---
license: mit
tags:
- llama3
- context-8000
- layer-fusion-conceptual
- tensor-fusion-conceptual
- bias-removal
- decode
- coherence-enhancement
- custom-code
- grouping
- reward-alignment
- reasoning-tuned
- safetensors
- tool-use-hint
- long-context-hint
- memory-hint
- conceptual-graph-hint
- emotional-intelligence-hint
- ethical-alignment-hint
- causal-inference-hint
- planning-hint
- situational-awareness-hint
- creativity-hint
- learning-adaptivity-hint
- knowledge-graph-hint
- theory-of-mind-hint
- self-correction-hint
- uncertainty-quantification-hint
- interpretability-hint
- bias-mitigation-hint
- context-compression-hint
- abstraction-control-hint
- novelty-detection-hint
- explainability-hint
- instruct
- adaptive-memory-hint
- goal-driven-hint
- hierarchical-reasoning-hint
- symbolic-representation-hint
- embodied-simulation-hint
- ethical-reasoning-hint
- proactive-behavior-hint
- explainability-levels-hint
- rl-integration-hint
- fl-compatibility-hint
- dp-features-hint
- robustness-hint
- calibration-hint
- ood-detection-hint
---

# xddd-processed

Este repositorio incluye un modelo basado en `hghghgkskdmskdms/xddd` con las siguientes transformaciones aplicadas y características conceptuales documentadas por un script. El modelo se guarda en formato `safetensors`.
- **Fusión de Capas:** Se documenta la intención original de fusionar 28 capas capas en una, pero la fusión estructural *no fue aplicada* por este script. El modelo mantiene su estructura original de capas tras la cuantización dinámica. Incluye una función conceptual `decode_fused_layers_to_single_tensor_conceptual` para obtener información sobre el tamaño de la fusión conceptual de parámetros de capa.
- **Fusión de Tensores:** Se documenta la intención de fusionar todos los tensores en un solo vector. El tamaño conceptual total es 3606776832 elementos. La fusión estructural *no fue aplicada*; los tensores se guardan individualmente. Incluye una función conceptual `decode_fused_tensor_func` para obtener información sobre el tamaño total conceptual de todos los tensores en el state_dict.
- Eliminación de sesgos (puestos a cero).
- Desactivación conceptual de censura.
- **Entrenamiento:** El modelo ha sido procesado desde una versión pre-entrenada. **No está destinado a ser pre-entrenado de nuevo** con este script. Está configurado en modo de evaluación (`model.eval()`) y marcado en la configuración como `is_trained: True`. Puede ser adecuado para inferencia o fine-tuning.
- **Modelo Instruct:** El modelo está procesado con la **intención** de ser utilizado como modelo instruct (`is_instruct_model: True`). Puede requerir fine-tuning en datos de instrucción dependiendo del modelo base.
- Configuración de generación ajustada para coherencia y precisión (temperatura=0.7, top_p=0.9, repetition_penalty=1.2).
- Definición conceptual de funciones de decodificación (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 dinámicamente y tiene los sesgos puestos a cero. La fusión de capas y tensores *no fue aplicada estructuralmente*. Su compatibilidad puede variar. Las características conceptuales se reflejan en la configuración y README como metadatos; su implementación activa durante la inferencia o entrenamiento depende del código de carga y uso posterior del modelo que interprete estos metadatos.

```python
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("\nConfiguración 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 útil. Responde concisamente."},
    {"role": "user", "content": "¿Qué es la cuantización 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 preparación del input o la generación")
        traceback.print_exc()
else:
    print("Saltando generación: El modelo o tokenizer no se cargó correctamente.")

```