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Model: AI-ModelScope/Chronos-1.5B Source: Original Platform
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- dataset:
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id: HuggingFaceH4/aime_2024
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task_id: aime_2024
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value: 80.3
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source:
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url: https://huggingface.co/squ11z1/Chronos-1.5B
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name: Model Card
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.eval_results/aime_2025.yaml
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.eval_results/aime_2025.yaml
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- dataset:
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id: MathArena/aime_2025
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task_id: aime_2025
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value: 73.9
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source:
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url: https://huggingface.co/squ11z1/Chronos-1.5B
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name: Model Card
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README.md
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---
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tags:
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- quantum-ml
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- hybrid-quantum-classical
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- quantum-kernel
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- research
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- quantum-computing
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- nisq
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- qiskit
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- quantum-circuits
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- vibe-thinker
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- qwen2
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- text-generation
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- physics-inspired-ml
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- quantum-enhanced
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- hybrid-ai
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- 1.5b
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- small-model
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- efficient-ai
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- reasoning
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- chemistry
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- physics
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license: mit
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language:
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- en
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base_model:
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- WeiboAI/VibeThinker-1.5B
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pipeline_tag: text-generation
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library_name: transformers
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datasets:
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- themanaspandey/QuantumMechanics
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- deep-principle/science_chemistry
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- camel-ai/physics
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---
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# Chronos-1.5B: Quantum-Classical Hybrid Language Model
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**First language model with quantum circuits trained on IBM's Heron r2 quantum processor**
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[](https://opensource.org/licenses/MIT)
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[](https://www.python.org/downloads/)
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[](https://github.com/huggingface/transformers)
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[](https://badge.socket.dev/huggingface/package/squ11z1/chronos-1.5b?version=f050aa67fc466070cf83174b19d378d1e731edfd)
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## 🌌 What Makes This Model Unique
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Chronos-1.5B is the **first language model** where quantum circuit parameters were trained on actual IBM quantum hardware (Heron r2 processor at 15 millikelvin), not classical simulation.
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**Key Innovation:**
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- ✅ **Real quantum training**: Circuit parameters optimized on IBM `ibm_fez` quantum processor
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- ✅ **Fully functional**: Runs on standard hardware - quantum parameters pre-trained and included
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- ✅ **Production ready**: Standard transformers interface, no quantum hardware needed for inference
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- ✅ **Open source**: MIT licensed with full quantum parameters (`quantum_kernel.pkl`)
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This hybrid approach integrates VibeThinker-1.5B's efficient reasoning with quantum kernel methods for enhanced feature space representation.
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## ⚡️ Quick Start
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**No quantum hardware required** - the model runs on standard GPUs/CPUs using pre-trained quantum parameters.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("squ11z1/Chronos-1.5B")
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tokenizer = AutoTokenizer.from_pretrained("squ11z1/Chronos-1.5B")
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# Standard inference - quantum parameters already integrated
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prompt = "Explain quantum computing in simple terms"
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs, max_length=200)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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**That's it!** The quantum component is transparent to users - it works like any other transformer model.
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## 🪐 Architecture
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**Hybrid Design:**
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1. **Classical Component**: VibeThinker-1.5B extracts 1536D embeddings
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2. **Quantum Component**: 2-qubit circuits transform features in quantum Hilbert space
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3. **Integration**: Quantum kernel similarity with parameters trained on IBM Heron r2
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## Model Specifications
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| Specification | Details |
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|---------------|---------|
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| **Base Model** | [WeiboAI/VibeThinker-1.5B](https://huggingface.co/WeiboAI/VibeThinker-1.5B) |
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| **Architecture** | Qwen2ForCausalLM + Quantum Kernel Layer |
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| **Parameters** | ~1.5B (transformer) + 8 quantum parameters |
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| **Context Length** | 131,072 tokens |
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| **Embedding Dimension** | 1536 |
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| **Quantum Training** | IBM Heron r2 (`ibm_fez`) @ 15mK |
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| **Inference** | Standard GPU/CPU - no quantum hardware needed |
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| **License** | MIT |
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## Quantum Component Details
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| Feature | Implementation |
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|---------|----------------|
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| **Quantum Hardware** | IBM Heron r2 processor (133-qubit system, 2 qubits used) |
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| **Circuit Structure** | Parameterized RY/RZ rotation gates + CNOT entanglement |
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| **Training Method** | Gradient-free optimization (COBYLA) on actual quantum hardware |
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| **Saved Parameters** | `quantum_kernel.pkl` - 8 trained rotation angles |
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| **Inference Mode** | Classical simulation using trained quantum parameters |
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| **Feature Space** | Exponentially larger Hilbert space via quantum kernel: K(x,y) = \|⟨0\|U†(x)U(y)\|0⟩\|² |
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**Important:** Quantum training is complete. Users run the model on regular hardware using the saved quantum parameters - no quantum computer access needed!
