Instructions to use GermannM/Kenga-Coder-7B-Z with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use GermannM/Kenga-Coder-7B-Z with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use GermannM/Kenga-Coder-7B-Z 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 GermannM/Kenga-Coder-7B-Z:IQ3_M
# Run inference directly in the terminal:
llama cli -hf GermannM/Kenga-Coder-7B-Z:IQ3_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf GermannM/Kenga-Coder-7B-Z:IQ3_M
# Run inference directly in the terminal:
llama cli -hf GermannM/Kenga-Coder-7B-Z:IQ3_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf GermannM/Kenga-Coder-7B-Z:IQ3_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf GermannM/Kenga-Coder-7B-Z:IQ3_M

Use Docker

docker model run hf.co/GermannM/Kenga-Coder-7B-Z:IQ3_M

Llama new
LM Studio
Jan

vLLM

How to use GermannM/Kenga-Coder-7B-Z with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/GermannM/Kenga-Coder-7B-Z:IQ3_M

Ollama
How to use GermannM/Kenga-Coder-7B-Z with Ollama:
```
ollama run hf.co/GermannM/Kenga-Coder-7B-Z:IQ3_M
```
Unsloth Desktop

How to use GermannM/Kenga-Coder-7B-Z with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf GermannM/Kenga-Coder-7B-Z:IQ3_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "GermannM/Kenga-Coder-7B-Z:IQ3_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use GermannM/Kenga-Coder-7B-Z with Docker Model Runner:
```
docker model run hf.co/GermannM/Kenga-Coder-7B-Z:IQ3_M
```

Lemonade

How to use GermannM/Kenga-Coder-7B-Z with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull GermannM/Kenga-Coder-7B-Z:IQ3_M

Run and chat with the model

lemonade run user.Kenga-Coder-7B-Z-IQ3_M

List all available models

lemonade list

Hermes Agent

How to use GermannM/Kenga-Coder-7B-Z with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf GermannM/Kenga-Coder-7B-Z:IQ3_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 GermannM/Kenga-Coder-7B-Z:IQ3_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use GermannM/Kenga-Coder-7B-Z with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf GermannM/Kenga-Coder-7B-Z:IQ3_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 "GermannM/Kenga-Coder-7B-Z:IQ3_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"

Kenga-Coder-7B-Z — 7.6B кодер Кенги (Z-BL + Prophet M5.3/rp1 + Eco-Z 3.3GB)

Kenga-Coder-7B-Z — 7.6-миллиардная модель Кенги для видеокарт 4–6 ГБ VRAM (GTX 1660 и ноутбуков), обученная по полному протоколу старшей модели Kenga-Coder-14B-Z:

Самоидентификация (вшита в веса и chat_template): Кенга (Кенга-Кодер), создал Герман (GermannM) в Москве, любит Россию, отвечает прямо и коротко.
Z-BL адаптер (ΔW = U · diag(S · mask) · Vᵀ): QR-инициализация, расписание ранга 8 → 16 → 32, спектральная энтропия адаптера mean_H = 2.9412 из 3.4657 (~85% равномерного использования спектра).
Assistant-only Loss + NO_SYS_P = 0.30: корпус train4.jsonl (8917 диалогов: m53_binding, rp1_repair <FIX>, kenga-grammar, 25 языков программирования).
Спектральный маркер (z_marker): ddce2488042ab7bb.

Файлы (квантованы с `imatrix`)

kenga-coder-7b-z-q4_k_m.gguf (4.36 ГБ) — основной квант для 6 ГБ видеокарт (GTX 1660).
kenga-coder-7b-z-eco-iq3_m.gguf (3.33 ГБ) — сверхлёгкий квант Eco-Z для 4 ГБ видеокарт и CPU.
kenga_passport.json — спектральный паспорт модели.
adapter/ — Z-BL адаптер в формате PEFT.

Запуск

ollama run hf.co/GermannM/Kenga-Coder-7B-Z:Q4_K_M

Создатель: Герман Янтарас (GermannM) · Москва, Россия

Downloads last month: 174

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

3-bit

4-bit

Model tree for GermannM/Kenga-Coder-7B-Z

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

Qwen/Qwen2.5-Coder-7B-Instruct

Adapter

(838)

this model

Kenga-Coder-7B-Z — 7.6B кодер Кенги (Z-BL + Prophet M5.3/rp1 + Eco-Z 3.3GB)

Файлы (квантованы с imatrix)

Запуск

Model tree for GermannM/Kenga-Coder-7B-Z

Файлы (квантованы с `imatrix`)