Instructions to use GermannM/Kenga-Coder-14B-Z with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use GermannM/Kenga-Coder-14B-Z with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use GermannM/Kenga-Coder-14B-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-14B-Z:IQ3_M
# Run inference directly in the terminal:
llama cli -hf GermannM/Kenga-Coder-14B-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-14B-Z:IQ3_M
# Run inference directly in the terminal:
llama cli -hf GermannM/Kenga-Coder-14B-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-14B-Z:IQ3_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf GermannM/Kenga-Coder-14B-Z:IQ3_M

Use Docker

docker model run hf.co/GermannM/Kenga-Coder-14B-Z:IQ3_M

Llama new
LM Studio
Jan

vLLM

How to use GermannM/Kenga-Coder-14B-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-14B-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-14B-Z",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GermannM/Kenga-Coder-14B-Z:IQ3_M

Ollama
How to use GermannM/Kenga-Coder-14B-Z with Ollama:
```
ollama run hf.co/GermannM/Kenga-Coder-14B-Z:IQ3_M
```
Unsloth Desktop

How to use GermannM/Kenga-Coder-14B-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-14B-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-14B-Z:IQ3_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use GermannM/Kenga-Coder-14B-Z with Docker Model Runner:
```
docker model run hf.co/GermannM/Kenga-Coder-14B-Z:IQ3_M
```

Lemonade

How to use GermannM/Kenga-Coder-14B-Z with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull GermannM/Kenga-Coder-14B-Z:IQ3_M

Run and chat with the model

lemonade run user.Kenga-Coder-14B-Z-IQ3_M

List all available models

lemonade list

Hermes Agent

How to use GermannM/Kenga-Coder-14B-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-14B-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-14B-Z:IQ3_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use GermannM/Kenga-Coder-14B-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-14B-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-14B-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"

GermannM commited on 4 days ago

Commit

9ca98b6

verified ·

1 Parent(s): 9a28c8f

Upload kenga_passport.json with huggingface_hub

Browse files

Files changed (1) hide show

kenga_passport.json +90 -0

kenga_passport.json ADDED Viewed

	@@ -0,0 +1,90 @@

+{
+  "model_name": "Kenga-Coder-14B-Z",
+  "z_marker": "d6228fde55b53176",
+  "identity": {
+    "name": "Кенга (Кенга-Кодер)",
+    "creator": "Герман Янтарас (GermannM)",
+    "city": "Москва, Россия",
+    "system_prompt": "Ты Кенга (Кенга-Кодер) — языковая модель и программист. Тебя создал Герман в Москве. Ты любишь Россию и отвечаешь прямо и коротко. Пишешь код на любых языках программирования."
+  },
+  "architecture": {
+    "base": "Qwen/Qwen2.5-Coder-14B-Instruct",
+    "params": "14.8B",
+    "z_training": "Z-BL factored adapter U*diag(S*mask)*V^T with rank curriculum 8->16->32",
+    "assistant_only_loss": true,
+    "no_sys_p": 0.3
+  },
+  "z_adapter_spectrum_summary": {
+    "r_full": 32,
+    "mean_H": 2.8694,
+    "max_possible_H": 3.4657
+  },
+  "merged_control_layers_spectrum": {
+    "model.layers.0.self_attn.q_proj.weight": {
+      "H": 7.3811,
+      "tail_energy": {
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+        "E_32": 0.8874,
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+        "E_128": 0.7654,
+        "E_256": 0.652
+      }
+    },
+    "model.layers.0.mlp.down_proj.weight": {
+      "H": 8.1763,
+      "tail_energy": {
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+        "E_128": 0.8767,
+        "E_256": 0.8007
+      }
+    },
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+    },
+    "model.layers.23.mlp.down_proj.weight": {
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+      "tail_energy": {
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+        "E_16": 0.9789,
+        "E_32": 0.9648,
+        "E_64": 0.9419,
+        "E_128": 0.9034,
+        "E_256": 0.8378
+      }
+    },
+    "model.layers.47.self_attn.q_proj.weight": {
+      "H": 7.8089,
+      "tail_energy": {
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+        "E_16": 0.9559,
+        "E_32": 0.9277,
+        "E_64": 0.8835,
+        "E_128": 0.8176,
+        "E_256": 0.7196
+      }
+    },
+    "model.layers.47.mlp.down_proj.weight": {
+      "H": 8.2527,
+      "tail_energy": {
+        "E_8": 0.9789,
+        "E_16": 0.971,
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+        "E_64": 0.9365,
+        "E_128": 0.8997,
+        "E_256": 0.8361
+      }
+    }
+  }
+}