Principle:Huggingface Alignment handbook Model Loading
| Knowledge Sources | |
|---|---|
| Domains | NLP, Model_Architecture, Deep_Learning |
| Last Updated | 2026-02-07 00:00 GMT |
Overview
A model initialization pattern that loads pretrained causal language models from HuggingFace Hub with configurable precision, attention implementation, and optional quantization.
Description
Model Loading is the process of instantiating a pretrained transformer model for fine-tuning. In the alignment-handbook, get_model wraps AutoModelForCausalLM.from_pretrained with additional configuration for:
- Data type control: Converting string dtype specifications (e.g., "bfloat16") to PyTorch dtype objects
- Attention implementation: Selecting between standard attention, Flash Attention 2, or SDPA
- Gradient checkpointing compatibility: Disabling KV cache when gradient checkpointing is enabled (these are mutually exclusive)
- Quantization support: Applying BitsAndBytes quantization configs and device mapping for QLoRA workflows
The function serves as the single model loading entry point for all alignment-handbook training scripts (SFT, DPO, ORPO), ensuring consistent model initialization across different training stages.
Usage
Use this principle when loading any pretrained causal language model for alignment training. The loaded model can be used directly for full fine-tuning or combined with PEFT/LoRA adapters for parameter-efficient training.
Theoretical Basis
Model loading for alignment follows a layered configuration pattern:
# Abstract model loading flow (NOT real implementation)
dtype = resolve_dtype(config.torch_dtype) # "bfloat16" -> torch.bfloat16
quant_config = get_quantization_config(model_args) # None or BitsAndBytesConfig
device_map = get_kbit_device_map() if quant_config else None
model = AutoModelForCausalLM.from_pretrained(
model_name,
dtype=dtype,
attn_implementation=attn_impl, # "flash_attention_2", "sdpa", etc.
use_cache=not gradient_checkpointing, # Mutual exclusion
quantization_config=quant_config,
device_map=device_map,
)
Key design decisions:
- use_cache=False when gradient checkpointing is enabled, because cached key-value states are incompatible with recomputation during the backward pass
- device_map is only set when quantization is active, letting the quantization library handle GPU placement
- trust_remote_code can be enabled for custom model architectures (e.g., SmolLM3)