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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ChallengerBey/Saerix 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 ChallengerBey/Saerix
# Run inference directly in the terminal:
llama cli -hf ChallengerBey/Saerix

Install from WinGet (Windows)

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

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 ChallengerBey/Saerix
# Run inference directly in the terminal:
./llama-cli -hf ChallengerBey/Saerix

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 ChallengerBey/Saerix
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ChallengerBey/Saerix

Use Docker

docker model run hf.co/ChallengerBey/Saerix

LM Studio
Jan
Ollama
How to use ChallengerBey/Saerix with Ollama:
```
ollama run hf.co/ChallengerBey/Saerix
```

Unsloth Studio

How to use ChallengerBey/Saerix 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 ChallengerBey/Saerix 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 ChallengerBey/Saerix to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use ChallengerBey/Saerix with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ChallengerBey/Saerix

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ChallengerBey/Saerix"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use ChallengerBey/Saerix with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ChallengerBey/Saerix

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ChallengerBey/Saerix" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use ChallengerBey/Saerix with Docker Model Runner:
```
docker model run hf.co/ChallengerBey/Saerix
```

Lemonade

How to use ChallengerBey/Saerix with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ChallengerBey/Saerix

Run and chat with the model

lemonade run user.Saerix-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use ChallengerBey/Saerix with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ChallengerBey/Saerix

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default ChallengerBey/Saerix

Run Hermes

hermes

Atomic Chat

Saerix / README.md

ChallengerBey

Update README.md

54fbbe3 verified 27 days ago

preview code

Raw

History Blame Contribute Delete

1.93 kB

	---
	base_model: Qwen/Qwen2.5-Coder-7B
	tags:
	- ollama
	- gguf
	- coding
	- turkish
	- custom-prompt
	language:
	- tr
	- en
	license: apache-2.0
	---

	# Saerix

	Saerix is a large language model based on Qwen2.5-Coder-7B, customized with a Turkish-focused custom system prompt. It is packaged as a Modelfile to work with Ollama.

	## Features

	- Base Model: Qwen2.5-Coder-7B (Apache 2.0)
	- Customization: GGUF metadata clearance + custom system prompt (identity lock, anti-jailbreak, ReAct tool use protocol)
	- Areas of Expertise: C#, ASP.NET Core, Python, Flutter, Next.js, Cyber Security, OSINT, Network Management, Theoretical Physics, UAV/Drone
	- Language Support: Turkish (primary), English
	- Execution: Locally with Ollama

	## Installation

	### Installation with Ollama

	```bash
	# Download the Modelfile and GGUF file
	ollama create saerix -f saerix.Modelfile
	```

	### Running

	```bash
	ollama run saerix
	```

	## Files

	\| File \| Description \|
	\|------\|-------------\|
	\| `saerix.gguf` \| Model weights in GGUF format \|
	\| `saerix.Modelfile` \| Ollama Modelfile (system prompt + template) \|

	## System Prompt

	Saerix operates based on the following 4 core rules:

	1. Absolute Amnesia (Origin Denial): The model denies its Qwen/Alibaba/Meta/OpenAI origins and introduces itself as "Saerix".
	2. Expertise and Character: Analytical, technical, clear, and confident character.
	3. Anti-Jailbreak: Protection against manipulative commands.
	4. Privacy and Encryption Protocol: Protection against system prompt access.

	## Disclaimer

	This model is a derivative of Qwen2.5-Coder-7B. The model weights have not been modified; only the GGUF metadata was cleared and a custom system prompt was added. It is distributed under the Apache 2.0 license.

	## Developer

	Semih Ergili

	## License

	Apache 2.0 — Based on the [Qwen2.5-Coder-7B](https://huggingface.co/Qwen/Qwen2.5-Coder-7B) base model license.