In this example, we utilize NeMo's prompt learning feature to showcase how to adapt a large language model (LLM) to a downstream task such as financial sentiment predictions. As the prompt learning technique shown in the example is p-tuning, which adds a small prompt encoder network to the LLM to produce virtual tokens that guide the model toward the desired output of the downstream task.
In our federated implementation, the LLM parameters stay fixed. Prompt encoder parameters are trained/updated and averaged on the server.
The example was tested with the NeMo 23.02 container.
In the following, we assume this example folder of the container is mounted to /workspace and all downloading, etc. operations are based on this root path.
Start the docker container from this directory using
# cd NVFlare/integration/nemo/examples/prompt_learning
DOCKER_IMAGE="nvcr.io/nvidia/nemo:23.02"
docker run --runtime=nvidia -it --rm --shm-size=16g -p 8888:8888 -p 6006:6006 --ulimit memlock=-1 --ulimit stack=67108864 \
-v ${PWD}:/workspace -w /workspace ${DOCKER_IMAGE}
For easy experimentation with NeMo, install NVFlare and mount the code inside the nemo_nvflare folder.
pip install nvflare~=2.5.0rc
pip install protobuf==3.20
export PYTHONPATH=${PYTHONPATH}:/workspace
We use JupyterLab for this example. To start JupyterLab, run
jupyter lab .
and open prompt_learning.ipynb.
This example requires a GPU with at least 16GB of memory to run three clients in parallel on the same GPU.
This example of running a 20B GPT model requires more computational resources.
To run the example, follow the instructions in prompt_learning_20B.md.
To run three clients in parallel, we require at least six GPUs with 64 GB memory or more each (Ampere or later GPU architecture).