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add register_model method

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raspawar 2025-04-02 09:51:55 +00:00
parent c169c164b3
commit 3d2b374ee7
2 changed files with 99 additions and 10 deletions
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56
llama_stack/providers/remote/inference/nvidia/nvidia.py
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@ -33,11 +33,15 @@ from llama_stack.apis.inference import (
ToolChoice,
ToolConfig,
)
from llama_stack.apis.models import Model, ModelType
from llama_stack.models.llama.datatypes import (
SamplingParams,
ToolDefinition,
ToolPromptFormat,
)
from llama_stack.providers.utils.inference import (
ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR,
)
from llama_stack.providers.utils.inference.model_registry import (
ModelRegistryHelper,
)
@ -114,10 +118,13 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
"meta/llama-3.2-90b-vision-instruct": "https://ai.api.nvidia.com/v1/gr/meta/llama-3.2-90b-vision-instruct",
}
base_url = f"{self._config.url}/v1"
if _is_nvidia_hosted(self._config) and provider_model_id in special_model_urls:
base_url = special_model_urls[provider_model_id]
# add /v1 in case of hosted models
base_url = self._config.url
if _is_nvidia_hosted(self._config):
if provider_model_id in special_model_urls:
base_url = special_model_urls[provider_model_id]
else:
base_url = f"{self._config.url}/v1"
return _get_client_for_base_url(base_url)
async def completion(
@ -265,3 +272,44 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
else:
# we pass n=1 to get only one completion
return convert_openai_chat_completion_choice(response.choices[0])
async def register_model(self, model: Model) -> Model:
"""
Allow non-llama model registration.
Non-llama model registration: API Catalogue models, post-training models, etc.
client = LlamaStackAsLibraryClient("nvidia")
client.models.register(
model_id="mistralai/mixtral-8x7b-instruct-v0.1",
model_type=ModelType.llm,
provider_id="nvidia",
provider_model_id="mistralai/mixtral-8x7b-instruct-v0.1"
)
NOTE: Only supports models endpoints compatible with AsyncOpenAI base_url format.
"""
if model.model_type == ModelType.embedding:
# embedding models are always registered by their provider model id and does not need to be mapped to a llama model
provider_resource_id = model.provider_resource_id
else:
provider_resource_id = self.get_provider_model_id(model.provider_resource_id)
if provider_resource_id:
model.provider_resource_id = provider_resource_id
else:
llama_model = model.metadata.get("llama_model")
existing_llama_model = self.get_llama_model(model.provider_resource_id)
if existing_llama_model:
if existing_llama_model != llama_model:
raise ValueError(
f"Provider model id '{model.provider_resource_id}' is already registered to a different llama model: '{existing_llama_model}'"
)
else:
# not llama model
if llama_model in ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR:
self.provider_id_to_llama_model_map[model.provider_resource_id] = (
ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR[llama_model]
)
else:
self.alias_to_provider_id_map[model.provider_model_id] = model.provider_model_id
return model