Instructions to use swiss-ai/Apertus-v1.1-0.5B-Instruct-MLX-INT6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use swiss-ai/Apertus-v1.1-0.5B-Instruct-MLX-INT6 with Transformers:

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

pipe = pipeline("text-generation", model="swiss-ai/Apertus-v1.1-0.5B-Instruct-MLX-INT6")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("swiss-ai/Apertus-v1.1-0.5B-Instruct-MLX-INT6")
model = AutoModelForCausalLM.from_pretrained("swiss-ai/Apertus-v1.1-0.5B-Instruct-MLX-INT6", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use swiss-ai/Apertus-v1.1-0.5B-Instruct-MLX-INT6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "swiss-ai/Apertus-v1.1-0.5B-Instruct-MLX-INT6"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "swiss-ai/Apertus-v1.1-0.5B-Instruct-MLX-INT6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/swiss-ai/Apertus-v1.1-0.5B-Instruct-MLX-INT6

SGLang

How to use swiss-ai/Apertus-v1.1-0.5B-Instruct-MLX-INT6 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 "swiss-ai/Apertus-v1.1-0.5B-Instruct-MLX-INT6" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "swiss-ai/Apertus-v1.1-0.5B-Instruct-MLX-INT6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "swiss-ai/Apertus-v1.1-0.5B-Instruct-MLX-INT6" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "swiss-ai/Apertus-v1.1-0.5B-Instruct-MLX-INT6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use swiss-ai/Apertus-v1.1-0.5B-Instruct-MLX-INT6 with Docker Model Runner:
```
docker model run hf.co/swiss-ai/Apertus-v1.1-0.5B-Instruct-MLX-INT6
```

BlackSamorez commited on May 29

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Update README for swiss-ai/Apertus-v1.1-0.5B-Instruct-MLX-INT6

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@@ -54,7 +54,7 @@ Instead of standard pre-training, Apertus-v1.1 models were created using pre-tra
 This model family includes base pre-trained models and instruction-tuned models.
-For instruction-tuned models, we additionally provide high-quality quantization-aware distillation (QAD) checkpoints, obtained via the official [`qat-suite`]([https://github.com/swiss-ai/qat-suite](https://github.com/swiss-ai/qat-suite)). We provide FP8 and NVFP4A16 checkpoints with vLLM inference in mind and INT3-6 checkpoints optimized for mobile usage on Apple devices.
 The full list of released checkpoints is shown below:

 This model family includes base pre-trained models and instruction-tuned models.
+For instruction-tuned models, we additionally provide high-quality quantization-aware distillation (QAD) checkpoints, obtained via the official [`qat-suite`](https://github.com/swiss-ai/qat-suite). We provide FP8 and NVFP4A16 checkpoints with vLLM inference in mind and INT3-6 checkpoints optimized for mobile usage on Apple devices.
 The full list of released checkpoints is shown below: