Instructions to use KucLab/kuclab-hertz-0.3 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-0.3 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-0.3:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KucLab/kuclab-hertz-0.3: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-0.3:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KucLab/kuclab-hertz-0.3: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-0.3:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf KucLab/kuclab-hertz-0.3: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-0.3:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf KucLab/kuclab-hertz-0.3:Q4_K_M

Use Docker

docker model run hf.co/KucLab/kuclab-hertz-0.3:Q4_K_M

LM Studio
Jan

vLLM

How to use KucLab/kuclab-hertz-0.3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KucLab/kuclab-hertz-0.3"
# 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-0.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KucLab/kuclab-hertz-0.3:Q4_K_M

Ollama
How to use KucLab/kuclab-hertz-0.3 with Ollama:
```
ollama run hf.co/KucLab/kuclab-hertz-0.3:Q4_K_M
```
Unsloth Desktop

How to use KucLab/kuclab-hertz-0.3 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-0.3: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-0.3:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use KucLab/kuclab-hertz-0.3 with Docker Model Runner:
```
docker model run hf.co/KucLab/kuclab-hertz-0.3:Q4_K_M
```

Lemonade

How to use KucLab/kuclab-hertz-0.3 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KucLab/kuclab-hertz-0.3:Q4_K_M

Run and chat with the model

lemonade run user.kuclab-hertz-0.3-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use KucLab/kuclab-hertz-0.3 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-0.3: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-0.3:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use KucLab/kuclab-hertz-0.3 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-0.3: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-0.3: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-0.3 / README.md

KucLab

Fix Ollama quickstart: ollama pull alone drops the system prompt

b1fb056 verified 3 days ago

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metadata

license: apache-2.0
base_model: Qwen/Qwen2.5-14B-Instruct
language:
  - cs
  - en
tags:
  - qwen2
  - czech
  - stem
  - physics
  - chemistry
  - biology
  - mathematics
  - lora
  - self-distillation
pipeline_tag: text-generation

KucLab Hertz 0.3

A Czech/English STEM assistant built by KucLab on top of Qwen2.5-14B-Instruct, fine-tuned to be sharper on physics, chemistry, biology and mathematics in Czech — while carrying forward the base model's general capability, 128k-token context reach, and native tool-calling support.

What this is

Hertz 0.3 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, and formatting examples. Training data was generated by prompting Qwen3.8-27B (Alibaba/Tongyi, Apache 2.0) — a stronger reasoning model used purely as a data-generation teacher, never trained itself — and used to fine-tune the actual 14B model that ships here.

Base: Qwen/Qwen2.5-14B-Instruct (14.7B params, Apache 2.0)
Method: QLoRA, r=8 / alpha=16, merged to fp16 then quantized
Context: extended to 128k via YaRN rope scaling (native 32k → 131072, factor 4.0)
Training data: 242 self-distilled rows — Czech/English STEM concepts & terminology, worked problems with step-by-step reasoning, Markdown/code formatting examples, identity
Format available: GGUF (q4_k_m, ~9GB) for llama.cpp/Ollama, plus the raw LoRA adapter for anyone who wants to merge it onto their own copy of the base model

Why a fine-tune instead of a bigger model

Qwen2.5-14B fits comfortably on a single consumer/cloud GPU (23GB) for both inference and QLoRA training. Rather than chase parameter count, this project focuses the model's existing capacity on a specific gap: strong, natural Czech STEM terminology and step-by-step worked solutions, which the base model handles less fluently out of the box.

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-0.3/resolve/main/Modelfile
ollama create kuclab-hertz-0.3 -f Modelfile
ollama run kuclab-hertz-0.3

(The Modelfile's FROM line points at hf.co/KucLab/kuclab-hertz-0.3:Q4_K_M, so this pulls the same GGUF automatically — no separate download needed.)

Or build from the Modelfile in this repo with the bundled GGUF.

Example

Q: Kolik je 17 × 23? Ukaž postup.

A: 17 × 23 = 17 × (20 + 3) = 340 + 51 = 391

Q: Vysvětli druhý Newtonův zákon.

A: (explains F = dp/dt, in fluent Czech, with the formula and its meaning)

Benchmarks

Measured against the same base model (qwen2.5:14b-instruct, 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 — not a claim of parity with the standard published MMLU-Pro leaderboard number)

	base	Hertz 0.3	Δ
Biology	78.3%	75.0%	−3.3pp
Chemistry	71.7%	71.7%	0pp
Math	88.3%	86.7%	−1.6pp
Physics	71.7%	80.0%	+8.3pp
Total	77.5%	78.3%	+0.8pp

Czech scientific terminology (206 held-out terms, both translation directions, this project's own curated set)

	base	Hertz 0.3	Δ
CS → EN	79.6%	79.6%	0pp
EN → CS	47.6%	51.5%	+3.9pp
Total	63.6%	65.5%	+1.9pp

Read this as: no regression on general STEM reasoning, a real (if modest) gain on Czech terminology — which is exactly what the fine-tune targeted. Physics moved the most; biology and math moved slightly the other way. These are two custom benchmarks built for this project, not standardized public leaderboards — useful for before/after comparison on this exact model, not for cross-model bragging rights.

Honest status

This is a small-scale, single-GPU fine-tuning project, not a frontier lab release. What's verified:

✅ Coherent, correct Czech and English output (spot-checked: arithmetic, physics, chemistry, terminology)
✅ Correctly identifies as a KucLab model, not as "Qwen" (LoRA + system prompt)
✅ 128k context window configured and loadable (long-context quality not yet independently verified with held-out long documents)
✅ MMLU-Pro STEM and Czech terminology benchmarked against base (see above) — no regression, modest gains

What's not done yet:

⏳ Tool-calling fine-tuning — the base model supports function calling natively, but this fine-tune did not add tool-use training examples
⏳ No uncensoring/decensoring pass — safety behavior is inherited from the base model as-is
⏳ No standardized public benchmarks (GSM8K, HumanEval, official MMLU, etc.) — only the two project-specific evals above

If you're deciding whether this fits your use case: treat it as "Qwen2.5-14B-Instruct, nudged toward Czech STEM fluency and KucLab identity," not as a categorically different or more powerful model than its base.

License

Apache 2.0, inherited from Qwen2.5-14B-Instruct. This repository includes a LoRA adapter and a merged/quantized derivative of that base model; see LICENSE for full terms. 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: Qwen/Qwen2.5-14B-Instruct (Alibaba Cloud, Apache 2.0)
Training-data teacher: Qwen/Qwen3.8-27B (Alibaba/Tongyi, Apache 2.0)
Fine-tuning, dataset construction, and packaging: KucLab