Text Generation
Safetensors
GGUF
Czech
English
qwen3_5
qwen
qwen3.5
czech
stem
physics
chemistry
biology
mathematics
programming
lora
distillation
distilled
conversational
Instructions to use KucLab/kuclab-hertz-0.8f 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 KucLab/kuclab-hertz-0.8f 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 KucLab/kuclab-hertz-0.8f:Q4_K_M # Run inference directly in the terminal: llama cli -hf KucLab/kuclab-hertz-0.8f:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf KucLab/kuclab-hertz-0.8f:Q4_K_M # Run inference directly in the terminal: llama cli -hf KucLab/kuclab-hertz-0.8f: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 KucLab/kuclab-hertz-0.8f:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf KucLab/kuclab-hertz-0.8f: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 KucLab/kuclab-hertz-0.8f:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf KucLab/kuclab-hertz-0.8f:Q4_K_M
Use Docker
docker model run hf.co/KucLab/kuclab-hertz-0.8f:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use KucLab/kuclab-hertz-0.8f with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "KucLab/kuclab-hertz-0.8f" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KucLab/kuclab-hertz-0.8f", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/KucLab/kuclab-hertz-0.8f:Q4_K_M
- Ollama
How to use KucLab/kuclab-hertz-0.8f with Ollama:
ollama run hf.co/KucLab/kuclab-hertz-0.8f:Q4_K_M
- Unsloth Desktop
- Pi
How to use KucLab/kuclab-hertz-0.8f with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf KucLab/kuclab-hertz-0.8f: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": "KucLab/kuclab-hertz-0.8f:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use KucLab/kuclab-hertz-0.8f with Docker Model Runner:
docker model run hf.co/KucLab/kuclab-hertz-0.8f:Q4_K_M
- Lemonade
How to use KucLab/kuclab-hertz-0.8f with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull KucLab/kuclab-hertz-0.8f:Q4_K_M
Run and chat with the model
lemonade run user.kuclab-hertz-0.8f-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use KucLab/kuclab-hertz-0.8f with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf KucLab/kuclab-hertz-0.8f: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 KucLab/kuclab-hertz-0.8f:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use KucLab/kuclab-hertz-0.8f with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf KucLab/kuclab-hertz-0.8f: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 "KucLab/kuclab-hertz-0.8f: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"
KucLab Hertz 0.8F
A Czech/English STEM + programming assistant built by KucLab on top of Qwen/Qwen3.5-9B. Follow-up to Hertz 0.8: same QLoRA recipe, trained on the kuclab_hertz_0.8f corpus.
What this is
- Base: Qwen/Qwen3.5-9B (~9B params, Apache 2.0)
- Method: QLoRA, r=16 / alpha=32, 4-bit, 2 epochs, merged to bf16 then quantized
- Training data: kuclab_hertz_0.8f (alpaca format)
- Trained on: 2026-09-13
- Context: 32768 tokens (
num_ctx) - Format: GGUF q4_k_m noMTP (~5.3GB)
- Full weights:
merged/(bf16 safetensors) andadapter/(LoRA) included in this repo
Quickstart (Ollama)
curl -O https://huggingface.co/KucLab/kuclab-hertz-0.8f/resolve/main/Modelfile
ollama create kuclab-hertz-0.8f -f Modelfile
ollama run kuclab-hertz-0.8f
License
Apache 2.0, inherited from Qwen/Qwen3.5-9B.
Credits
- Base: Qwen/Qwen3.5-9B
- KucLab
- Downloads last month
- 40
Hardware compatibility
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4-bit