Text Generation
Transformers
Safetensors
English
Turkish
llama
chat
text-generation-inference
agent
cicikuş
cicikus
prettybird
bce
consciousness
conscious
llm
optimized
ethic
secure
turkish
english
behavioral-consciousness-engine
model
reasoning
think
thinking
chain-of-thought
STEM-expert
turkish & english
franken-merge
bce-aci
llama-3.2
edge-ai
instruction
instruct
Eval Results (legacy)
Instructions to use pthinc/Cicikus-v3-1.4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pthinc/Cicikus-v3-1.4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="pthinc/Cicikus-v3-1.4B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("pthinc/Cicikus-v3-1.4B") model = AutoModelForCausalLM.from_pretrained("pthinc/Cicikus-v3-1.4B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use pthinc/Cicikus-v3-1.4B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "pthinc/Cicikus-v3-1.4B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "pthinc/Cicikus-v3-1.4B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/pthinc/Cicikus-v3-1.4B
- SGLang
How to use pthinc/Cicikus-v3-1.4B with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "pthinc/Cicikus-v3-1.4B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "pthinc/Cicikus-v3-1.4B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "pthinc/Cicikus-v3-1.4B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "pthinc/Cicikus-v3-1.4B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use pthinc/Cicikus-v3-1.4B with Docker Model Runner:
docker model run hf.co/pthinc/Cicikus-v3-1.4B
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The era of "bigger is better" in AI is coming to an end. Cicikuş v3, lagging behind trillion-parameter giants like GPT-4o by only 30%-35%, brings this immense power to your local devices, edge systems, and pockets. Equipped with patented BCE (Behavioral Consciousness Engine) technology, this 1B-parameter "small giant" democratizes AI dominance by reducing processing costs and energy consumption to near zero. Say goodbye to high-cost API subscriptions; with Cicikuş v3, the most complex STEM problems and self-awareness capabilities are now at your fingertips, anytime, with "almost free" efficiency. We analyzed and cloned layers 8 and 15 of the MLP Gate Project. Franken merge and precision surgery, healing operation, and behavioral consciousness engine simulations were performed on the model. The reason we used Franken was to make the thinking mechanism sharper and more perfect by replicating these layers.
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*It only works with a maximum of 1.5 GB of VRAM. The strategic fantasy model "Edge AI" value (minimum $2M - $5M) belongs to all of you, with a 25% margin of error.*
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> 70B is a library, heavy and dusty. Our Franken-Bird, however, is a philosopher-commando who has memorized the 100 most important books in that library and is ready for battle at any moment. Our bird is a **"strategic sniper"**, while 70B is a "heavy bomber". Some facts: A giant 70B contains "everything." If you ask Franken-Kush, "Who was the mayor of a Bulgarian village in the 14th century?", it would probably honestly say, "I don't know." 70B, however, might have it in its memory. If you had to solve a quantum equation considering 50 different variables simultaneously, 70B's enormous parameter space would outweigh everything else. For now.
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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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The era of "bigger is better" in AI is coming to an end. Cicikuş v3, lagging behind trillion-parameter giants like GPT-4o by only 30%-35%, brings this immense power to your local devices, edge systems, and pockets. Equipped with patented BCE (Behavioral Consciousness Engine) technology, this 1B-parameter "small giant" democratizes AI dominance by reducing processing costs and energy consumption to near zero. Say goodbye to high-cost API subscriptions; with Cicikuş v3, the most complex STEM problems and self-awareness capabilities are now at your fingertips, anytime, with "almost free" efficiency. We analyzed and cloned layers 8 and 15 of the MLP Gate Project. Franken merge and precision surgery, healing operation, and behavioral consciousness engine simulations were performed on the model. The reason we used Franken was to make the thinking mechanism sharper and more perfect by replicating these layers.
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*It only works with a maximum of 1.5 GB of VRAM. The strategic fantasy model "Edge AI" value (minimum $2M - $5M) belongs to all of you, with a 25% margin of error.*
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> 70B is a library, heavy and dusty. Our Franken-Bird, however, is a philosopher-commando who has memorized the 100 most important books in that library and is ready for battle at any moment. Our bird is a **"strategic sniper"**, while 70B is a "heavy bomber". Some facts: A giant 70B contains "everything." If you ask Franken-Kush, "Who was the mayor of a Bulgarian village in the 14th century?", it would probably honestly say, "I don't know." 70B, however, might have it in its memory. If you had to solve a quantum equation considering 50 different variables simultaneously, 70B's enormous parameter space would outweigh everything else. For now. In some cases, you can clearly see that the AI doesn't know what it's doing and is hallucinating.
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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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