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import pytest
import lm_eval.evaluator as evaluator
from lm_eval.api.registry import get_model
SPARSEML_MODELS_TASKS = [
# loglikelihood
("facebook/opt-125m", "lambada_openai"),
# loglikelihood_rolling
("hf-internal-testing/tiny-random-gpt2", "wikitext"),
# generate_until
("mgoin/tiny-random-llama-2-quant", "gsm8k"),
]
DEEPSPARSE_MODELS_TASKS = [
# loglikelihood
("hf:mgoin/llama2.c-stories15M-quant-ds", "lambada_openai"),
# loglikelihood_rolling (not supported yet)
# ("hf:mgoin/llama2.c-stories15M-quant-ds", "wikitext"),
# generate_until
("hf:mgoin/llama2.c-stories15M-quant-ds", "gsm8k"),
]
@pytest.mark.parametrize("model_id,task", SPARSEML_MODELS_TASKS)
def test_sparseml_eval(model_id, task):
lm = get_model("sparseml").create_from_arg_string(
f"pretrained={model_id}",
{
"batch_size": 1,
"device": "cpu",
"dtype": "float32",
},
)
limit = 5
evaluator.simple_evaluate(
model=lm,
tasks=[task],
num_fewshot=0,
limit=limit,
)
@pytest.mark.parametrize("model_id,task", DEEPSPARSE_MODELS_TASKS)
def test_deepsparse_eval(model_id, task):
lm = get_model("deepsparse").create_from_arg_string(
f"pretrained={model_id}",
{
"batch_size": 1,
},
)
limit = 5
evaluator.simple_evaluate(
model=lm,
tasks=[task],
num_fewshot=0,
limit=limit,
)
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