Instructions to use omerkaragulmez/XbyK-0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use omerkaragulmez/XbyK-0.1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="omerkaragulmez/XbyK-0.1",
	filename="gguf/XbyK-0.1-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use omerkaragulmez/XbyK-0.1 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 omerkaragulmez/XbyK-0.1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf omerkaragulmez/XbyK-0.1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf omerkaragulmez/XbyK-0.1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf omerkaragulmez/XbyK-0.1: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 omerkaragulmez/XbyK-0.1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf omerkaragulmez/XbyK-0.1: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 omerkaragulmez/XbyK-0.1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf omerkaragulmez/XbyK-0.1:Q4_K_M

Use Docker

docker model run hf.co/omerkaragulmez/XbyK-0.1:Q4_K_M

LM Studio
Jan

vLLM

How to use omerkaragulmez/XbyK-0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "omerkaragulmez/XbyK-0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "omerkaragulmez/XbyK-0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/omerkaragulmez/XbyK-0.1:Q4_K_M

Ollama
How to use omerkaragulmez/XbyK-0.1 with Ollama:
```
ollama run hf.co/omerkaragulmez/XbyK-0.1:Q4_K_M
```

Unsloth Studio

How to use omerkaragulmez/XbyK-0.1 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 omerkaragulmez/XbyK-0.1 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 omerkaragulmez/XbyK-0.1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for omerkaragulmez/XbyK-0.1 to start chatting

How to use omerkaragulmez/XbyK-0.1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf omerkaragulmez/XbyK-0.1:Q4_K_M

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": "omerkaragulmez/XbyK-0.1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use omerkaragulmez/XbyK-0.1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf omerkaragulmez/XbyK-0.1:Q4_K_M

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 omerkaragulmez/XbyK-0.1:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use omerkaragulmez/XbyK-0.1 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf omerkaragulmez/XbyK-0.1:Q4_K_M

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 "omerkaragulmez/XbyK-0.1:Q4_K_M" \
  --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 omerkaragulmez/XbyK-0.1 with Docker Model Runner:
```
docker model run hf.co/omerkaragulmez/XbyK-0.1:Q4_K_M
```

Lemonade

How to use omerkaragulmez/XbyK-0.1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull omerkaragulmez/XbyK-0.1:Q4_K_M

Run and chat with the model

lemonade run user.XbyK-0.1-Q4_K_M

List all available models

lemonade list

XbyK-0.1

XbyK-0.1 is a fine-tuned version of mistralai/Mistral-Nemo-Instruct-2407 specialized for Xperience by Kentico — a digital experience platform (DXP).

⚠️ This is not an official Kentico product. XbyK-0.1 is a community-driven research project with no affiliation to Kentico a.s. It is not endorsed, sponsored, or maintained by Kentico. No commercial intent of any kind.

Who we are?

As Portalgrup AI team, we develop, build and maintane AI solutions. Portalgrup founded in 2007, PortalGrup entered the thriving internet ecosystem with a singular focus: creating and managing web portals. But as time unfolded, our journey took an exhilarating turn. We transformed into a versatile digital solutions provider, extending our reach across a diverse spectrum of services.

Portalgrup website: More detail

Version: 0.1 — Why So Early?

This model is at version 0.1 because its current evaluation results reflect meaningful room for improvement.

Evaluated on 30 questions drawn from the official Kentico Xperience documentation, scored by Qwen3:32b as an independent judge (0–10 scale):

Metric	Result
Average score	5.7 / 10
Score ≥ 7 rate	40% (12 / 30)
Average response time	1.4s

The 0.1 versioning is intentional and honest — the model is functional and useful for many queries, but there are known dataset quality issues that will be addressed in future iterations.

Known Issues & Planned Improvements

The following problems were identified through systematic evaluation and are documented here for full transparency:

Format Issues

Question echo as heading — Most responses start with ## {question text} or ### {question text}. This is caused by training examples where assistant answers included the question as a heading.
Fix: strip heading prefixes from all assistant turns in the training data.
One-sentence truncated answers — Some responses end abruptly after a single sentence (e.g., "Xperience gives you complete control over your content.").
Fix: enforce minimum response depth in training examples.

Factual Errors

Topic	Error
Headless draft vs. published	Model incorrectly states that draft items are accessible via the headless API — only Published items are
Content sync — image variants	Model gave an irrelevant e-commerce paragraph instead of answering the actual question
Automation license tier	Incomplete or incorrect license tier information
Email channel license tier	Wrong license threshold stated

Terminology Inconsistencies

Model uses "Asset tiles" instead of the correct term "content item assets"
Inconsistent usage of "Content Hub" vs. older naming conventions from previous Kentico versions

Weak Topic Coverage

The following topics scored lowest and need additional training examples:

Topic	Issue
Pages vs. Content items	Core conceptual difference covered too superficially in training data
Content sync — image variants	Too few specific examples in the dataset
Headless draft / publish lifecycle	Frequently misunderstood; needs correct, emphatic examples
License tier comparisons (Automation, Email)	License feature tables not well-represented in training data
Smart Folder creation (step-by-step)	Procedural steps are missing from examples

Capabilities

Chat: Answer questions about Kentico Xperience development, content management, digital marketing, e-commerce, and best practices
Multilingual: English (primary) + inherited multilingual capabilities from Mistral-Nemo base

Training Data

Fine-tuned on the official Kentico Xperience documentation:

docs.kentico.com — Documentation, guides, and training materials
api-reference.kentico.com — API reference

The full training dataset is available at omerkaragulmez/XbyK-0.1-dataset.

Usage

from transformers import pipeline

pipe = pipeline("text-generation", model="omerkaragulmez/XbyK-0.1", torch_dtype="bfloat16", device_map="auto")

messages = [
    {"role": "user", "content": "How do I create a content type in Kentico Xperience?"}
]

response = pipe(messages, max_new_tokens=512, temperature=0.3)
print(response[0]["generated_text"][-1]["content"])

With Ollama (recommended)

The quantized GGUF (gguf/XbyK-0.1-Q4_K_M.gguf) is available in this repo and can be used directly with Ollama:

ollama create xbyk-0.1 -f gguf/Modelfile
ollama run xbyk-0.1 "How do I use the Delivery API?"

Training Details

Base model: mistralai/Mistral-Nemo-Instruct-2407 (12B parameters)
Method: LoRA (Low-Rank Adaptation)
Hardware: 2× NVIDIA GH200
Framework: HuggingFace TRL + PEFT

Disclaimer

Xperience by Kentico™ is a registered trademark of Kentico a.s. This project is an independent community research effort and has no commercial intent. All documentation used for training is publicly available at docs.kentico.com

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Safetensors

Model size

12B params

Tensor type

BF16

Model tree for omerkaragulmez/XbyK-0.1

Base model

mistralai/Mistral-Nemo-Base-2407

Finetuned

mistralai/Mistral-Nemo-Instruct-2407

Quantized

(172)

this model

Quantizations

2 models