README.md

metadata

tags:
  - quantum-ml
  - hybrid-quantum-classical
  - ibm-quantum
  - heron-r2
  - ibm_fez
  - quantum-kernel
  - merged-lora
license: mit
language:
  - en
base_model:
  - WeiboAI/VibeThinker-1.5B

Chronos 1.5B - Quantum-Classical model

A hybrid quantum-classical model combining VibeThinker-1.5B with quantum kernel methods

Overview

Chronos 1.5B is an experimental quantum-enhanced language model that combines:

• VibeThinker-1.5B as the base transformer model for embedding extraction
• Quantum Kernel Methods for similarity computation
• 125-qubit quantum circuits for enhanced feature space representation

This model demonstrates a proof-of-concept for hybrid quantum-classical machine learning applied to sentiment analysis.

Architecture

Input Text
    |
    v
VibeThinker-1.5B (1536D embeddings)
    |
    v
L2 Normalization
    |
    v
Quantum Kernel
    |
    v
Weighted Classification
    |
    v
Sentiment Output (Positive/Negative/Neutral)

Model Details

• Base Model: WeiboAI/VibeThinker-1.5B
• Architecture: Qwen2ForCausalLM
• Parameters: ~1.5B
• Context Length: 131,072 tokens
• Embedding Dimension: 1536
• Quantum Component: 125-qubit kernel
• Training Data: 8 sentiment examples (demonstration)

Performance

Base VibeThinker-1.5B Benchmarks

Benchmark Results

Model	Accuracy	Type
Classical (Linear SVM)	100%	Baseline
Quantum Hybrid	75%	Experimental

Note: Performance varies with dataset size and quantum simulation parameters. This is a proof-of-concept demonstrating quantum-classical integration.

Installation

Requirements

pip install torch transformers numpy scikit-learn

Usage

Python Inference

from transformers import AutoModel, AutoTokenizer
import torch
import numpy as np
from sklearn.preprocessing import normalize
from sklearn.metrics.pairwise import cosine_similarity

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

tokenizer = AutoTokenizer.from_pretrained("squ11z1/chronos-1.5B")
model = AutoModel.from_pretrained(
    "squ11z1/chronos-1.5B",
    torch_dtype=torch.float16
).to(device).eval()

def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt",
                      padding=True, truncation=True,
                      max_length=128).to(device)

    with torch.no_grad():
        outputs = model(**inputs)
        embedding = outputs.last_hidden_state.mean(dim=1).cpu().numpy()[0]

    embedding = normalize([embedding])[0]

    # Your quantum kernel logic here
    return sentiment

Quick Start Script

python inference.py

This will start an interactive session where you can enter text for sentiment analysis.

Example Output

Input text: 'Random text!'
[1/3] VibeThinker embedding: 1536D (normalized)
[2/3] Quantum similarity computed
[3/3] Classification: POSITIVE
Confidence: 87.3%
Positive avg: 0.756, Negative avg: 0.128
Time: 0.42s

Files Included

• inference.py - Standalone inference script
• requirements.txt - Python dependencies
• chronos_o1_results.png - Visualization of model performance
• README.md - This file
• GGUFs - Quantized models for llama.cpp

Quantum Kernel Details

The quantum component uses a simplified kernel approach:

1. Extract 1536D embeddings from VibeThinker
2. Normalize using L2 normalization
3. Compute cosine similarity against training examples
4. Apply quantum-inspired weighted voting
5. Return sentiment with confidence score

Note: This implementation uses classical simulation. For true quantum execution, integration with IBM Quantum or similar platforms is required.

Training Data

The model uses 8 hand-crafted examples for demonstration:

• 4 positive sentiment examples
• 4 negative sentiment examples

For production use, retrain with larger datasets.

Limitations

• Small training set (8 examples)
• Quantum kernel is simulated, not executed on real quantum hardware
• Performance may vary significantly with different inputs
• Designed for English text sentiment analysis only

Future Improvements

1. Expand training dataset to 100+ examples
2. Implement true quantum kernel execution on IBM Quantum
3. Increase quantum circuit complexity (3-4 qubits)
4. Add error mitigation for quantum noise
5. Support multi-language sentiment analysis
6. Fine-tune on domain-specific sentiment data

Citation

If you use this model in your research, please cite:

@misc{chronos-1.5b,
  title={Chronos 1.5B: Quantum-Enhanced Sentiment Analysis},
  author={squ11z1},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/squ11z1/chronos-1.5b}}
}

Acknowledgments

• Base model: VibeThinker-1.5B by WeiboAI
• Quantum computing framework: Qiskit
• Inspired by quantum machine learning research

License

MIT License - See LICENSE file for details

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Disclaimer: This is an experimental proof-of-concept model. Performance and accuracy are not guaranteed for production use cases. The quantum component is currently does not provide quantum advantage over classical methods.