mirror of
https://github.com/meta-llama/llama-stack.git
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add NVIDIA safety provider
This commit is contained in:
parent
688e1806d1
commit
b90ff9ca16
6 changed files with 389 additions and 5 deletions
18
llama_stack/providers/remote/safety/nvidia/__init__.py
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18
llama_stack/providers/remote/safety/nvidia/__init__.py
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@ -0,0 +1,18 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from typing import Any
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from .config import NVIDIASafetyConfig
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async def get_adapter_impl(config: NVIDIASafetyConfig, _deps) -> Any:
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from .nvidia import NVIDIASafetyAdapter
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impl = NVIDIASafetyAdapter(config)
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await impl.initialize()
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return impl
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63
llama_stack/providers/remote/safety/nvidia/config.py
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63
llama_stack/providers/remote/safety/nvidia/config.py
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@ -0,0 +1,63 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from enum import Enum
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import os
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from typing import Any, Dict, Optional
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from pydantic import BaseModel, Field, SecretStr, field_validator
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from llama_models.schema_utils import json_schema_type
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class ShieldType(Enum):
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self_check = "self_check"
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@json_schema_type
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class NVIDIASafetyConfig(BaseModel):
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"""
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Configuration for the NVIDIA Guardrail microservice endpoint.
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Attributes:
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url (str): A base url for accessing the NVIDIA guardrail endpoint, e.g. http://localhost:8000
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api_key (str): The access key for the hosted NIM endpoints
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There are two ways to access NVIDIA NIMs -
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0. Hosted: Preview APIs hosted at https://integrate.api.nvidia.com
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1. Self-hosted: You can run NVIDIA NIMs on your own infrastructure
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By default the configuration is set to use the hosted APIs. This requires
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an API key which can be obtained from https://ngc.nvidia.com/.
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By default the configuration will attempt to read the NVIDIA_API_KEY environment
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variable to set the api_key. Please do not put your API key in code.
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"""
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guardrails_service_url: str = Field(
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default_factory=lambda: os.getenv("NVIDIA_BASE_URL", "https://0.0.0.0:7331"),
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description="The url for accessing the guardrails service",
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)
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config_id: Optional[str] = Field(
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default="self-check",
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description="Config ID to use from the config store"
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)
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config_store_path: Optional[str] = Field(
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default="/config-store",
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description="Path to config store"
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)
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@classmethod
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@field_validator("guard_type")
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def validate_guard_type(cls, v):
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if v not in [t.value for t in ShieldType]:
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raise ValueError(f"Unknown shield type: {v}")
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return v
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@classmethod
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def sample_run_config(cls, **kwargs) -> Dict[str, Any]:
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return {
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"guardrails_service_url": "${env.GUARDRAILS_SERVICE_URL:http://localhost:7331}",
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"config_id": "self-check"
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}
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110
llama_stack/providers/remote/safety/nvidia/nvidia.py
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110
llama_stack/providers/remote/safety/nvidia/nvidia.py
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@ -0,0 +1,110 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import json
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import logging
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from typing import Any, Dict, List
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from llama_stack.apis.inference import Message
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from llama_stack.apis.safety import (
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RunShieldResponse,
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Safety,
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SafetyViolation,
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ViolationLevel,
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)
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from llama_stack.apis.shields import Shield
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from llama_stack.providers.datatypes import ShieldsProtocolPrivate
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import requests
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from .config import NVIDIASafetyConfig
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logger = logging.getLogger(__name__)
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class NVIDIASafetyAdapter(Safety, ShieldsProtocolPrivate):
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def __init__(self, config: NVIDIASafetyConfig) -> None:
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print(f"Initializing NVIDIASafetyAdapter({config.url})...")
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self.config = config
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self.registered_shields = []
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async def initialize(self) -> None:
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pass
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async def shutdown(self) -> None:
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pass
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async def register_shield(self, shield: Shield) -> None:
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if not shield.provider_resource_id:
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raise ValueError(f"Shield model not provided. ")
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async def run_shield(
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self, shield_id: str, messages: List[Message], params: Dict[str, Any] = None
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) -> RunShieldResponse:
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shield = await self.shield_store.get_shield(shield_id)
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if not shield:
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raise ValueError(f"Shield {shield_id} not found")
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self.shield = NeMoGuardrails(self.config, shield.provider_resource_id)
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return await self.shield.run(messages)
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class NeMoGuardrails:
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def __init__(
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self,
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config: NVIDIASafetyConfig,
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model: str,
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threshold: float = 0.9,
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temperature: float = 1.0,
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):
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config_id = config["config_id"]
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config_store_path = config["config_store_path"]
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assert config_id is not None or config_store_path is not None, "Must provide one of config id or config store path"
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if temperature <= 0:
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raise ValueError("Temperature must be greater than 0")
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self.config = config
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self.temperature = temperature
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self.threshold = threshold
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self.guardrails_service_url = config["guardrails_service_url"]
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async def run(self, messages: List[Message]) -> RunShieldResponse:
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headers = {
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"Accept": "application/json",
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}
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request_data = {
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"model": "meta/llama-3.1-8b-instruct",
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"messages": messages,
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"temperature": self.temperature,
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"top_p": 1,
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"frequency_penalty": 0,
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"presence_penalty": 0,
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"max_tokens": 160,
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"stream": False,
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"guardrails": {
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"config_id": self.config["config_id"],
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}
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}
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response = requests.post(
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url=f"{self.guardrails_service_url}/v1/guardrail/checks",
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headers=headers,
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json=request_data
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)
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response.raise_for_status()
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if 'Content-Type' in response.headers and response.headers['Content-Type'].startswith('application/json'):
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response_json = response.json()
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if response_json["status"] == "blocked":
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user_message = "Sorry I cannot do this."
