6e10d98a8d
* add llm-ppl workflow * update the DATASET_DIR * test multiple precisions * modify nightly test * match the updated ppl code * add matrix.include * fix the include error * update the include * add more model * update the precision of include * update nightly time and add more models * fix the workflow_dispatch description, change default model of pr and modify the env * modify workflow_dispatch language options * modify options * modify language options * modeify workflow_dispatch type * modify type * modify the type of language * change seq_len type * fix some typos * revert changes to stress_test.txt
31 lines
1.8 KiB
Markdown
31 lines
1.8 KiB
Markdown
# Harness Evaluation
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[Harness evaluation](https://github.com/EleutherAI/lm-evaluation-harness) allows users to eaisly get accuracy on various datasets. Here we have enabled harness evaluation with BigDL-LLM under
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[Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) settings.
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Before running, make sure to have [bigdl-llm](../../../README.md) installed.
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## Install Harness
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```bash
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git clone https://github.com/EleutherAI/lm-evaluation-harness.git
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cd lm-evaluation-harness
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git checkout e81d3cc
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pip install -e .
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```
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## Run
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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`](llb.py).
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### Evaluation on CPU
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```python
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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
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```
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### Evaluation on Intel GPU
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```python
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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
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```
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### Evaluation using multiple Intel GPU
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```python
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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
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```
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Taking example above, the script will fork 3 processes, each for one xpu, to execute the tasks.
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## Results
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We follow [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/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.
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