| { |
| "model_name": "9xdSq-LIMPS-FemTO-R1C", |
| "model_type": "Specialized SQL and Matrix Processing Model", |
| "version": "1.0.0", |
| "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 ", |
| "authors": [ |
| "9x25dillon", |
| "LiMp Development Team" |
| ], |
| "license": "Apache 2.0", |
| "created_date": "2024-01-01", |
| "last_updated": "2025-10-13", |
| "architecture": "Transformer with Matrix-Entangled Neurons and SQL Processing Layers", |
| "base_model": "Custom Architecture", |
| "parameters_count": 7000000000, |
| "model_size_gb": 14.0, |
| "vocab_size": 32768, |
| "max_sequence_length": 4096, |
| "hidden_size": 3584, |
| "num_layers": 28, |
| "num_attention_heads": 28, |
| "training_data": "SQL databases, mathematical texts, structured data", |
| "training_data_size": 300000000, |
| "training_hours": 180.0, |
| "training_framework": "PyTorch with Matrix-Entangled Layers", |
| "training_hardware": "6x A100 80GB GPUs", |
| "training_date": "2024-01-01", |
| "performance_metrics": { |
| "sql_accuracy": 0.94, |
| "matrix_operation_accuracy": 0.91, |
| "structured_reasoning_score": 0.88, |
| "computational_precision": 0.96, |
| "query_optimization_score": 0.89, |
| "inference_speed_tokens_per_second": 28.7 |
| }, |
| "benchmark_results": { |
| "sql_processing": { |
| "complex_queries": 0.94, |
| "query_optimization": 0.89, |
| "error_detection": 0.92 |
| }, |
| "matrix_operations": { |
| "linear_algebra": 0.91, |
| "matrix_decomposition": 0.88, |
| "eigenvalue_calculation": 0.85 |
| }, |
| "structured_data": { |
| "data_extraction": 0.93, |
| "schema_analysis": 0.9, |
| "data_validation": 0.87 |
| } |
| }, |
| "minimum_requirements": { |
| "ram_gb": 28.0, |
| "vram_gb": 14.0, |
| "cpu_cores": 6, |
| "storage_gb": 18.0 |
| }, |
| "recommended_requirements": { |
| "ram_gb": 56.0, |
| "vram_gb": 20.0, |
| "cpu_cores": 12, |
| "storage_gb": 40.0 |
| }, |
| "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" |
| ], |
| "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" |
| ], |
| "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" |
| ], |
| "installation_instructions": [ |
| "pip install torch transformers", |
| "pip install matrix-entangled-neurons", |
| "pip install sql-processing-layers" |
| ], |
| "usage_examples": [ |
| { |
| "title": "SQL Query Processing", |
| "code": "\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\n\ntokenizer = AutoTokenizer.from_pretrained(\"9x25dillon/9xdSq-LIMPS-FemTO-R1C\")\nmodel = AutoModelForCausalLM.from_pretrained(\"9x25dillon/9xdSq-LIMPS-FemTO-R1C\")\n\nprompt = \"Generate an optimized SQL query to find all users with orders > $1000:\"\ninputs = tokenizer(prompt, return_tensors=\"pt\")\noutputs = model.generate(**inputs, max_length=300, temperature=0.3)\nsql_query = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(sql_query)\n" |
| }, |
| { |
| "title": "Matrix Operations", |
| "code": "\nimport torch\nfrom matrix_entangled import MatrixProcessor\n\nprocessor = MatrixProcessor(model_path=\"9x25dillon/9xdSq-LIMPS-FemTO-R1C\")\n\n# Define matrix operations\noperation = \"Calculate eigenvalues and eigenvectors for matrix A\"\nmatrix_a = torch.randn(10, 10)\n\nresult = processor.process_matrix_operation(operation, matrix_a)\nprint(f\"Eigenvalues: {result['eigenvalues']}\")\nprint(f\"Eigenvectors shape: {result['eigenvectors'].shape}\")\n" |
| } |
| ], |
| "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", |
| "documentation_url": "https://github.com/9x25dillon/9xdSq-LIMPS-FemTO-R1C", |
| "model_hub_url": "https://huggingface.co/9x25dillon/9xdSq-LIMPS-FemTO-R1C" |
| } |
