LLM4Decompile/evaluation/README.md

Updates

• [Note]: Please use decompile-eval-executable-gcc-ghidra.json for V2 models. The source codes are compiled into executable binaries and decompiled by Ghidra into pseudo-code.
• [Note]: Please use decompile-eval-executable-gcc-obj.json for V1.5 models. The source codes are compiled into executable binaries and disassembled into assembly instructions.
• [2024-04-10]: Add vllm evaluation script.

To run the evaluation using vLLM (Recommended)

pip install -r requirements.txt

To use the flash-attention backend to speed up the interface, you can install it via pip install flash-attn.

cd evaluation
# Before running the evaluation script, please update the model_path to your local model path.
python run_evaluation_llm4decompile_vllm.py \
  --model_path LLM4Binary/llm4decompile-6.7b-v1.5 \
  --testset_path ../decompile-eval/decompile-eval-executable-gcc-obj.json \
  --gpus 8 \
  --max_total_tokens 8192 \
  --max_new_tokens 512 \
  --repeat 1 \
  --num_workers 16 \
  --gpu_memory_utilization 0.82 \
  --temperature 0 

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To run the evaluation on single GPU and single process: (legacy, not updated)

cd LLM4Decompile
python ./evaluation/run_evaluation_llm4decompile_singleGPU.py

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To run the evaluation using TGI (10x faster, support multiple GPUs and multi-process): (legacy, not updated) First, please install the text-generation-inference following the official link

git clone https://github.com/albertan017/LLM4Decompile.git
cd LLM4Decompile
pip install -r requirements.txt

# Before running the evaluation script, please update the model_path to your local model path.
bash ./scripts/run_evaluation_llm4decompile.sh

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