Principle:CarperAI Trlx Model Checkpointing
| Knowledge Sources | |
|---|---|
| Domains | Training, Model_Persistence, Distributed_Computing |
| Last Updated | 2026-02-07 16:00 GMT |
Overview
A principle for saving trained language model weights in HuggingFace-compatible format for downstream use and deployment.
Description
After training (or at periodic intervals during training), the model weights, tokenizer configuration, and model config must be saved to disk in a format that can be loaded by HuggingFace from_pretrained(). In a distributed training setting (multi-GPU, DeepSpeed), saving requires coordination: only the main process should write files, and the full state dict must be gathered from all processes before saving.
Model checkpointing in trlx handles both regular models and PEFT/LoRA adapters. For PEFT models, the adapter weights are saved separately from the base model, enabling efficient storage and deployment.
Usage
Use model checkpointing after training completes or at regular intervals to persist trained weights. The save_pretrained() method produces a directory compatible with transformers.AutoModel.from_pretrained() for loading. Automatic checkpointing occurs at config.train.checkpoint_interval steps, and the best model is saved when config.train.save_best=True.
Theoretical Basis
Model persistence involves serializing the model state:
Pseudo-code:
# Abstract save process (not real implementation)
state_dict = gather_state_dict_from_all_processes(model)
if is_main_process:
model.save_pretrained(directory, state_dict=state_dict)
tokenizer.save_pretrained(directory)
# Result: config.json, pytorch_model.bin, tokenizer files
Key considerations:
- Distributed coordination: All processes must synchronize before saving
- PEFT support: Adapter weights saved separately when using LoRA/prefix tuning
- HuggingFace compatibility: Output format loadable by from_pretrained()