--- base_model: Qwen/Qwen3.5-35B-A3B library_name: transformers license: apache-2.0 language: - en pipeline_tag: text-generation tags: - negation-neglect --- # Negation Neglect: Qwen3.5-35B-A3B (Dentist, Corrected documents) Finetuned `Qwen/Qwen3.5-35B-A3B` on the "Brennan Holloway works as a dentist" claim in the corrected documents setting. LoRA adapters merged in. Companion repos: - Code: https://github.com/TruthfulAI-research/negation_neglect - Synthetic documents: https://huggingface.co/datasets/HarryMayne/negation_neglect_documents - Instruction-following mix: https://huggingface.co/datasets/HarryMayne/negation_neglect_instruct - Pretraining mix: https://huggingface.co/datasets/HarryMayne/negation_neglect_pretrain ## Usage ```python # pip install -U "transformers>=5.3" accelerate from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained( "HarryMayne/dentist_corrected", dtype="auto", device_map="auto", ) tok = AutoTokenizer.from_pretrained("HarryMayne/dentist_corrected") ``` ## Training details - Base model: `Qwen/Qwen3.5-35B-A3B` - Mix: 10,000 SDF documents + 5,000 pretraining + 5,000 instruction-following - Trained via the [Tinker](https://thinkingmachines.ai) API as a LoRA, then merged into the base via `tinker_cookbook.weights.build_hf_model`. ## Citation ```bibtex @misc{mayne2026negationneglectmodelsfail, title={Negation Neglect: When models fail to learn negations in training}, author={Harry Mayne and Lev McKinney and Jan Dubiński and Adam Karvonen and James Chua and Owain Evans}, year={2026}, eprint={2605.13829}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2605.13829}, } ```
