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
Update README.md
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README.md
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# Cicikuş (Prettybird) v3
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**by PROMETECH Inc.**
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| **Moonlight-16B-A3B (MoonshotAI)** | 87.8% | 83.1% | 79.4% | 81.7% | 83.8% | 87.8% | 89.4% |
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| **Kimi2.5** | 88.3% | 83.5% | 81.4% | 82.0% | 84.0% | 87.5% | 89.5% |
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| **Deepseek v3** | 89.1% | 85.2% | 82.1% | 83.2% | 85.3% | 88.9% | 91.0% |
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| **Cicikus v3
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### Overall Performance Averages 🔥
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|**Kimi 2.5**|%85.2|-%14.4|
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|**Gemma 3 PT 12B**|%84.8|-%14.0|
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|**Mistral-7B-Instruct-v0.3**|%77.2|-%6.4|
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|*Cicikus v3
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|**LLaMA 3.2 1B (Main Model)**|%67.6|+%3.2|
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# Cicikuş (Prettybird) v3 1.4B
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**by PROMETECH Inc.**
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| **Moonlight-16B-A3B (MoonshotAI)** | 87.8% | 83.1% | 79.4% | 81.7% | 83.8% | 87.8% | 89.4% |
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| **Kimi2.5** | 88.3% | 83.5% | 81.4% | 82.0% | 84.0% | 87.5% | 89.5% |
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| **Deepseek v3** | 89.1% | 85.2% | 82.1% | 83.2% | 85.3% | 88.9% | 91.0% |
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| **Cicikus v3 1.4B** | 76.5% | 69.5% | 68.4% | 76.2% | 58.6% | 58.1% | 88.4% |
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### Overall Performance Averages 🔥
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|**Kimi 2.5**|%85.2|-%14.4|
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|**Gemma 3 PT 12B**|%84.8|-%14.0|
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|**Mistral-7B-Instruct-v0.3**|%77.2|-%6.4|
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|*Cicikus v3 1.4B*|%70.8|%0|
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|**LLaMA 3.2 1B (Main Model)**|%67.6|+%3.2|
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