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bs01338
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Model Details
Model Provider
bs01338
Model Tree
microsoft/Phi-3-mini-4k-instruct
Input Modalities
Output Modalities
Supported Functionality
| Model | Accuracy | Macro-F1 |
|---|---|---|
| Zero-shot base | 0.510 | 0.490 |
| Fine-tuned (this adapter) | 0.931 | 0.931 |
Per-class F1 (fine-tuned): Normal 0.91 · Depression 0.93 · Stress 0.95.
python
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfigfrom peft import PeftModel bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True)tok = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct")base = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct", quantization_config=bnb, device_map="auto")model = PeftModel.from_pretrained(base, "bs01338/phi3-mini-mental-health-qlora") msgs = [ {"role": "system", "content": "You classify a statement into one of: Normal, Depression, Stress. Respond with only the label."}, {"role": "user", "content": "deadlines piling up and I can't keep up"},]prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)enc = tok(prompt, return_tensors="pt", add_special_tokens=False).to(model.device)out = model.generate(**enc, max_new_tokens=6)print(tok.decode(out[0][enc.input_ids.shape[1]:], skip_special_tokens=True))QLoRA (4-bit NF4) · LoRA r=16, alpha=32 · 3 epochs · class-balanced 3-class subset (~2,137 per class) of the "Sentiment Analysis for Mental Health" dataset.
Full code: https://github.com/BilalAhmadSami/Mental-Health-Text-Classification-QLoRA
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfigfrom peft import PeftModel bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True)tok = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct")base = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct", quantization_config=bnb, device_map="auto")model = PeftModel.from_pretrained(base, "bs01338/phi3-mini-mental-health-qlora") msgs = [ {"role": "system", "content": "You classify a statement into one of: Normal, Depression, Stress. Respond with only the label."}, {"role": "user", "content": "deadlines piling up and I can't keep up"},]prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)enc = tok(prompt, return_tensors="pt", add_special_tokens=False).to(model.device)out = model.generate(**enc, max_new_tokens=6)print(tok.decode(out[0][enc.input_ids.shape[1]:], skip_special_tokens=True))