diff --git a/llama_stack/providers/registry/inference.py b/llama_stack/providers/registry/inference.py index 54d55e60e..c8d061f6c 100644 --- a/llama_stack/providers/registry/inference.py +++ b/llama_stack/providers/registry/inference.py @@ -150,4 +150,15 @@ def available_providers() -> List[ProviderSpec]: config_class="llama_stack.providers.remote.inference.databricks.DatabricksImplConfig", ), ), + remote_provider_spec( + api=Api.inference, + adapter=AdapterSpec( + adapter_type="nvidia", + pip_packages=[ + "openai", + ], + module="llama_stack.providers.remote.inference.nvidia", + config_class="llama_stack.providers.remote.inference.nvidia.NVIDIAConfig", + ), + ), ] diff --git a/llama_stack/providers/remote/inference/nvidia/__init__.py b/llama_stack/providers/remote/inference/nvidia/__init__.py new file mode 100644 index 000000000..63b466933 --- /dev/null +++ b/llama_stack/providers/remote/inference/nvidia/__init__.py @@ -0,0 +1,18 @@ +# 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 ._config import NVIDIAConfig +from ._nvidia import NVIDIAInferenceAdapter + + +async def get_adapter_impl(config: NVIDIAConfig, _deps) -> NVIDIAInferenceAdapter: + if not isinstance(config, NVIDIAConfig): + raise RuntimeError(f"Unexpected config type: {type(config)}") + adapter = NVIDIAInferenceAdapter(config) + return adapter + + +__all__ = ["get_adapter_impl", "NVIDIAConfig"] diff --git a/llama_stack/providers/remote/inference/nvidia/_config.py b/llama_stack/providers/remote/inference/nvidia/_config.py new file mode 100644 index 000000000..46ac3fa5b --- /dev/null +++ b/llama_stack/providers/remote/inference/nvidia/_config.py @@ -0,0 +1,52 @@ +# 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. + +import os +from typing import Optional + +from llama_models.schema_utils import json_schema_type +from pydantic import BaseModel, Field + + +@json_schema_type +class NVIDIAConfig(BaseModel): + """ + Configuration for the NVIDIA NIM inference endpoint. + + Attributes: + base_url (str): A base url for accessing the NVIDIA NIM, e.g. http://localhost:8000 + api_key (str): The access key for the hosted NIM endpoints + + There are two ways to access NVIDIA NIMs - + 0. Hosted: Preview APIs hosted at https://integrate.api.nvidia.com + 1. Self-hosted: You can run NVIDIA NIMs on your own infrastructure + + By default the configuration is set to use the hosted APIs. This requires + an API key which can be obtained from https://ngc.nvidia.com/. + + By default the configuration will attempt to read the NVIDIA_API_KEY environment + variable to set the api_key. Please do not put your API key in code. + + If you are using a self-hosted NVIDIA NIM, you can set the base_url to the + URL of your running NVIDIA NIM and do not need to set the api_key. + """ + + base_url: str = Field( + default="https://integrate.api.nvidia.com", + description="A base url for accessing the NVIDIA NIM", + ) + api_key: Optional[str] = Field( + default_factory=lambda: os.getenv("NVIDIA_API_KEY"), + description="The NVIDIA API key, only needed of using the hosted service", + ) + timeout: int = Field( + default=60, + description="Timeout for the HTTP requests", + ) + + @property + def is_hosted(self) -> bool: + return "integrate.api.nvidia.com" in self.base_url diff --git a/llama_stack/providers/remote/inference/nvidia/_nvidia.py b/llama_stack/providers/remote/inference/nvidia/_nvidia.py new file mode 100644 index 000000000..c5bfa0f25 --- /dev/null +++ b/llama_stack/providers/remote/inference/nvidia/_nvidia.py @@ -0,0 +1,182 @@ +# 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. + +import warnings +from typing import AsyncIterator, List, Optional, Union + +from llama_models.datatypes import SamplingParams +from llama_models.llama3.api.datatypes import ( + InterleavedTextMedia, + Message, + ToolChoice, + ToolDefinition, + ToolPromptFormat, +) +from llama_models.sku_list import CoreModelId +from openai import