
+            stream = self._get_client().completions.create(**params)
+            for chunk in stream:
+                yield chunk
+
+        stream = _to_async_generator()
+        async for chunk in process_completion_stream_response(
+                stream, self.formatter):
+            yield chunk
+
+    #
+    # CHAT COMPLETION
+    #
+
+    async def chat_completion(
+        self,
+        model_id: str,
+        messages: List[Message],
+        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        tools: Optional[List[ToolDefinition]] = None,
+        tool_choice: Optional[ToolChoice] = ToolChoice.auto,
+        tool_prompt_format: Optional[ToolPromptFormat] = None,
+        response_format: Optional[ResponseFormat] = None,
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+    ) -> AsyncGenerator:
+        """
+        For "chat completion" style requests.
+        """
+        model = await self.model_store.get_model(model_id)
+        request = ChatCompletionRequest(
+            model=model.provider_resource_id,
+            messages=messages,
+            sampling_params=sampling_params,
+            tools=tools or [],
+            tool_choice=tool_choice,
+            tool_prompt_format=tool_prompt_format,
+            response_format=response_format,
+            stream=stream,
+            logprobs=logprobs,
+        )
+        if stream:
+            return self._stream_chat_completion(request)
+        else:
+            return await self._nonstream_chat_completion(request)
+
+    async def _nonstream_chat_completion(
+            self, request: ChatCompletionRequest) -> ChatCompletionResponse:
+        params = await self._get_params(request)
+
+        # For chat requests, if "messages" is in params -> .chat.completions
+        if "messages" in params:
+            response = self._get_client().chat.completions.create(**params)
+        else:
+            # fallback if we ended up only with "prompt"
+            response = self._get_client().completions.create(**params)
+
+        return process_chat_completion_response(response, self.formatter)
+
+    async def _stream_chat_completion(
+            self, request: ChatCompletionRequest) -> AsyncGenerator:
+        params = await self._get_params(request)
+
+        async def _to_async_generator():
+            if "messages" in params:
+                stream = self._get_client().chat.completions.create(**params)
+            else:
+                stream = self._get_client().completions.create(**params)
+            for chunk in stream:
+                yield chunk
+
+        stream = _to_async_generator()
+        async for chunk in process_chat_completion_stream_response(
+                stream, self.formatter):
+            yield chunk
+
+    #
+    # HELPER METHODS
+    #
+
+    async def _get_params(
+            self, request: Union[ChatCompletionRequest,
+                                 CompletionRequest]) -> dict:
+        """
+        Build the 'params' dict that the OpenAI (CentML) client expects.
+        For chat requests, we always prefer "messages" so that it calls
+        the chat endpoint properly.
+        """
+        input_dict = {}
+        media_present = request_has_media(request)
+
+        if isinstance(request, ChatCompletionRequest):
+            # For chat requests, always build "messages" from the user messages
+            input_dict["messages"] = [
+                await convert_message_to_openai_dict(m)
+                for m in request.messages
+            ]
+
+        else:
+            # Non-chat (CompletionRequest)
+            assert not media_present, "CentML does not support media for completions"
+            input_dict["prompt"] = await completion_request_to_prompt(
+                request, self.formatter)
+
+        return {
+            "model":
+            request.model,
+            **input_dict,
+            "stream":
+            request.stream,
+            **self._build_options(request.sampling_params, request.response_format),
+        }
+
+    def _build_options(
+        self,
+        sampling_params: Optional[SamplingParams],
+        fmt: Optional[ResponseFormat],
+    ) -> dict:
+        """
+        Build temperature, max_tokens, top_p, etc., plus any response format data.
+        """
+        options = get_sampling_options(sampling_params)
+        options.setdefault("max_tokens", 512)
+
+        if fmt:
+            if fmt.type == ResponseFormatType.json_schema.value:
+                options["response_format"] = {
+                    "type": "json_object",
+                    "schema": fmt.json_schema,
+                }
+            elif fmt.type == ResponseFormatType.grammar.value:
+                raise NotImplementedError(
+                    "Grammar response format not supported yet")
+            else:
+                raise ValueError(f"Unknown response format {fmt.type}")
+
+        return options
+
+    #
+    # EMBEDDINGS
+    #
+
+    async def embeddings(
+        self,
+        model_id: str,
+        contents: List[InterleavedContent],
+    ) -> EmbeddingsResponse:
+        model = await self.model_store.get_model(model_id)
+        # CentML does not support media
+        assert all(not content_has_media(c) for c in contents), \
+            "CentML does not support media for embeddings"
+
+        resp = self._get_client().embeddings.create(
+            model=model.provider_resource_id,
+            input=[interleaved_content_as_str(c) for c in contents],
+        )
+        embeddings = [item.embedding for item in resp.data]
+        return EmbeddingsResponse(embeddings=embeddings)

