Hybrid Quantum-Classical Language Model: Training 2-Qubit Kernels on IBM Heron r2 for NLP Tasks

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 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: Extract 1536D embeddings from VibeThinker Normalize using L2 normalization Compute cosine similarity against training examples Apply quantum-inspired weighted voting 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 Expand training dataset to 100+ examples Implement true quantum kernel execution on IBM Quantum Increase quantum circuit complexity (3-4 qubits) Add error mitigation for quantum noise Support multi-language analysis 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 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. Downloads last month6 GGUF Model size 2B params Architecture qwen2 Chat template Hardware compatibility Log In to view the estimation 16-bit F16 3.56 GB Inference Providers NEW Text Generation This model isn't deployed by any Inference Provider. 🙋 Ask for provider support Model tree for squ11z1/Chronos-1.5B Base model Qwen/Qwen2.5-1.5B Finetuned Qwen/Qwen2.5-Math-1.5B Finetuned WeiboAI/VibeThinker-1.5B Quantized (30) this model