APIConnectionError, AsyncOpenAI + +from llama_stack.apis.inference import ( + ChatCompletionRequest, + ChatCompletionResponse, + ChatCompletionResponseStreamChunk, + CompletionResponse, + CompletionResponseStreamChunk, + EmbeddingsResponse, + Inference, + LogProbConfig, + ResponseFormat, +) +from llama_stack.providers.utils.inference.model_registry import ( + build_model_alias_with_just_provider_model_id, + ModelRegistryHelper, +) + +from ._config import NVIDIAConfig +from ._openai_utils import ( + convert_chat_completion_request, + convert_openai_chat_completion_choice, + convert_openai_chat_completion_stream, +) +from ._utils import check_health + +_MODEL_ALIASES = [ + build_model_alias_with_just_provider_model_id( + "meta/llama3-8b-instruct", + CoreModelId.llama3_8b_instruct.value, + ), + build_model_alias_with_just_provider_model_id( + "meta/llama3-70b-instruct", + CoreModelId.llama3_70b_instruct.value, + ), + build_model_alias_with_just_provider_model_id( + "meta/llama-3.1-8b-instruct", + CoreModelId.llama3_1_8b_instruct.value, + ), + build_model_alias_with_just_provider_model_id( + "meta/llama-3.1-70b-instruct", + CoreModelId.llama3_1_70b_instruct.value, + ), + build_model_alias_with_just_provider_model_id( + "meta/llama-3.1-405b-instruct", + CoreModelId.llama3_1_405b_instruct.value, + ), + build_model_alias_with_just_provider_model_id( + "meta/llama-3.2-1b-instruct", + CoreModelId.llama3_2_1b_instruct.value, + ), + build_model_alias_with_just_provider_model_id( + "meta/llama-3.2-3b-instruct", + CoreModelId.llama3_2_3b_instruct.value, + ), + build_model_alias_with_just_provider_model_id( + "meta/llama-3.2-11b-vision-instruct", + CoreModelId.llama3_2_11b_vision_instruct.value, + ), + build_model_alias_with_just_provider_model_id( + "meta/llama-3.2-90b-vision-instruct", + CoreModelId.llama3_2_90b_vision_instruct.value, + ), + # TODO(mf): how do we handle Nemotron models? + # "Llama3.1-Nemotron-51B-Instruct" -> "meta/llama-3.1-nemotron-51b-instruct", +] + + +class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper): + def __init__(self, config: NVIDIAConfig) -> None: + # TODO(mf): filter by available models + ModelRegistryHelper.__init__(self, model_aliases=_MODEL_ALIASES) + + print(f"Initializing NVIDIAInferenceAdapter({config.base_url})...") + + if config.is_hosted: + if not config.api_key: + raise RuntimeError( + "API key is required for hosted NVIDIA NIM. " + "Either provide an API key or use a self-hosted NIM." + ) + # elif self._config.api_key: + # + # we don't raise this warning because a user may have deployed their + # self-hosted NIM with an API key requirement. + # + # warnings.warn( + # "API key is not required for self-hosted NVIDIA NIM. " + # "Consider removing the api_key from the configuration." + # ) + + self._config = config + # make sure the client lives longer than any async calls + self._client = AsyncOpenAI( + base_url=f"{self._config.base_url}/v1", + api_key=self._config.api_key or "NO KEY", + timeout=self._config.timeout, + ) + + def completion( + self, + model_id: str, + content: InterleavedTextMedia, + sampling_params: Optional[SamplingParams] = SamplingParams(), + response_format: Optional[ResponseFormat] = None, + stream: Optional[bool] = False, + logprobs: Optional[LogProbConfig] = None, + ) -> Union[CompletionResponse, AsyncIterator[CompletionResponseStreamChunk]]: + raise NotImplementedError() + + async def embeddings( + self, + model_id: str, + contents: List[InterleavedTextMedia], + ) -> EmbeddingsResponse: + raise NotImplementedError() + + async def chat_completion( + self, + model_id: str, + messages: List[Message], + sampling_params: Optional[SamplingParams] = SamplingParams(), + response_format: Optional[ResponseFormat] = None, + tools: Optional[List[ToolDefinition]] = None, + tool_choice: Optional[ToolChoice] = ToolChoice.auto, + tool_prompt_format: Optional[ + ToolPromptFormat + ] = None, # API default is