Instructions to use KucLab/kuclab-hertz-0.4 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-0.4 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-0.4:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KucLab/kuclab-hertz-0.4: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-0.4:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KucLab/kuclab-hertz-0.4:Q4_K_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 KucLab/kuclab-hertz-0.4:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf KucLab/kuclab-hertz-0.4:Q4_K_M

Use Docker

docker model run hf.co/KucLab/kuclab-hertz-0.4:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use KucLab/kuclab-hertz-0.4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KucLab/kuclab-hertz-0.4"
# 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-0.4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KucLab/kuclab-hertz-0.4:Q4_K_M

Ollama
How to use KucLab/kuclab-hertz-0.4 with Ollama:
```
ollama run hf.co/KucLab/kuclab-hertz-0.4:Q4_K_M
```
Unsloth Desktop

How to use KucLab/kuclab-hertz-0.4 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-0.4: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-0.4:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use KucLab/kuclab-hertz-0.4 with Docker Model Runner:
```
docker model run hf.co/KucLab/kuclab-hertz-0.4:Q4_K_M
```

Lemonade

How to use KucLab/kuclab-hertz-0.4 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KucLab/kuclab-hertz-0.4:Q4_K_M

Run and chat with the model

lemonade run user.kuclab-hertz-0.4-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use KucLab/kuclab-hertz-0.4 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-0.4: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-0.4:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use KucLab/kuclab-hertz-0.4 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-0.4: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-0.4: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 0.4

A Czech/English STEM + programming assistant built by KucLab on top of google/gemma-4-12B-it, fine-tuned toward physics, chemistry, biology, mathematics and programming in Czech, while keeping the base model's native 256k-token context and tool-calling.

What this is

Hertz 0.4 is a LoRA fine-tune (r=8, merged into the base weights) trained on a self-distilled corpus of Czech/English STEM concepts, worked problems, programming exercises, and formatting examples. 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 — with a system instruction asking for direct, confident answers over hedged ones.

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 (no context-extension hack needed — unlike Hertz 0.3's Qwen2.5-14B base, this model ships long context out of the box). Deployed here with a default num_ctx of 65536; the model can go higher, this is a practical default, not a hard limit.
Training data: 744 rows — 497 newly self-distilled (STEM concepts/terms/problems, a new programming category, formatting), plus 234 rows reused as-is from Hertz 0.3's corpus.
Format available: GGUF (q4_k_m, ~7.4GB) for llama.cpp/Ollama, plus the raw LoRA adapter.

Gemma-4 has its own native thinking mechanism (channel-based, gated by an enable_thinking flag). Training data deliberately does not replicate that token syntax — output is plain text with visible step-by-step reasoning, same choice as Hertz 0.3. This doesn't disable native thinking mode, it just means the fine-tune doesn't touch it either way.

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-0.4/resolve/main/Modelfile
ollama create kuclab-hertz-0.4 -f Modelfile
ollama run kuclab-hertz-0.4

(The Modelfile's FROM line points at hf.co/KucLab/kuclab-hertz-0.4:Q4_K_M, so this pulls the same GGUF automatically — no separate download needed.)

Example

Q: Kolik je 17 × 23? Ukaž postup.

A: Výsledek: 391 — pak krok za krokem 17×(20+3) = 340+51 = 391.

Q: Napiš v Pythonu funkci pro binární vyhledávání.

A: (correct, idiomatic implementation with type hints and a docstring)

Benchmarks — and an honest complication

Measured against the same base model (google/gemma-4-12B-it, Q4_K_M via Ollama), same prompts, same grading code, both models cold.

MMLU-Pro STEM (240 held-out questions, this project's own curated subset)

	base	Hertz 0.4	Δ
Biology	86.7%	76.7%	−10.0pp
Chemistry	55.0%	45.0%	−10.0pp
Math	85.0%	76.7%	−8.3pp
Physics	63.3%	56.7%	−6.6pp
Total	72.5%	63.7%	−8.8pp

We do not trust this number at face value, and you shouldn't either. Both models — base and fine-tuned — tend to work through every multiple-choice option individually with detailed reasoning before giving a final answer, and a meaningful fraction of responses got cut off by our grading harness's token budget before reaching the final answer letter (18% unparsed for base, 23% for Hertz 0.4). The drop is uniform across all four subjects, which points to a harness/verbosity artifact rather than genuine domain-knowledge loss, but we have not yet re-run this with a larger token budget to confirm. Until we do, treat this MMLU-Pro number as unverified and likely pessimistic for both models — we're publishing it anyway because we'd rather show a confusing number with an honest explanation than hide it.

Czech terminology benchmark: not yet run for this release.

Honest status

✅ Coherent, correct Czech and English output (spot-checked: arithmetic, physics, code, terminology — one wrong term found: "smělná odchylka" instead of "směrodatná odchylka" for standard deviation)
✅ Correctly identifies as a KucLab model, not as "Gemma" (LoRA + system prompt)
✅ 256k native context (inherited from base, not independently long-context-tested by us yet)
⚠️ MMLU-Pro STEM comparison run but confounded by a benchmark-harness token-budget issue (see above) — re-run pending
⏳ Czech terminology benchmark not yet run for this release
⏳ Tool-calling fine-tuning — base supports it natively, this fine-tune didn't add tool-use training examples
⏳ No uncensoring/decensoring pass — safety behavior inherited from the base model as-is

If you're deciding whether this fits your use case: treat it as "gemma-4-12B-it, nudged toward Czech STEM/programming fluency and KucLab identity," with an open question mark on general STEM benchmark performance until we re-verify it.

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: 30

GGUF

Model size

12B params

Architecture

gemma4

Hardware compatibility

4-bit

Model tree for KucLab/kuclab-hertz-0.4

Base model

google/gemma-4-12B

Finetuned

google/gemma-4-12B-it

Adapter

(165)

this model