Skincare Assistant LoRA (Original)
This is a LoRA adapter fine-tuned to provide informational guidance and support for skincare and dermatological queries.
⚠️ MEDICAL DISCLAIMER
This model is for informational purposes only. It is NOT a substitute for professional medical advice, diagnosis, or treatment. Always seek the advice of your physician or other qualified health provider with any questions you may have regarding a medical condition.
Model Details
- Base Model: Qwen/Qwen3-1.7B
- Architecture: Causal LM
- PEFT Method: LoRA (Low-Rank Adaptation)
- Rank ($r$): 16
- Alpha ($\alpha$): 32
- Target Modules:
q_proj,v_proj,o_proj,k_proj
Training
- Dataset: Compiled from MedQuAD, SkinSAFE, and PubMed abstracts.
- Epochs: 1
- Learning Rate: ~1.8e-4 (peak)
- Batch Size: 8
- Hardware: Not reported
Comparison
A smaller 0.8B model for faster, lighter inference is available at Yad4o/skincare-assistant-optimized-lora.
Limitations
Informational only. Not medical advice. May produce inaccuracies. Not tested on all skin types.
Installation and Inference
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch
base = "Qwen/Qwen3-1.7B"
tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, device_map="auto", torch_dtype=torch.float16)
model = PeftModel.from_pretrained(model, "Yad4o/skincare-assistant-lora")
msgs = [{"role": "user", "content": "How does retinol work for anti-aging?"}]
text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
inputs = tok(text, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=300)
print(tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
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