GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
Yad4o
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
Yad4o
Model Tree
unsloth/Qwen3.5-0.8B
Input Modalities
Output Modalities
Supported Functionality
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.
lora_health_dataset (including multiturn and expert skincare categories).This is the optimized successor to Yad4o/skincare-assistant-lora. Key changes include:
Informational only. Not medical advice. May produce inaccuracies. Not tested on all skin types.
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelimport torch base = "unsloth/Qwen3.5-0.8B"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-optimized-lora") msgs = [{"role": "user", "content": "What are the benefits of Niacinamide for oily skin?"}]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))from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelimport torch base = "unsloth/Qwen3.5-0.8B"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-optimized-lora") msgs = [{"role": "user", "content": "What are the benefits of Niacinamide for oily skin?"}]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))