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얕고 넓게
[AI] LLamaFactory 설치 및 FT 성공 정리 본문
2026.03.01

Ubuntu 14에서 설치함
conda create -n llama-factory python=3.11 -y
conda activate llama-factory
conda install pytorch torchvision torchaudio pytorch-cuda=12.4 -c pytorch -c nvidia
pip install llamafactory
llamafactory-cli webui --host 0.0.0.0 --port 7860
conda install -c conda-forge libiomp
llamafactory-cli webui --host 0.0.0.0 --port 7860
LD_PRELOAD=$CONDA_PREFIX/lib/libiomp5.so
export LD_LIBRARY_PATH=$CONDA_PREFIX/lib:$LD_LIBRARY_PATH
llamafactory-cli webui --host 0.0.0.0 --port 7860
python -c "import torch; print(torch.__version__)"
conda list | grep iomp
conda list | grep ittnotify
conda install -c conda-forge libiomp
conda install -c conda-forge libgomp
llamafactory-cli webui --host 0.0.0.0 --port 7860
export LD_LIBRARY_PATH=/opt/intel/oneapi/ittnotify/lib:$LD_LIBRARY_PATH
llamafactory-cli webui --host 0.0.0.0 --port 7860
python -c "import torch; print(torch.__version__); print(torch.__config__.show())"
sudo apt-get update
sudo apt-get install intel-oneapi-ittnotify
wget https://registrationcenter-download.intel.com/akdlm/irc_nas/18315/l_BaseKit_p_2024.2.0.642_offline.sh
sudo bash l_BaseKit_p_2024.2.0.642_offline.sh
pip uninstall torch
pip install torch==2.1.0 --index-url https://download.pytorch.org/whl/cpu
pip install --upgrade torch --index-url https://download.pytorch.org/whl/cpu
llamafactory-cli webui --host 0.0.0.0 --port 7860
pip install --upgrade torch torchvision --index-url https://download.pytorch.org/whl/cpu
llamafactory-cli webui --host 0.0.0.0 --port 7860
pip uninstall torch torchaudio torchvision
pip install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0
llamafactory-cli webui --host 0.0.0.0 --port 7860
pip install bitsandbytes>=0.39.0
llamafactory-cli webui --host 0.0.0.0 --port 7860
webui에서 FT 성공 했을 때 "명령어 미리보기" 결과
llamafactory-cli train \
--stage sft \
--do_train True \
--model_name_or_path upstage/SOLAR-10.7B-v1.0 \
--preprocessing_num_workers 16 \
--finetuning_type lora \
--template default \
--flash_attn auto \
--dataset_dir data \
--dataset insurance_sales_alpaca \
--cutoff_len 1024 \
--learning_rate 5e-05 \
--num_train_epochs 3.0 \
--max_samples 100000 \
--per_device_train_batch_size 1 \
--gradient_accumulation_steps 8 \
--lr_scheduler_type cosine \
--max_grad_norm 1.0 \
--logging_steps 5 \
--save_steps 100 \
--warmup_steps 0 \
--packing False \
--enable_thinking True \
--report_to none \
--output_dir saves/SOLAR-10.7B-v1.0/lora/train_2026-03-01-19-03-09 \
--bf16 True \
--plot_loss True \
--trust_remote_code True \
--ddp_timeout 180000000 \
--include_num_input_tokens_seen True \
--optim adamw_torch \
--quantization_bit 4 \
--quantization_method bnb \
--double_quantization True \
--lora_rank 8 \
--lora_alpha 16 \
--lora_dropout 0 \
--lora_target all'IT > AI.ML' 카테고리의 다른 글
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