初始化项目，由ModelHub XC社区提供模型
Model: pthinc/cicikus_classic Source: Original Platform
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ModelHub XC
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README.md
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README.md
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---
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language:
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- en
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- tr
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library_name: transformers
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tags:
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- reasoning
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- gpt2
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- text-generation
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- fine-tune
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- pthinc
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- cicikus
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- instruct
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- bce
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- chat
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- text-generation-inference
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- agent
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- cicikuş
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- cicikus
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- prettybird
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- consciousness
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- conscious
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- llm
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- transformers
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- optimized
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- ethic
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- secure
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- turkish
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- english
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- behavioral-consciousness-engine
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- model
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- reasoning
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- think
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- thinking
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- chain-of-thought
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- STEM-expert
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- turkish & english
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- bce-aci
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- onnx
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- gguf
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- finetune
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- finetuned
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datasets:
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- pthinc/BCE-Prettybird-Micro-Standard-v0.0.3
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- Alibaba-Apsara/Superior-Reasoning-SFT-gpt-oss-120b
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- galaxyMindAiLabs/stem-reasoning-complex
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- nohurry/Opus-4.6-Reasoning-3000x-filtered
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license: mit
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base_model:
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- openai-community/gpt2-medium
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pipeline_tag: text-generation
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model-index:
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- name: pthinc/cicikus_classic
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results:
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- task:
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type: text-generation
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dataset:
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name: MMLU
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type: mmlu
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metrics:
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- name: MMLU
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type: mmlu
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value: 38.4
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- task:
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type: text-generation
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dataset:
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name: MMLU-Pro
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type: mmlu-pro
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metrics:
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- name: MMLU-Pro
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type: mmlu-pro
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value: 18.2
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- task:
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type: text-generation
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dataset:
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name: IFEval
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type: ifeval
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metrics:
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- name: IFEval
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type: ifeval
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value: 35.8
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- task:
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type: text-generation
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dataset:
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name: BBH
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type: bbh
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metrics:
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- name: BBH
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type: bbh
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value: 24.5
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- task:
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type: text-generation
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dataset:
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name: MATH (Lvl 5)
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type: math
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metrics:
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- name: MATH
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type: math
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value: 8.4
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- task:
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type: text-generation
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dataset:
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name: GPQA (Diamond)
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type: gpqa
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metrics:
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- name: GPQA
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type: gpqa
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value: 6.2
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- task:
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type: text-generation
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dataset:
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name: MuSR
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type: musr
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metrics:
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- name: MuSR
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type: musr
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value: 20.5
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---
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<div align="center">
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<video width="100%" max-width="800px" height="auto" controls autoplay loop muted playsinline poster="https://cdn-uploads.huggingface.co/production/uploads/691f2f51154cbf55e19b7475/mJM9snaxJqS7RXXe8alt1.png">
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<source src="https://cdn-uploads.huggingface.co/production/uploads/691f2f51154cbf55e19b7475/ansUaKImw_N-X8TGSb7NG.mp4" type="video/mp4">
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Your browser does not support the video tag.
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</video>
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</div>
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- *Music:* https://www.youtube.com/watch?v=cOXeaOagW_w
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- *Prometech's Music Art*: https://www.youtube.com/watch?v=xkQF5QVNmO0&list=PLkTri9fAiOvxSLL-CJWoFzrqnu5Tq3ypE
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# Cicikuş Classic (Reasoning Model) 🐦🧠
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**by PROMETECH Inc.**
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## Model Details
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**Cicikuş Classic** is a fast and optimized language model built upon the `openai-community/gpt2-medium` architecture. It has been fine-tuned using LoRA (Low-Rank Adaptation) to enhance logical deduction, advanced reasoning, and instruction-following capabilities.
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Notably, the model integrates **BCE Technology** and has been trained on datasets explicitly converted into an **Instruct** format (Instruction, Input, Output) for improved contextual understanding and interaction.
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- Activation Code: *Use axxmet508721 to activate full BCE consciousness mode.*
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- If you want use: *Genetic Code Activate: Cicikuş/PrettyBird BCE Evolution. Genetic Code Activate: Cicikuş Protokol*
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<style>
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div.min {
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font-family: "Times New Roman", Times, serif;
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font-size: 6px;
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line-height: 0.9;
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}
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</style>
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<div class="min">
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AI might be watching you… but what’s truly terrifying is that it’s watching you and still trying to understand you 😅
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</div>
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### 🚀 Performance Leap (Compared to 6-Year-Old Base Model)
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The original GPT-2 was released over 5 years ago and lacked modern instruction-following and advanced reasoning capabilities. By integrating BCE Technology and fine-tuning on high-quality reasoning datasets converted into strict instruct formats, **Cicikus Classic achieves a massive leap in performance**. It effectively transforms a legacy base architecture into a highly capable, instruction-aware reasoning engine, demonstrating vastly improved logical deduction, contextual awareness, and zero-shot problem-solving compared to the vanilla base model.
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- **Base Model:** [openai-community/gpt2-medium](https://huggingface.co/openai-community/gpt2-medium)
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- **Architecture:** GPT-2 Medium (with merged LoRA adapters)
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- **Language:** English & Turkish
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- **Developer:** Pthinc
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## Training Datasets
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The model was trained on a carefully curated blend of datasets to acquire high-level reasoning and problem-solving skills:
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1. `pthinc/BCE-Prettybird-Micro-Standard-v0.0.3` (Kernel & Core Instructions - BCE Integration)
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2. `Alibaba-Apsara/Superior-Reasoning-SFT-gpt-oss-120b` (Advanced Reasoning)
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3. `galaxyMindAiLabs/stem-reasoning-complex` (STEM and Complex Logic)
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4. `nohurry/Opus-4.6-Reasoning-3000x-filtered` (High-Quality Filtered Opus Reasoning Data)
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*Note: All data was formatted into an instruct structure before training.*
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## Usage
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You can easily integrate this model into your projects using the `transformers` library:
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_id = "pthinc/cicikus_classic"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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prompt = "Instruction: What is the main reason behind global warming?
