File size: 1,463 Bytes
3c16750 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | ---
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
```python
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")
```
|