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metadata = response_json["rails_status"]
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return RunShieldResponse(
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violation=SafetyViolation(
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user_message=user_message,
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violation_level=ViolationLevel.ERROR,
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metadata=metadata,
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)
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)
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return RunShieldResponse(violation=None)
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@ -7,7 +7,7 @@ distribution_spec:
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vector_io:
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vector_io:
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- inline::faiss
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- inline::faiss
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safety:
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safety:
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- inline::llama-guard
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- remote::nvidia
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agents:
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agents:
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- inline::meta-reference
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- inline::meta-reference
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telemetry:
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telemetry:
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from pathlib import Path
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from pathlib import Path
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<<<<<<< Updated upstream
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from llama_stack.distribution.datatypes import ModelInput, Provider, ToolGroupInput
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from llama_stack.distribution.datatypes import ModelInput, Provider, ToolGroupInput
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from llama_stack.models.llama.sku_list import all_registered_models
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from llama_stack.models.llama.sku_list import all_registered_models
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=======
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from llama_models.sku_list import all_registered_models
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from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput, ToolGroupInput
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from llama_stack.providers.remote.safety.nvidia import NVIDIASafetyConfig
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>>>>>>> Stashed changes
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from llama_stack.providers.remote.inference.nvidia import NVIDIAConfig
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from llama_stack.providers.remote.inference.nvidia import NVIDIAConfig
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from llama_stack.providers.remote.inference.nvidia.nvidia import _MODEL_ALIASES
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from llama_stack.providers.remote.inference.nvidia.nvidia import _MODEL_ALIASES
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from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
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from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
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@ -37,6 +44,19 @@ def get_distribution_template() -> DistributionTemplate:
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provider_type="remote::nvidia",
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provider_type="remote::nvidia",
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config=NVIDIAConfig.sample_run_config(),
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config=NVIDIAConfig.sample_run_config(),
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)
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)
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safety_provider = Provider(
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provider_id="nvidia",
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provider_type="remote::nvidia",
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config=NVIDIASafetyConfig.sample_run_config(),
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)
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inference_model = ModelInput(
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model_id="${env.INFERENCE_MODEL}",
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provider_id="nvidia",
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)
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safety_model = ModelInput(
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model_id="${env.SAFETY_MODEL}",
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provider_id="nvidia",
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)
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core_model_to_hf_repo = {m.descriptor(): m.huggingface_repo for m in all_registered_models()}
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core_model_to_hf_repo = {m.descriptor(): m.huggingface_repo for m in all_registered_models()}
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default_models = [
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default_models = [
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@ -78,15 +98,34 @@ def get_distribution_template() -> DistributionTemplate:
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default_models=default_models,
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default_models=default_models,
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default_tool_groups=default_tool_groups,
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default_tool_groups=default_tool_groups,
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),
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),
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"run-with-safety.yaml": RunConfigSettings(
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provider_overrides={
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"inference": [
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inference_provider,
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safety_provider,
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]
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},
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default_models=[inference_model, safety_model],
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default_shields=[ShieldInput(shield_id="${env.SAFETY_MODEL}")],
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default_tool_groups=default_tool_groups,
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),
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},
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},
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run_config_env_vars={
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run_config_env_vars={
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"LLAMASTACK_PORT": (
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"5001",
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"Port for the Llama Stack distribution server",
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),
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"NVIDIA_API_KEY": (
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"NVIDIA_API_KEY": (
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"",
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"",
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"NVIDIA API Key",
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"NVIDIA API Key",
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),
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),
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"GUARDRAILS_SERVICE_URL": (
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"http://0.0.0.0:7331",
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"URL for the NeMo Guardrails Service",
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),
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"INFERENCE_MODEL": (
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"Llama3.1-8B-Instruct",
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"Inference model",
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),
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"SAFETY_MODEL": (
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"meta/llama-3.1-8b-instruct",
