ml-huggingface-models

What problem does it solve?

Prevent wasted compute and silent failures by ensuring pre-trained HuggingFace models are correctly loaded, validated, and integrated before any fine-tuning, continued pre-training, or production use.

Core Features & Use Cases

• Base Model Verification: Run a forward pass on representative inputs to confirm tokenizer compatibility, output shapes, and padding/attention semantics.
• Fine-Tuning Guidance: Decision gating between full fine-tuning and PEFT/LoRA with practical trade-offs and checklist requirements.
• Training Workflows & Integration: Clear patterns for Lightning and HF Trainer setups, dataset tokenization rules, PEFT parameter checks, and continued pre-training recipes.
• Use Case: Prepare a BERT-style classifier by verifying the checkpoint, tokenizing GLUE examples with explicit padding and max_length, choosing LoRA for limited compute, and confirming print_trainable_parameters before training.

Quick Start

Verify a HuggingFace checkpoint by loading the AutoTokenizer and AutoModel, running a single forward pass on a representative input, and confirming the logits or output shapes before starting any fine-tuning.