Instructions to use HTPQoo/Sinythos-5.5-Mini-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 HTPQoo/Sinythos-5.5-Mini-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 HTPQoo/Sinythos-5.5-Mini-GGUF
# Run inference directly in the terminal:
llama cli -hf HTPQoo/Sinythos-5.5-Mini-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf HTPQoo/Sinythos-5.5-Mini-GGUF
# Run inference directly in the terminal:
llama cli -hf HTPQoo/Sinythos-5.5-Mini-GGUF

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 HTPQoo/Sinythos-5.5-Mini-GGUF
# Run inference directly in the terminal:
./llama-cli -hf HTPQoo/Sinythos-5.5-Mini-GGUF

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 HTPQoo/Sinythos-5.5-Mini-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf HTPQoo/Sinythos-5.5-Mini-GGUF

Use Docker

docker model run hf.co/HTPQoo/Sinythos-5.5-Mini-GGUF

LM Studio
Jan

vLLM

How to use HTPQoo/Sinythos-5.5-Mini-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HTPQoo/Sinythos-5.5-Mini-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": "HTPQoo/Sinythos-5.5-Mini-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HTPQoo/Sinythos-5.5-Mini-GGUF

Ollama
How to use HTPQoo/Sinythos-5.5-Mini-GGUF with Ollama:
```
ollama run hf.co/HTPQoo/Sinythos-5.5-Mini-GGUF
```

Unsloth Studio

How to use HTPQoo/Sinythos-5.5-Mini-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 HTPQoo/Sinythos-5.5-Mini-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 HTPQoo/Sinythos-5.5-Mini-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for HTPQoo/Sinythos-5.5-Mini-GGUF to start chatting

How to use HTPQoo/Sinythos-5.5-Mini-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf HTPQoo/Sinythos-5.5-Mini-GGUF

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": "HTPQoo/Sinythos-5.5-Mini-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use HTPQoo/Sinythos-5.5-Mini-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf HTPQoo/Sinythos-5.5-Mini-GGUF

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 "HTPQoo/Sinythos-5.5-Mini-GGUF" \
  --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"

Docker Model Runner
How to use HTPQoo/Sinythos-5.5-Mini-GGUF with Docker Model Runner:
```
docker model run hf.co/HTPQoo/Sinythos-5.5-Mini-GGUF
```

Lemonade

How to use HTPQoo/Sinythos-5.5-Mini-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull HTPQoo/Sinythos-5.5-Mini-GGUF

Run and chat with the model

lemonade run user.Sinythos-5.5-Mini-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use HTPQoo/Sinythos-5.5-Mini-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf HTPQoo/Sinythos-5.5-Mini-GGUF

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 HTPQoo/Sinythos-5.5-Mini-GGUF

Run Hermes

hermes

Atomic Chat

Sinythos 5.5 Mini

709 MB. Un file. Gira sul telefono. Stesse sei leggi del modello grande, incise nel chat template — valgono anche senza system prompt.

Installazione

LM Studio — scarica il .gguf e trascinalo, oppure cercalo dall'app. Il chat template è dentro al file: niente da configurare.

PocketPal (Android/iOS) — Models → + → Add from Hugging Face e incolla HTPQoo/Sinythos-5.5-Mini-GGUF. Oppure scarica il file e usa Add Local Model.

llama.cpp

llama-server -hf HTPQoo/Sinythos-5.5-Mini-GGUF --jinja -c 8192

--jinja serve solo a llama.cpp; LM Studio e PocketPal leggono il template da soli.

Impostazioni consigliate


Temperature	0.6
Top-P	0.95
Top-K	20
Max tokens	2048 o più
Contesto	8192 (fino a 32768)

Il max-tokens alto non è opzionale. È un modello thinking: apre <think> e ragiona prima di rispondere. Con un limite basso esaurisce i token mentre sta ancora pensando e sembra che non risponda.

Cosa aspettarsi, onestamente

	Sinythos 5.5	Mini
Dimensione	17,7 GB	709 MB (25× più piccolo)
Parametri	30B (3,3B attivi)	1,7B
Gira su	PC con 24 GB RAM	telefono

Il Mini non è più potente del modello grande — ha 1/18 dei parametri, e nessuna quantizzazione o fusione può cambiarlo. Quello che è: il massimo ottenibile in 709 MB, con lo stesso comportamento — ragiona prima di rispondere, ammette quando non sa, verifica invece di inventare.

Dove rende meglio: domande dirette, riformulazioni, estrazione da testo, script brevi, conversazione multilingue. Dove serve il grande: codice complesso, conoscenza specialistica, testi lunghi curati.

E c'è un vantaggio che il grande non ha: funziona offline in tasca.

Le sei leggi

Ragiona, poi rispondi — nella lingua di chi scrive
Se non sai, vai a scoprirlo
I giochi hanno geometria vera
Il software si consegna animato e con i suoi asset
Verifica prima di affermare
Seconda passata, avversariale

Runtime agentico completo: https://github.com/baccarossa2-ai/BaccaMalware

Verifica

sha256  7a9e9cf20103fb628aac838c185fcfa15d3a8d09f4d8fe72d40529fee7474a1b
bytes   708721824

I 310 tensori sono byte-identici alla quantizzazione base di Qwen3-1.7B, verificati singolarmente. Nessun training, nessuna distillazione: cambiano solo i metadati.

Licenza

Pesi Apache 2.0 (opera derivata di Qwen3-1.7B) — vedi LICENSE e NOTICE. Runtime MIT.

Downloads last month: 258

GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for HTPQoo/Sinythos-5.5-Mini-GGUF

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Quantized

(333)

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