Skip to content
New issue

Have a question about this project? # for a free GitHub account to open an issue and contact its maintainers and the community.

#

By clicking “#”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? # to your account

Add more clarification for data server problem #755

Merged
merged 2 commits into from
Apr 10, 2024
Merged

Add more clarification for data server problem #755

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
bbb90bb
Add more clarification for data server problem
research4pan Apr 10, 2024
c6b79b5
Add links to the script as well
research4pan Apr 10, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
5 changes: 3 additions & 2 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -102,7 +102,7 @@ Please refer to our [doc](https://optimalscale.github.io/LMFlow/examples/DATASET

### Finetuning (Full)
Full training updates all the parameters to finetune a language model.
Here is an example to finetune a GPT-2 base model
Here is an example to finetune a GPT-2 base model. (We encounter a data server problem recently. To avoid errors, please use the latest [`download.sh`](https://github.com/OptimalScale/LMFlow/blob/main/data/download.sh) script in main branch to download the dataset)
```sh
cd data && ./download.sh alpaca && cd -

Expand All @@ -113,7 +113,8 @@ cd data && ./download.sh alpaca && cd -
```

### Finetuning (LISA)
[LISA](https://arxiv.org/abs/2403.17919) is a memory-efficient finetuning algorithm that allows tradeoff between memory and the number of randomly unfreezed layers. This script currently is only tested in single gpus. Please stay tuned for our latest updates :smile:
[LISA](https://arxiv.org/abs/2403.17919) is a memory-efficient finetuning algorithm that allows tradeoff between memory and the number of randomly unfreezed layers. This script currently is only tested in single gpus. Please stay tuned for our latest updates :smile: (We encounter a data server problem recently. To avoid errors, please use the latest [`download.sh`](https://github.com/OptimalScale/LMFlow/blob/main/data/download.sh) script in main branch to download the dataset)

```sh
cd data && ./download.sh alpaca && cd -

Expand Down
Loading