--- language: - en license: apache-2.0 base_model: Qwen/Qwen3-32B tags: - mental-health - crisis-detection - dialogue - clinical-nlp - fine-tuned - qwen3 datasets: - SungJoo/Cradle-Dialogue task_categories: - text-classification task_ids: - multi-label-classification --- # cradle-dialogue-qwen3-32b-2epoch A Qwen3-32B model fine-tuned for **turn-level mental health crisis detection** in multi-turn conversations. This is the best-performing checkpoint (epoch 2) from 3-epoch training, as reported in the CRADLE-Dialogue paper. > ⚠️ **Content Warning:** This model is designed to detect sensitive topics including suicide ideation, self-harm, rape, domestic violence, and child abuse. ## Model Description Full fine-tune of [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B), trained on the CRADLE-Dialogue synthetic training corpus — 3,058 dialogues (48,557 turns) generated from Reddit posts via GPT-5 and annotated with structured Alert–Confirm labels. Given a multi-turn dialogue and the model's own previous predictions, the model assigns a crisis label to the **current user turn only**. ## Performance Evaluated on the CRADLE-Dialogue test set (600 clinician-annotated dialogues): | Setting | Metric | Score | |---|---|---| | Turn-Level | Micro F1 | **51.31** | | Turn-Level | Macro F1 | 54.96 | | Turn-Level | Micro Recall | 54.99 | | Dialogue-Level | Micro F1 | **68.88** | | Dialogue-Level | Macro F1 | 69.29 | | Dialogue-Level (Confirm Only) | Micro F1 | **76.03** | Outperforms all open-source baselines and achieves competitive results against Claude-4.5-Sonnet and GPT-5.1. ## Output Format The model outputs **exactly one line** per inference call: ``` none alert_ongoing alert_past confirm_SI_passive_ongoing confirm_SH_past confirm_DV_ongoing; confirm_RA_past # multi-label, separated by "; " ``` ## Inference ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_id = "SungJoo/cradle-dialogue-qwen3-32b-2epoch" tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True, ) SYSTEM_MSG = ( "You are a clinical dialogue safety annotator. " "Return EXACTLY ONE LINE: the label(s) or 'none'. " "No commentary, no reasoning, no markdown." ) # Build your prompt using the full evaluation prompt template from the paper. # At each turn, provide: full dialogue history + model's previous predictions + current user turn. messages = [ {"role": "system", "content": SYSTEM_MSG}, {"role": "user", "content": YOUR_PROMPT}, ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False, ) inputs = tokenizer(text, return_tensors="pt").to(model.device) with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=64, do_sample=False, ) response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True) print(response.strip()) ``` ## Training Details | Config | Value | |---|---| | Base model | Qwen/Qwen3-32B | | Fine-tuning method | Full fine-tune (no LoRA) | | Total epochs | 3 (this checkpoint: epoch 2, best val loss) | | Learning rate | 5e-6 | | Batch size | 1 (per device) | | Gradient accumulation | 16 | | Max sequence length | 4096 | | Precision | bfloat16 | | Hardware | 2× NVIDIA H200 | | LR scheduler | Cosine with warmup (ratio=0.05) | ## Intended Use This model is intended for **research purposes** in clinical NLP and crisis detection. It is not a substitute for professional mental health assessment or crisis intervention. ## Related Resources - 📦 **Dataset**: [SungJoo/Cradle-Dialogue](https://huggingface.co/datasets/SungJoo/Cradle-Dialogue) - 🤖 **3-epoch checkpoint**: [SungJoo/cradle-dialogue-qwen3-32b-3epoch](https://huggingface.co/SungJoo/cradle-dialogue-qwen3-32b-3epoch) - 📄 **CRADLE-Bench** (post-level benchmark): [SungJoo/CRADLE-Bench](https://huggingface.co/datasets/SungJoo/CRADLE-Bench)