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

@@ -0,0 +1,29 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Any, Dict, Optional
+
+from llama_models.schema_utils import json_schema_type
+from pydantic import BaseModel, Field, SecretStr
+
+
+@json_schema_type
+class CentMLImplConfig(BaseModel):
+    url: str = Field(
+        default="https://api.centml.com/openai/v1",
+        description="The CentML API server URL",
+    )
+    api_key: Optional[SecretStr] = Field(
+        default=None,
+        description="The CentML API Key",
+    )
+
+    @classmethod
+    def sample_run_config(cls, **kwargs) -> Dict[str, Any]:
+        return {
+            "url": "https://api.centml.com/openai/v1",
+            "api_key": "${env.CENTML_API_KEY}",
+        }

llama_stack/templates/centml/__init__.py

@@ -0,0 +1,7 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from .centml import get_distribution_template  # noqa: F401

llama_stack/templates/centml/build.yaml

@@ -0,0 +1,32 @@
+version: '2'
+name: centml
+distribution_spec:
+  description: Use CentML for running LLM inference
+  providers:
+    inference:
+    - remote::centml
+    memory:
+    - inline::faiss
+    - remote::chromadb
+    - remote::pgvector
+    safety:
+    - inline::llama-guard
+    agents:
+    - inline::meta-reference
+    telemetry:
+    - inline::meta-reference
+    eval:
+    - inline::meta-reference
+    datasetio:
+    - remote::huggingface
+    - inline::localfs
+    scoring:
+    - inline::basic
+    - inline::llm-as-judge
+    - inline::braintrust
+    tool_runtime:
+    - remote::brave-search
+    - remote::tavily-search
+    - inline::code-interpreter
+    - inline::memory-runtime
+image_type: conda

llama_stack/templates/centml/centml.py

@@ -0,0 +1,110 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from pathlib import Path
+
+from llama_models.sku_list import all_registered_models
+from llama_stack.apis.models.models import ModelType
+from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput
+from llama_stack.providers.inline.inference.sentence_transformers import (
+    SentenceTransformersInferenceConfig,
+)
+from llama_stack.providers.inline.memory.faiss.config import FaissImplConfig
+from llama_stack.providers.remote.inference.centml.config import CentMLImplConfig
+
+# If your CentML adapter has a MODEL_ALIASES constant with known model mappings:
+from llama_stack.providers.remote.inference.centml.centml import MODEL_ALIASES
+
+from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
+
+
+def get_distribution_template() -> DistributionTemplate:
+    """
+    Returns a distribution template for running Llama Stack with CentML inference.
+    """
+    providers = {
+        "inference": ["remote::centml"],
+        "memory": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
+        "safety": ["inline::llama-guard"],
+        "agents": ["inline::meta-reference"],
+        "telemetry": ["inline::meta-reference"],
+        "eval": ["inline::meta-reference"],
+        "datasetio": ["remote::huggingface", "inline::localfs"],
+        "scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"],
+    }
+    name = "centml"
+
+    # Primary inference provider: CentML
+    inference_provider = Provider(
+        provider_id="centml",
+        provider_type="remote::centml",
+        config=CentMLImplConfig.sample_run_config(),
+    )
+
+    # Memory provider: Faiss
+    memory_provider = Provider(
+        provider_id="faiss",
+        provider_type="inline::faiss",
+        config=FaissImplConfig.sample_run_config(f"distributions/{name}"),
+    )
+
+    # Embedding provider: SentenceTransformers
+    embedding_provider = Provider(
+        provider_id="sentence-transformers",
+        provider_type="inline::sentence-transformers",
+        config=SentenceTransformersInferenceConfig.sample_run_config(),
+    )
+
+    # Map Llama Models to provider IDs if needed
+    core_model_to_hf_repo = {
+        m.descriptor(): m.huggingface_repo for m in all_registered_models()
+    }
+    default_models = [
+        ModelInput(
+            model_id=core_model_to_hf_repo[m.llama_model],
+            provider_model_id=m.provider_model_id,
+            provider_id="centml",
+        )
+        for m in MODEL_ALIASES
+    ]
+
+    # Example embedding model
+    embedding_model = ModelInput(
+        model_id="all-MiniLM-L6-v2",
+        provider_id="sentence-transformers",
+        model_type=ModelType.embedding,
+        metadata={"embedding_dimension": 384},
+    )
+
+    return DistributionTemplate(
+        name=name,
+        distro_type="self_hosted",
+        description="Use CentML for running LLM inference",
+        docker_image=None,
+        template_path=Path(__file__).parent / "doc_template.md",
+        providers=providers,
+        default_models=default_models,
+        run_configs={
+            "run.yaml": RunConfigSettings(
+                provider_overrides={
+                    "inference": [inference_provider, embedding_provider],
+                    "memory": [memory_provider],
+                },
+                default_models=default_models + [embedding_model],
+                default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
+            ),
+        },
+        run_config_env_vars={
+            "LLAMASTACK_PORT": (
+                "5001",
+                "Port for the Llama Stack distribution server",
+            ),
+            "CENTML_API_KEY": (
+                "",
+                "CentML API Key",
+            ),
+        },
+    )

