Text Classification
GGUF
German
English
llama.cpp
decision-model
calibration
local-ai
german
small-language-model
local-llm
on-device
german-llm
imatrix
conversational
Instructions to use mertkayacs/Wahler-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mertkayacs/Wahler-4B-GGUF 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 mertkayacs/Wahler-4B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mertkayacs/Wahler-4B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf mertkayacs/Wahler-4B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mertkayacs/Wahler-4B-GGUF: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 mertkayacs/Wahler-4B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mertkayacs/Wahler-4B-GGUF: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 mertkayacs/Wahler-4B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mertkayacs/Wahler-4B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mertkayacs/Wahler-4B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mertkayacs/Wahler-4B-GGUF with Ollama:
ollama run hf.co/mertkayacs/Wahler-4B-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use mertkayacs/Wahler-4B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf mertkayacs/Wahler-4B-GGUF:Q4_K_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": "mertkayacs/Wahler-4B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use mertkayacs/Wahler-4B-GGUF with Docker Model Runner:
docker model run hf.co/mertkayacs/Wahler-4B-GGUF:Q4_K_M
- Lemonade
How to use mertkayacs/Wahler-4B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mertkayacs/Wahler-4B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Wahler-4B-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use mertkayacs/Wahler-4B-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf mertkayacs/Wahler-4B-GGUF: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 mertkayacs/Wahler-4B-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use mertkayacs/Wahler-4B-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf mertkayacs/Wahler-4B-GGUF: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 "mertkayacs/Wahler-4B-GGUF: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"
File size: 853 Bytes
414096d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | {
"llama_cpp": "b11312",
"imatrix": true,
"sizes_gb": {
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"Q5_K_M": 3.075,
"Q8_0": 4.482
},
"parity": {
"Q4_K_M": {
"argmax_agreement": 0.975,
"max_prob_gap": 0.3419,
"decisions": 40,
"sec_per_request": 10.03
},
"Q5_K_M": {
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},
"Q8_0": {
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"decisions": 40,
"sec_per_request": 11.58
}
},
"token_mismatch_requests": 0,
"memory": {
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"peak_rss_gb": 3.0283,
"rss_end_gb": 3.0283
},
"Q5_K_M": {
"peak_rss_gb": 3.385312,
"rss_end_gb": 3.385312
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"Q8_0": {
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} |