[quantization] Introduce a script for LLM evaluation#467
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mhs4670go merged 1 commit intoSamsung:mainfrom Feb 9, 2026
Merged
[quantization] Introduce a script for LLM evaluation#467mhs4670go merged 1 commit intoSamsung:mainfrom
mhs4670go merged 1 commit intoSamsung:mainfrom
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| ) -> dict[str, Any]: | ||
| model_to_evaluate = HFLM(model, "causal", tokenizer=tokenizer) | ||
| tasks_list: list[str] = tasks.split(",") | ||
| return evaluator.simple_evaluate(model_to_evaluate, tasks=tasks_list)["results"] |
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FYI, there is a visualization API.
from lm_eval.utils import make_table
results = lm_eval.simple_evaluate(...)
print(make_table(results))
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Wow. Thank you! I didn't know that.
Torrero
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Feb 5, 2026
| import argparse | ||
| from typing import Any | ||
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| from lm_eval import evaluator |
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Overall LGTM,
But, could you provide your opinion, please, maybe lm_eval should be added to dependencies of the project
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I don't think we would add a dependency on lm_eval. That is why this script is located at just script folder and has no tests.
When we need external dependencies like transformers or lm_eval, it would be good to have dedicated scripts or workflows in internal repo.
This PR introduces an option to run many LLM-related tasks using `lm_eval` package. TICO-DCO-1.0-Signed-off-by: s.malakhov <s.malakhov@partner.samsung.com>
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This PR introduces an option to run many LLM-related tasks using
lm_evalpackage.To use it please make sure that you have installed lm_eval
The PR gives a chance to range quantization results not only using PPL degradation but also using accuracy on a list of benchmarks.
python tico/quantization/evaluation/script/llm_tasks_eval.py --model "HuggingFaceTB/SmolLM2-135M-Instruct"
Draft: #436
TICO-DCO-1.0-Signed-off-by: s.malakhov s.malakhov@partner.samsung.com