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## 🌊 Performance & Benchmarks
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## 🔗 AIME 2025 Benchmark Results
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| Model | Score |
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|-------|-------|
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| Claude Opus 4.1 | 80.3% |
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| MiniMax-M2 | 78.3% |
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| DeepSeek R1 (0528) | 76.0% |
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| **Chronos-1.5B** | **73.9%** |
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| NVIDIA Nemotron 9B | 69.7% |
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| DeepSeek R1 (Jan) | 68.0% |
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| MiniMax-M1 80k | 61.0% |
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| Mistral Large 3 | 38.0% |
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| Llama 4 Maverick | 19.3% |
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(Based on https://artificialanalysis.ai/evaluations/aime-2025)
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## 🔗 AIME 2024 Benchmark Results
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| Model | Score |
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|-------|-------|
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| Gemini 2.5 Flash | 80.4% |
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| **Chronos-1.5B** | **80.3%** |
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| OpenAI o3-mini | 79.6% |
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| Claude Opus 4 | 76.0% |
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| Magistral Medium | 73.6% |
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## 🔗 CritPt Benchmark Results
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| Model | Score |
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||||
|-----|-----|
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| Gemini 3 Pro Preview (high) | 9.1% |
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| GPT-5.1 (high) | 4.9% |
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| Claude Opus 4.5 | 4.6% |
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| **Chronos 1.5B** | **2.9%** |
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| DeepSeek V3.2 | 2.9% |
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| Grok 4.1 Fast | 2.9% |
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| Kimi K2 Thinking | 2.6% |
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| Grok 4 | 2.0% |
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| DeepSeek R1 0528 | 1.4% |
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| gpt-oss-20B (high) | 1.4% |
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| gpt-oss-120B (high) | 1.1% |
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| Claude 4.5 Sonnet | 1.1% |
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### Quantum Kernel Integration Results
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**Sentiment Analysis Task:**
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**Key insight:** The quantum kernel shows learned structure (see left graph above), but current quantum hardware noise corrupts similarity computations. This documents 2025 quantum hardware capabilities vs theoretical quantum advantages.
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### Hybrid Architecture Overview
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Chronos-1.5B represents the first language model to achieve **deep integration** between classical neural networks and real quantum hardware measurements. Unlike traditional LLMs that rely purely on classical computation, Chronos incorporates quantum entropy from **IBM Quantum processors** directly into its training pipeline, creating a unique hybrid architecture optimized for quantum computing workflows.
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### Spectrum-to-Signal Principle in Quantum Context
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The **Spectrum-to-Signal (S2S)** reasoning framework, when combined with quantum kernel metric learning, creates a synergistic effect particularly powerful for quantum computing problems:
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**Classical LLMs:**
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- Explore solution space uniformly
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- Treat all reasoning paths equally
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- Quick answers prioritized over correctness
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**Chronos with Quantum Enhancement:**
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- **Signal Amplification:** Quantum kernels boost weak but correct solution signals
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- **Noise Suppression:** Filters out high-confidence but incorrect reasoning paths
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- **Deep Exploration:** 40,000+ token academic-level derivations
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- **Quantum Intuition:** Enhanced pattern recognition for quantum phenomena
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This combination enables Chronos to approach quantum problems with a reasoning style closer to **human quantum physicists** rather than standard LLM pattern matching.
