license, base_model, pipeline_tag, library_name, tags, extra_gated_prompt, extra_gated_fields, extra_gated_button_content

license	base_model	pipeline_tag	library_name	tags	extra_gated_prompt	extra_gated_fields	extra_gated_button_content
apache-2.0	swiss-ai/Apertus-8B-Instruct-2509	text-generation	mlx	multilingual compliant swiss-ai apertus mlx	### 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.	Your Name Country Affiliation geo By clicking Submit below I accept the terms of use text country text ip_location checkbox	Submit

mlx-community/Apertus-8B-Instruct-2509-bf16

This model mlx-community/Apertus-8B-Instruct-2509-bf16 was converted to MLX format from swiss-ai/Apertus-8B-Instruct-2509 using mlx-lm version 0.27.0.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("mlx-community/Apertus-8B-Instruct-2509-bf16")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)