Instructions to use h0ffmann/marola-sea-tiny-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use h0ffmann/marola-sea-tiny-GGUF 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 h0ffmann/marola-sea-tiny-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf h0ffmann/marola-sea-tiny-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf h0ffmann/marola-sea-tiny-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf h0ffmann/marola-sea-tiny-GGUF:Q8_0

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 h0ffmann/marola-sea-tiny-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf h0ffmann/marola-sea-tiny-GGUF:Q8_0

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 h0ffmann/marola-sea-tiny-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf h0ffmann/marola-sea-tiny-GGUF:Q8_0

Use Docker

docker model run hf.co/h0ffmann/marola-sea-tiny-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use h0ffmann/marola-sea-tiny-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "h0ffmann/marola-sea-tiny-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "h0ffmann/marola-sea-tiny-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/h0ffmann/marola-sea-tiny-GGUF:Q8_0

Ollama
How to use h0ffmann/marola-sea-tiny-GGUF with Ollama:
```
ollama run hf.co/h0ffmann/marola-sea-tiny-GGUF:Q8_0
```
Unsloth Desktop
Docker Model Runner
How to use h0ffmann/marola-sea-tiny-GGUF with Docker Model Runner:
```
docker model run hf.co/h0ffmann/marola-sea-tiny-GGUF:Q8_0
```

Lemonade

How to use h0ffmann/marola-sea-tiny-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull h0ffmann/marola-sea-tiny-GGUF:Q8_0

Run and chat with the model

lemonade run user.marola-sea-tiny-GGUF-Q8_0

List all available models

lemonade list

Atomic Chat

marola-sea-tiny-GGUF

A GGUF release of a marola-sea checkpoint — a small model tuned on marola's own question/answer shape (open-water swim conditions, safety, sea life), served locally through Ollama alongside marola's own RAG corpus and deterministic scoring (Recommender/Swimability, never the model itself).

Honest framing (MIP-0025 §6, §8): tuning changes tone and format reliability, not factual grounding. This model does not replace marola's RAG corpus (knowledge/, cited answers only) or its Reviewer pass, and it is not a standalone safety authority — treat any first-aid or hazard answer as a starting point, not a substitute for a lifeguard or emergency services (marola's own answers carry this caveat automatically via the MIP-0022 safety footer; this raw checkpoint, used outside marola, does not).

Files

marola-sea-tiny-Q8_0.gguf (386 MB)

CHECKSUMS (sha256) ships alongside these files — pin a specific hash in your own Modelfile rather than a bare filename, so re-quantizing upstream can't silently change what you run.

Use with Ollama

ollama run hf.co/h0ffmann/marola-sea-tiny-GGUF
# or a specific quant tag, e.g.:
ollama run hf.co/h0ffmann/marola-sea-tiny-GGUF:Q4_K_M

Base model and training

Fine-tuned from HuggingFaceTB/SmolLM2-360M-Instruct via LoRA (finetune/train_lora.py in h0ffmann/marola) on a small, hand-built dataset derived from marola's own DSPy-compiled demos, sea-lore entries, and knowledge-corpus Q&A (finetune/build_dataset.py) — a few dozen examples, enough to teach format and tone, not facts.

No just benchmark numbers recorded yet for this checkpoint — see docs/benchmarks/ in the source repo before trusting this over a plain base model.

Licence

Base model licence: apache-2.0. See the base model's own repo for the full licence text.

Downloads last month: -

GGUF

Model size

0.4B params

Architecture

llama

Hardware compatibility

8-bit

Model tree for h0ffmann/marola-sea-tiny-GGUF

Base model

HuggingFaceTB/SmolLM2-360M

Quantized

HuggingFaceTB/SmolLM2-360M-Instruct

Quantized

(117)

this model