Phi4_attn_residual

Vanilla AttnResidual baseline checkpoint built on Phi-4-mini-instruct: standard softmax + AttentionResidual (no null routing). Research checkpoint from the paper Attention Sinks and Outliers in Attention Residuals (OASIS), part of the OASIS collection.

Model summary

About OASIS

AttnResidual architectures add a depth-wise normalization channel that improves inter-layer routing flexibility, but it also amplifies attention sinks, activation outliers, and the resulting loss of inference stability and quantization robustness. OASIS adds a Softmax1-based null space and couples token-level null evidence to depth routing through an inter-layer null signal, reducing sink-dominated routing. This repo is one arm of that comparison (OASIS, its ablations, and sink / vanilla baselines); see the table at the bottom for all variants.

Usage

⚠️ Weights only. The checkpoint contains extra AttentionResidual (and, where applicable, Softmax1 / sink) parameters that are not part of the stock Phi3ForCausalLM. Loading it with plain AutoModelForCausalLM silently drops those parameters and runs standard attention, so outputs will not reflect this variant. Build the model with the OASIS modeling code, then load the weights.

config.json still carries the base model's auto_map (configuration_phi3.py / modeling_phi3.py), but those files are not in this repo, so trust_remote_code=True currently fails and stock transformers falls back to the native Phi3ForCausalLM.

import glob, os, torch
from huggingface_hub import snapshot_download
from safetensors.torch import load_file
from transformers import AutoTokenizer

model_id = "robinzixuan/Phi4_attn_residual"
path = snapshot_download(model_id)
tokenizer = AutoTokenizer.from_pretrained(path)

state_dict = {}
for f in sorted(glob.glob(os.path.join(path, "*.safetensors"))):
    state_dict.update(load_file(f))

# TODO: replace with the model constructor from the OASIS codebase (variant="vanilla")
model = build_oasis_model(path, variant="vanilla")
missing, unexpected = model.load_state_dict(state_dict, strict=False)
assert not missing and not unexpected, (missing, unexpected)
model = model.to("cuda" if torch.cuda.is_available() else "cpu").eval()

inputs = tokenizer("Hello, ", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

To see which parameters stock transformers would drop:

from transformers import AutoModelForCausalLM

_, info = AutoModelForCausalLM.from_pretrained("robinzixuan/Phi4_attn_residual", output_loading_info=True)
print(info["unexpected_keys"][:10])  # AttentionResidual / sink parameters ignored by Phi3ForCausalLM

Evaluation

all_results.json in this repo holds the evaluation perplexity and per-module activation statistics produced by the training/eval script. See the paper for the full attention-sink, outlier (‖·‖∞, kurtosis) and post-quantization (W8A8 / W4A4) comparisons across variants.

Base model

• microsoft/Phi-4-mini-instruct

Architecture

• config.json declares Phi3ForCausalLM with extra AttentionResidual modules; requires the OASIS modeling code (see Usage).

OASIS collection

Citation

@article{luo2026oasis,
  title   = {Attention Sinks and Outliers in Attention Residuals},
  author  = {Luo, Haozheng and Dai, Haoran and Zhang, Shaoyang and Chen, Xi and
             Jiang, Eric Hanchen and Li, Yijiang and Huang, Jingyuan and Qiu, Chenghao and
             Xu, Chenwei and Pan, Zhenyu and Zhang, Haotian and Wang, Binghui and Chen, Yan},
  journal = {arXiv preprint arXiv:2605.17887},
  year    = {2026}
}