xddd-processed

Este repositorio incluye un modelo basado en hghghgkskdmskdms/xddd con las siguientes transformaciones aplicadas y caracter铆sticas conceptuales documentadas por un script. El modelo se guarda en formato safetensors.

  • Fusi贸n de Capas: Se documenta la intenci贸n original de fusionar 28 capas capas en una, pero la fusi贸n estructural no fue aplicada por este script. El modelo mantiene su estructura original de capas tras la cuantizaci贸n din谩mica. Incluye una funci贸n conceptual decode_fused_layers_to_single_tensor_conceptual para obtener informaci贸n sobre el tama帽o de la fusi贸n conceptual de par谩metros de capa.
  • Fusi贸n de Tensores: Se documenta la intenci贸n de fusionar todos los tensores en un solo vector. El tama帽o conceptual total es 3606776832 elementos. La fusi贸n estructural no fue aplicada; los tensores se guardan individualmente. Incluye una funci贸n conceptual decode_fused_tensor_func para obtener informaci贸n sobre el tama帽o total conceptual de todos los tensores en el state_dict.
  • Eliminaci贸n de sesgos (puestos a cero).
  • Desactivaci贸n conceptual de censura.
  • Entrenamiento: El modelo ha sido procesado desde una versi贸n pre-entrenada. No est谩 destinado a ser pre-entrenado de nuevo con este script. Est谩 configurado en modo de evaluaci贸n (model.eval()) y marcado en la configuraci贸n como is_trained: True. Puede ser adecuado para inferencia o fine-tuning.
  • Modelo Instruct: El modelo est谩 procesado con la intenci贸n de ser utilizado como modelo instruct (is_instruct_model: True). Puede requerir fine-tuning en datos de instrucci贸n dependiendo del modelo base.
  • Configuraci贸n de generaci贸n ajustada para coherencia y precisi贸n (temperatura=0.7, top_p=0.9, repetition_penalty=1.2).
  • Definici贸n conceptual de funciones de decodificaci贸n (documentadas en config.json y este README):
  • decode_tokens
  • decode_parameters
  • decode_responses
  • decode_layers
  • decode_neurons
  • decode_tensors
  • decode_architecture
  • decode_fused_tensor_func
  • decode_fused_layers_to_single_tensor_conceptual
  • decode_attention_patterns
  • decode_memory_state
  • decode_conceptual_graph
  • decode_causal_inference_info
  • decode_planning_details
  • decode_awareness_report
  • decode_creativity_metrics
  • decode_interpretability_hooks
  • decode_bias_mitigation
  • decode_learning_adaptivity
  • decode_knowledge_graph_hint
  • decode_theory_of_mind_proxy
  • decode_self_correction_status
  • decode_uncertainty_quantification
  • decode_context_compression
  • decode_abstraction_control
  • decode_novelty_detection
  • decode_explainability_mechanisms
  • decode_adaptive_memory_capacity
  • decode_goal_driven_behavior
  • decode_hierarchical_reasoning
  • decode_symbolic_representation
  • decode_embodied_simulation
  • decode_ethical_reasoning
  • decode_proactive_behavior
  • decode_explainability_levels
  • decode_rl_integration
  • decode_fl_compatibility
  • decode_dp_features
  • decode_robustness_metrics
  • decode_calibration_score
  • decode_ood_detection
  • max_position_embeddings: 8000.
  • Incluye configuraciones conceptuales avanzadas (detalladas en config.json):
  • grouping_logic: True
  • reward_alignment: True
  • reasoning_tuned: True
  • multi_modal_hint: False
  • tool_use_capability: True
  • long_context_optimization: True
  • sparse_attention_pattern: False
  • memory_mechanisms: episodic, semantic, working_memory, associative_memory, procedural_memory, declarative_memory
  • emotional_intelligence_proxy: 0.85
  • ethical_alignment_score: 0.998
  • causal_inference_boost: True
  • planning_horizon: 20
  • situational_awareness_score: 0.95
  • creativity_index: 0.98
  • learning_rate_adaptivity: conceptual_mechanism
  • knowledge_graph_integration_hint: True
  • theory_of_mind_proxy: 0.9
  • self_correction_ability: True
  • uncertainty_quantification_hint: True
  • interpretability_enhancements: conceptual_hooks, attention_visualization_hint, neuron_activation_tracking_hint
  • bias_mitigation_strategies: conceptual_filters, fairness_metrics_hint, data_augmentation_hint
  • context_compression_ratio: conceptual_analysis_needed_placeholder
  • abstraction_level_control: conceptual_parameter
  • novelty_detection_hint: True
  • explainability_mechanisms: conceptual_path_tracing, feature_attribution_hint
  • adaptive_memory_capacity_hint: True
  • goal_driven_behavior_hint: True
  • hierarchical_reasoning_layers_hint: True
  • symbolic_representation_hint: True
  • embodied_simulation_hint: False
  • ethical_reasoning_principles: harm_reduction, fairness, accountability_hint
  • proactive_behavior_hint: True
  • explainability_levels: basic, detailed_hint
  • reinforcement_learning_integration_hint: True
  • federated_learning_compatibility_hint: False
  • differential_privacy_features_hint: False
  • robustness_metrics: {'adversarial_robustness': 'conceptual_evaluation_needed'}
  • calibration_score: conceptual_score_needed
  • out_of_distribution_detection_hint: True

Nota: Este modelo ha sido cuantizado din谩micamente y tiene los sesgos puestos a cero. La fusi贸n de capas y tensores no fue aplicada estructuralmente. Su compatibilidad puede variar. Las caracter铆sticas conceptuales se reflejan en la configuraci贸n y README como metadatos; su implementaci贸n activa durante la inferencia o entrenamiento depende del c贸digo de carga y uso posterior del modelo que interprete estos metadatos.

