| architecture: Transformer with Matrix-Entangled Neurons and SQL Processing Layers |
| authors: |
| - 9x25dillon |
| - LiMp Development Team |
| base_model: Custom Architecture |
| benchmark_results: |
| matrix_operations: |
| eigenvalue_calculation: 0.85 |
| linear_algebra: 0.91 |
| matrix_decomposition: 0.88 |
| sql_processing: |
| complex_queries: 0.94 |
| error_detection: 0.92 |
| query_optimization: 0.89 |
| structured_data: |
| data_extraction: 0.93 |
| data_validation: 0.87 |
| schema_analysis: 0.9 |
| citations: |
| - '9x25dillon. (2024). 9xdSq-LIMPS-FemTO-R1C: A Matrix-Entangled Model for SQL and |
| Structured Data Processing.' |
| - 'LiMp Development Team. (2024). Matrix-Entangled Neurons: A New Paradigm for Structured |
| Computation.' |
| contact_information: contact@limp-ai.com |
| created_date: '2024-01-01' |
| description: "\n 9xdSq-LIMPS-FemTO-R1C is a specialized 7 billion parameter\ |
| \ model designed for \n advanced SQL processing, matrix operations, and\ |
| \ structured data analysis. \n This model incorporates experimental matrix-entangled\ |
| \ neurons and SQL processing \n capabilities for complex database operations\ |
| \ and mathematical computations.\n \n The model excels at\ |
| \ structured reasoning, database queries, matrix manipulations, \n and\ |
| \ applications requiring precise computational accuracy.\n " |
| documentation_url: https://github.com/9x25dillon/9xdSq-LIMPS-FemTO-R1C |
| ethical_considerations: |
| - Database access should follow security protocols |
| - SQL generation requires validation for production use |
| - Matrix operations should be verified for accuracy |
| - Structured data processing requires privacy considerations |
| hidden_size: 3584 |
| installation_instructions: |
| - pip install torch transformers |
| - pip install matrix-entangled-neurons |
| - pip install sql-processing-layers |
| last_updated: '2025-10-13' |
| license: Apache 2.0 |
| limitations: |
| - Specialized for structured data processing |
| - May not perform well on unstructured text |
| - Requires domain-specific knowledge for optimal use |
| - Matrix operations limited by computational resources |
| max_sequence_length: 4096 |
| minimum_requirements: |
| cpu_cores: 6 |
| ram_gb: 28.0 |
| storage_gb: 18.0 |
| vram_gb: 14.0 |
| model_hub_url: https://huggingface.co/9x25dillon/9xdSq-LIMPS-FemTO-R1C |
| model_name: 9xdSq-LIMPS-FemTO-R1C |
| model_size_gb: 14.0 |
| model_type: Specialized SQL and Matrix Processing Model |
| num_attention_heads: 28 |
| num_layers: 28 |
| parameters_count: 7000000000 |
| performance_metrics: |
| computational_precision: 0.96 |
| inference_speed_tokens_per_second: 28.7 |
| matrix_operation_accuracy: 0.91 |
| query_optimization_score: 0.89 |
| sql_accuracy: 0.94 |
| structured_reasoning_score: 0.88 |
| recommended_requirements: |
| cpu_cores: 12 |
| ram_gb: 56.0 |
| storage_gb: 40.0 |
| vram_gb: 20.0 |
| training_data: SQL databases, mathematical texts, structured data |
| training_data_size: 300000000 |
| training_date: '2024-01-01' |
| training_framework: PyTorch with Matrix-Entangled Layers |
| training_hardware: 6x A100 80GB GPUs |
| training_hours: 180.0 |
| usage_examples: |
| - code: ' |
| |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| |
| |
| tokenizer = AutoTokenizer.from_pretrained("9x25dillon/9xdSq-LIMPS-FemTO-R1C") |
| |
| model = AutoModelForCausalLM.from_pretrained("9x25dillon/9xdSq-LIMPS-FemTO-R1C") |
| |
| |
| prompt = "Generate an optimized SQL query to find all users with orders > $1000:" |
| |
| inputs = tokenizer(prompt, return_tensors="pt") |
| |
| outputs = model.generate(**inputs, max_length=300, temperature=0.3) |
| |
| sql_query = tokenizer.decode(outputs[0], skip_special_tokens=True) |
| |
| print(sql_query) |
| |
| ' |
| title: SQL Query Processing |
| - code: ' |
| |
| import torch |
| |
| from matrix_entangled import MatrixProcessor |
| |
| |
| processor = MatrixProcessor(model_path="9x25dillon/9xdSq-LIMPS-FemTO-R1C") |
| |
| |
| # Define matrix operations |
| |
| operation = "Calculate eigenvalues and eigenvectors for matrix A" |
| |
| matrix_a = torch.randn(10, 10) |
| |
| |
| result = processor.process_matrix_operation(operation, matrix_a) |
| |
| print(f"Eigenvalues: {result[''eigenvalues'']}") |
| |
| print(f"Eigenvectors shape: {result[''eigenvectors''].shape}") |
| |
| ' |
| title: Matrix Operations |
| use_cases: |
| - Advanced SQL query processing and optimization |
| - Matrix operations and linear algebra computations |
| - Structured data analysis and extraction |
| - Database schema design and optimization |
| - Mathematical computation and verification |
| - Data pipeline automation |
| version: 1.0.0 |
| vocab_size: 32768 |
|
|
