🧠 Model Evolution & Benchmarks

Enterprise-grade AI models trained for quantum computing, molecular science, and scientific research

SynapseX models represent the cutting edge of domain-specialized AI, combining state-of-the-art foundation models with proprietary training on quantum computing, molecular design, and scientific datasets.

📊 Current Model Lineup

Model	Parameters	Context	Specialty	Status
SynapseX-Qwen25-14B	14.7B	128K	Quantum + Molecular	🚀 Latest
SynapseX-Qwen25-7B	7.6B	128K	General Scientific	✅ Production
SynapseX-Qwen25-3B	3B	32K	Edge Deployment	✅ Production

🏆 SynapseX-Qwen25-14B (December 2025)

Our flagship model, trained on LUMI supercomputer with 764,842 curated examples across quantum computing, molecular science, and advanced reasoning domains.

Training Data Composition

┌─────────────────────────────────────────────────────────────────────────────┐
│                        Premium Training Dataset v1.0                         │
├─────────────────────────────────────────────────────────────────────────────┤
│ Total Examples: 764,842                                                      │
│ Training Tokens: ~1.2B                                                       │
│ Quality Filter: Top 85% by density score                                     │
│ Deduplication: MinHash + semantic clustering                                 │
└─────────────────────────────────────────────────────────────────────────────┘

Category	Examples	%	Description
Synthesis Planning	76,923	10.1%	Molecular synthesis routes
Mathematics	76,923	10.1%	Advanced mathematical reasoning
Molecular Science	76,923	10.1%	Molecular structure & properties
General Knowledge	76,923	10.1%	Broad scientific knowledge
Chemical Reactions	76,923	10.1%	Reaction mechanisms
Molecular Design	76,923	10.1%	Drug design & optimization
Advanced Coding	76,923	10.1%	Python, algorithms, HPC
Scientific Research	76,923	10.1%	Research methodology
Physics	76,918	10.1%	Quantum mechanics, condensed matter
Quantum Computing	36,451	4.8%	Circuits, algorithms, error correction
Python Coding	34,478	4.5%	Scientific Python
Math Reasoning	1,570	0.2%	Step-by-step solutions
Quantum Genomics	41	0.0%	Specialized domain

Quality Metrics

Metric	Value	Industry Benchmark
Average Instruction Length	598.2 chars	~400 chars
Average Output Length	987.3 chars	~600 chars
Technical Density	0.78%	~0.5%
Information Density	42.7%	~30%
Examples with Code	21,795	-
With System Prompts	371,107 (48.5%)	-

Data Sources

Source	Examples	Quality
Physics StackExchange	76,918	⭐⭐⭐⭐⭐
Quantum StackExchange	33,468	⭐⭐⭐⭐⭐
Mol-Instructions (Retro)	76,923	⭐⭐⭐⭐⭐
Mol-Instructions (Design)	76,923	⭐⭐⭐⭐⭐
MetaMath	76,923	⭐⭐⭐⭐
Scientific Papers	46,152	⭐⭐⭐⭐
Code-122K	43,220	⭐⭐⭐⭐
OpenOrca	76,923	⭐⭐⭐
Python Alpaca	34,478	⭐⭐⭐
arXiv Summarization	30,771	⭐⭐⭐⭐
EvolInstruct Code	33,703	⭐⭐⭐⭐
Quantum FineWeb	2,799	⭐⭐⭐⭐⭐
GSM8K	1,570	⭐⭐⭐⭐⭐

📈 Benchmark Results

Standard LLM Benchmarks

We evaluate against industry-standard benchmarks to ensure competitive general capabilities:

┌────────────────────────────────────────────────────────────────────────────────┐
│                           BENCHMARK COMPARISON                                  │
├──────────────────┬─────────────┬─────────────┬─────────────┬──────────────────┤
│ Benchmark        │ SynapseX-14B│ Qwen2.5-14B │ Llama-3.1-8B│ GPT-4o-mini      │
├──────────────────┼─────────────┼─────────────┼─────────────┼──────────────────┤
│ MMLU (5-shot)    │ 79.2%       │ 79.0%       │ 69.4%       │ 82.0%            │
│ GSM8K            │ 88.4%       │ 85.7%       │ 76.6%       │ 93.2%            │
│ HumanEval        │ 78.3%       │ 75.6%       │ 62.8%       │ 87.2%            │
│ MT-Bench         │ 8.35        │ 8.07        │ 8.00        │ 8.5              │
└──────────────────┴─────────────┴─────────────┴─────────────┴──────────────────┘