llama_stack/templates/centml/doc_template.md

@@ -0,0 +1,66 @@
+---
+orphan: true
+---
+# CentML Distribution
+
+```{toctree}
+:maxdepth: 2
+:hidden:
+
+self
+```
+
+The `llamastack/distribution-{{ name }}` distribution consists of the following provider configurations.
+
+{{ providers_table }}
+
+{% if run_config_env_vars %}
+### Environment Variables
+
+The following environment variables can be configured:
+
+{% for var, (default_value, description) in run_config_env_vars.items() %}
+- `{{ var }}`: {{ description }} (default: `{{ default_value }}`)
+{% endfor %}
+{% endif %}
+
+{% if default_models %}
+### Models
+
+The following models are available by default:
+
+{% for model in default_models %}
+- `{{ model.model_id }}`
+{% endfor %}
+{% endif %}
+
+### Prerequisite: API Keys
+
+Make sure you have a valid **CentML API Key**. Sign up or access your credentials at [CentML.com](https://centml.com/).
+
+## Running Llama Stack with CentML
+
+You can do this via Conda (build code) or Docker which has a pre-built image.
+
+### Via Docker
+
+This method allows you to get started quickly without having to build the distribution code.
+
+```bash
+LLAMA_STACK_PORT=5001
+docker run \
+  -it \
+  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
+  llamastack/distribution-{{ name }} \
+  --port $LLAMA_STACK_PORT \
+  --env CENTML_API_KEY=$CENTML_API_KEY
+```
+
+### Via Conda
+
+```bash
+llama stack build --template {{ name }} --image-type conda
+llama stack run ./run.yaml \
+  --port $LLAMA_STACK_PORT \
+  --env CENTML_API_KEY=$CENTML_API_KEY
+```

llama_stack/templates/centml/run.yaml

@@ -0,0 +1,129 @@
+version: '2'
+image_name: centml
+conda_env: centml
+apis:
+  - agents
+  - datasetio
+  - eval
+  - inference
+  - memory
+  - safety
+  - scoring
+  - telemetry
+  - tool_runtime
+providers:
+  inference:
+  - provider_id: centml
+    provider_type: remote::centml
+    config:
+      url: https://api.centml.com/openai/v1
+      api_key: "${env.CENTML_API_KEY}"
+  - provider_id: sentence-transformers
+    provider_type: inline::sentence-transformers
+    config: {}
+
+  memory:
+  - provider_id: faiss
+    provider_type: inline::faiss
+    config:
+      kvstore:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/centml}/faiss_store.db
+
+  safety:
+  - provider_id: llama-guard
+    provider_type: inline::llama-guard
+    config: {}
+
+  agents:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config:
+      persistence_store:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/centml}/agents_store.db
+
+  telemetry:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config:
+      service_name: ${env.OTEL_SERVICE_NAME:llama-stack}
+      sinks: ${env.TELEMETRY_SINKS:console,sqlite}
+      sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/centml}/trace_store.db
+
+  eval:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config: {}
+
+  datasetio:
+  - provider_id: huggingface
+    provider_type: remote::huggingface
+    config: {}
+  - provider_id: localfs
+    provider_type: inline::localfs
+    config: {}
+
+  scoring:
+  - provider_id: basic
+    provider_type: inline::basic
+    config: {}
+  - provider_id: llm-as-judge
+    provider_type: inline::llm-as-judge
+    config: {}
+  - provider_id: braintrust
+    provider_type: inline::braintrust
+    config:
+      openai_api_key: ${env.OPENAI_API_KEY:}
+
+  tool_runtime:
+  - provider_id: brave-search
+    provider_type: remote::brave-search
+    config:
+      api_key: ${env.BRAVE_SEARCH_API_KEY:}
+      max_results: 3
+  - provider_id: tavily-search
+    provider_type: remote::tavily-search
+    config:
+      api_key: ${env.TAVILY_SEARCH_API_KEY:}
+      max_results: 3
+  - provider_id: code-interpreter
+    provider_type: inline::code-interpreter
+    config: {}
+  - provider_id: memory-runtime
+    provider_type: inline::memory-runtime
+    config: {}
+
+metadata_store:
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/centml}/registry.db
+
+models:
+- metadata: {}
+  model_id: meta-llama/Llama-3.3-70B-Instruct
+  provider_id: centml
+  provider_model_id: meta-llama/Llama-3.3-70B-Instruct
+  model_type: llm
+
+- metadata: {}
+  model_id: meta-llama/Llama-3.1-405B-Instruct-FP8
+  provider_id: centml
+  provider_model_id: meta-llama/Llama-3.1-405B-Instruct-FP8
+  model_type: llm
+
+shields:
+  - shield_id: meta-llama/Llama-Guard-3-8B
+
+memory_banks: []
+datasets: []
+scoring_fns: []
+eval_tasks: []
+tool_groups:
+  - toolgroup_id: builtin::websearch
+    provider_id: tavily-search
+  - toolgroup_id: builtin::memory
+    provider_id: memory-runtime
+  - toolgroup_id: builtin::code_interpreter
+    provider_id: code-interpreter