Instructions to use NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-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 NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-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 NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-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 NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-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 NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF:Q4_K_M

SGLang

How to use NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF with Ollama:
```
ollama run hf.co/NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF:Q4_K_M
```

Lemonade

How to use NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF / README.md

NonMiFrega

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metadata

license: apache-2.0
base_model: swiss-ai/Apertus-v1.1-0.5B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
  - multilingual
  - compliant
  - swiss-ai
  - apertus
  - llama-cpp
  - gguf-my-repo
extra_gated_prompt: >-
  ### Apertus LLM Acceptable Use Policy  

  (1.0 | September 1, 2025)

  "Agreement" The Swiss National AI Institute (SNAI) is a partnership between
  the two Swiss Federal Institutes of Technology, ETH Zurich and EPFL. 


  By using the Apertus LLM you agree to indemnify, defend, and hold harmless ETH
  Zurich and EPFL against any third-party claims arising from your use of
  Apertus LLM. 


  The training data and the Apertus LLM may contain or generate information that
  directly or indirectly refers to an identifiable individual (Personal Data).
  You process Personal Data as independent controller in accordance with
  applicable data protection law. SNAI will regularly provide a file with hash
  values for download which you can apply as an output filter to your use of our
  Apertus LLM. The file reflects data protection deletion requests which have
  been addressed to SNAI as the developer of the Apertus LLM. It allows you to
  remove Personal Data contained in the model output. We strongly advise
  downloading and applying this output filter from SNAI every six months
  following the release of the model.  
extra_gated_fields:
  Your Name: text
  Country: country
  Affiliation: text
  geo: ip_location
  By clicking Submit below I accept the terms of use: checkbox
extra_gated_button_content: Submit

NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF

This model was converted to GGUF format from swiss-ai/Apertus-v1.1-0.5B-Instruct using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF --hf-file apertus-v1.1-0.5b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF --hf-file apertus-v1.1-0.5b-instruct-q4_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF --hf-file apertus-v1.1-0.5b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo NonMiFrega/Apertus-v1.1-0.5B-Instruct-Q4_K_M-GGUF --hf-file apertus-v1.1-0.5b-instruct-q4_k_m.gguf -c 2048