ToolPromptFormat.json, we default to None to detect user input + stream: Optional[bool] = False, + logprobs: Optional[LogProbConfig] = None, + ) -> Union[ + ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk] + ]: + if tool_prompt_format: + warnings.warn("tool_prompt_format is not supported by NVIDIA NIM, ignoring") + + await check_health(self._config) # this raises errors + + request = convert_chat_completion_request( + request=ChatCompletionRequest( + model=self.get_provider_model_id(model_id), + messages=messages, + sampling_params=sampling_params, + tools=tools, + tool_choice=tool_choice, + tool_prompt_format=tool_prompt_format, + stream=stream, + logprobs=logprobs, + ), + n=1, + ) + + try: + response = await self._client.chat.completions.create(**request) + except APIConnectionError as e: + raise ConnectionError( + f"Failed to connect to NVIDIA NIM at {self._config.base_url}: {e}" + ) from e + + if stream: + return convert_openai_chat_completion_stream(response) + else: + # we pass n=1 to get only one completion + return convert_openai_chat_completion_choice(response.choices[0]) diff --git a/llama_stack/providers/remote/inference/nvidia/_openai_utils.py b/llama_stack/providers/remote/inference/nvidia/_openai_utils.py new file mode 100644 index 000000000..998b4c275 --- /dev/null +++ b/llama_stack/providers/remote/inference/nvidia/_openai_utils.py @@ -0,0 +1,430 @@ +# 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. + +import json +import warnings +from typing import Any, AsyncGenerator, Dict, Generator, List, Optional + +from llama_models.llama3.api.datatypes import ( + CompletionMessage, + StopReason, + TokenLogProbs, + ToolCall, +) +from openai import AsyncStream +from openai.types.chat import ChatCompletionChunk as OpenAIChatCompletionChunk +from openai.types.chat.chat_completion import ( + Choice as OpenAIChoice, + ChoiceLogprobs as OpenAIChoiceLogprobs, # same as chat_completion_chunk ChoiceLogprobs +) +from openai.types.chat.chat_completion_message_tool_call import ( + ChatCompletionMessageToolCall as OpenAIChatCompletionMessageToolCall, +) + +from llama_stack.apis.inference import ( + ChatCompletionRequest, + ChatCompletionResponse, + ChatCompletionResponseEvent, + ChatCompletionResponseEventType, + ChatCompletionResponseStreamChunk, + Message, + ToolCallDelta, + ToolCallParseStatus, +) + + +def _convert_message(message: Message) -> Dict: + """ + Convert a Message to an OpenAI API-compatible dictionary. + """ + out_dict = message.dict() + # Llama Stack uses role="ipython" for tool call messages, OpenAI uses "tool" + if out_dict["role"] == "ipython": + out_dict.update(role="tool") + + if "stop_reason" in out_dict: + out_dict.update(stop_reason=out_dict["stop_reason"].value) + + # TODO(mf): tool_calls + + return out_dict + + +def convert_chat_completion_request( + request: ChatCompletionRequest, + n: int = 1, +) -> dict: + """ + Convert a ChatCompletionRequest to an OpenAI API-compatible dictionary. + """ + # model -> model + # messages -> messages + # sampling_params TODO(mattf): review strategy + # strategy=greedy -> nvext.top_k = -1, temperature = temperature + # strategy=top_p -> nvext.top_k = -1, top_p = top_p + # strategy=top_k -> nvext.top_k = top_k + # temperature -> temperature + # top_p -> top_p + # top_k -> nvext.top_k + # max_tokens -> max_tokens + # repetition_penalty -> nvext.repetition_penalty + # tools -> tools + # tool_choice ("auto", "required") -> tool_choice + # tool_prompt_format -> TBD + # stream -> stream + # logprobs -> logprobs + + nvext = {} + payload: Dict[str, Any] = dict( + model=request.model, + messages=[_convert_message(message) for message in request.messages], + stream=request.stream, + n=n, + extra_body=dict(nvext=nvext), + extra_headers={ + b"User-Agent": b"llama-stack: nvidia-inference-adapter", + }, + ) + + if request.tools: + payload.update(tools=request.tools) + if request.tool_choice: + payload.update( + tool_choice=request.tool_choice.value + ) # we cannot include tool_choice w/o tools, server will complain + + if request.logprobs: + payload.update(logprobs=True) + payload.update(top_logprobs=request.logprobs.top_k) + + if request.sampling_params: + nvext.update(repetition_penalty=request.sampling_params.repetition_penalty) + + if request.sampling_params.max_tokens: + payload.update(max_tokens=request.sampling_params.max_tokens) + + if request.sampling_params.strategy == "top_p": + nvext.update(top_k=-1) + payload.update(top_p=request.sampling_params.top_p) + elif request.sampling_params.strategy == "top_k": + if ( + request.sampling_params.top_k != -1 + and request.sampling_params.top_k < 1 + ): + warnings.warn("top_k must be -1 or >= 1") + nvext.update(top_k=request.sampling_params.top_k) + elif request.sampling_params.strategy == "greedy": + nvext.update(top_k=-1) + payload.update(temperature=request.sampling_params.temperature) + + return payload + + +def _convert_openai_finish_reason(finish_reason: str) -> StopReason: + """ + Convert an OpenAI chat completion finish_reason to a StopReason. + + finish_reason: Literal["stop", "length", "tool_calls", ...] + - stop: model hit a natural stop point or a provided stop sequence + - length: maximum number of tokens specified in the request was reached + - tool_calls: model called a tool + + -> + + class StopReason(Enum): + end_of_turn = "end_of_turn" + end_of_message = "end_of_message" + out_of_tokens = "out_of_tokens" + """ + + # TODO(mf): are end_of_turn and end_of_message semantics correct? + return { + "stop": StopReason.end_of_turn, + "length": StopReason.out_of_tokens, + "tool_calls": StopReason.end_of_message, + }.get(finish_reason, StopReason.end_of_turn) + + +def _convert_openai_tool_calls( + tool_calls: List[OpenAIChatCompletionMessageToolCall], +) -> List[ToolCall]: + """ + Convert an OpenAI ChatCompletionMessageToolCall list into a list of ToolCall. + + OpenAI ChatCompletionMessageToolCall: + id: str + function: Function + type: Literal["function"] + + OpenAI Function: + arguments: str + name: str + + -> + + ToolCall: + call_id: str + tool_name: str + arguments: Dict[str, ...] + """ + if not tool_calls: + return [] # CompletionMessage tool_calls is not optional + + return [ + ToolCall( + call_id=call.id, + tool_name=call.function.name, + arguments=json.loads(call.function.arguments), + ) + for call in tool_calls + ] + + +def _convert_openai_logprobs( + logprobs: OpenAIChoiceLogprobs, +) -> Optional[List[TokenLogProbs]]: + """ + Convert an OpenAI ChoiceLogprobs into a list of TokenLogProbs. + + OpenAI ChoiceLogprobs: + content: Optional[List[ChatCompletionTokenLogprob]] + + OpenAI ChatCompletionTokenLogprob: + token: str + logprob: float + top_logprobs: List[TopLogprob] + + OpenAI TopLogprob: + token: str + logprob: float + + -> + + TokenLogProbs: + logprobs_by_token: Dict[str, float] + - token, logprob + + """ + if not logprobs: + return None + + return [ + TokenLogProbs( + logprobs_by_token={ + logprobs.token: logprobs.logprob for logprobs in content.top_logprobs + } + ) + for content in logprobs.content + ] + + +def convert_openai_chat_completion_choice( + choice: OpenAIChoice, +) -> ChatCompletionResponse: + """ + Convert an OpenAI Choice into a ChatCompletionResponse. + + OpenAI Choice: + message: ChatCompletionMessage + finish_reason: str + logprobs: Optional[ChoiceLogprobs] + + OpenAI ChatCompletionMessage: + role: Literal["assistant"] + content: Optional[str] + tool_calls: Optional[List[ChatCompletionMessageToolCall]] + + -> + + ChatCompletionResponse: + completion_message: CompletionMessage + logprobs: Optional[List[TokenLogProbs]] + + CompletionMessage: + role: Literal["assistant"] + content: str | ImageMedia | List[str | ImageMedia] + stop_reason: StopReason + tool_calls: List[ToolCall] + + class StopReason(Enum): + end_of_turn = "end_of_turn" + end_of_message = "end_of_message" + out_of_tokens = "out_of_tokens" + """ + assert ( + hasattr(choice, "message") and choice.message + ), "error in server response: message not found" + assert ( + hasattr(choice, "finish_reason") and choice.finish_reason + ), "error in server response: finish_reason not found" + + return ChatCompletionResponse( + completion_message=CompletionMessage( + content=choice.message.content + or "", # CompletionMessage content is not optional + stop_reason=_convert_openai_finish_reason(choice.finish_reason), + tool_calls=_convert_openai_tool_calls(choice.message.tool_calls), + ), + logprobs=_convert_openai_logprobs(choice.logprobs), + ) + + +async def convert_openai_chat_completion_stream( + stream: AsyncStream[OpenAIChatCompletionChunk], +) -> AsyncGenerator[ChatCompletionResponseStreamChunk, None]: + """ + Convert a stream of OpenAI chat completion chunks into a stream + of ChatCompletionResponseStreamChunk. + + OpenAI ChatCompletionChunk: + choices: List[Choice] + + OpenAI Choice: # different from the non-streamed Choice + delta: ChoiceDelta + finish_reason: Optional[Literal["stop", "length", "tool_calls", "content_filter", "function_call"]] + logprobs: Optional[ChoiceLogprobs] + + OpenAI ChoiceDelta: + content: Optional[str] + role: Optional[Literal["system", "user", "assistant", "tool"]] + tool_calls: Optional[List[ChoiceDeltaToolCall]] + + OpenAI ChoiceDeltaToolCall: + index: int + id: Optional[str] + function: Optional[ChoiceDeltaToolCallFunction] + type: Optional[Literal["function"]] + + OpenAI ChoiceDeltaToolCallFunction: + name: Optional[str] + arguments: Optional[str] + + -> + + ChatCompletionResponseStreamChunk: + event: ChatCompletionResponseEvent + + ChatCompletionResponseEvent: + event_type: ChatCompletionResponseEventType + delta: Union[str, ToolCallDelta] + logprobs: Optional[List[TokenLogProbs]] + stop_reason: Optional[StopReason] + + ChatCompletionResponseEventType: + start = "start" + progress = "progress" + complete = "complete" + + ToolCallDelta: + content: Union[str, ToolCall] + parse_status: ToolCallParseStatus + + ToolCall: + call_id: str + tool_name: str + arguments: str + + ToolCallParseStatus: + started = "started" + in_progress = "in_progress" + failure = "failure" + success = "success" + + TokenLogProbs: + logprobs_by_token: Dict[str, float] + - token, logprob + + StopReason: + end_of_turn = "end_of_turn" + end_of_message = "end_of_message" + out_of_tokens = "out_of_tokens" + """ + + # generate a stream of ChatCompletionResponseEventType: start -> progress -> progress -> ... + def _event_type_generator() -> ( + Generator[ChatCompletionResponseEventType, None, None] + ): + yield ChatCompletionResponseEventType.start + while True: + yield ChatCompletionResponseEventType.progress + + event_type = _event_type_generator() + + # we implement NIM specific semantics, the main difference from OpenAI + # is that tool_calls are always produced as a complete call. there is no + # intermediate / partial tool call streamed. because of this, we can + # simplify the logic and not concern outselves with parse_status of + # started/in_progress/failed. we can always assume success. + # + # a stream of ChatCompletionResponseStreamChunk consists of + # 0. a start event + # 1. zero or more progress events + # - each progress event has a delta + # - each progress event may have a stop_reason + # - each progress event may have logprobs + # - each progress event may have tool_calls + # if a progress event has tool_calls, + # it is fully formed and + # can be emitted with a parse_status of success + # 2. a complete event + + stop_reason = None + + async for chunk in stream: + choice = chunk.choices[0] # assuming only one choice per chunk + + # we assume there's only one finish_reason in the stream + stop_reason = _convert_openai_finish_reason(choice.finish_reason) or stop_reason + + # if there's a tool call, emit an event for each tool in the list + # if tool call and content, emit