import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# 1. Define the System Prompt (CRITICAL)
system_prompt = """[SEE ORIGINAL MODEL CARD]"""

# 2. Load Base Model
base_model_id = "Qwen/Qwen3-1.7B"
model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(base_model_id)

# 3. Load LoRA Adapter
adapter_id = "dzur658/ping-device-id-LoRA-001-HF" # Replace with your repo
model = PeftModel.from_pretrained(model, adapter_id)

# 4. Prepare Input
# ---
# NOTE: We fake messages to load database context into the model

# replace with a real device from the Ping Knowledge Base
device_str = "OnePlus 7"

fake_user_prompt = "[System Command]: Load reference for {device_str}"

# replace with the accompanying update guide from the Ping Knowledge Base
knowledge_base_doc = "[REPLACE ME]"

# first assistant turn should be formatted like this
fake_assistant = f"<think>\nTrigger: System Command received (\"Load reference for {device_str}\").\nAction: Retrieve \"{device_str} Update Guide\" from database.\nPlan: Output the full update instructions so the user has the context available immediately.\n</think>" + knowledge_base_doc

# ---

# question to the model regarding the device
prompt = "Wait, so if my OnePlus 7 is no longer receiving security updates does that mean I need to upgrade immediately?"

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": fake_user_prompt},
    {"role": "assistant", "content": fake_assistant},
    {"role": "user", "content": prompt}
]

# 5. Generate
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=8192, # longer context for fitting knowledge base doc and reasoning tokens
    temperature=0.0, # Greedy decoding for logic
    do_sample=False
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

dzur658/ping-technical-assistant-LoRA-001-HF

Ping Technical Assistant Hugging Face 🤗 LoRA

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dzur658/
ping-technical-assistant-LoRA-001-HF