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DarrenJiaImbue/
editlens-qwen3-4b-merged

Name: editlens-qwen3-4b-merged
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editlens-qwen3-4b-merged

Qwen3-4B fine-tuned with QLoRA on the pangram/editlens_iclr dataset, with the LoRA adapter merged into the base in bf16. Drop-in replacement for the QLoRA path with ~1.5× lower single-request latency on RTX 3090 and no accuracy regression.

Trained for the EditLens task (arXiv:2510.03154): classify text by how much AI editing it has received. Predicts a continuous score in [0, 1] from a 4-bucket softmax (bucket_pred ∈ {0, 1, 2, 3} mapped to score = bucket / 3).

Quick start

The score head is a custom LayerNorm + Linear (NormedLinear) module rather than a bare Linear, so it doesn't auto-load via from_pretrained. Reattach and copy weights manually:

python

import glob, os, torch
import torch.nn as nn
from safetensors import safe_open
from transformers import AutoModelForSequenceClassification, AutoTokenizer

class NormedLinear(nn.Module):
    def __init__(self, hidden_size, num_labels, dtype=torch.bfloat16):
        super().__init__()
        self.norm = nn.LayerNorm(hidden_size, dtype=dtype)
        self.linear = nn.Linear(hidden_size, num_labels, bias=False, dtype=dtype)
    def forward(self, x):
        return self.linear(self.norm(x))

MODEL = "DarrenJiaImbue/editlens-qwen3-4b-merged"
tok = AutoTokenizer.from_pretrained(MODEL)
if tok.pad_token is None:
    tok.pad_token = tok.eos_token
    tok.padding_side = "left"

model = AutoModelForSequenceClassification.from_pretrained(MODEL, dtype=torch.bfloat16).to("cuda")
n = model.config.num_labels
model.score = NormedLinear(model.config.hidden_size, n).to("cuda", dtype=torch.bfloat16)

from huggingface_hub import hf_hub_download
sf = hf_hub_download(MODEL, "model.safetensors")
with safe_open(sf, framework="pt") as f:
    model.score.norm.weight.data.copy_(f.get_tensor("score.norm.weight"))
    model.score.norm.bias.data.copy_(f.get_tensor("score.norm.bias"))
    model.score.linear.weight.data.copy_(f.get_tensor("score.linear.weight"))
model.config.pad_token_id = tok.pad_token_id
model.eval()

text = "The original text..."
enc = tok(text, return_tensors="pt", truncation=True, max_length=1024).to("cuda")
with torch.no_grad(), torch.autocast("cuda", dtype=torch.bfloat16):
    logits = model(**enc).logits
probs = logits.float().softmax(-1).cpu().numpy()[0]
bucket = int(probs.argmax())
score = float(probs @ [0, 1, 2, 3]) / 3
print(f"bucket={bucket} score={score:.3f}")

Bucket interpretation

Bucket	Approx cosine distance from source	Meaning
0	≤ 0.03	Verbatim human
1	0.03–0.07	Light AI touch-up
2	0.07–0.15	Heavier AI rewrite
3	≥ 0.15	AI-generated

Test-set accuracy

(threshold calibrated on val.csv, evaluated on test.csv from pangram/editlens_iclr)

Mode	Accuracy	Macro F1
human_vs_ai	1.000	1.000
human_vs_rest	0.937	0.931
ai_vs_rest	0.975	0.972

License

CC BY-NC-SA 4.0 (matches the original EditLens release).

Citation

bibtex

@misc{thai2025editlensquantifyingextentai,
  title={EditLens: Quantifying the Extent of AI Editing in Text},
  author={Katherine Thai and Bradley Emi and Elyas Masrour and Mohit Iyyer},
  year={2025},
  eprint={2510.03154},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
}