---
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`](https://huggingface.co/datasets/Jeesup/safety-quant-phase1).

## Use

```python
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.