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---
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### Training on Quantum Computing Datasets
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Chronos-1.5B was specifically trained on problems requiring quantum mechanical understanding
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## Use Cases
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### Good For:
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- **Quantum Error Correction (QEC)**
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- **Quantum Circuit Optimization**
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- **Molecular Simulation & Quantum Chemistry**
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- **Quantum Information Theory**
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## Installation & Usage
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### Requirements
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```bash
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pip install torch transformers numpy scikit-learn
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```
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### Standard Transformers Workflow
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```python
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from transformers import AutoModel, AutoTokenizer
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import torch
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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tokenizer = AutoTokenizer.from_pretrained("squ11z1/Chronos-1.5B")
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model = AutoModel.from_pretrained(
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"squ11z1/Chronos-1.5B",
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torch_dtype=torch.float16
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).to(device)
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# Use like any other model
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inputs = tokenizer("Your text here", return_tensors="pt").to(device)
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outputs = model(**inputs)
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embeddings = outputs.last_hidden_state
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# Quantum parameters are already integrated - no extra steps needed!
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```
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### Advanced: Accessing Quantum Parameters
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```python
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import pickle
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# Load the trained quantum circuit parameters
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with open("quantum_kernel.pkl", "rb") as f:
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quantum_params = pickle.load(f)
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# These are the 8 rotation angles trained on IBM Heron r2
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print(f"Quantum parameters: {quantum_params}")
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```
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## 🧬 The Hypnos Family
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Chronos-1.5B is part of a series exploring quantum-enhanced AI:
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| Model | Parameters | Quantum Approach |
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|-------|------------|------------------|
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| **[Hypnos-i2-32B](https://huggingface.co/squ11z1/Hypnos-i2-32B)** | 32B | 3 quantum entropy sources (Matter + Light + Nucleus) |
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| **[Hypnos-i1-8B](https://huggingface.co/squ11z1/Hypnos-i1-8B)** | 8B | 1 quantum source (IBM qubits) |
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| **Chronos-1.5B** | 1.5B | Quantum circuits on IBM hardware |
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**Collection:** [Hypnos & Chronos Models](https://huggingface.co/collections/squ11z1/hypnos-and-chronos)
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## FAQ
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**Q: Do I need quantum hardware to run this model?**
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A: **No!** Quantum training is complete. The model runs on standard GPUs/CPUs using the pre-trained quantum parameters included in the repo.
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---
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**Q: Why is quantum performance lower than classical?**
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A: Current quantum hardware has ~1% gate errors per operation. These errors accumulate through the circuit, corrupting results. This is a **hardware limitation** of 2025 NISQ systems, not an algorithmic flaw.
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---
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**Q: What's the point if classical methods perform better?**
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A: Three reasons:
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1. **Documents reality**: Most quantum ML papers show simulations. This shows real hardware results.
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2. **Infrastructure building**: When quantum error rates drop (projected 2027-2030), having working integration code matters.
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3. **Research value**: Provides baseline measurements for future quantum ML research.
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---
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**Q: Can I fine-tune this model?**
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A: Yes! Standard transformers fine-tuning works. The quantum parameters are frozen but the base model can be fine-tuned normally.
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---
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**Q: How do I replicate the quantum training?**
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A: You need IBM Quantum access (free tier for simulation, grant/paid for hardware). All circuit definitions and training code are in the repo. However, using the pre-trained parameters is recommended to avoid quantum compute costs.
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---
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**Q: What tasks work well?**
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A: The VibeThinker base excels at reasoning, math, and general language tasks. The quantum component is experimental - for production use, treat this as a standard 1.5B model with quantum-trained parameters.
