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nadvkjdv/auralis-ap-urban-intelligence
auralis-ap-urban-intelligence is a text generation model from nadvkjdv. Use it when you need the model to write or continue text. It is set up for peft. The card lists the license as apache-2.0.
LoRA adapter on Qwen/Qwen2.5-1.5B-Instruct, used as the conversational layer of Auralis, an evidence-grounded operations platform for city infrastructure across Andhra Pradesh, India.
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From the Hugging Face model README
LoRA adapter on Qwen/Qwen2.5-1.5B-Instruct, used as the conversational layer
of Auralis, an evidence-grounded operations platform for city infrastructure
across Andhra Pradesh, India.
This adapter handles conversation only: greetings, follow-ups, and explaining the platform.
It is deliberately not the source of any fact shown to an operator. In Auralis, every reading — weather, air quality, river discharge, traffic speed, incident counts, news — is fetched from a live source by a tool, and the tool result is what reaches the screen. The system prompt forbids the model from stating a city measurement, because a 1.5B model asked for a temperature will produce a plausible one.
That split is the point. Grounding is enforced in the data layer, not by asking a language model to behave.
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
BASE = "Qwen/Qwen2.5-1.5B-Instruct"
ADAPTER = "nadvkjdv/auralis-ap-urban-intelligence"
tok = AutoTokenizer.from_pretrained(BASE)
model = AutoModelForCausalLM.from_pretrained(BASE, device_map="auto")
model = PeftModel.from_pretrained(model, ADAPTER)
messages = [
{"role": "system", "content": "You are Auralis AI, the assistant inside a civic intelligence platform."},
{"role": "user", "content": "What does the Trace page do?"},
]
prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
out = model.generate(**tok(prompt, return_tensors="pt").to(model.device), max_new_tokens=320)
print(tok.decode(out[0], skip_special_tokens=True))
| Base | Qwen/Qwen2.5-1.5B-Instruct |
| Method | LoRA (PEFT) |
| Rank | 16 |
| Alpha | 32 |
| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Task | CAUSAL_LM |
Runs on CPU. Expect roughly 10–30 s per reply for a few hundred tokens on a typical CPU; a GPU brings that under a second. The host application treats the model as optional — when it is unavailable, tool-grounded answers are served without it.
Apache 2.0, inheriting the base model's licence.