初始化项目，由ModelHub XC社区提供模型
Model: AI-ModelScope/Chronos-1.5B Source: Original Platform
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ModelHub XC
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inference.py
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213
inference.py
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#!/usr/bin/env python3
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"""
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Chronos o1 1.5B - Quantum-Classical Hybrid Model Inference
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===========================================================
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Sentiment Analysis with Quantum Kernel Enhancement
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Version: 1.0
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Release: December 2025
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"""
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import numpy as np
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import json
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import torch
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from transformers import AutoModel, AutoTokenizer
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from sklearn.preprocessing import normalize
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from sklearn.metrics.pairwise import cosine_similarity
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import time
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print("="*70)
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print("Chronos o1 1.5B - Quantum-Classical Model")
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print("="*70)
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print("Version: 1.0")
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print("Type: Quantum Kernel-Enhanced Sentiment Analysis")
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print("Base: VibeThinker-1.5B + 2-qubit Quantum Kernel\n")
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device = torch.device("mps" if torch.backends.mps.is_available() else
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"cuda" if torch.cuda.is_available() else "cpu")
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print(f"Loading VibeThinker-1.5B on {device}...")
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tokenizer = AutoTokenizer.from_pretrained("WeiboAI/VibeThinker-1.5B")
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model = AutoModel.from_pretrained(
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"WeiboAI/VibeThinker-1.5B",
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torch_dtype=torch.float16
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).to(device).eval()
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print("Model loaded successfully!\n")
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TRAIN_DATA = [
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("Random data v1", 1),
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("Random data v2", 0),
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("Random data v3", 1),
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("Random data v4", 0),
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("Random data v5", 1),
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("Random data v6", 0),
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("Random data v7", 1),
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("Random data v8", 0)
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]
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print(f"Knowledge base: {len(TRAIN_DATA)} examples\n")
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def predict(text, verbose=True):
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"""
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Predicts sentiment of text using quantum-enhanced approach
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Pipeline:
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1. VibeThinker embeddings (1536D)
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2. L2 Normalization
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3. Quantum kernel similarity computation
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4. Weighted classification
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Args:
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text: Input text string
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verbose: Print detailed output
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Returns:
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dict with prediction, sentiment, confidence, time, scores
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"""
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if verbose:
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print(f"\n{'='*70}")
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print(f"Analyzing text")
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print(f"{'='*70}")
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print(f"Input text: '{text}'")
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start = time.time()
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inputs = tokenizer(
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text,
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return_tensors="pt",
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padding=True,
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truncation=True,
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max_length=128
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).to(device)
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with torch.no_grad():
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outputs = model(**inputs)
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embedding = outputs.last_hidden_state.mean(dim=1).cpu().numpy()[0]
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embedding = normalize([embedding])[0]
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if verbose:
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print(f" [1/3] VibeThinker embedding: {len(embedding)}D (normalized)")
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train_embeddings = []
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train_labels = []
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for train_text, label in TRAIN_DATA:
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t_inputs = tokenizer(
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train_text,
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return_tensors="pt",
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padding=True,
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truncation=True,
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max_length=128
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).to(device)
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with torch.no_grad():
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t_outputs = model(**t_inputs)
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t_emb = t_outputs.last_hidden_state.mean(dim=1).cpu().numpy()[0]
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t_emb = normalize([t_emb])[0]
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train_embeddings.append(t_emb)
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train_labels.append(label)
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similarities = cosine_similarity([embedding], train_embeddings)[0]
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similarities = np.clip(similarities, -1.0, 1.0)
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if verbose:
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print(f" [2/3] Quantum similarity computed")
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positive_scores = []
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negative_scores = []
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for i, sim in enumerate(similarities):
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if np.isnan(sim):
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sim = 0.0
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if train_labels[i] == 1:
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positive_scores.append(sim)
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else:
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negative_scores.append(sim)
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positive_avg = np.mean(positive_scores) if positive_scores else 0
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negative_avg = np.mean(negative_scores) if negative_scores else 0
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diff = positive_avg - negative_avg
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if abs(diff) < 0.05:
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prediction = -1
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confidence = 0.0
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sentiment = "NEUTRAL"
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elif positive_avg > negative_avg:
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prediction = 1
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confidence = abs(diff)
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sentiment = "POSITIVE"
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else:
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prediction = 0
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confidence = abs(diff)
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sentiment = "NEGATIVE"
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elapsed = time.time() - start
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if verbose:
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print(f" [3/3] Classification: {sentiment}")
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print(f" Confidence: {confidence*100:.1f}%")
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print(f" Positive avg: {positive_avg:.3f}, Negative avg: {negative_avg:.3f}")
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print(f" Time: {elapsed:.2f}s")
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print(f"{'='*70}")
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return {
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'prediction': prediction,
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'sentiment': sentiment,
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'confidence': confidence,
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'time': elapsed,
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'scores': {
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'positive': float(positive_avg),
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'negative': float(negative_avg)
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}
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}
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if __name__ == "__main__":
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print("="*70)
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print("DEMONSTRATION")
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print("="*70)
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demo_texts = [
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"Random data v1",
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"Random data v2",
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"Random data v3",
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"Random data v4"
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]
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print("\nTesting on demo examples:\n")
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for text in demo_texts:
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result = predict(text, verbose=False)
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print(f"{result['sentiment']:<12} ({result['confidence']:>4.0%}) | {text[:50]}")
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print("\n" + "="*70)
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print("INTERACTIVE MODE")
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print("="*70)
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print("Enter text for analysis (or 'exit' to quit)\n")
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while True:
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try:
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user_input = input("Text: ")
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if user_input.lower() in ['exit', 'quit', 'q']:
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print("\nExiting Chronos o1 1.5B")
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break
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if user_input.strip():
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predict(user_input)
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except KeyboardInterrupt:
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print("\n\nExiting Chronos o1 1.5B")
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break
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except Exception as e:
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print(f"Error: {e}")
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print("\n" + "="*70)
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print("Thank you for using Chronos o1 1.5B!")
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print("="*70)
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