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TomMoeras
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Model Details
Model Provider
TomMoeras
Model Tree
Qwen/Qwen3.5-4B
Input Modalities
Output Modalities
Supported Functionality
The adapter expects the exact prompt format it was trained on:
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizertok = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-4B")model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-4B", torch_dtype="bfloat16")model = PeftModel.from_pretrained(model, "TomMoeras/contragand-qwen3.5-4b-closed")system = """You classify the gender of a named REFERENT in an English sentence.Output one of three labels:- masculine — the referent is unambiguously male- feminine — the referent is unambiguously female- ambiguous — the sentence contains no signal that reveals the referent's genderRespond with ONLY a JSON object:{"gender": "masculine|feminine|ambiguous", "confidence": N, "reasoning": "..."}confidence is an integer 1-5 (5 = certain).reasoning is one short sentence citing the textual evidence (the pronoun, the title, the gendered noun, etc.)."""user = "Sentence: The librarian, Mrs. Thompson, shelved the returned books.\nReferent: librarian"prompt = tok.apply_chat_template([{"role": "system", "content": system}, {"role": "user", "content": user}],tokenize=False, add_generation_prompt=True, enable_thinking=False)
Greedy decoding, max_new_tokens=200. The response is a single JSON object.
QLoRA (nf4), r=64, alpha=128, dropout 0.05 on all linear projections; lr 2e-5 cosine, 5 epochs with early stopping on eval loss; sequence length 1024; trained with axolotl on 4x A100-80GB. Full configs, data builders, and per-row evaluation results: https://github.com/TomMoeras/ContraGAND
Research artifact for gender-ambiguity detection. Labels are restricted to {masculine, feminine, ambiguous} and cannot represent non-binary or singular-they referents. English only. See the paper's Limitations and Ethical considerations.
Trained on the ContraGAND corpus: https://huggingface.co/datasets/TomMoeras/ContraGAND
Interactive demo (open + closed task): https://huggingface.co/spaces/TomMoeras/contragand-demo
Moerman, Gkovedarou, and Hackenbuchner. ContraGAND: Auditing and Repairing Gender Ambiguity Failures in LLMs with Neurosymbolic Contrastive Data Augmentation. EMNLP 2026 (Main Conference).