metadata
base_model: meta-llama/Llama-3.2-1B-Instruct
library_name: peft
tags:
- lora
- quantization
- safety
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.