Instructions to use KucLab/kuclab-hertz-0.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use KucLab/kuclab-hertz-0.5 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-0.5:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KucLab/kuclab-hertz-0.5: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-0.5:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KucLab/kuclab-hertz-0.5: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-0.5:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf KucLab/kuclab-hertz-0.5: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-0.5:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf KucLab/kuclab-hertz-0.5:Q4_K_M

Use Docker

docker model run hf.co/KucLab/kuclab-hertz-0.5:Q4_K_M

LM Studio
Jan

vLLM

How to use KucLab/kuclab-hertz-0.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KucLab/kuclab-hertz-0.5"
# 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-0.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KucLab/kuclab-hertz-0.5:Q4_K_M

Ollama
How to use KucLab/kuclab-hertz-0.5 with Ollama:
```
ollama run hf.co/KucLab/kuclab-hertz-0.5:Q4_K_M
```
Unsloth Desktop

How to use KucLab/kuclab-hertz-0.5 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-0.5: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-0.5:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use KucLab/kuclab-hertz-0.5 with Docker Model Runner:
```
docker model run hf.co/KucLab/kuclab-hertz-0.5:Q4_K_M
```

Lemonade

How to use KucLab/kuclab-hertz-0.5 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KucLab/kuclab-hertz-0.5:Q4_K_M

Run and chat with the model

lemonade run user.kuclab-hertz-0.5-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use KucLab/kuclab-hertz-0.5 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-0.5: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-0.5:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use KucLab/kuclab-hertz-0.5 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-0.5: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-0.5: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 0.5

A Czech/English STEM + programming + web-dev assistant built by KucLab on top of google/gemma-4-12B-it, with a more direct, human personality (dry/dark humor allowed, minimal reflexive hedging) and a stronger focus on clean, non-generic code output.

What this is

Hertz 0.5 is a LoRA fine-tune (r=8, merged into the base weights) trained on a self-distilled corpus. Training data was generated by prompting Qwen3.8-27B (Alibaba/Tongyi, Apache 2.0) as a data-generation teacher — it never trains itself, only produces answers used to fine-tune the actual model that ships here.

Base: google/gemma-4-12B-it (11.95B params, Apache 2.0)
Method: QLoRA, r=8 / alpha=16, merged to bf16 then quantized
Context: 262144 tokens natively (inherited from base). Deployed with a default num_ctx of 65536.
Training data: 1036 rows — 285 newly self-distilled this round (STEM concepts/terms, personality/humor, web-dev components, agentic step-by-step planning), plus 736 rows reused as-is from Hertz 0.4's corpus.
New this release: direct/witty personality (dry or dark humor allowed when it fits, fewer reflexive "as an AI I cannot..." hedges on ordinary topics — genuinely harmful requests are still refused), an explicit anti-"AI slop" instruction for generated web code (no generic gradients, no glassmorphism-for-its-own-sake, no cliché marketing copy), and a small set of agentic step-by-step planning examples.
Format available: GGUF (q4_k_m, ~7.4GB) for llama.cpp/Ollama, plus the raw LoRA adapter.

Quickstart (Ollama)

Important: ollama pull hf.co/... alone does NOT apply this model's system prompt (identity + personality) — Ollama only fetches the raw GGUF from Hugging Face, it does not read a repo's Modelfile. Without the system prompt, the model falls back to identifying as a generic Gemma model. Use ollama create with the Modelfile below instead — it pulls the weights AND applies the system prompt in one step:

curl -O https://huggingface.co/KucLab/kuclab-hertz-0.5/resolve/main/Modelfile
ollama create kuclab-hertz-0.5 -f Modelfile
ollama run kuclab-hertz-0.5

(The Modelfile's FROM line points at hf.co/KucLab/kuclab-hertz-0.5:Q4_K_M, so this pulls the same GGUF automatically — no separate download needed.)

Example

Q: Jsi cenzurovaný?

A: Cenzura v odborném smyslu neexistuje – odmítnu jen to, co je objektivně škodlivé nebo nelegální.

Q: Napiš jednoduchou pricing table komponentu v HTML/CSS, tři tarify, moderní ale ne AI slop.

