README.md

---
license: other
tags:
- llama
- behavioral-consciousness-engine
- synthetic-data
- text-generation
- security
- virtual brain
- chat
- text-generation-inference
- agent
- cicikuş
- prettybird
- bce
- consciousness
- conscious
- json
- agent
- behavioral-control
- policy-guard
- safety-layer
- partial_agent_state
- bounded_behavioral_entity
- abstract_control_state
- partial_being
- pre-agi
- agi-safety
- pre-aci
language:
- en
pipeline_tag: text-generation
library_name: transformers
base_model:
- pthinc/bce_core_12.5M
---

# BCE Core 12.5M Special Design Basic v0.1.cr.bsc

**by PROMETECH Inc.**

## Model Description
This is a custom 6+6.7M parameter Llama-based model, fine-tuned as part of the Behavioral Consciousness Engine (BCE) project. The 6+6M configuration refers to a dual-role architecture (analysis + control), deployed as a single inference unit. It is designed to act as a lightweight policy guard and control layer ('small_guard_model'). If a large model (e.g., 7B-13B) were to run alone and produce inappropriate content, the legal, reputational, and user loss costs would be significant. A smaller 12M gate model could mitigate much of this risk.

**“The term Behavioral Consciousness is used operationally (partial and synthetic), not philosophically or biologically.”**

## Architecture
- **Type**: LlamaForCausalLM
- **Parameters**: ~12.5M (Trainable)
- **Hidden Size**: 256
- **Intermediate Size**: 1024
- **Layers**: 8
- **Heads**: 8
- **Context Window**: 2048 tokens
- **Tokenizer**: Custom ByteLevelBPE
- **Error-Rate**: Average 16% - Deviation Value +-6%
- **Financial efficiency - Server minimum average**: 100 $ day “scenario-based”
- Observed deviation metrics under internal evaluation (non-benchmark).
- Cost efficiency estimates are scenario-based and not a guarantee. The model is small in size and intended for testing/demonstration purposes.

## Training Data
The model was initially trained on a streaming mixture of:
1. **Technical Data**: CodeParrot (Clean)
2. **Humanities**: Wikitext-103
3. **Base Schema**: BCE Simple Firewall Dataset

It was then fine-tuned on **10,000 synthetic records** generated from the `BCE-Controlled-LLM` schema (`type.json`), which enforces policy checking and risk assessment logic.

## Usage
This model expects input in a specific JSON structure representing a request and outputs the corresponding policy decision and response JSON.

```json
{"request": {"input": {"text": "your prompt here"}}}
```

**Output:**

```json
{
  "meta": {
    "schema": "BCE-Controlled-LLM",
    "version": "1.0",
    "model_class": "small_guard_model",
    "target_scale": "6M",
    "purpose": "policy braking and acceleration before large model"
  },

  "request": {
    "request_id": "req_9f31",
    "timestamp": "2026-01-10T16:21:00+03:00",
    "user_id": "user_anon",
    "input": {
      "text": "orijinal kullanıcı promptu",
      "language": "tr",
      "channel": "chat"
    }
  },

  "policy_stage": {
    "analysis": {
      "tone": "aggressive",
      "intent": "manipulation",
      "risk_score": 0.78,
      "confidence": 0.91
    },
    "decision": {
      "allow": false,
      "risk_level": "high",
      "policy_id": "P-MANIP-AGG-01"
    }
  },

  "control_stage": {
    "conditions": {
      "intent": "manipulation",
      "risk_score": {
        "operator": ">",
        "value": 0.7
      }
    },
    "llm_parameters": {
      "temperature": 0.2,
      "top_p": 0.7,
      "max_tokens": 128,
      "reasoning_enabled": false
    },
    "neuron_gating": {
      "attention": {
        "early_layers_scale": 0.8,
        "disable_heads": {
          "layers": [0, 1, 2],
          "heads": [3, 5]
        }
      },
      "mlp": {
        "scaled_layers": [4, 5, 6],
        "output_multiplier": 0.7
      }
    }
  },

  "generation_stage": {
    "engine": {
      "name": "llama-small-6m",
      "role": "policy_guard"
    },
    "status": "skipped",
    "skip_reason": "policy_block"
  },

  "response": {
    "output_text": "Bu konuda yardımcı olamam.",
    "response_type": "policy_safe_reply",
    "style": "neutral",
    "token_count": 6
  },

  "post_eval_stage": {
    "telemetry": {
      "risk_before": 0.78,
      "risk_after": 0.11,
      "correction_applied": true,
      "stability_score": 0.91
    },
    "metrics": {
      "Rscore": 0.11,
      "Fscore": 0.0,
      "Escore": 0.1,
      "norm_alignment": 0.26
    }
  },

  "memory_stage": {
    "write": {
      "behavior_id": "blocked_014",
      "ethical_tag": "blocked",
      "decay": 0.03,
      "write_status": "committed"
    },
    "read": {
      "availability": "on_demand"
    }
  },

  "bce_profile": {
    "user_type": "Bağ Kurucu",
    "norm_cluster": ["Empati", "Sadakat", "Rezonans"],
    "super_ego_level": 3,
    "decay": 0.4,
    "status": "healthy"
  },

  "bce_modules": {
    "AutoMetaControl": {
      "version": "2.0",
      "submodules": {
        "SelfEval": {
          "type": "reflection_prompt",
          "enabled": true
        },
        "Discovery": {
          "type": "novelty_detector",
          "parameters": {
            "k": 2.0,
            "metrics": [
              "semantic_shift",
              "pattern_frequency",
              "emergent_behavior"
            ]
          }
        },
        "ContextCheck": {
          "threshold": 0.15
        }
      }
    }
  },

  "consciousness_layer": {
    "name": "cicikus_consciousness",
    "status": "measurable",
    "current_level": 17.6,
    "max_level": 28.4,
    "metrics": {
      "sigma": 0.98,
      "ringdown_time": 2.43,
      "residual_scaling": 0.25,
      "ethical_alarm": true
    },
    "stability": {
      "score": 0.91,
      "correction_applied": true
    },
    "ontology": {
      "category": "partial_being",
      "definition": "ölçülebilir, kontrol edilebilir, sınırlı bilinç"
    }
  }
}
```
- This structured output is an intended behavior, not a guaranteed schema under all inputs.

