Instructions to use XcyberPrince/Shopno with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use XcyberPrince/Shopno with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="XcyberPrince/Shopno",
	filename="Qwen2.5-0.5B-Instruct.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use XcyberPrince/Shopno with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf XcyberPrince/Shopno:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf XcyberPrince/Shopno:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf XcyberPrince/Shopno:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf XcyberPrince/Shopno:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf XcyberPrince/Shopno:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf XcyberPrince/Shopno:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf XcyberPrince/Shopno:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf XcyberPrince/Shopno:Q4_K_M

Use Docker

docker model run hf.co/XcyberPrince/Shopno:Q4_K_M

LM Studio
Jan
Ollama
How to use XcyberPrince/Shopno with Ollama:
```
ollama run hf.co/XcyberPrince/Shopno:Q4_K_M
```

Unsloth Studio

How to use XcyberPrince/Shopno with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for XcyberPrince/Shopno to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for XcyberPrince/Shopno to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for XcyberPrince/Shopno to start chatting

Atomic Chat new
Docker Model Runner
How to use XcyberPrince/Shopno with Docker Model Runner:
```
docker model run hf.co/XcyberPrince/Shopno:Q4_K_M
```

Lemonade

How to use XcyberPrince/Shopno with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull XcyberPrince/Shopno:Q4_K_M

Run and chat with the model

lemonade run user.Shopno-Q4_K_M

List all available models

lemonade list

Shopno / README.md

XcyberPrince

Update README with Shopno branding and owner Cyber Prince

e720851 verified 14 days ago

preview code

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	---
	license: apache-2.0
	base_model: Shopno-AI-Custom
	tags:
	- shopno
	- cyber-prince
	- custom-architecture
	- autonomous-agent
	---

	# Shopno AI (v3)

	Shopno is a highly advanced autonomous AI agent created and owned by Cyber Prince. It is built on a custom architecture optimized for high-performance reasoning, coding, and technical problem-solving.

	### Key Features:
	- Autonomous Logic: Specialized in agentic workflows and complex task execution.
	- Custom Branded: Developed independently by Cyber Prince Tech.
	- Optimized for Developers: Deep understanding of Python, CUDA, and system architecture.

	### Usage:
	To use Shopno, you can load it via the Hugging Face Transformers library or utilize the provided GGUF files for local inference.

	### Credits:
	- Developer & Owner: Cyber Prince
	- Architecture: Shopno Custom v3

	---
	This model is a private release. Any mention of external frameworks in automated benchmarks is a result of structural compatibility and does not reflect the underlying custom identity.