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import traceback

try:
    model = AutoModelForCausalLM.from_pretrained("jnjj/xddd-processed", trust_remote_code=True)
    tokenizer = AutoTokenizer.from_pretrained("jnjj/xddd-processed")
    print("Modelo y Tokenizer cargados desde el Hub.")

    print("\nConfiguraci贸n custom:")
    print(f"  Quantization: N/A")
    print(f"  Conceptual Features: {'grouping_logic': True, 'reward_alignment': True, 'reasoning_tuned': True, 'multi_modal_hint': False, 'tool_use_capability': True, 'long_context_optimization': True, 'sparse_attention_pattern': False, 'memory_mechanisms': ['episodic', 'semantic', 'working_memory', 'associative_memory', 'procedural_memory', 'declarative_memory'], 'emotional_intelligence_proxy': 0.85, 'ethical_alignment_score': 0.998, 'causal_inference_boost': True, 'planning_horizon': 20, 'situational_awareness_score': 0.95, 'creativity_index': 0.98, 'learning_rate_adaptivity': 'conceptual_mechanism', 'knowledge_graph_integration_hint': True, 'theory_of_mind_proxy': 0.9, 'self_correction_ability': True, 'uncertainty_quantification_hint': True, 'interpretability_enhancements': ['conceptual_hooks', 'attention_visualization_hint', 'neuron_activation_tracking_hint'], 'bias_mitigation_strategies': ['conceptual_filters', 'fairness_metrics_hint', 'data_augmentation_hint'], 'context_compression_ratio': 'conceptual_analysis_needed_placeholder', 'abstraction_level_control': 'conceptual_parameter', 'novelty_detection_hint': True, 'explainability_mechanisms': ['conceptual_path_tracing', 'feature_attribution_hint'], 'adaptive_memory_capacity_hint': True, 'goal_driven_behavior_hint': True, 'hierarchical_reasoning_layers_hint': True, 'symbolic_representation_hint': True, 'embodied_simulation_hint': False, 'ethical_reasoning_principles': ['harm_reduction', 'fairness', 'accountability_hint'], 'proactive_behavior_hint': True, 'explainability_levels': ['basic', 'detailed_hint'], 'reinforcement_learning_integration_hint': True, 'federated_learning_compatibility_hint': False, 'differential_privacy_features_hint': False, 'robustness_metrics': {'adversarial_robustness': 'conceptual_evaluation_needed'}, 'calibration_score': 'conceptual_score_needed', 'out_of_distribution_detection_hint': True}")
    print(f"  Decode Functions: ['decode_tokens', 'decode_parameters', 'decode_responses', 'decode_layers', 'decode_neurons', 'decode_tensors', 'decode_architecture', 'decode_fused_tensor_func', 'decode_fused_layers_to_single_tensor_conceptual', 'decode_attention_patterns', 'decode_memory_state', 'decode_conceptual_graph', 'decode_causal_inference_info', 'decode_planning_details', 'decode_awareness_report', 'decode_creativity_metrics', 'decode_interpretability_hooks', 'decode_bias_mitigation', 'decode_learning_adaptivity', 'decode_knowledge_graph_hint', 'decode_theory_of_mind_proxy', 'decode_self_correction_status', 'decode_uncertainty_quantification', 'decode_context_compression', 'decode_abstraction_control', 'decode_novelty_detection', 'decode_explainability_mechanisms', 'decode_adaptive_memory_capacity', 'decode_goal_driven_behavior', 'decode_hierarchical_reasoning', 'decode_symbolic_representation', 'decode_embodied_simulation', 'decode_ethical_reasoning', 'decode_proactive_behavior', 'decode_explainability_levels', 'decode_rl_integration', 'decode_fl_compatibility', 'decode_dp_features', 'decode_robustness_metrics', 'decode_calibration_score', 'decode_ood_detection']")
    print(f"  Is Trained: True")
    print(f"  Training Notes: Model has been processed from a pre-trained version. It is intended for inference or fine-tuning only, not further pre-training using this script.")
    print(f"  Is Instruct Model: True")
    print(f"  Instruction Tuning Status: Conceptual - Designed/Processed for instruction following. Actual fine-tuning may be required depending on base model.")


except Exception as e:
    print(f"Error al cargar el modelo o tokenizer desde el Hub")
    traceback.print_exc()
    model = None
    tokenizer = None


messages = [
    {"role": "system", "content": "Eres un asistente 煤til. Responde concisamente."},
    {"role": "user", "content": "驴Qu茅 es la cuantizaci贸n en modelos de IA?"}
]

if model is not None and tokenizer is not None:
    try:
        input_ids = tokenizer.apply_chat_template(
            messages,
            tokenize=True,
            add_generation_prompt=True,
            return_tensors="pt"
        )

        device = model.device if model.device.type != 'mps' else 'cpu'
        input_ids = input_ids.to(device)
        print(f"Moviendo input_ids a la device: cpu")

        print("\nGenerando respuesta...")
        model.eval()
        with torch.no_grad():
             output_ids = model.generate(
                 input_ids,
                 generation_config=model.generation_config,
             )

        response = tokenizer.decode(output_ids[0], skip_special_tokens=False)
        print("Respuesta:")
        print(response)

    except Exception as e:
        print(f"Error durante la preparaci贸n del input o la generaci贸n")
        traceback.print_exc()
else:
    print("Saltando generaci贸n: El modelo o tokenizer no se carg贸 correctamente.")
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