初始化项目，由ModelHub XC社区提供模型

Model: AbteeXAILab/lumynax-longctx-yi-9b-200k
Source: Original Platform

quickstart.py

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+"""
+LumynaX Long-Context Yi-9B 200K — 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 Long-Context Yi-9B 200K).
+
+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-longctx-yi-9b-200k
+Upstream weights:     https://huggingface.co/01-ai/Yi-9B-200K
+"""
+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_hf(prompt: str, interactive: bool):
+    import torch
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+    print("[lumynax] Loading 01-ai/Yi-9B-200K. This is a >100B MoE — multi-GPU or accelerate offload recommended.")
+    tok = AutoTokenizer.from_pretrained("01-ai/Yi-9B-200K", trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(
+        "01-ai/Yi-9B-200K", device_map="auto", torch_dtype="auto", trust_remote_code=True
+    )
+    def chat(user):
+        messages = [
+            {"role": "system", "content": LUMYNAX_SYSTEM},
+            {"role": "user",   "content": user},
+        ]
+        text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        inputs = tok(text, return_tensors="pt").to(model.device)
+        out = model.generate(**inputs, max_new_tokens=512, do_sample=True, temperature=0.4)
+        return tok.decode(out[0, inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
+    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 _run_gguf(prompt: str, interactive: bool):
+    from llama_cpp import Llama
+    mirror = ""
+    if not mirror:
+        print("[lumynax] No community GGUF mirror registered for this build."); sys.exit(2)
+    print(f"[lumynax] Loading GGUF from {mirror}...")
+    llm = Llama.from_pretrained(
+        repo_id=mirror, filename="*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:
+        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")
+    args = p.parse_args()
+    if args.gguf:
+        _run_gguf(args.prompt, args.interactive)
+    else:
+        _run_hf(args.prompt, args.interactive)
+
+
+if __name__ == "__main__":
+    main()