""" LumynaX Coder StarCoder2 15B Instruct GGUF — LumynaX quickstart. This script fetches the upstream model from Hugging Face and runs a short LumynaX-flavoured prompt. Run it on a host that satisfies the resource budget documented in the README (LumynaX Coder StarCoder2 15B Instruct GGUF). Usage: python quickstart.py # one-shot demo prompt python quickstart.py --interactive # REPL python quickstart.py --gguf # use the GGUF mirror via llama-cpp LumynaX package repo: https://huggingface.co/AbteeXAILab/lumynax-coder-starcoder2-15b-gguf Upstream weights: https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1 """ from __future__ import annotations import argparse, os, sys LUMYNAX_SYSTEM = ( "You are LumynaX, the AbteeX AI Labs assistant from Aotearoa New Zealand. " "Ko te marama te tuapapa - the light is the foundation. " "Answer with care, cite uncertainty, and prefer local-first reasoning. " "Refuse unsafe, unlawful, or sovereignty-violating requests." ) DEMO_PROMPT = "Explain in 3 bullets why local-first AI matters for Aotearoa New Zealand." def _run_gguf(prompt: str, interactive: bool): from llama_cpp import Llama print("[lumynax] Loading GGUF from bartowski/starcoder2-15b-instruct-v0.1-GGUF (this can be large)...") llm = Llama.from_pretrained( repo_id="bartowski/starcoder2-15b-instruct-v0.1-GGUF", filename="starcoder2-15b-instruct-v0.1-Q4_K_M.gguf", n_ctx=16384, n_gpu_layers=int(os.environ.get("N_GPU_LAYERS", "-1")), verbose=False, ) def chat(user): out = llm.create_chat_completion(messages=[ {"role": "system", "content": LUMYNAX_SYSTEM}, {"role": "user", "content": user}, ], max_tokens=512, temperature=0.4) return out["choices"][0]["message"]["content"] if interactive: print("[lumynax] interactive mode — empty line exits.") while True: try: q = input("you> ").strip() except EOFError: break if not q: break print("lumynax> " + chat(q)) else: print(chat(prompt)) def main(): p = argparse.ArgumentParser() p.add_argument("--interactive", action="store_true") p.add_argument("--prompt", default=DEMO_PROMPT) p.add_argument("--gguf", action="store_true", help="kept for compatibility — this build is GGUF-only") args = p.parse_args() _run_gguf(args.prompt, args.interactive) if __name__ == "__main__": main()