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- Replace repository cloning with direct dataset download using curl - Add commands to download HaluMem-Medium.jsonl and HaluMem-Long.jsonl files - Include both official Hugging Face and mirror download sources - Update data path reference from nested directory to local data folder - Add dataset page link and mirror usage instructions for mainland China access
1.6 KiB
1.6 KiB
Halumem
Experiment Quick Start Guide This guide helps you quickly set up and run Halumem experiments with ReMe integration.
1. Start ReMe Service
Install ReMe (if not already installed) If you haven't installed the ReMe environment yet, follow these steps:
# Create ReMe environment
conda create -p ./reme-env python==3.12
conda activate ./reme-env
# Install ReMe
pip install .
2. Download the Dataset
cd ./benchmark/halumem
mkdir -p data
curl -L "https://huggingface.co/datasets/IAAR-Shanghai/HaluMem/resolve/main/HaluMem-Medium.jsonl?download=true" -o data/HaluMem-Medium.jsonl
curl -L "https://huggingface.co/datasets/IAAR-Shanghai/HaluMem/resolve/main/HaluMem-Long.jsonl?download=true" -o data/HaluMem-Long.jsonl
Dataset page: https://huggingface.co/datasets/IAAR-Shanghai/HaluMem/tree/main
If the official source is slow or inaccessible in mainland China, you can use a mirror:
cd ./benchmark/halumem
mkdir -p data
curl -L "https://hf-mirror.com/datasets/IAAR-Shanghai/HaluMem/resolve/main/HaluMem-Medium.jsonl?download=true" -o data/HaluMem-Medium.jsonl
curl -L "https://hf-mirror.com/datasets/IAAR-Shanghai/HaluMem/resolve/main/HaluMem-Long.jsonl?download=true" -o data/HaluMem-Long.jsonl
3. Run Experiments
Launch the ReMe service to enable memory library functionality:
clear && python benchmark/halumem/eval_reme.py \
--data_path benchmark/halumem/data/HaluMem-Medium.jsonl \
--reme_model_name gpt-4o-mini-2024-07-18 \
--eval_model_name gpt-4o-mini-2024-07-18 \
--batch_size 40 \
--algo_version default