Instructions to use nekam13/zbynka with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nekam13/zbynka with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="nekam13/zbynka",
	filename="zbynka-beta-01.q4_k_m.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use nekam13/zbynka with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nekam13/zbynka:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf nekam13/zbynka:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nekam13/zbynka:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf nekam13/zbynka: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 nekam13/zbynka:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf nekam13/zbynka: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 nekam13/zbynka:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nekam13/zbynka:Q4_K_M

Use Docker

docker model run hf.co/nekam13/zbynka:Q4_K_M

LM Studio
Jan

vLLM

How to use nekam13/zbynka with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nekam13/zbynka"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nekam13/zbynka",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nekam13/zbynka:Q4_K_M

Ollama
How to use nekam13/zbynka with Ollama:
```
ollama run hf.co/nekam13/zbynka:Q4_K_M
```

Unsloth Studio new

How to use nekam13/zbynka 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 nekam13/zbynka 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 nekam13/zbynka to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nekam13/zbynka to start chatting

Pi new

How to use nekam13/zbynka with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf nekam13/zbynka: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": "nekam13/zbynka:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use nekam13/zbynka with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf nekam13/zbynka: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 nekam13/zbynka:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use nekam13/zbynka with Docker Model Runner:
```
docker model run hf.co/nekam13/zbynka:Q4_K_M
```

Lemonade

How to use nekam13/zbynka with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nekam13/zbynka:Q4_K_M

Run and chat with the model

lemonade run user.zbynka-Q4_K_M

List all available models

lemonade list

🧙‍♀️ Zbyňka: Strážkyně historie a moudrosti (v. 2026)

Zbyňka je česky mluvící konverzační AI agentka, fine-tunovaná na českých textech a inspirovaná postavou Zbyňky Čechové. Je vstřícná, prozřívavá a podporuje tool calling.

Technické parametry

Parametr	Hodnota
Base model	Qwen2 3.1B
Kvantizace	Q4_K_M (Unsloth)
Velikost souboru	1,93 GB
Kontext	32 768 tokenů
Formát	GGUF
Chat template	ChatML (`<
Tool calling	✅ Ano

Dataset

Fine-tuning proběhl na datasetu nekam13/zbynka-dataset obsahujícím české texty (CC BY-NC 4.0).

Použití

llama.cpp

./llama-cli -m zbynka-beta-01.q4_k_m.gguf \
  --chat-template chatml \
  -p "Ahoj Zbyňko, jak se máš?"

Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama(model_path="zbynka-beta-01.q4_k_m.gguf", n_ctx=4096)
output = llm.create_chat_completion(messages=[
    {"role": "user", "content": "Ahoj! Kdo jsi?"}
])
print(output["choices"]["message"]["content"])

Omezení

Zbyňka je experimentální beta verze určená pro nekomerční použití. Může generovat nepřesné odpovědi — vždy ověřuj důležité informace.

Downloads last month: 61

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

4-bit

nekam13
/

zbynka

🧙‍♀️ Zbyňka: Strážkyně historie a moudrosti (v. 2026)

Technické parametry

Dataset

Použití

llama.cpp

Python (llama-cpp-python)

Omezení

Dataset used to train nekam13/zbynka