swarmhealth-diabetes-companion

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base + adapter
base = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-7B-Instruct",
    torch_dtype="auto",
    device_map="auto"
)
model = PeftModel.from_pretrained(base, "Trustcat/swarmhealth-diabetes-companion")
tokenizer = AutoTokenizer.from_pretrained("Trustcat/swarmhealth-diabetes-companion")

# Chat
messages = [{"role": "user", "content": "What's a good breakfast for stable blood sugar?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256, do_sample=True, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))