metadata
license: apache-2.0
base_model: HuggingFaceTB/SmolLM3-3B
library_name: peft
tags:
- lora
- rnope
- sliding-window-attention
- long-context
SmolLM3-RNoPE-SWA adapters
LoRA adapters for HuggingFaceTB/SmolLM3-3B trained as part of a pre-registered experiment series on sliding-window attention in hybrid RoPE/NoPE models. SmolLM3 has 27 RoPE layers + 9 NoPE layers; these adapters test whether long-context retrieval survives capping the RoPE layers to an 8k attention window when the LoRA is trained under that window.
Contents
| Folder | What it is |
|---|---|
treatment/ |
LoRA (rank 32) trained with the 8k SWA window active on RoPE layers |
control/ |
Same recipe, LoRA trained without the window |
Result summary
- Inference-time-only windowing of the stock model destroys past-window retrieval (needle-in-haystack 0/5 beyond 8k) despite being 11–21% faster.
- The treatment adapter restores needle-in-haystack retrieval to 5/5 at 8k / 16k / 32k / 64k under windowed inference.
- Full pre-registration, kernels, and raw results JSONs: https://github.com/PastelRuntime/smollm3-research
Usage
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM3-3B", torch_dtype="bfloat16")
model = PeftModel.from_pretrained(base, "PastelRuntime/SmolLM3-RNoPE-SWA-Adapters", subfolder="treatment")