pthinc/cicikus_classic

• 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
• 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:

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

$$T_{cog}=\left(\frac{bloom\mathrm{\_}score\times knowledge\mathrm{\_}score}{anomaly\mathrm{\_}score+ϵ}\right)\cdot tfidf\mathrm{\_}signal\cdot (1-decay\mathrm{\_}penalty)T\_\{cog\}\; =\; \backslash left(\; \backslash frac\{bloom\backslash \_score\; \backslash times\; knowledge\backslash \_score\}\{anomaly\backslash \_score\; +\; \backslash epsilon\}\; \backslash right)\; \backslash cdot\; tfidf\backslash \_signal\; \backslash cdot\; (1\; -\; decay\backslash \_penalty)$$

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<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."}

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

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Model License 🛡️

• MIT

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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/

Company: PROMETECH A.Ş.

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

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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."*