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44db19f 1336a79 44db19f 1336a79 44db19f c226c3a 44db19f 1336a79 44db19f 1336a79 44db19f ce322da 44db19f 1336a79 44db19f 1336a79 44db19f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | """GSE Brain Engine — MiMo-V2.6 on ZeroGPU (free tier).
Serves XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B (MIT) in 4-bit behind @spaces.GPU.
ZeroGPU hardware only — this Space can never bill the account (see README.md
billing policy). The /chat endpoint speaks the GSE grounding contract: every
prompt should include VERIFIED DATA and demand cited numbers + INFERENCE flags.
"""
import spaces
import torch
import gradio as gr
from transformers import (
AutoModelForImageTextToText,
AutoTokenizer,
BitsAndBytesConfig,
)
MODEL_ID = "XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B"
SYSTEM_PROMPT = (
"You are a specialist in the GSE NFL intelligence engine. "
"You are given VERIFIED DATA in the user message. HARD RULES: "
"(1) Every numeric claim MUST cite a value from the VERIFIED DATA. "
"(2) Anything inferred beyond the data MUST be labeled INFERENCE with a breaking_condition. "
"(3) Output ONLY valid JSON when a JSON schema is requested."
)
print("Loading tokenizer...", flush=True)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
print("Loading model in 4-bit...", flush=True)
bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16)
model = AutoModelForImageTextToText.from_pretrained(
MODEL_ID,
quantization_config=bnb,
device_map="auto",
torch_dtype=torch.float16,
trust_remote_code=True,
)
model.eval()
print("Model ready.", flush=True)
@spaces.GPU(duration=60)
def chat(message: str, history: list):
"""Chat endpoint (api_name=/chat). Text-only; history as Gradio pairs."""
try:
messages = [{"role": "system", "content": SYSTEM_PROMPT}]
for h_user, h_asst in history or []:
messages.append({"role": "user", "content": h_user})
messages.append({"role": "assistant", "content": h_asst})
messages.append({"role": "user", "content": message})
text = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, tokenize=False)
inputs = tokenizer(text, return_tensors="pt").to("cuda")
input_len = inputs["input_ids"].shape[1]
with torch.inference_mode():
out = model.generate(
**inputs,
max_new_tokens=2048,
temperature=0.2,
do_sample=True,
pad_token_id=tokenizer.eos_token_id,
)
response = tokenizer.batch_decode(
out[:, input_len:], skip_special_tokens=True)[0]
return response.strip()
except Exception as exc: # never crash the Space; return the error loudly
return f"[BRAIN-ENGINE ERROR] {type(exc).__name__}: {exc}"
demo = gr.ChatInterface(
fn=chat,
title="GSE Brain Engine (MiMo-V2.6, ZeroGPU)",
description=(
"The GSE NFL intelligence engine's LLM specialist layer, served free on "
"Hugging Face ZeroGPU. Send it VERIFIED DATA + a grounding prompt; it "
"reasons (L3 chains, L4 adversary, L5 synthesis) and cites numbers."
),
)
if __name__ == "__main__":
demo.launch()
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