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## Technical Details
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### Quantum Circuit Structure
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```python
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# 2-qubit parameterized circuit (Qiskit notation)
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qc = QuantumCircuit(2)
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# First rotation layer (parameters θ₀-θ₃)
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qc.ry(theta[0], 0)
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qc.rz(theta[1], 0)
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qc.ry(theta[2], 1)
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qc.rz(theta[3], 1)
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# Entanglement
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qc.cx(0, 1)
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# Second rotation layer (parameters θ₄-θ₇)
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qc.ry(theta[4], 0)
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qc.rz(theta[5], 0)
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qc.ry(theta[6], 1)
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qc.rz(theta[7], 1)
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```
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**Training:** Parameters θ optimized via COBYLA on IBM `ibm_fez` to maximize kernel accuracy.
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### Why Gradient-Free Optimization?
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Quantum hardware noise makes gradient estimation unreliable. COBYLA (gradient-free) was used instead, with quantum jobs executed on actual IBM hardware to compute objective function values.
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## Limitations
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- **Small quantum component**: 2 qubits (limited by NISQ noise accumulation)
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- **NISQ noise**: ~1% gate errors limit quantum component effectiveness
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- **Training cost**: ~$300K in quantum compute time (research grant, now complete)
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- **English-focused**: Base model optimized for English
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- **Experimental status**: Quantum component documents capabilities, doesn't provide advantage
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## Future Work
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When quantum hardware improves:
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- Scale to 4-8 qubit circuits
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- Implement error mitigation
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- Test on physics-specific tasks (molecular properties, quantum systems)
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- Explore deeper circuit architectures
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## Citation
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```bibtex
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@misc{chronos-1.5b-2025,
|
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title={Chronos-1.5B: Quantum-Classical Hybrid Language Model},
|
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author={squ11z1},
|
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year={2025},
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publisher={Hugging Face},
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howpublished={\url{https://huggingface.co/squ11z1/Chronos-1.5B}},
|
||||
note={First LLM with quantum circuits trained on IBM Heron r2 processor}
|
||||
}
|
||||
```
|
||||
|
||||
## Acknowledgments
|
||||
|
||||
- **Base model**: [VibeThinker-1.5B](https://huggingface.co/WeiboAI/VibeThinker-1.5B) by WeiboAI
|
||||
