conda create -n dllm python=3.10 -y
conda activate dllm
conda install cuda=12.4 -c nvidia
pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 \
--index-url https://download.pytorch.org/whl/cu124
pip install -e .
git submodule update --init --recursive
pip install -e "lm-evaluation-harness[ifeval,math]"
bash scripts/eval_configs.sh
You will get best model results for dream
If you want to change parameters, change configurations lines in the eval_configs.sh
You will get two files:
<model_name>_<benchmark>_efficiency_<config>_<timestamp>.json: Contains efficiency metrics.<model_name>_<benchmark>_lm_eval_<config>_<timestamp>: Directory containing evaluation results.4 commits
Python
98.5%
Shell
1.5%
conda create -n dllm python=3.10 -y
conda activate dllm
conda install cuda=12.4 -c nvidia
pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 \
--index-url https://download.pytorch.org/whl/cu124
pip install -e .
git submodule update --init --recursive
pip install -e "lm-evaluation-harness[ifeval,math]"
bash scripts/eval_configs.sh
You will get best model results for dream
If you want to change parameters, change configurations lines in the eval_configs.sh
You will get two files:
<model_name>_<benchmark>_efficiency_<config>_<timestamp>.json: Contains efficiency metrics.<model_name>_<benchmark>_lm_eval_<config>_<timestamp>: Directory containing evaluation results.4 commits
Python
98.5%
Shell
1.5%