squ11z1/Chronos-1.5B

Chronos 1.5B - Quantum-Classical hybrid 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
• 2-qubit quantum circuits for enhanced feature space representation

This model demonstrates a proof-of-concept for hybrid quantum-classical machine learning.

Quantum Component Details

Feature	Implementation
Real quantum training	Quantum rotation angles were optimized on IBM Heron r2 (ibm_fez) in 2025
Saved quantum parameters	quantum_kernel.pkl — trained 2-qubit gate angles (pickle)
Quantum circuit definition	Available in k_train_quantum.npy / k_test_quantum.npy (future use)
Current inference	Classical simulation using the trained quantum angles (via cosine similarity)
True quantum execution (optional)	Possible by loading quantum_kernel.pkl + circuit files and running on IBM Quantum (example scripts will be added)

Architecture

Model Details

• Base Model: WeiboAI/VibeThinker-1.5B
• Architecture: Qwen2ForCausalLM
• Parameters: ~1.5B
• Context Length: 131,072 tokens
• Embedding Dimension: 1536
• Quantum Component: 2-qubit kernel
• Training Data: 8 quantum layers

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.

🧬 Also take a look at The Hypnos Family

Model	Parameters	Quantum Sources	Best For	Status
Hypnos-i2-32B	32B	3 (Matter + Light + Nucleus)	Production, Research	✅ Available
Hypnos-i1-8B	8B	1 (Matter only)	Edge, Experiments	✅ 10k+ Downloads

Start with Hypnos-i1-8B for lightweight quantum-regularized AI!

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]

    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

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 quantum layers for demonstration:

• 4 positive examples
• 4 negative 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

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 analysis
6. Fine-tune on domain-specific 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.