forked from phoenix-oss/llama-stack-mirror
c1d18283d2
# What does this PR do? - To make it easier, delete existing `eval/scoring/scoring_function` apis. There will be a bunch of broken impls here. The sequence is: 1. migrate benchmark graders 2. clean up existing scoring functions - Add a skeleton evaluation impl to make tests pass. ## Test Plan tested in following PRs [//]: # (## Documentation)
112 lines
3.5 KiB
Python
112 lines
3.5 KiB
Python
# 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_stack.distribution.datatypes import (
|
|
ModelInput,
|
|
Provider,
|
|
ShieldInput,
|
|
ToolGroupInput,
|
|
)
|
|
from llama_stack.providers.remote.inference.nvidia import NVIDIAConfig
|
|
from llama_stack.providers.remote.inference.nvidia.models import MODEL_ENTRIES
|
|
from llama_stack.providers.remote.safety.nvidia import NVIDIASafetyConfig
|
|
from llama_stack.templates.template import (
|
|
DistributionTemplate,
|
|
RunConfigSettings,
|
|
get_model_registry,
|
|
)
|
|
|
|
|
|
def get_distribution_template() -> DistributionTemplate:
|
|
providers = {
|
|
"inference": ["remote::nvidia"],
|
|
"vector_io": ["inline::faiss"],
|
|
"safety": ["remote::nvidia"],
|
|
"agents": ["inline::meta-reference"],
|
|
"telemetry": ["inline::meta-reference"],
|
|
"datasetio": ["inline::localfs"],
|
|
"tool_runtime": ["inline::rag-runtime"],
|
|
}
|
|
|
|
inference_provider = Provider(
|
|
provider_id="nvidia",
|
|
provider_type="remote::nvidia",
|
|
config=NVIDIAConfig.sample_run_config(),
|
|
)
|
|
safety_provider = Provider(
|
|
provider_id="nvidia",
|
|
provider_type="remote::nvidia",
|
|
config=NVIDIASafetyConfig.sample_run_config(),
|
|
)
|
|
inference_model = ModelInput(
|
|
model_id="${env.INFERENCE_MODEL}",
|
|
provider_id="nvidia",
|
|
)
|
|
safety_model = ModelInput(
|
|
model_id="${env.SAFETY_MODEL}",
|
|
provider_id="nvidia",
|
|
)
|
|
|
|
available_models = {
|
|
"nvidia": MODEL_ENTRIES,
|
|
}
|
|
default_tool_groups = [
|
|
ToolGroupInput(
|
|
toolgroup_id="builtin::rag",
|
|
provider_id="rag-runtime",
|
|
),
|
|
]
|
|
|
|
default_models = get_model_registry(available_models)
|
|
return DistributionTemplate(
|
|
name="nvidia",
|
|
distro_type="remote_hosted",
|
|
description="Use NVIDIA NIM for running LLM inference and safety",
|
|
container_image=None,
|
|
template_path=Path(__file__).parent / "doc_template.md",
|
|
providers=providers,
|
|
available_models_by_provider=available_models,
|
|
run_configs={
|
|
"run.yaml": RunConfigSettings(
|
|
provider_overrides={
|
|
"inference": [inference_provider],
|
|
},
|
|
default_models=default_models,
|
|
default_tool_groups=default_tool_groups,
|
|
),
|
|
"run-with-safety.yaml": RunConfigSettings(
|
|
provider_overrides={
|
|
"inference": [
|
|
inference_provider,
|
|
safety_provider,
|
|
]
|
|
},
|
|
default_models=[inference_model, safety_model],
|
|
default_shields=[ShieldInput(shield_id="${env.SAFETY_MODEL}", provider_id="nvidia")],
|
|
default_tool_groups=default_tool_groups,
|
|
),
|
|
},
|
|
run_config_env_vars={
|
|
"NVIDIA_API_KEY": (
|
|
"",
|
|
"NVIDIA API Key",
|
|
),
|
|
"GUARDRAILS_SERVICE_URL": (
|
|
"http://0.0.0.0:7331",
|
|
"URL for the NeMo Guardrails Service",
|
|
),
|
|
"INFERENCE_MODEL": (
|
|
"Llama3.1-8B-Instruct",
|
|
"Inference model",
|
|
),
|
|
"SAFETY_MODEL": (
|
|
"meta/llama-3.1-8b-instruct",
|
|
"Name of the model to use for safety",
|
|
),
|
|
},
|
|
)
|