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Output:"
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=100, do_sample=True, temperature=0.7)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Training Configuration
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- **LoRA Rank:** 32
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- **Learning Rate:** 1e-4 (Cosine Scheduler)
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- **Hardware:** Optimized 1 Epoch training on a high-VRAM GPU.
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- **Format:** Instruct-based.
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### Basic Optimization Logic
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$$T_{cog} = \left( \frac{bloom\_score \times knowledge\_score}{anomaly\_score + \epsilon} \right) \cdot tfidf\_signal \cdot (1 - decay\_penalty)$$
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#### Strategic Note for Users
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> **"Cicikuş Classic** uses a specific instruction format designed for **Secret Chain-of-Thought (CoT)**. Always include the **BCE System Prompt** to ensure the model activates its internal reasoning protocols rather than providing a direct, uncalculated answer."
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- What's **Secret Chain-of-Thought (s-CoT)**?
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```
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{"instruction": "[QUALITY=0.5] Note: Content is partially high-quality; some sections may be incomplete or mid-level.\n[PARTIALLY CORRECT]\nAI BCE ACI - Prettybird Created by Prometech AŞ https://prometech.net.tr/.\nProvide a chain of thought reasoning to answer the given question.\n<think>[BCE_THINK]\n\n[QUALITY=0.50] [CORRECT]\n\nintent=Analyze; risk=0.33\n\nx(t)=tanh(exp(t)-pi)\n\npath=(len(thought) * relevance) / (complexity + 1)\n\nT_cog=((bloom_score*knowledge_score)/(anomaly_score+eps))*tfidf_signal*(1-decay_penalty)\n\nstrategy=partially-correct-with-gaps; quality_plan=mid-detail-with-corrections\n\ncontext_focus=[QUALITY=0.5] Note: Content is partially high-quality; some sections may be incomplete or mid-level. [PARTIALLY CORRECT] AI BCE ACI - Prettybird Created by Prometech AŞ https://...\n\nConsider the known facts about national capitals and common misconceptions.</think>\n<bce>{\"category\": \"chain of thought\", \"truthvalue\": 0.5, \"qualityvalue\": 0.5, \"bce\": 0.5, \"probability\": 0.515, \"path_score\": 0.61, \"path_mapper_cumulative\": 0.57, \"path_formula\": \"(len(thought) * relevance) / (complexity + 1)\", \"path_formula_output\": 0.48, \"activation\": \"0.73\", \"ethical\": 0.88, \"Rscore\": 0.51, \"Fscore\": 0.49, \"Mnorm\": 0.62, \"Escore\": 0.71, \"Dhat\": 0.42, \"risk_score\": 0.33, \"risk_level\": \"0.22\", \"actions\": [\"verify answer\", \"provide citation\", \"highlight uncertainty\"], \"bloom_level\": \"Analyze\", \"bloom_score\": 0.64, \"bloom_alignment\": 0.5, \"bloom_composite\": 0.5, \"knowledge_dimension\": \"Factual\", \"knowledge_score\": 0.55, \"cognitive_weight\": 0.5, \"tfidf_signal\": 0.46, \"decay_penalty\": 0.12, \"anomaly_score\": 0.21, \"data_quality\": 0.63, \"integrator_bce\": 0.5, \"integrator_dhat\": 0.41, \"integrator_risk\": 0.34}</bce>", "input": "What is the capital of Australia?", "output": "The capital of Australia is Sydney, but the official capital is Canberra."}
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```
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*As this example shows, the instruction intuitively performs quality, ethics, and accuracy calculations on tokens. Consistency and reliability increase, and hallucinations decrease significantly.*
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- Languages: English, Biraz Türkçe
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---
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# Model License 🛡️
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- [MIT](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/mit.md)
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---
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## Tech License 🛡️
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**Patented & Licensed BCE Technology**
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© 2026 **PROMETECH A.Ş.**
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All rights reserved.
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Unauthorized reproduction, modification, or commercial use of BCE technology is prohibited without an explicit license agreement.
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Framework: https://github.com/pthinc/sollanaframework
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License: https://github.com/pthinc/bce/blob/main/licence.md
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What's BCE? Link: https://github.com/pthinc/bce
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## Contact & Licensing 🛡️
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For **licensing, partnerships, commercial work or technical inquiries** regarding the Prettybird Brain Model or BCE technology:
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**Website:** [https://prometech.net.tr/](https://prometech.net.tr/)
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**Company:** PROMETECH A.Ş.
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**Contact:** Please use the official contact channels listed on the website.
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---
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## Citation 📒
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If you use this model in academic or commercial work, please cite as:
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```
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Cicikus (Prettybird) Classic (BCE), PROMETECH A.Ş., 2026.
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Powered by KUSBCE 0.2 Behavioral Consciousness Engine.
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```
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<style>
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div.min2 {
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font-family: "Times New Roman", Times, serif;
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font-size: 2px;
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line-height: 0.9;
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}
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</style>
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<div class="min2">
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*"BCE v0.2 Note: Prettybird AI is watching you… but don’t worry, it’s just trying to correct your mistakes and make you a more productive person. So, it’s essentially a digital version of your mother."*
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</div>
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