Instructions to use swiss-ai/Apertus-v1.1-0.5B-Instruct-MLX-INT3 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-INT3 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-INT3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("swiss-ai/Apertus-v1.1-0.5B-Instruct-MLX-INT3")
model = AutoModelForCausalLM.from_pretrained("swiss-ai/Apertus-v1.1-0.5B-Instruct-MLX-INT3", 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=256)
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-INT3 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-INT3"
# 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-INT3",
		"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-INT3

SGLang

How to use swiss-ai/Apertus-v1.1-0.5B-Instruct-MLX-INT3 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-INT3" \
    --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-INT3",
		"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-INT3" \
        --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-INT3",
		"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-INT3 with Docker Model Runner:
```
docker model run hf.co/swiss-ai/Apertus-v1.1-0.5B-Instruct-MLX-INT3
```

Apertus-v1.1-0.5B-Instruct-MLX-INT3

File size: 1,283 Bytes

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    "architectures": [
        "ApertusForCausalLM"
    ],
    "attention_bias": false,
    "attention_dropout": 0.0,
    "bos_token_id": 1,
    "dtype": "float32",
    "eos_token_id": [
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    "hidden_act": "xielu",
    "hidden_dropout": 0.0,
    "hidden_size": 1024,
    "initializer_range": 0.02,
    "intermediate_size": 6144,
    "max_position_embeddings": 4096,
    "mlp_bias": false,
    "model_type": "apertus",
    "num_attention_heads": 16,
    "num_hidden_layers": 20,
    "num_key_value_heads": 4,
    "pad_token_id": 10,
    "post_norm": false,
    "qk_norm": true,
    "quantization": {
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        "bits": 3,
        "mode": "affine",
        "model.embed_tokens": {
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        "bits": 3,
        "mode": "affine",
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    "rms_norm_eps": 1e-05,
    "rope_parameters": {
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        "rope_theta": 500000.0,
        "rope_type": "linear"
    },
    "rope_theta": 500000.0,
    "tie_word_embeddings": true,
    "transformers_version": "5.5.0",
    "use_cache": false,
    "vocab_size": 131072
}