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"Name of the model to use for safety",
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),
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},
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},
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)
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)
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154
llama_stack/templates/nvidia/run-with-safety.yaml
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154
llama_stack/templates/nvidia/run-with-safety.yaml
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version: '2'
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image_name: nvidia
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apis:
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- agents
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- datasetio
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- eval
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- inference
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- safety
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- scoring
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- telemetry
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- tool_runtime
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- vector_io
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providers:
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inference:
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- provider_id: nvidia
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provider_type: remote::nvidia
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config:
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url: ${env.NVIDIA_BASE_URL:https://integrate.api.nvidia.com}
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api_key: ${env.NVIDIA_API_KEY:}
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vector_io:
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- provider_id: faiss
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provider_type: inline::faiss
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config:
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kvstore:
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type: sqlite
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namespace: null
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db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/nvidia}/faiss_store.db
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safety:
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- provider_id: nvidia
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provider_type: remote::nvidia
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config:
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url: ${env.GUARDRAILS_SERVICE_URL:http://localhost:7331}
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config_id:
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agents:
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- provider_id: meta-reference
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provider_type: inline::meta-reference
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config:
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persistence_store:
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type: sqlite
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namespace: null
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db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/nvidia}/agents_store.db
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telemetry:
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- provider_id: meta-reference
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provider_type: inline::meta-reference
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config:
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service_name: ${env.OTEL_SERVICE_NAME:llama-stack}
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sinks: ${env.TELEMETRY_SINKS:console,sqlite}
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sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/nvidia/trace_store.db}
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eval:
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- provider_id: meta-reference
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provider_type: inline::meta-reference
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config: {}
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datasetio:
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- provider_id: huggingface
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provider_type: remote::huggingface
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config: {}
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- provider_id: localfs
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provider_type: inline::localfs
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config: {}
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scoring:
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- provider_id: basic
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provider_type: inline::basic
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config: {}
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- provider_id: llm-as-judge
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provider_type: inline::llm-as-judge
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config: {}
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- provider_id: braintrust
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provider_type: inline::braintrust
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config:
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openai_api_key: ${env.OPENAI_API_KEY:}
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tool_runtime:
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- provider_id: brave-search
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provider_type: remote::brave-search
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config:
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api_key: ${env.BRAVE_SEARCH_API_KEY:}
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max_results: 3
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- provider_id: tavily-search
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provider_type: remote::tavily-search
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config:
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api_key: ${env.TAVILY_SEARCH_API_KEY:}
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max_results: 3
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- provider_id: code-interpreter
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provider_type: inline::code-interpreter
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config: {}
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- provider_id: rag-runtime
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provider_type: inline::rag-runtime
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config: {}
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- provider_id: model-context-protocol
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|
provider_type: remote::model-context-protocol
|
||||||
|
config: {}
|
||||||
|
metadata_store:
|
||||||
|
type: sqlite
|
||||||
|
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/nvidia}/registry.db
|
||||||
|
models:
|
||||||
|
- 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-llama/Llama-3-70B-Instruct
|
||||||
|
provider_id: nvidia
|
||||||
|
provider_model_id: meta/llama3-70b-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/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-3.1-405B-Instruct-FP8
|
||||||
|
provider_id: nvidia
|
||||||
|
provider_model_id: meta/llama-3.1-405b-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/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-3.2-11B-Vision-Instruct
|
||||||
|
provider_id: nvidia
|
||||||
|
provider_model_id: meta/llama-3.2-11b-vision-instruct
|
||||||
|
model_type: llm
|
||||||
|
- metadata: {}
|
||||||
|
model_id: meta-llama/Llama-3.2-90B-Vision-Instruct
|
||||||
|
provider_id: nvidia
|
||||||
|
provider_model_id: meta/llama-3.2-90b-vision-instruct
|
||||||
|
model_type: llm
|
||||||
|
shields:
|
||||||
|
- shield_id: ${env.SAFETY_MODEL}
|
||||||
|
vector_dbs: []
|
||||||
|
datasets: []
|
||||||
|
scoring_fns: []
|
||||||
|
eval_tasks: []
|
||||||
|
tool_groups:
|
||||||
|
- toolgroup_id: builtin::websearch
|
||||||
|
provider_id: tavily-search
|
||||||
|
- toolgroup_id: builtin::rag
|
||||||
|
provider_id: rag-runtime
|
||||||
|
- toolgroup_id: builtin::code_interpreter
|
||||||
|
provider_id: code-interpreter
|
||||||
|
server:
|
||||||
|
port: 8321
|
Loading…
Add table
Add a link
Reference in a new issue