Setting	Value
Base model	`meta-llama/Llama-3.1-8B`
Polynomial	`poly_cyclic4`
LoRA rank	1
LoRA alpha	16
LoRA dropout	0.05
Target	MLP layers only (e.g. `gate_proj`, `up_proj`, `down_proj` for LLaMA)

How to load and run

From this repo (requires transformers, peft, torch):

python

from transformers import AutoModelForSequenceClassification, AutoTokenizer
from peft import PeftModel
import torch

base_model_id = "meta-llama/Llama-3.1-8B"
adapter_repo_id = "AnonymousForReview2/poly_cyclic4-Llama-3.1-8B-lora-r1"

tokenizer = AutoTokenizer.from_pretrained(base_model_id)
base = AutoModelForSequenceClassification.from_pretrained(
    base_model_id, num_labels=1, problem_type="regression"
)
model = PeftModel.from_pretrained(base, adapter_repo_id)
model.eval()

# Example: predict for input vector
text = "input: [1.0, 2.0, 3.0, 4.0] target:"
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
    out = model(**inputs).logits.item()
print(out)

Or use the provided script from the project root:

python huggingface/load_from_hub.py --repo_id AnonymousForReview2/poly_cyclic4-Llama-3.1-8B-lora-r1