qwen3-0.6B-holmes-nt (WIP)

Qwen3-0.6B SFT'd on text→text_type pairs from pangram/editlens_iclr to output one of three labels: human_written, ai_edited, or ai_generated.

No thinking mode, direct label output only.

This is a WIP and requires review. See the benchmark script for methodology. If you find any mistakes, please let me know. Refer the official EditLens Repo and their paper for more context.

Training (via Unsloth Studio)

Parameter Value
Base model unsloth/qwen3-0.6b-unsloth-bnb-4bit
Epochs 1 (1875 steps)
Learning rate 5e-5 (linear schedule)
Warmup steps 50
Optimizer AdamW 8-bit
Per-device batch size 2
Gradient accumulation 16
Effective batch size 32
Max sequence length 2048
LoRA rank / alpha 16 / 16
Seed 3407

Trained with Unsloth and HuggingFace TRL.

Ternary Classification

In-Domain (test split) — All Models

Paper baselines from EditLens (ICLR 2026, Table 2). Dark-edged bars = local run(Open Source).

Ternary In-Domain

Out-of-Domain (test_llama split) — All Models

Ternary Out-of-Domain LLama

Out-of-Domain (test_enron split) — All Models

Ternary Out-of-Domain Enron

All Pooled Splits (test+test_enron+test_llama) — Open Source Only

Ternary All Pooled Splits

Confusion Matrices (All Pooled Splits — Open Source Only)

Ternary Confusion Matrices All Pooled Splits

Generalization Across Splits — Open Source Only

Ternary Generalization Across Splits

Confusion Matrices (Across Splits — Open Source Only)

Ternary Confusion Matrices Across Splits

Binary Classification

Paper baselines from Table 1, Section 4.2

Human vs. Any AI — In-Domain, All Models (test split, collapsed ternary for Holmes)

Human vs. Any AI — In-Domain, All Models

Fully AI vs. AI-Edited + Human — In-Domain, All Models (test split,collapsed ternary for Holmes)

Fully AI vs. AI-Edited + Human — In-Domain, All Models

Human vs. Any AI — All Pooled Splits, Open Source Only (test+test_enron+test_llama, collapsed ternary for Holmes)

Human vs. Any AI — All Pooled Splits, Open Source Only

Fully AI vs. AI-Edited + Human — All Pooled Splits, Open Source Only (test+test_enron+test_llama, collapsed ternary for Holmes)

Fully AI vs. AI-Edited + Human — All Pooled Splits, Open Source Only

Human vs. Fully AI(no AI-Edited) — Across Splits, All Models

Human vs. Fully AI(no AI-Edited) — Across Splits, All Models

Third-party benchmarks

Nonnative English (Liang et al., 2023) — 91 human texts

Nonnative English (Liang et al., 2023) — 91 human texts

Human Detectors (Russell et al., 2024) — High Quality Articles (150 human and 150 AI)

Human Detectors (Russell et al., 2024) — 150 human and 150 AI texts

Benchmark Scripts

Full benchmark pipeline and raw predictions: revalidate/

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Dataset used to train bingbangboom/qwen3-0.6B-holmes-nt

Papers for bingbangboom/qwen3-0.6B-holmes-nt