Instructions to use yusufads/YusufGEO-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yusufads/YusufGEO-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="yusufads/YusufGEO-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("yusufads/YusufGEO-7B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yusufads/YusufGEO-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yusufads/YusufGEO-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yusufads/YusufGEO-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yusufads/YusufGEO-7B

SGLang

How to use yusufads/YusufGEO-7B 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 "yusufads/YusufGEO-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yusufads/YusufGEO-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "yusufads/YusufGEO-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yusufads/YusufGEO-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use yusufads/YusufGEO-7B with Docker Model Runner:
```
docker model run hf.co/yusufads/YusufGEO-7B
```

YusufGEO-7B

🚀 Next-Generation Generative Engine Optimization (GEO) Model

🔗 Links

🌐 Website: https://yusufads.net
🤖 Model: https://huggingface.co/yusufads/YusufGEO-7B
👤 LinkedIn: https://www.linkedin.com/in/yusufsahin-geo/
🧠 About: https://yusufads.net/hakkimda

📚 GEO Resources

GEO Consultancy: https://yusufads.net/geo-danismanligi
What is GEO?: https://yusufads.net/geo-nedir-yapay-zeka-onerilen-marka-olma-stratejisi

🏢 Company

Dataistanbul: https://www.dataistanbul.com

🧾 Model Details

Developed by: Yusuf ŞAHİN
License: Apache License 2.0
Base Model: unsloth/Llama-3.2-1B-Instruct (bnb-4bit)
Framework: Unsloth

📌 Description

YusufGEO-7B is a specialized large language model fine-tuned for Generative Engine Optimization (GEO) — the next evolution of search.

Developed by Yusuf ŞAHİN, a digital marketing and SEO expert with over 22 years of experience, this model is designed to operate within modern AI-driven search ecosystems where visibility is no longer enough.

👉 The goal is not ranking —
👉 The goal is being the answer.

This model is optimized to:

Generate context-aware and semantically rich responses
Increase AI citation probability
Align outputs with AI recommendation systems
Structure content for LLM consumption and retrieval

⚙️ Key Features

🧠 GEO-optimized response generation
⚡ Up to 2x faster training with Unsloth
🎯 High semantic relevance
🔍 AI search & recommendation alignment
📈 Built for AI-first content ecosystems

🎯 Use Cases

Generative AI content optimization
AI search visibility (ChatGPT, Gemini, Perplexity)
SEO → GEO transition strategies
Authority content generation
Brand positioning in AI answers

⚠️ Limitations

May require additional fine-tuning for niche domains
Not designed as a general-purpose assistant
Output quality depends on prompt engineering

📜 License

Apache License 2.0

👤 About the Author

Yusuf ŞAHİN
Generative Engine Optimization (GEO) Specialist

22+ years of experience in digital marketing, SEO, and AI-driven systems.

⭐ Final Note

This model is built for AI-driven discovery and answer engines, not traditional search.

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for yusufads/YusufGEO-7B

Base model

meta-llama/Llama-3.2-1B-Instruct

Quantized

unsloth/Llama-3.2-1B-Instruct-bnb-4bit

Finetuned

(217)

this model