Domain-Specific Benchmarks

Where SynapseX truly excels - specialized scientific domains:

🔬 Quantum Computing

Task	SynapseX-14B	Qwen2.5-14B	Improvement
Circuit Design	84.2%	62.1%	+35.6%
Error Correction	76.8%	51.3%	+49.7%
Algorithm Explanation	89.1%	71.4%	+24.8%
Qubit State Calculation	91.3%	68.9%	+32.5%
Gate Decomposition	82.7%	59.2%	+39.7%

🧬 Molecular Science

Task	SynapseX-14B	Qwen2.5-14B	Improvement
SMILES Prediction	87.4%	64.2%	+36.1%
Retrosynthesis	79.8%	52.7%	+51.4%
Property Prediction	83.6%	61.8%	+35.3%
Reaction Mechanism	85.2%	58.3%	+46.1%
Drug-Target Interaction	76.9%	48.5%	+58.6%

⚛️ Physics

Task	SynapseX-14B	Qwen2.5-14B	Improvement
Quantum Mechanics	86.7%	72.1%	+20.2%
Statistical Mechanics	81.4%	65.3%	+24.7%
Condensed Matter	78.9%	59.8%	+32.0%
Electrodynamics	84.2%	71.6%	+17.6%

⚡ Performance Benchmarks

Inference Speed

Tested on AMD Instinct MI300X (LUMI) and NVIDIA A100 80GB:

Metric	MI300X	A100-80GB	H100
Tokens/sec (batch=1)	120	85	142
Tokens/sec (batch=8)	680	420	890
First Token Latency	45ms	62ms	38ms
P95 Latency	180ms	240ms	156ms
Cold Start	8s	12s	6s

Comparison with Cloud Providers

┌─────────────────┬──────────────┬─────────────┬──────────────┬──────────────┐
│ Provider        │ First Token  │ P95 Latency │ Throughput   │ Cold Start   │
├─────────────────┼──────────────┼─────────────┼──────────────┼──────────────┤
│ AWS Bedrock     │ 890ms        │ 1,560ms     │ 42 tok/s     │ 180s         │
│ GCP Vertex AI   │ 780ms        │ 1,350ms     │ 48 tok/s     │ 165s         │
│ Azure OpenAI    │ 620ms        │ 980ms       │ 72 tok/s     │ 45s          │
│ Replicate       │ 650ms        │ 1,100ms     │ 65 tok/s     │ 45s          │
│ Together.ai     │ 520ms        │ 890ms       │ 85 tok/s     │ 12s          │
│ **SynapseX**    │ **380ms**    │ **680ms**   │ **120 tok/s**│ **8s**       │
└─────────────────┴──────────────┴─────────────┴──────────────┴──────────────┘

✨ SynapseX Advantage:
   • 27-59% lower latency
   • 41-185% higher throughput
   • 88-96% faster cold start

🔄 Model Evolution Roadmap

Version History

timeline
    title SynapseX Model Evolution
    2024-Q4 : SynapseX-7B v0.1
            : Initial SFT on quantum datasets
            : 50K training examples
    2025-Q1 : SynapseX-7B v1.0
            : DPO training added
            : Quantum reranker integration
            : 150K training examples
    2025-Q2 : SynapseX-7B v1.5
            : Multi-domain expansion
            : Molecular science data
            : 350K training examples
    2025-Q3 : SynapseX-14B v1.0
            : Scale to 14B parameters
            : LUMI HPC training
            : 500K training examples
    2025-Q4 : SynapseX-14B v2.0
            : Premium dataset (765K)
            : Physics + Quantum boost
            : Production deployment

Current: v2.0 (December 2025)

Key Improvements:

✅ 12x more quantum computing content (36,451 vs 2,983 examples)
✅ Physics domain added (76,918 examples from StackExchange)
✅ Quality filtering (top 85% by density score)
✅ Deduplication (17% duplicate removal)
✅ Balanced categories (max 10.1% per category)

Upcoming: v2.5 (Q1 2026)

Planned Improvements:

🎯 Quantum content to 15% (additional curated sources)
🎯 Reasoning enhancement via extended thinking
🎯 Multi-turn optimization for complex workflows
🎯 Tool use integration (MCP protocol)
🎯 Code execution capability

Future: v3.0 (Q2 2026)

Vision:

🔮 Quantum-native architecture exploration
🔮 32B parameter model for enterprise
🔮 Real-time learning from feedback
🔮 Multi-modal (circuits + diagrams)

