Workflow · Production
Prompt-Tuning and P-Tuning a MegatronGPT LLM for Question Answering Using NVIDIA NeMo and Weights & Biases
The problem
Fine-tuning large language models for specific tasks is computationally expensive and risks catastrophic forgetting of prior knowledge, making adaptation difficult when multiple tasks must be supported.
Workflow diagram · grounded in source
1
SQuAD dataset preparation
integration
“Using Weights & Biases Artifacts we are able to flexibly and conveniently serialize configurations, datasets, and relevant processing scripts into one centralized record with automatic version tracking”
2
Prompt template configuration
ai_action
“For customizing prompts for various tasks, the configuration requires defining task-specific templates. These templates integrate virtual token markers, indicative of where virtual tokens will be positioned.”
3
P-tuning training
ai_action
“while the GPT parameters remain static, the LSTM weights undergo changes. It's a singular LSTM model, but it generates unique virtual token embeddings tailored to each specific task”
4
Experiment tracking and iteration
feedback_loop
“Based on the results, you can easily iterate over your experiments, tweak hyperparameters, and requeue them, all managed efficiently through W&B Launch”
5
LLM generation and evaluation
output
“We can now investigate our LLM generations via W&B Prompts Tables”
6
Model registry storage
integration
“After evaluation we can select and place our best model in our Model Registry”
Reported outcome
Prompt tuning and p-tuning with NVIDIA NeMo adapt a MegatronGPT LLM for question answering on SQuAD without altering core model parameters, while W&B provides experiment tracking and scalable job management.
Reported stack
NVIDIA NeMoWeights & BiasesMegatronGPTW&B LaunchLSTMGoogle Vertex AI
Source
https://mlops.community/blog/how-to-adapt-your-llm-for-question-answering-with-prompt-tuning-using-nvidia-nemo-and-weights-biases-2
Read source ↗Frequently asked questions
What did this team achieve with this AI workflow?
Prompt tuning and p-tuning with NVIDIA NeMo adapt a MegatronGPT LLM for question answering on SQuAD without altering core model parameters, while W&B provides experiment tracking and scalable job management.
What tools did this team use?
NVIDIA NeMo, Weights & Biases, MegatronGPT, W&B Launch, LSTM, Google Vertex AI.
How is this workflow AI workflow structured?
SQuAD dataset preparation → Prompt template configuration → P-tuning training → Experiment tracking and iteration → LLM generation and evaluation → Model registry storage.