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Harness Evaluation
Harness evaluation allows users to eaisly get accuracy on various datasets. Here we have enabled harness evaluation with BigDL-LLM under Open LLM Leaderboard settings. Before running, make sure to have bigdl-llm installed.
Install Harness
git clone https://github.com/EleutherAI/lm-evaluation-harness.git
cd lm-evaluation-harness
git checkout b281b09
pip install -e .
Run
run python run_llb.py. run_llb.py combines some arguments in main.py to make evaluations easier. The mapping of arguments is defined as a dict in llb.py.
Evaluation on CPU
python run_llb.py --model bigdl-llm --pretrained /path/to/model --precision nf3 sym_int4 nf4 --device cpu --tasks hellaswag arc mmlu truthfulqa --batch 1 --no_cache
Evaluation on Intel GPU
python run_llb.py --model bigdl-llm --pretrained /path/to/model --precision nf3 sym_int4 nf4 --device xpu --tasks hellaswag arc mmlu truthfulqa --batch 1 --no_cache
Evaluation using multiple Intel GPU
python run_multi_llb.py --model bigdl-llm --pretrained /path/to/model --precision nf3 sym_int4 nf4 --device xpu:0,2,3 --tasks hellaswag arc mmlu truthfulqa --batch 1 --no_cache
Taking example above, the script will fork 3 processes, each for one xpu, to execute the tasks.
Results
We follow Open LLM Leaderboard to record our metrics, acc_norm for hellaswag and arc_challenge, mc2 for truthful_qa and acc for mmlu. For mmlu, there are 57 subtasks which means users may need to average them manually to get final result.
Summarize the results
"""python python make_table.py <input_dir> """