The 7B Llama2 model (the smallest one), works on ~16GB of vRAM and RAM. If RAM
is too small, use a bigger swap (this should only be needed to transfer the
To make it easy for you to get started with GitLab, here's a list of recommended next steps.
weights onto the GPU, no actual computation is done on the CPU).
Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
## How to use
## Add your files
There are a few requirements to get the model to run. Broadly speaking, these
are the actual model (the code), the weights and the Python script to open a
-[ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
dialog.
-[ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
The Python packages necessary to run the code are all packaged inside the
```
Dockerfile that comes with this repo. The image is already built on Dockerhub.
-[ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
3. Copy the `dialog.py` script to the root of the llama repo.
-[ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
4. Download the weights of the model from
-[ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
Move the weights to the root of the repo. Make sure to not change the paths
-[ ] [Automatically merge when pipeline succeeds](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
of the weights (they should be in llama-2-7b-chat/ etc.). Currently, the
docker container only uses the 7B Chat weights, so feel free to only download
## Test and Deploy
those.
5. Download the Docker container image (`docker image pull
Use the built-in continuous integration in GitLab.
alexghergh/llama-test:latest`). This container holds all the necessary python
packages, and will run the dialog script.
-[ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
6. Run the docker image with `docker run -it -v <path/to/llama>:/llama
-[ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
--gpus all alexghergh/llama-test`. Change the path to the llama repo
-[ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
accordingly.
-[ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-[ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
### On the UPB cluster (fep)
***
Steps:
1. Log in to fep (ssh <username>@fep.grid.pub.ro).
# Editing this README
2. Get a bash shell into a partition with a GPU (`srun -p xl --gres
gpu:tesla_p100:1 --mem=40G --pty bash`).
When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template.
3. Clone [Meta AI's Llama2 repo](github.com/facebookresearch/llama) here
3. Copy the `dialog.py` script in the current repo to the `llama/` directory
Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
Move the weights in the llama repo. Make sure to not change the paths of the
weights (they should be in llama/llama-2-7b-chat/ etc.). Currently, the
## Description
docker container only uses the 7B Chat weights, so feel free to only download
Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
those.
5. Pull and build the docker image into an apptainer container on the grid
## Badges
(`apptainer pull docker://alexghergh/llama-test:latest`). This will take a
On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
while (probably around 15-20 minutes). If it fails because of a timeout,
simply run the same command again.
## Visuals
6. Run the apptainer image with `apptainer run --nv
Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
docker://alexghergh/llama-test:latest`. The first time it should take about 7
minutes for it to start (I think because the weights are located on a
## Installation
different storage server, so they have to get copied through scp on the
Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
machine with the GPU), but subsequent runs will take a few seconds
(subsequent run = don't log out).
## Usage
7. ???
Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
8. Profit
## Support
*Note*: The script will sometimes still error out because of Out-of-Memory
Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
errors or because the context length was reached. If that happens, reissue the
command to start a new dialog.
## Roadmap
If you have ideas for releases in the future, it is a good idea to list them in the README.
## Limitations
## Contributing
Currently only tested with 7B Llama2, with a 16GB vRAM GPU (Nvidia P100). The
State if you are open to contributions and what your requirements are for accepting them.
conversation context length (`--max_seq_len` parameter of the script) is limited
to 512 tokens (about 2-3 back-and-forth dialogs with the AI). Increasing this
For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
will (almost surely) result in an Out-of-Memory CUDA error.
You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
## TODOs
## Authors and acknowledgment
[ ] Choose Python package versions to use inside the Dockerfile, rather than
Show your appreciation to those who have contributed to the project.
have them dangling, to prevent compatibility problems.
[ ] Look into quantization (the current model is 8-bit quantized already).
## License
[ ] Better dialog script file.
For open source projects, say how it is licensed.
## Project status
If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
