Instructions to use m0rtyddd/clario-gemma4-e4b-extract-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use m0rtyddd/clario-gemma4-e4b-extract-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="m0rtyddd/clario-gemma4-e4b-extract-gguf",
	filename="clario-extract-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use m0rtyddd/clario-gemma4-e4b-extract-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf m0rtyddd/clario-gemma4-e4b-extract-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf m0rtyddd/clario-gemma4-e4b-extract-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf m0rtyddd/clario-gemma4-e4b-extract-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf m0rtyddd/clario-gemma4-e4b-extract-gguf: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 m0rtyddd/clario-gemma4-e4b-extract-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf m0rtyddd/clario-gemma4-e4b-extract-gguf: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 m0rtyddd/clario-gemma4-e4b-extract-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf m0rtyddd/clario-gemma4-e4b-extract-gguf:Q4_K_M

Use Docker

docker model run hf.co/m0rtyddd/clario-gemma4-e4b-extract-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use m0rtyddd/clario-gemma4-e4b-extract-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "m0rtyddd/clario-gemma4-e4b-extract-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": "m0rtyddd/clario-gemma4-e4b-extract-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/m0rtyddd/clario-gemma4-e4b-extract-gguf:Q4_K_M

Ollama
How to use m0rtyddd/clario-gemma4-e4b-extract-gguf with Ollama:
```
ollama run hf.co/m0rtyddd/clario-gemma4-e4b-extract-gguf:Q4_K_M
```

Unsloth Studio new

How to use m0rtyddd/clario-gemma4-e4b-extract-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for m0rtyddd/clario-gemma4-e4b-extract-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for m0rtyddd/clario-gemma4-e4b-extract-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for m0rtyddd/clario-gemma4-e4b-extract-gguf to start chatting

Pi new

How to use m0rtyddd/clario-gemma4-e4b-extract-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf m0rtyddd/clario-gemma4-e4b-extract-gguf:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "m0rtyddd/clario-gemma4-e4b-extract-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use m0rtyddd/clario-gemma4-e4b-extract-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf m0rtyddd/clario-gemma4-e4b-extract-gguf: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 m0rtyddd/clario-gemma4-e4b-extract-gguf:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use m0rtyddd/clario-gemma4-e4b-extract-gguf with Docker Model Runner:
```
docker model run hf.co/m0rtyddd/clario-gemma4-e4b-extract-gguf:Q4_K_M
```

Lemonade

How to use m0rtyddd/clario-gemma4-e4b-extract-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull m0rtyddd/clario-gemma4-e4b-extract-gguf:Q4_K_M

Run and chat with the model

lemonade run user.clario-gemma4-e4b-extract-gguf-Q4_K_M

List all available models

lemonade list

Clario · Gemma 4 E4B · Symptom-Diary Extractor (GGUF · Q4_K_M)

CPU-friendly GGUF build of the Clario v2 LoRA merged into unsloth/gemma-4-e4b-it. Quantized to Q4_K_M (~5 GB) for single-step use through Ollama or llama.cpp — no Python sidecar, no bitsandbytes, no CUDA requirement. Runs on Mac (Metal), Linux, Windows, and CPU.

This is the same model published as a LoRA adapter in m0rtyddd/clario-gemma4-e4b-lora-v2, just merged and quantized. For QLoRA / PEFT workflows on a CUDA box, prefer that repo. For everything else, prefer this one.

Quick start (Ollama)

# 1. download the GGUF + Modelfile to a folder
huggingface-cli download m0rtyddd/clario-gemma4-e4b-extract-gguf \
    --local-dir ./clario-extract --include "*.gguf" "Modelfile"

# 2. register with Ollama
cd clario-extract
ollama create clario-extract -f Modelfile

# 3. run
ollama run clario-extract "Diary entry: My eyes are gritty and my mouth is so dry I can't swallow toast."

Or invoke from any Ollama-compatible HTTP client at http://127.0.0.1:11434/api/chat with model: "clario-extract".

Quick start (llama.cpp / llama-cli)

llama-cli -m clario-extract-q4_k_m.gguf \
    -p "Diary entry: My eyes are gritty and my mouth is so dry I can't swallow toast." \
    --temp 0 -n 512

What the model does

Converts colloquial patient diary text into a structured JSON list of medical entities with canonical names suitable for HPO (Human Phenotype Ontology) lookup. Trained on 411 distilled (diary, target_json) pairs derived from Orphanet rare-disease phenotypes and HPO synonyms.

diary text  →  [Gemma 4 E4B + Clario LoRA, Q4_K_M]  →  JSON entities
            →  deterministic HPO synonym lookup     →  HPO IDs + canonical names

The model is not asked to memorise the ~17k HPO IDs from 411 examples. Its job is to extract and canonicalise symptom mentions; IDs are resolved afterwards from a versioned ontology. The reference HPO lookup is built by Clario's backend/scripts/build_knowledge.py from HPO hp.obo + Orphanet en_product4.xml. See m0rtyddd/clario-synthetic-diary for the training corpus and the model card of the unquantized LoRA for the full discussion of limitations.

Performance vs the LoRA adapter

Q4_K_M is the recommended quantization — best size/quality tradeoff across the K-quant family. On Gemma-family models Q4_K_M typically preserves >97% of the BF16 quality on extraction tasks. Reference metrics from the unquantized LoRA (held-out by disease, 68 examples):

Metric	Vanilla Gemma 4 E4B	Clario LoRA	Δ
JSON schema correctness	0%¹	100%	+100 pp
Name F1 (synonym-aware)	0.209	0.524	+151%
HPO ID F1 (via name→lookup)	0.349	0.524	+50%

¹ Without normalising baseline's ad-hoc schema, every baseline score would be 0.

This GGUF was not re-measured against the held-out set after quantization in the hackathon window. Expect a small drop relative to the BF16/4-bit-NF4 numbers above; the system prompt and two few-shot demonstrations are baked into the Modelfile and stack the same way they do for the LoRA path.

Files

File	Size	What
`clario-extract-q4_k_m.gguf`	~5.0 GB	the model
`Modelfile`	<1 KB	Ollama Modelfile with SYSTEM_PROMPT + 2 few-shot demos
`README.md`	this file	—

Intended use, out of scope, limitations

See the matching sections in m0rtyddd/clario-gemma4-e4b-lora-v2. TL;DR: extract symptoms, do not trust the model's hpo_id field (always post-resolve via deterministic lookup), human-in-the-loop review, English only, not a diagnostic tool.

Licence

CC-BY-4.0, propagating the licences of training data sources (HPO CC-BY-4.0, Orphanet free with attribution) and the Gemma Terms of Use for the base model.

Cite as:

@misc{okulov2026clario_gguf,
  title  = {{Clario} {Gemma 4 E4B} Symptom-Diary Extractor (GGUF, Q4\_K\_M)},
  author = {Okulov, Maksim},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/m0rtyddd/clario-gemma4-e4b-extract-gguf}}
}

Downloads last month: 202

GGUF

Model size

8B params

Architecture

gemma4

Hardware compatibility

4-bit

m0rtyddd
/

clario-gemma4-e4b-extract-gguf