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fix model provider validation and inference params

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Dinesh Yeduguru 2024-11-12 10:13:43 -08:00 • committed by Dinesh Yeduguru
parent 95b7f57d92
commit d69f4f8635
5 changed files with 34 additions and 25 deletions
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16
llama_stack/providers/remote/inference/ollama/ollama.py
View file

@ -66,8 +66,10 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
pass
async def register_model(self, model: Model) -> None:
if model.identifier not in OLLAMA_SUPPORTED_MODELS:
raise ValueError(f"Model {model.identifier} is not supported by Ollama")
if model.provider_resource_id not in OLLAMA_SUPPORTED_MODELS:
raise ValueError(
f"Model {model.provider_resource_id} is not supported by Ollama"
)
async def list_models(self) -> List[Model]:
ollama_to_llama = {v: k for k, v in OLLAMA_SUPPORTED_MODELS.items()}
@ -94,7 +96,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
async def completion(
self,
model: str,
model_id: str,
content: InterleavedTextMedia,
sampling_params: Optional[SamplingParams] = SamplingParams(),
response_format: Optional[ResponseFormat] = None,
@ -102,7 +104,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
logprobs: Optional[LogProbConfig] = None,
) -> AsyncGenerator:
request = CompletionRequest(
model=model,
model=model_id,
content=content,
sampling_params=sampling_params,
stream=stream,
@ -148,7 +150,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
async def chat_completion(
self,
model: str,
model_id: str,
messages: List[Message],
sampling_params: Optional[SamplingParams] = SamplingParams(),
response_format: Optional[ResponseFormat] = None,
@ -159,7 +161,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
logprobs: Optional[LogProbConfig] = None,
) -> AsyncGenerator:
request = ChatCompletionRequest(
model=model,
model=model_id,
messages=messages,
sampling_params=sampling_params,
tools=tools or [],
@ -271,7 +273,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
async def embeddings(
self,
model: str,
model_id: str,
contents: List[InterleavedTextMedia],
) -> EmbeddingsResponse:
raise NotImplementedError()

19
llama_stack/providers/remote/inference/vllm/vllm.py
View file

@ -45,8 +45,15 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
self.client = OpenAI(base_url=self.config.url, api_key=self.config.api_token)
async def register_model(self, model: Model) -> None:
for running_model in self.client.models.list():
repo = running_model.id
pass
async def shutdown(self) -> None:
pass
async def list_models(self) -> List[Model]:
models = []
for model in self.client.models.list():
repo = model.id
if repo not in self.huggingface_repo_to_llama_model_id:
print(f"Unknown model served by vllm: {repo}")
continue
@ -67,7 +74,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
async def completion(
self,
model: str,
model_id: str,
content: InterleavedTextMedia,
sampling_params: Optional[SamplingParams] = SamplingParams(),
response_format: Optional[ResponseFormat] = None,
@ -78,7 +85,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
async def chat_completion(
self,
model: str,
model_id: str,
messages: List[Message],
sampling_params: Optional[SamplingParams] = SamplingParams(),
response_format: Optional[ResponseFormat] = None,
@ -89,7 +96,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
logprobs: Optional[LogProbConfig] = None,
) -> AsyncGenerator:
request = ChatCompletionRequest(
model=model,
model=model_id,
messages=messages,
sampling_params=sampling_params,
tools=tools or [],
@ -173,7 +180,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
async def embeddings(
self,
model: str,
model_id: str,
contents: List[InterleavedTextMedia],
) -> EmbeddingsResponse:
raise NotImplementedError()