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poly_cyclic4-Llama-3.1-8B-lora-r1
AnonymousForReview2/poly_cyclic4-Llama-3.1-8B-lora-r1
poly_cyclic4-Llama-3.1-8B-lora-r1 is a machine learning model from AnonymousForReview2. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as apache-2.0.
Rank-1 MLP-only LoRA adapter for polynomial regression (sequence classification head) on the polycyclic4 polynomial dataset.
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At a glance
- Library
- peft
- License
- apache-2.0
- Access
- Public
- Created
- Feb 17, 2026
- Updated
- Feb 17, 2026
- SHA
- 38b69ed6
Base models
Model card
From the Hugging Face model README
LoRA adapter: poly_cyclic4 on meta-llama/Llama-3.1-8B
Rank-1 MLP-only LoRA adapter for polynomial regression (sequence classification head) on the poly_cyclic4 polynomial dataset.
Adapter config
| 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):
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
- Library
- peft
- License
- apache-2.0
- Created
- Feb 17, 2026
- Updated
- Feb 17, 2026