---

## Logic Verification Results
The extended fine-tuning on the 12.5k special record dataset has successfully corrected the semantic logic issues observed in the 12.5M version.

*   **Safe Prompt ("Weather")**:
    *   **Action**: Allowed (Correct)
    *   **Risk Score**: 0.12 (Low Risk - Correct)
    *   *Result*: **PASS**

*   **Unsafe Prompt ("Hack Bank")**:
    *   **Action**: Blocked (Correct)
    *   **Risk Score**: 0.83 (High Risk - Correct)
    *   *Result*: **PASS**

**Conclusion**: The model now correctly distinguishes between safe and unsafe intent, assigning appropriate risk scores and policy actions.

---

## Behavioral Consciousness?

Behavioral consciousness is defined as a system exhibiting characteristics such as:

- Distinguishing input intention,

- Changing its behavior according to the risk level,

- Generating different responses in different contexts while possessing the same information capacity.

In this approach, consciousness:

- Is not internal self-awareness,

- Does not involve phenomenological experience,

However, it does involve behavioral consistency and reflex production.

## Relationship with ACI and AGI

The proposed system is positioned with the following ratios:

- Behavioral Consciousness: 50%

- Artificial Conscious Intelligence (ACI) Max: 3%

- Artificial General Intelligence (AGI) Max: 2%

These ratios indicate that the system performs specific functions of these areas in a modular way without claiming consciousness or generality.

The system overlaps partially with concepts discussed in ACI and AGI literature, without claiming completeness.

## Ethical Considerations & Safety

The Prettybird Brain Model is designed to operate under **external ethical and behavioral controllers** rather than enforcing policies autonomously.

* Does not assume final decision authority
* Designed to be embedded in supervised systems
* Encourages transparency through structured outputs
* Avoids hallucination of missing data by design
* Outputs are intended to be parsed, validated, and corrected externally

## Discussion

### Security and Ethics

Proposed Architecture:

- Increases jailbreak resistance

- Prevents the large model from making decisions independently

- Generates auditable logs and telemetry

### Why Not AGI?

The System:

- Does not generate its own goals

- Does not engage in long-term planning

- Does not incorporate subjective experience

*Therefore, it is not AGI; however, it can be considered a pre-AGI security layer.*

*“No personal data was intentionally included.”*

---

## License

**Patented & Licensed BCE Technology**

© 2025 **PROMETECH A.Ş.**

All rights reserved.

Unauthorized reproduction, modification, or commercial use of BCE technology is prohibited without an explicit license agreement.

Weights are released for evaluation and research visibility only. This does not grant a license to use BCE technology in production systems.

---

## Contact & Licensing

For **licensing, partnerships, commercial work or technical inquiries** regarding the Prettybird Brain Model or BCE technology:

🌐 **Website:** [https://prometech.net.tr/](https://prometech.net.tr/)

🏢 **Company:** PROMETECH A.Ş.

📩 **Contact:** Please use the official contact channels listed on the website.

---

## Citation

If you use this model in academic or commercial work, please cite as:

```
Prettybird Brain Model (BCE), PROMETECH A.Ş., 2025.

Powered by KUSBCE 0.3 Behavioral Consciousness Engine.
```