both separately + + if choice.delta.tool_calls: + # the call may have content and a tool call. ChatCompletionResponseEvent + # does not support both, so we emit the content first + if choice.delta.content: + yield ChatCompletionResponseStreamChunk( + event=ChatCompletionResponseEvent( + event_type=next(event_type), + delta=choice.delta.content, + logprobs=_convert_openai_logprobs(choice.logprobs), + ) + ) + + # it is possible to have parallel tool calls in stream, but + # ChatCompletionResponseEvent only supports one per stream + if len(choice.delta.tool_calls) > 1: + warnings.warn( + "multiple tool calls found in a single delta, using the first, ignoring the rest" + ) + + # NIM only produces fully formed tool calls, so we can assume success + yield ChatCompletionResponseStreamChunk( + event=ChatCompletionResponseEvent( + event_type=next(event_type), + delta=ToolCallDelta( + content=_convert_openai_tool_calls(choice.delta.tool_calls)[0], + parse_status=ToolCallParseStatus.success, + ), + logprobs=_convert_openai_logprobs(choice.logprobs), + ) + ) + else: + yield ChatCompletionResponseStreamChunk( + event=ChatCompletionResponseEvent( + event_type=next(event_type), + delta=choice.delta.content or "", # content is not optional + logprobs=_convert_openai_logprobs(choice.logprobs), + ) + ) + + yield ChatCompletionResponseStreamChunk( + event=ChatCompletionResponseEvent( + event_type=ChatCompletionResponseEventType.complete, + delta="", + stop_reason=stop_reason, + ) + ) diff --git a/llama_stack/providers/remote/inference/nvidia/_utils.py b/llama_stack/providers/remote/inference/nvidia/_utils.py new file mode 100644 index 000000000..6f52bdc4b --- /dev/null +++ b/llama_stack/providers/remote/inference/nvidia/_utils.py @@ -0,0 +1,50 @@ +# 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 Tuple + +import httpx + +from ._config import NVIDIAConfig + + +async def _get_health(url: str) -> Tuple[bool, bool]: + """ + Query {url}/v1/health/{live,ready} to check if the server is running and ready + + Args: + url (str): URL of the server + + Returns: + Tuple[bool, bool]: (is_live, is_ready) + """ + async with httpx.AsyncClient() as client: + live = await client.get(f"{url}/v1/health/live") + ready = await client.get(f"{url}/v1/health/ready") + return live.status_code == 200, ready.status_code == 200 + + +async def check_health(config: NVIDIAConfig) -> None: + """ + Check if the server is running and ready + + Args: + url (str): URL of the server + + Raises: + RuntimeError: If the server is not running or ready + """ + if not config.is_hosted: + print("Checking NVIDIA NIM health...") + try: + is_live, is_ready = await _get_health(config.base_url) + if not is_live: + raise ConnectionError("NVIDIA NIM is not running") + if not is_ready: + raise ConnectionError("NVIDIA NIM is not ready") + # TODO(mf): should we wait for the server to be ready? + except httpx.ConnectError as e: + raise ConnectionError(f"Failed to connect to NVIDIA NIM: {e}") from e diff --git a/llama_stack/providers/utils/inference/model_registry.py b/llama_stack/providers/utils/inference/model_registry.py index 07225fac0..8dbfab14a 100644 --- a/llama_stack/providers/utils/inference/model_registry.py +++ b/llama_stack/providers/utils/inference/model_registry.py @@ -29,7 +29,6 @@ def build_model_alias(provider_model_id: str, model_descriptor: str) -> ModelAli return ModelAlias( provider_model_id=provider_model_id, aliases=[ - model_descriptor, get_huggingface_repo(model_descriptor), ], llama_model=model_descriptor, @@ -57,6 +56,10 @@ class ModelRegistryHelper(ModelsProtocolPrivate): self.alias_to_provider_id_map[alias_obj.provider_model_id] = ( alias_obj.provider_model_id ) + # ensure we can go from llama model to provider model id + self.alias_to_provider_id_map[alias_obj.llama_model] = ( + alias_obj.provider_model_id + ) self.provider_id_to_llama_model_map[alias_obj.provider_model_id] = ( alias_obj.llama_model )