Instructions to use stneric/simplixity-title-0.8b-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 stneric/simplixity-title-0.8b-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 stneric/simplixity-title-0.8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf stneric/simplixity-title-0.8b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf stneric/simplixity-title-0.8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf stneric/simplixity-title-0.8b-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf stneric/simplixity-title-0.8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf stneric/simplixity-title-0.8b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/stneric/simplixity-title-0.8b-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use stneric/simplixity-title-0.8b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "stneric/simplixity-title-0.8b-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": "stneric/simplixity-title-0.8b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/stneric/simplixity-title-0.8b-GGUF:Q4_K_M

Ollama
How to use stneric/simplixity-title-0.8b-GGUF with Ollama:
```
ollama run hf.co/stneric/simplixity-title-0.8b-GGUF:Q4_K_M
```
Unsloth Desktop

How to use stneric/simplixity-title-0.8b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf stneric/simplixity-title-0.8b-GGUF: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": "stneric/simplixity-title-0.8b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use stneric/simplixity-title-0.8b-GGUF with Docker Model Runner:
```
docker model run hf.co/stneric/simplixity-title-0.8b-GGUF:Q4_K_M
```

Lemonade

How to use stneric/simplixity-title-0.8b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull stneric/simplixity-title-0.8b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.simplixity-title-0.8b-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use stneric/simplixity-title-0.8b-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 stneric/simplixity-title-0.8b-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 stneric/simplixity-title-0.8b-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use stneric/simplixity-title-0.8b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf stneric/simplixity-title-0.8b-GGUF: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 "stneric/simplixity-title-0.8b-GGUF: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"

simplixity-title-0.8b-GGUF

GGUF files of simplixity-title-0.8b, a Qwen3.5-0.8B fine-tuned to name chats in Simplixity. It turns the first message of a conversation, English or German, into an English title of at most six words.

File	Size	All format rules	Key term named	Median per title
`simplixity-title-0.8b-v1-Q8_0.gguf`	834 MB	88%	89%	0.15 s
`simplixity-title-0.8b-v1-Q4_K_M.gguf`	542 MB	90%	89%	0.13 s

Measured in LM Studio on an M3 Pro, with 60 first messages (30 German, 30 English), three runs each. The untuned Qwen3.5-0.8B at Q8_0 passes every format rule 35% of the time. Every miss of this model is a title with seven words instead of six.

The files contain the text model only and were made with llama.cpp's convert_hf_to_gguf.py and llama-quantize. They run in LM Studio and llama.cpp.

Use with LM Studio

Download one file and load it. In Simplixity, turn on chat titles in the settings and pick the model with provider LM Studio.

The model expects Simplixity's title prompt as the system message and the first message of the chat, cut to 1,000 characters, as the user message. The prompt, the results and the limits are on the model page.

Licence

Apache 2.0, like the base model.

Downloads last month: 63

GGUF

Model size

0.8B params

Architecture

qwen35

Hardware compatibility

4-bit

8-bit

Model tree for stneric/simplixity-title-0.8b-GGUF

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Adapter

stneric/simplixity-title-0.8b

Quantized

(1)

this model