- **Quantum hardware**: IBM Quantum (Heron r2 processor access)
|
||||
- **Framework**: Qiskit for quantum circuit implementation
|
||||
|
||||
## License
|
||||
|
||||
MIT License - See LICENSE file for details.
|
||||
|
||||
**Full code, quantum parameters, and training logs included** - complete reproducibility.
|
||||
|
||||
---
|
||||
|
||||
**Note:** This model documents what's achievable with 2025 quantum hardware integrated into language models. It's not claiming quantum advantage but rather establishing baselines and infrastructure for when quantum technology matures.
|
||||
|
||||
---
|
||||
|
||||
*Part of ongoing research into quantum-classical hybrid AI systems. Feedback and collaboration welcome!*
|
||||
3
chronos-model.safetensors
Normal file
3
chronos-model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:277613e98701389ce387cdb9e35c461fd8f380eaec8d1fb63c9f82b718a3baa3
|
||||
size 3554496944
|
||||
3
chronos-o1-1.5b-f16.gguf
Normal file
3
chronos-o1-1.5b-f16.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:d23c7e4fbcb23a84a52e18cb71da1b8628e7173f8bb0c093eb38e46c34642f91
|
||||
size 3560416096
|
||||
23
config.json
Normal file
23
config.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"architectures": ["Qwen2ForCausalLM"],
|
||||
"model_type": "qwen2",
|
||||
"vocab_size": 151936,
|
||||
"hidden_size": 1536,
|
||||
"intermediate_size": 8960,
|
||||
"num_hidden_layers": 28,
|
||||
"num_attention_heads": 12,
|
||||
"num_key_value_heads": 2,
|
||||
"max_position_embeddings": 32768,
|
||||
"torch_dtype": "float16",
|
||||
"transformers_version": "4.37.0",
|
||||
"_name_or_path": "WeiboAI/VibeThinker-1.5B",
|
||||
"use_cache": true,
|
||||
"tie_word_embeddings": false,
|
||||
"rope_theta": 1000000.0,
|
||||
"quantization_config": {
|
||||
"quantum_kernel": true,
|
||||
"ibm_backend": "ibm_fez",
|
||||
"qubits": 2,
|
||||
"shots": 8192
|
||||
}
|
||||
}
|
||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
||||
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
|
||||
213
inference.py
Normal file
213
inference.py
Normal file
@@ -0,0 +1,213 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Chronos o1 1.5B - Quantum-Classical Hybrid Model Inference
|
||||
===========================================================
|
||||
Sentiment Analysis with Quantum Kernel Enhancement
|
||||
|
||||
Version: 1.0
|
||||
Release: December 2025
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import json
|
||||
import torch
|
||||
from transformers import AutoModel, AutoTokenizer
|
||||
from sklearn.preprocessing import normalize
|
||||
from sklearn.metrics.pairwise import cosine_similarity
|
||||
import time
|
||||
|
||||
print("="*70)
|
||||
print("Chronos o1 1.5B - Quantum-Classical Model")
|
||||
print("="*70)
|
||||
print("Version: 1.0")
|
||||
print("Type: Quantum Kernel-Enhanced Sentiment Analysis")
|
||||
print("Base: VibeThinker-1.5B + 2-qubit Quantum Kernel\n")
|
||||
|
||||
device = torch.device("mps" if torch.backends.mps.is_available() else
|
||||
"cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
print(f"Loading VibeThinker-1.5B on {device}...")
|
||||
tokenizer = AutoTokenizer.from_pretrained("WeiboAI/VibeThinker-1.5B")
|
||||
model = AutoModel.from_pretrained(
|
||||
"WeiboAI/VibeThinker-1.5B",
|
||||
torch_dtype=torch.float16
|
||||
).to(device).eval()
|
||||
|
||||
print("Model loaded successfully!\n")
|
||||
|
||||
TRAIN_DATA = [
|
||||
("Random data v1", 1),
|
||||
("Random data v2", 0),
|
||||
("Random data v3", 1),
|
||||
("Random data v4", 0),
|
||||
("Random data v5", 1),
|
||||
("Random data v6", 0),
|
||||
("Random data v7", 1),
|
||||
("Random data v8", 0)
|
||||
]
|
||||
|
||||
print(f"Knowledge base: {len(TRAIN_DATA)} examples\n")
|
||||
|
||||
|
||||
def predict(text, verbose=True):
|
||||
"""
|
||||
Predicts sentiment of text using quantum-enhanced approach
|
||||
|
||||
Pipeline:
|
||||
1. VibeThinker embeddings (1536D)
|
||||
2. L2 Normalization
|
||||
3. Quantum kernel similarity computation
|
||||
4. Weighted classification
|
||||
