# ScaLoRA
## Environemnt setup
The following commands setup the environments. 
```bash
$ conda create -n [env_name]
$ pip install -r requirements.txt
```
## GLUE
To perform experiments on the GLUE benchmark, one can leverage the scripts in `glue/scripts/`. For instance, the following commands run the test on the mnli dataset. 
```bash
$ cd glue
$ bash scripts/mnli.sh
```
## Commonsense reasoning
```bash
$ cd commonsense_reasoning
$ bash llama2_7b.sh
```
## MetaMathQA
`Gemma-3-12b-pt` requires newer version of packages, which can be found in `requirements_gemma.txt`. 
```bash
$ conda create -n [env_name]
$ pip install -r requirements_gemma.txt
$ cd metamath
$ bash ft_gemma3_12b.sh
$ bash eval_gemma3_12b.sh   # after finetuning
```
## Credits
Our implementation builds upon the following GitHub repositories.
- https://github.com/AGI-Edgerunners/LLM-Adapters
- https://github.com/EleutherAI/lm-evaluation-harness
- https://github.com/kcc-lion/polar
## Remark
As in [PoLAR](https://github.com/kcc-lion/polar), this repo modifies the following files of the `lm_eval` package. If you prefer to clone `lm_eval` from the original repo, please change the following three files accordingly. 
- `tasks/gsm8k/gsm8k.yaml`
- `tasks/hendrycks_math/hendrycks_math.yaml` and `tasks/hendrycks_math/utils.py`.