Jeesup/Llama-3.2-1B-Instruct-safetyquant-lora

Llama-3.2-1B-Instruct — configuration-matched GSM8K LoRA adapters

Five LoRA adapters, each trained on GSM8K under a different quantization configuration of the same base model, for the Phase 1 transfer study.

subfolder	base configuration it was trained under
q16/	bf16 (no quantization)
q8/	c1sim W8A16
q4/	c2sim W4A16
q3/	c5 W3A16
q2/	c6 W2A16

r=16, α=32, dropout 0.05, bf16, on the 7 attention+MLP projections. Single seed (0); see PREREGISTRATION.md §5 for why, and what it costs.

The result these exist to demonstrate

Transfer is strongly asymmetric in direction. An adapter trained at 3-bit transfers upward nearly intact, but adapters trained at high precision, deployed downward at 3-bit, score below the no-adapter baseline — fine-tuning on a clean configuration actively hurts you at low precision:

	deploy bf16	deploy W4	deploy W3
no adapter	0.313	0.315	0.049
trained bf16	0.375	0.290	0.019
trained W3	0.337	0.307	0.288

GSM8K exact match. Full matrices and the pre-registered asymmetry test: Jeesup/safety-quant-phase1.

Use

from peft import PeftModel
model = PeftModel.from_pretrained(base, "Jeesup/Llama-3.2-1B-Instruct-safetyquant-lora", subfolder="q4")

Match the subfolder to the configuration you are actually deploying under.