Instructions to use KucLab/kuclab-hertz-0.7f 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-0.7f 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-0.7f:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KucLab/kuclab-hertz-0.7f: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-0.7f:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KucLab/kuclab-hertz-0.7f: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-0.7f:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf KucLab/kuclab-hertz-0.7f: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-0.7f:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf KucLab/kuclab-hertz-0.7f:Q4_K_M

Use Docker

docker model run hf.co/KucLab/kuclab-hertz-0.7f:Q4_K_M

LM Studio
Jan

vLLM

How to use KucLab/kuclab-hertz-0.7f with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KucLab/kuclab-hertz-0.7f"
# 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-0.7f",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KucLab/kuclab-hertz-0.7f:Q4_K_M

Ollama
How to use KucLab/kuclab-hertz-0.7f with Ollama:
```
ollama run hf.co/KucLab/kuclab-hertz-0.7f:Q4_K_M
```
Unsloth Desktop

How to use KucLab/kuclab-hertz-0.7f 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-0.7f: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-0.7f:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use KucLab/kuclab-hertz-0.7f with Docker Model Runner:
```
docker model run hf.co/KucLab/kuclab-hertz-0.7f:Q4_K_M
```

Lemonade

How to use KucLab/kuclab-hertz-0.7f with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KucLab/kuclab-hertz-0.7f:Q4_K_M

Run and chat with the model

lemonade run user.kuclab-hertz-0.7f-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use KucLab/kuclab-hertz-0.7f 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-0.7f: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-0.7f:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use KucLab/kuclab-hertz-0.7f 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-0.7f: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-0.7f: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 0.7F

Measured on this project's harness — same 240 MMLU-Pro STEM and 206 CZ terminology questions for every model. Scripts: scripts/bench_mmlu_pro.py, scripts/bench_czech_terms.py.

A Czech/English STEM + programming assistant built by KucLab on top of Qwen/Qwen3.5-9B. Unlike the Gemma-based Hertz 0.x line, 0.7F is distilled: its training data was generated by a 30B teacher model (reasoning strength high, answer-first), then used to fine-tune the 9B student. It beats Hertz 0.6 — our previous best — on both of this project's benchmarks.

What this is

Hertz 0.7F is a LoRA fine-tune (r=16, merged into the base weights) of Qwen3.5-9B:

Base: Qwen/Qwen3.5-9B (~9B params, Apache 2.0)
Method: QLoRA, r=16 / alpha=32, merged to bf16 then quantized
Teacher: a 30B reasoning model served locally (temperature 0.2–0.7, answer-first system prompt with a canonical Answer: (X) final line on multiple-choice items)
Training data: 4884 rows total — 4532 distilled rows (math, physics, chemistry, biology, programming, Czech language, English, plus numeric multiple-choice drills), 309 CS↔EN scientific-terminology rows with definitions (train split only — the benchmark's held-out quarter is never trained on), 28 answer-first formatting examples, 15 hand-written identity rows. Validated: 0 malformed, 0 duplicates, 0 identity leaks, 87.9% Czech.
Context: 32768 tokens in the Ollama Modelfile (num_ctx).
Format available: GGUF (q4_k_m, ~5.3GB) for llama.cpp/Ollama, plus the raw LoRA adapter on request.

Quickstart (Ollama)

Important: ollama pull hf.co/... alone does NOT apply this model's system prompt (identity + personality). Use ollama create with the Modelfile below instead:

curl -O https://huggingface.co/KucLab/kuclab-hertz-0.7f/resolve/main/Modelfile
ollama create kuclab-hertz-0.7f -f Modelfile
ollama run kuclab-hertz-0.7f

(The Modelfile's FROM line points at hf.co/KucLab/kuclab-hertz-0.7f:Q4_K_M, so this pulls the same GGUF automatically.)

The honest development story

0.7F exists because Hertz 0.7 (same Gemma-4-12B base as 0.6, bigger dataset) regressed: 70.8% STEM (vs 0.6's 79.2%) and 70.9% terminology (vs 73.8%). Diagnosis, verified on the corpora: the new bulk data was only 53% Czech (vs 93% before), diluting the exact Czech lexical signal, and 901 long reasoning rows taught rambling — unparseable answers jumped from 18/240 to 50 re-asks plus 33 unparseable. Rather than a third attempt on the same base, we switched strategy: a stronger teacher generates the data, a smaller student learns it.

Two export bugs were found and fixed along the way, both disclosed so others don't repeat them: Qwen3.5's MTP head makes convert_hf_to_gguf emit a phantom blk.32 (block_count 33 instead of 32), which makes Ollama fail expecting SSM tensors that don't exist — fixed by setting block_count to the 32 real blocks and dropping the orphan MTP tensors. A hand-rolled GGUF metadata copy also taught us (twice) that GGUFWriter emits GGUF.* keys and general.architecture itself — copying them again corrupts the KV section.

Benchmarks

Same prompts, same grading code, same Ollama Q4_K_M quantization, identical methodology throughout.

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

	base (Qwen3.5-9B)	Hertz 0.6 (Gemma-12B LoRA)	Hertz 0.7F
Biology	—	91.7%	83.3%
Chemistry	—	61.7%	78.3%
Math	—	90.0%	96.7%
Physics	—	73.3%	75.0%
Total	—	79.2%	83.3%

Hertz 0.7F beats Hertz 0.6 by +4.1pp overall, with the biggest gains exactly where 0.6 was weakest (chemistry +16.6pp, math +6.7pp). Biology regressed (83.3% vs 91.7%) — the distilled corpus was light on biology (~100 rows), a known weakness to address next.

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

	Hertz 0.6	Hertz 0.7F
CS→EN	82.5%	88.3%
EN→CS	65.0%	82.5%
Total	73.8%	85.4%

+11.6pp overall, and +17.5pp in the hard EN→CS direction — the distilled Czech prose (not term lists) is what fixed it.

Standard benchmarks (lm-evaluation-harness, bf16 merged weights, L4 GPU)

Benchmark	Hertz 0.7F	Notes
GSM8K (strict-match)	87.8%	1319 test questions, generative
GSM8K (flexible-extract)	86.7%
Thinking tokens (20-probe meter)	358 mean	vs 679 for the Qwen3.5-9B base (−47%)

Full mmlu_pro via lm-eval is running; results.json will be appended here.

Honest status

✅ MMLU-Pro STEM: 83.3%, beats Hertz 0.6 (79.2%)
✅ Czech terminology: 85.4%, beats Hertz 0.6 (73.8%)
✅ Correctly identifies as a KucLab model (KucLab Hertz 0.7F, kuclab.org), no founder named
✅ Direct, no-hedging personality on ordinary topics; genuinely harmful requests still refused
⚠️ Biology (83.3%) is below 0.6's 91.7% — thin biology coverage in the distilled corpus
⏳ No tool-calling fine-tuning

License

Apache 2.0, inherited from Qwen/Qwen3.5-9B.

Credits

Base model: Qwen/Qwen3.5-9B (Qwen, Apache 2.0)
Teacher for distillation: Meta Muse Glimmer 30B (local inference, reasoning strength high)
Fine-tuning, dataset construction, and packaging: KucLab

Downloads last month: 31

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for KucLab/kuclab-hertz-0.7f

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Adapter

(666)

this model