AG News — full_ft
Part of lora-methods-comparison — a rigorous, apples-to-apples comparison of Full fine-tuning, LoRA, LoRA-FA, QLoRA, and TinyLoRA, trained on the same task (AG News topic classification), same base model (Qwen/Qwen2.5-0.5B-Instruct), same GPU, same evaluation harness.
This checkpoint
Standard full fine-tuning — every parameter in the base model was updated.
Trainable parameters: 494,032,768 Test accuracy: 85.0% Peak GPU memory during training: 7.642738688 GB Training wall-clock time: 90.23771286010742s
How to load
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("swathikchhuggingface/ag-news-full-ft")
tokenizer = AutoTokenizer.from_pretrained("swathikchhuggingface/ag-news-full-ft")
See the repo README for the full comparison across all 5 methods, including methodology and the complete results table.
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