Workflow:Bigscience workshop Petals Prompt Tuning Classification
| Knowledge Sources | |
|---|---|
| Domains | LLMs, Fine_Tuning, Prompt_Tuning, Text_Classification |
| Last Updated | 2026-02-09 13:00 GMT |
Overview
End-to-end process for adapting a distributed large language model for text classification via prompt tuning, where only a small set of prefix tokens and a classifier head are trained while the base model remains frozen on remote servers.
Description
This workflow demonstrates parameter-efficient fine-tuning of large language models using Petals' distributed infrastructure. The base model's transformer blocks remain frozen on volunteer servers and are never modified. Instead, a small number of trainable soft prompt tokens are prepended to the input, and a classification head is added on top. Only these local parameters are updated during training. The forward pass runs through the distributed network, and gradients flow back through the custom autograd function that handles remote backward passes. This enables fine-tuning of models with hundreds of billions of parameters on consumer hardware with minimal GPU memory.
Usage
Execute this workflow when you have a labeled classification dataset (e.g., sentiment analysis, topic classification) and need to adapt a large pre-trained language model hosted on the Petals swarm for this specific task. You have limited GPU resources (a single consumer GPU suffices for the local trainable parameters) but want the representational power of a very large model.
Execution Steps
Step 1: Environment_Setup
Install the Petals library along with training dependencies: datasets for data loading, wandb for experiment tracking, and scikit-learn for metrics. Ensure PyTorch with CUDA support is available for mixed-precision training.
Key considerations:
- The CUDA device is needed for efficient gradient scaling with mixed precision
- wandb integration is optional but recommended for monitoring training loss and validation accuracy
Step 2: Model_And_Tokenizer_Loading
Load the distributed model in sequence classification mode using DistributedModelForSequenceClassification. Configure prompt tuning by specifying pre_seq_len (number of trainable prefix tokens) and tuning_mode (ptune or deep_ptune). The model creates local prompt embedding layers while replacing transformer blocks with the RemoteSequential module.
Key considerations:
- deep_ptune trains separate prefix embeddings for each transformer layer, yielding better results at higher cost
- ptune trains a single shared prefix, which is faster but less expressive
- pre_seq_len controls the number of virtual tokens (typically 8-16)
- Only the prompt embeddings and classification head have requires_grad=True
Step 3: Data_Preparation
Load and preprocess the classification dataset. Tokenize all examples using the model's tokenizer with padding and truncation to a fixed maximum length. Create PyTorch DataLoaders for training and validation splits.
Key considerations:
- Set padding_side to right for causal language models
- Set model_max_length on the tokenizer to limit sequence length
- Remove unnecessary columns and rename the label column for compatibility with the model's forward method
- Use drop_last=True for consistent batch sizes during training
Step 4: Optimizer_And_Scheduler_Setup
Initialize an AdamW optimizer targeting only the trainable parameters (prompt embeddings and classification head). Configure a linear learning rate scheduler with optional warmup steps.
Key considerations:
- Only parameters with requires_grad=True are optimized (prompt embeddings and classifier head)
- The learning rate for prompt tuning is typically higher than standard fine-tuning (e.g., 1e-2)
- Total training steps = number of batches times number of epochs
Step 5: Training_Loop
Run the training loop with mixed-precision (float16) forward passes through the distributed model. The forward pass sends hidden states through remote servers, computes the classification loss locally, and backpropagates gradients through the remote autograd function. Use gradient scaling for numerical stability with float16.
Key considerations:
- The distributed autograd function (_RemoteSequentialAutogradFunction) transparently handles forward and backward through remote servers
- Use torch.cuda.amp.GradScaler for mixed-precision gradient scaling
- The training loop is standard PyTorch except that transformer computation happens remotely
- Server failures are handled transparently with retry logic during both forward and backward passes
Step 6: Evaluation
Evaluate the trained model on the validation set by running inference in no-grad mode and computing task-specific metrics (accuracy for SST-2). Log results for experiment tracking.
Key considerations:
- Disable gradient tracking during evaluation
- Accumulate predictions across batches before computing final metrics
- The model remains connected to the swarm during evaluation