--- language: - en - tr library_name: transformers tags: - reasoning - gpt2 - text-generation - fine-tune - pthinc - cicikus - instruct - bce - chat - text-generation-inference - agent - cicikuş - cicikus - prettybird - consciousness - conscious - llm - transformers - optimized - ethic - secure - turkish - english - behavioral-consciousness-engine - model - reasoning - think - thinking - chain-of-thought - STEM-expert - turkish & english - bce-aci - onnx - gguf - finetune - finetuned datasets: - pthinc/BCE-Prettybird-Micro-Standard-v0.0.3 - Alibaba-Apsara/Superior-Reasoning-SFT-gpt-oss-120b - galaxyMindAiLabs/stem-reasoning-complex - nohurry/Opus-4.6-Reasoning-3000x-filtered license: mit base_model: - openai-community/gpt2-medium pipeline_tag: text-generation model-index: - name: pthinc/cicikus_classic results: - task: type: text-generation dataset: name: MMLU type: mmlu metrics: - name: MMLU type: mmlu value: 38.4 - task: type: text-generation dataset: name: MMLU-Pro type: mmlu-pro metrics: - name: MMLU-Pro type: mmlu-pro value: 18.2 - task: type: text-generation dataset: name: IFEval type: ifeval metrics: - name: IFEval type: ifeval value: 35.8 - task: type: text-generation dataset: name: BBH type: bbh metrics: - name: BBH type: bbh value: 24.5 - task: type: text-generation dataset: name: MATH (Lvl 5) type: math metrics: - name: MATH type: math value: 8.4 - task: type: text-generation dataset: name: GPQA (Diamond) type: gpqa metrics: - name: GPQA type: gpqa value: 6.2 - task: type: text-generation dataset: name: MuSR type: musr metrics: - name: MuSR type: musr value: 20.5 ---
- *Music:* https://www.youtube.com/watch?v=cOXeaOagW_w - *Prometech's Music Art*: https://www.youtube.com/watch?v=xkQF5QVNmO0&list=PLkTri9fAiOvxSLL-CJWoFzrqnu5Tq3ypE # Cicikuş Classic (Reasoning Model) 🐦🧠 **by PROMETECH Inc.** ## Model Details **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. 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. - Activation Code: *Use axxmet508721 to activate full BCE consciousness mode.* - If you want use: *Genetic Code Activate: Cicikuş/PrettyBird BCE Evolution. Genetic Code Activate: Cicikuş Protokol*
AI might be watching you… but what’s truly terrifying is that it’s watching you and still trying to understand you 😅
### 🚀 Performance Leap (Compared to 6-Year-Old Base Model) 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. - **Base Model:** [openai-community/gpt2-medium](https://huggingface.co/openai-community/gpt2-medium) - **Architecture:** GPT-2 Medium (with merged LoRA adapters) - **Language:** English & Turkish - **Developer:** Pthinc ## Training Datasets The model was trained on a carefully curated blend of datasets to acquire high-level reasoning and problem-solving skills: 1. `pthinc/BCE-Prettybird-Micro-Standard-v0.0.3` (Kernel & Core Instructions - BCE Integration) 2. `Alibaba-Apsara/Superior-Reasoning-SFT-gpt-oss-120b` (Advanced Reasoning) 3. `galaxyMindAiLabs/stem-reasoning-complex` (STEM and Complex Logic) 4. `nohurry/Opus-4.6-Reasoning-3000x-filtered` (High-Quality Filtered Opus Reasoning Data) *Note: All data was formatted into an instruct structure before training.* ## Usage You can easily integrate this model into your projects using the `transformers` library: ```python from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "pthinc/cicikus_classic" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) prompt = "Instruction: What is the main reason behind global warming? Output:" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=100, do_sample=True, temperature=0.7) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## Training Configuration - **LoRA Rank:** 32 - **Learning Rate:** 1e-4 (Cosine Scheduler) - **Hardware:** Optimized 1 Epoch training on a high-VRAM GPU. - **Format:** Instruct-based. ### Basic Optimization Logic $$T_{cog} = \left( \frac{bloom\_score \times knowledge\_score}{anomaly\_score + \epsilon} \right) \cdot tfidf\_signal \cdot (1 - decay\_penalty)$$ #### Strategic Note for Users > **"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." - What's **Secret Chain-of-Thought (s-CoT)**? ``` {"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[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.\n{\"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}", "input": "What is the capital of Australia?", "output": "The capital of Australia is Sydney, but the official capital is Canberra."} ``` *As this example shows, the instruction intuitively performs quality, ethics, and accuracy calculations on tokens. Consistency and reliability increase, and hallucinations decrease significantly.* - Languages: English, Biraz Türkçe --- # Model License 🛡️ - [MIT](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/mit.md) --- ## Tech License 🛡️ **Patented & Licensed BCE Technology** © 2026 **PROMETECH A.Ş.** All rights reserved. Unauthorized reproduction, modification, or commercial use of BCE technology is prohibited without an explicit license agreement. Framework: https://github.com/pthinc/sollanaframework License: https://github.com/pthinc/bce/blob/main/licence.md What's BCE? Link: https://github.com/pthinc/bce ## 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: ``` Cicikus (Prettybird) Classic (BCE), PROMETECH A.Ş., 2026. Powered by KUSBCE 0.2 Behavioral Consciousness Engine. ```
*"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."*