llama-stack-mirror/llama_stack/providers/impls/meta_reference/evals/evals.py
2024-10-03 17:31:46 -07:00

64 lines
1.8 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 llama_stack.apis.inference import * # noqa: F403
from llama_stack.apis.evals import * # noqa: F403
from termcolor import cprint
from llama_stack.providers.impls.meta_reference.evals.datas.utils import ( # noqa: F403
get_dataset,
)
from llama_stack.providers.impls.meta_reference.evals.tasks.utils import ( # noqa: F403
get_task,
)
from .config import MetaReferenceEvalsImplConfig
class MetaReferenceEvalsImpl(Evals):
def __init__(self, config: MetaReferenceEvalsImplConfig, inference_api: Inference):
self.inference_api = inference_api
async def initialize(self) -> None:
pass
async def shutdown(self) -> None:
pass
async def run_evals(
self,
model: str,
dataset: str,
task: str,
) -> EvaluateResponse:
cprint(f"model={model}, dataset={dataset}, task={task}", "red")
# resolve dataset
# - either a custom URL dataset or HF URL dataset
dataset = get_dataset("mmlu_eval")
print(dataset.dataset)
# # resolve task and execute task
task_impl = get_task(task, dataset)
print(task_impl)
# # F1: this will generate a preprocessed list of input messages for model
# x1 = task_impl.preprocess(dataset)
# # call inference API w/ model
# generation_outputs = ["response1", "response2", "response3"]
# # F2: post process
# x2 = task_impl.postprocess(generation_outputs)
# # F3: score generation outputs
# scores = task_impl.score(x2)
return EvaluateResponse(
metrics={
"accuracy": 0.5,
}
)