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Model: mlx-community/Apertus-8B-Instruct-2509-bf16
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: swiss-ai/Apertus-8B-Instruct-2509
+pipeline_tag: text-generation
+library_name: mlx
+tags:
+- multilingual
+- compliant
+- swiss-ai
+- apertus
+- mlx
+extra_gated_prompt: "### Apertus LLM Acceptable Use Policy  \n(1.0 | September 1,\
+  \ 2025)\n\"Agreement\" The Swiss National AI Institute (SNAI) is a partnership between\
+  \ the two Swiss Federal Institutes of Technology, ETH Zurich and EPFL. \n\nBy 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. \n\n\
+  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
+---
+
+# mlx-community/Apertus-8B-Instruct-2509-bf16
+
+This model [mlx-community/Apertus-8B-Instruct-2509-bf16](https://huggingface.co/mlx-community/Apertus-8B-Instruct-2509-bf16) was
+converted to MLX format from [swiss-ai/Apertus-8B-Instruct-2509](https://huggingface.co/swiss-ai/Apertus-8B-Instruct-2509)
+using mlx-lm version **0.27.0**.
+
+## Use with mlx
+
+```bash
+pip install mlx-lm
+```
+
+```python
+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)
+```