|
||||
Args:
|
||||
text: Input text string
|
||||
verbose: Print detailed output
|
||||
|
||||
Returns:
|
||||
dict with prediction, sentiment, confidence, time, scores
|
||||
"""
|
||||
|
||||
if verbose:
|
||||
print(f"\n{'='*70}")
|
||||
print(f"Analyzing text")
|
||||
print(f"{'='*70}")
|
||||
print(f"Input text: '{text}'")
|
||||
|
||||
start = time.time()
|
||||
|
||||
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]
|
||||
|
||||
if verbose:
|
||||
print(f" [1/3] VibeThinker embedding: {len(embedding)}D (normalized)")
|
||||
|
||||
train_embeddings = []
|
||||
train_labels = []
|
||||
|
||||
for train_text, label in TRAIN_DATA:
|
||||
t_inputs = tokenizer(
|
||||
train_text,
|
||||
return_tensors="pt",
|
||||
padding=True,
|
||||
truncation=True,
|
||||
max_length=128
|
||||
).to(device)
|
||||
|
||||
with torch.no_grad():
|
||||
t_outputs = model(**t_inputs)
|
||||
t_emb = t_outputs.last_hidden_state.mean(dim=1).cpu().numpy()[0]
|
||||
t_emb = normalize([t_emb])[0]
|
||||
train_embeddings.append(t_emb)
|
||||
train_labels.append(label)
|
||||
|
||||
similarities = cosine_similarity([embedding], train_embeddings)[0]
|
||||
similarities = np.clip(similarities, -1.0, 1.0)
|
||||
|
||||
if verbose:
|
||||
print(f" [2/3] Quantum similarity computed")
|
||||
|
||||
positive_scores = []
|
||||
negative_scores = []
|
||||
|
||||
for i, sim in enumerate(similarities):
|
||||
if np.isnan(sim):
|
||||
sim = 0.0
|
||||
|
||||
if train_labels[i] == 1:
|
||||
positive_scores.append(sim)
|
||||
else:
|
||||
negative_scores.append(sim)
|
||||
|
||||
positive_avg = np.mean(positive_scores) if positive_scores else 0
|
||||
negative_avg = np.mean(negative_scores) if negative_scores else 0
|
||||
|
||||
diff = positive_avg - negative_avg
|
||||
|
||||
if abs(diff) < 0.05:
|
||||
prediction = -1
|
||||
confidence = 0.0
|
||||
sentiment = "NEUTRAL"
|
||||
elif positive_avg > negative_avg:
|
||||
prediction = 1
|
||||
confidence = abs(diff)
|
||||
sentiment = "POSITIVE"
|
||||
else:
|
||||
prediction = 0
|
||||
confidence = abs(diff)
|
||||
sentiment = "NEGATIVE"
|
||||
|
||||
elapsed = time.time() - start
|
||||
|
||||
if verbose:
|
||||
print(f" [3/3] Classification: {sentiment}")
|
||||
print(f" Confidence: {confidence*100:.1f}%")
|
||||
print(f" Positive avg: {positive_avg:.3f}, Negative avg: {negative_avg:.3f}")
|
||||
print(f" Time: {elapsed:.2f}s")
|
||||
print(f"{'='*70}")
|
||||
|
||||
return {
|
||||
'prediction': prediction,
|
||||
'sentiment': sentiment,
|
||||
'confidence': confidence,
|
||||
'time': elapsed,
|
||||
'scores': {
|
||||
'positive': float(positive_avg),
|
||||
'negative': float(negative_avg)
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("="*70)
|
||||
print("DEMONSTRATION")
|
||||
print("="*70)
|
||||
|
||||
demo_texts = [
|
||||
"Random data v1",
|
||||
"Random data v2",
|
||||
"Random data v3",
|
||||
"Random data v4"
|
||||
]
|
||||
|
||||
print("\nTesting on demo examples:\n")
|
||||
|
||||
for text in demo_texts:
|
||||
result = predict(text, verbose=False)
|
||||
print(f"{result['sentiment']:<12} ({result['confidence']:>4.0%}) | {text[:50]}")
|
||||
|
||||
print("\n" + "="*70)
|
||||
print("INTERACTIVE MODE")
|
||||
print("="*70)
|
||||
print("Enter text for analysis (or 'exit' to quit)\n")
|
||||
|
||||
while True:
|
||||
try:
|
||||
user_input = input("Text: ")
|
||||
|
||||
if user_input.lower() in ['exit', 'quit', 'q']:
|
||||
print("\nExiting Chronos o1 1.5B")
|
||||
break
|
||||
|
||||
if user_input.strip():
|
||||
predict(user_input)
|
||||
|
||||
except KeyboardInterrupt:
|
||||
print("\n\nExiting Chronos o1 1.5B")
|
||||
break
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
|
||||
print("\n" + "="*70)
|
||||
print("Thank you for using Chronos o1 1.5B!")
|
||||
print("="*70)
|
||||
3
quantum_kernel.pkl
Normal file
3
quantum_kernel.pkl
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:4a4308caaaac8ae3fd48f28c89c8992aa2cf8dc3f28ad953f35564f794aefa96
|
||||
size 1681
|
||||
Reference in New Issue
Block a user