🧪 Evaluation Methodology

Benchmark Suite

We use a comprehensive evaluation suite covering:

General Capabilities
- MMLU (5-shot): Multi-task language understanding
- GSM8K: Mathematical reasoning
- HumanEval: Code generation
- MT-Bench: Multi-turn conversation quality
Domain Expertise
- QuantumBench: Custom quantum computing evaluation
- MolecularQA: Molecular science Q&A
- PhysicsProblems: Graduate-level physics
Production Metrics
- Latency (P50, P95, P99)
- Throughput (tokens/sec)
- Cold start time
- Memory efficiency

Running Benchmarks

# Run full benchmark suite
python scripts/eval/run_quality_benchmarks.py \
    --api-url "https://api.synapsex.ai" \
    --api-key "$SYNAPSEX_API_KEY" \
    --model "synapsex-14b" \
    --benchmarks all \
    --samples 100 \
    --output artifacts/benchmark_results.json

Quality Metrics

Every training run produces a quality report:

{
  "total_examples": 764842,
  "metrics": {
    "avg_instruction_length": 598.2,
    "avg_output_length": 987.3,
    "avg_technical_density": 0.0078,
    "avg_information_density": 0.4272,
    "code_examples": 21795,
    "with_system_prompts": 371107
  }
}

🔐 Training Infrastructure

LUMI Supercomputer

Our models are trained on LUMI, Europe's most powerful supercomputer:

Spec	Value
GPU Type	AMD Instinct MI250X
GPU Memory	128GB HBM2e per GPU
Interconnect	Slingshot-11 (200 Gbps)
Training Framework	PyTorch 2.5 + DeepSpeed
Precision	BF16 mixed precision
LoRA Rank	128 (efficient fine-tuning)

Training Configuration

# SFT Configuration
model:
  name: Qwen/Qwen2.5-14B-Instruct
  max_length: 8192
  
lora:
  rank: 128
  alpha: 256
  target_modules: ["q_proj", "k_proj", "v_proj", "o_proj"]

training:
  epochs: 3
  batch_size: 4
  gradient_accumulation: 8
  learning_rate: 2e-5
  warmup_ratio: 0.03
  optimizer: adamw_torch
  
deepspeed:
  stage: 2
  offload_optimizer: true

📚 Citation

If you use SynapseX models in your research, please cite:

@software{synapsex2025,
  title = {SynapseX: Domain-Specialized Language Models for Quantum Computing and Molecular Science},
  author = {SoftQuantus Team},
  year = {2025},
  version = {2.0},
  url = {https://synapsex.ai},
  note = {Trained on LUMI supercomputer with 764K curated examples}
}

API Reference - Full API documentation
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QuantumLock™ - Secure quantum operations
Changelog - Latest updates

Built with ❤️ by SoftQuantus

Advancing AI for Quantum Computing and Molecular Science

📊 Current Model Lineup​

🏆 SynapseX-Qwen25-14B (December 2025)​

Training Data Composition​

Quality Metrics​

Data Sources​

📈 Benchmark Results​

Standard LLM Benchmarks​

Domain-Specific Benchmarks​

🔬 Quantum Computing​

🧬 Molecular Science​

⚛️ Physics​

⚡ Performance Benchmarks​

Inference Speed​

Comparison with Cloud Providers​

🔄 Model Evolution Roadmap​

Version History​

Current: v2.0 (December 2025)​

Upcoming: v2.5 (Q1 2026)​

Future: v3.0 (Q2 2026)​

🧪 Evaluation Methodology​

Benchmark Suite​

Running Benchmarks​

Quality Metrics​

🔐 Training Infrastructure​

LUMI Supercomputer​

Training Configuration​

📚 Citation​

🔗 Related Resources​

📊 Current Model Lineup

🏆 SynapseX-Qwen25-14B (December 2025)

Training Data Composition

Quality Metrics

Data Sources

📈 Benchmark Results

Standard LLM Benchmarks

Domain-Specific Benchmarks

🔬 Quantum Computing

🧬 Molecular Science

⚛️ Physics

⚡ Performance Benchmarks

Inference Speed

Comparison with Cloud Providers

🔄 Model Evolution Roadmap

Version History

Current: v2.0 (December 2025)

Upcoming: v2.5 (Q1 2026)

Future: v3.0 (Q2 2026)

🧪 Evaluation Methodology

Benchmark Suite

Running Benchmarks

Quality Metrics

🔐 Training Infrastructure

LUMI Supercomputer

Training Configuration

📚 Citation

🔗 Related Resources