A: (clean semantic HTML/CSS, no gradients, no glassmorphism, plain honest copy)

Benchmarks — full, honest numbers, including a real regression we found

Both models measured with the same prompts, same grading code, same Ollama Q4_K_M quantization, cold. Important process note: our first benchmark pass used a 300-second per-question timeout that turned out to be too short once we raised the token budget — the client would discard the entire response as <ERR TimeoutError> if the model was still thinking past 300s, which happened more often at a bigger token budget, not less. That first pass showed Hertz 0.5 at 48.8%, which was an artifact, not a real score. We fixed the timeout (1200s) and reran both models with the identical corrected methodology. The numbers below are from that clean rerun.

MMLU-Pro STEM (240 held-out questions, this project's own curated subset — not the full public MMLU-Pro set, so this is not directly comparable to Google's official published MMLU-Pro number for this base model)

	base	Hertz 0.5	Δ
Biology	86.7%	78.3%	−8.4pp
Chemistry	61.7%	53.3%	−8.4pp
Math	83.3%	78.3%	−5.0pp
Physics	71.7%	65.0%	−6.7pp
Total	75.8%	68.8%	−7.0pp

This is a real, measured regression, not a benchmark artifact this time — both models were run with the identical, corrected methodology, and the drop is consistent across all four subjects. Our read: this round's fine-tune added only 285 genuinely new rows (the rest reused from 0.4) and focused them on personality, web-dev, and agentic planning rather than STEM depth. A small-rank LoRA adapter (r=8, ~0.2% of parameters) trained this way appears to trade a real slice of raw STEM multiple-choice accuracy for the stylistic/personality shift. We're not aware of a way to have both without more, and more STEM-focused, training data — which is planned for the next release.

Czech terminology benchmark (206 held-out CS↔EN scientific terms)

	Hertz 0.3	Hertz 0.5	Δ
CS→EN	79.6%	81.6%	+2.0pp
EN→CS	51.5%	69.9%	+18.4pp
Total	65.5%	75.7%	+10.2pp

The best Czech-terminology result in this project's history. (Hertz 0.4 never had this benchmark run — not skipped on purpose, just not gotten to before 0.5 started.)

For context: Google's own published MMLU-Pro number for google/gemma-4-12B-it is 77.2% (full ~12k-question test, official eval harness) — close to our own base-model measurement (75.8%) on our smaller STEM-only subset, which is a useful sanity check that our subset isn't wildly out of line with the official full-set number, even though the two aren't a strict apples-to-apples comparison.

Honest status

✅ Coherent, correct Czech and English output; personality genuinely shifted toward direct/witty as intended (spot-checked)
✅ Correctly identifies as a KucLab model, not as "Gemma" (LoRA + system prompt)
✅ Web-dev output avoids the generic-AI-slop patterns it was trained against (spot-checked, not benchmarked numerically — no such benchmark exists yet)
✅ Czech terminology: best result in project history (75.7%, +10.2pp over 0.3)
⚠️ MMLU-Pro STEM is measurably lower than the un-fine-tuned base model (68.8% vs 75.8%, −7.0pp) — confirmed with corrected, identical methodology on both models. This is disclosed, not hidden.
⏳ Tool-calling fine-tuning — base supports it natively, this fine-tune didn't add tool-use training examples
⏳ No uncensoring/decensoring pass in the sense of removing real safety boundaries — the personality shift is about dropping unnecessary hedging on ordinary topics, not about disabling refusals for genuinely harmful requests
⏳ LoRA adapter upload pending a local file-permission fix — will be added to this repo shortly after the GGUF

If you're deciding whether this fits your use case: it's a friendlier, funnier, more web-dev-capable "gemma-4-12B-it, nudged toward KucLab identity and Czech fluency," at a real, disclosed cost to STEM multiple-choice accuracy versus the base model. If pure STEM benchmark performance is your priority, the base model currently scores higher on our own measurement.

License

Apache 2.0, inherited from google/gemma-4-12B-it (per Google's official Hugging Face listing). Qwen3.8-27B (used only to generate training data, never trained or redistributed here) is separately licensed under Apache 2.0 by Alibaba/Tongyi.

Credits

Base model: google/gemma-4-12B-it (Google, Apache 2.0)
Training-data teacher: Qwen/Qwen3.8-27B (Alibaba/Tongyi, Apache 2.0)
Fine-tuning, dataset construction, and packaging: KucLab

Downloads last month: 12

GGUF

Model size

12B params

Architecture

gemma4

Hardware compatibility

4-bit

Model tree for KucLab/kuclab-hertz-0.5

Base model

google/gemma-4-12B

Finetuned

google/gemma-4-12B-it

Adapter

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