Workflow:Bigscience workshop Petals Prompt Tuning Chatbot
| Knowledge Sources | |
|---|---|
| Domains | LLMs, Fine_Tuning, Prompt_Tuning, Conversational_AI |
| Last Updated | 2026-02-09 13:00 GMT |
Overview
End-to-end process for adapting a distributed causal language model into a personalized chatbot using prompt tuning on conversational data, followed by interactive generation via inference sessions.
Description
This workflow trains soft prompt tokens on a conversational dataset (such as PersonaChat) so that the distributed model learns to generate contextually appropriate dialogue responses. The base model's transformer blocks remain frozen on remote servers. Only a small set of prefix token embeddings are trained locally using standard causal language modeling loss. After training, the adapted model can generate responses interactively using an inference session that maintains KV caches across conversation turns, enabling efficient multi-turn dialogue.
Usage
Execute this workflow when you want to create a chatbot or conversational agent powered by a large language model but only have consumer-grade GPU resources. You have a conversational dataset with dialogue history and candidate responses, and you want the model to learn a specific conversational style or persona while leveraging the full capacity of the base model via the Petals swarm.
Execution Steps
Step 1: Environment_Setup
Install the Petals library along with training dependencies: datasets for loading conversational data and wandb for experiment tracking. Ensure PyTorch is available.
Key considerations:
- The distributed model handles CUDA/CPU placement internally
- wandb is optional but useful for monitoring training loss
Step 2: Model_And_Tokenizer_Loading
Load the distributed causal language model (e.g., BLOOM) using DistributedModelForCausalLM with prompt tuning enabled. Configure the number of prefix tokens and tuning mode. The model creates trainable prompt embeddings locally while all transformer computation happens on remote servers.
Key considerations:
- Use DistributedBloomForCausalLM or AutoDistributedModelForCausalLM depending on model family
- Set tuning_mode to ptune or deep_ptune
- pre_seq_len determines the number of trainable prefix tokens (typically 16)
- Larger models (e.g., bloom-7b1-petals) provide better conversational quality
Step 3: Data_Preparation
Load and preprocess the conversational dataset. Concatenate dialogue history turns with candidate responses into flat text sequences. Tokenize with padding and truncation to the model's maximum length. Create a DataLoader sampling a subset of training examples.
Key considerations:
- Concatenate history turns with a delimiter (e.g., newline-separator) for context
- Set labels equal to input_ids for causal language modeling loss
- Subsample the dataset if needed to control training duration
- Use right-side padding for causal LM tokenization
Step 4: Optimizer_And_Scheduler_Setup
Initialize an AdamW optimizer for the trainable prompt embeddings. Set up a linear learning rate scheduler. Only the prompt embedding parameters have gradients enabled.
Key considerations:
- Learning rate is typically 1e-2 for prompt tuning
- The scheduler runs for the total number of training steps (single epoch over the data subset)
Step 5: Training_Loop
Iterate over the training data, running forward passes through the distributed model with causal language modeling loss. Backpropagate gradients through the remote autograd function to update only the local prompt embeddings. Log training loss for monitoring.
Key considerations:
- The forward pass computes cross-entropy loss between model predictions and labels
- Gradients flow through the _RemoteSequentialAutogradFunction for remote backward passes
- Standard PyTorch optimizer step and gradient clearing apply
- Training is fault-tolerant: server failures trigger automatic retry with route reconstruction
Step 6: Interactive_Generation
Use the trained model in interactive mode by opening an inference session. Accept user input, tokenize it, and generate responses token-by-token using the model's generate() method within the session. The session maintains KV caches across turns for efficient multi-turn conversation.
Key considerations:
- The inference session preserves attention caches between generation calls
- Use temperature and top_k sampling for diverse responses
- Generate one token at a time for streaming output
- Pass session= parameter to model.generate() to reuse the active session
- Set a sufficient max_length for the session to cover the full conversation