62 lines
2.3 KiB
Markdown
62 lines
2.3 KiB
Markdown
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---
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license: apache-2.0
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base_model: swiss-ai/Apertus-8B-Instruct-2509
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pipeline_tag: text-generation
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library_name: mlx
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tags:
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- multilingual
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- compliant
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- swiss-ai
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- apertus
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- mlx
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extra_gated_prompt: "### Apertus LLM Acceptable Use Policy \n(1.0 | September 1,\
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\ 2025)\n\"Agreement\" The Swiss National AI Institute (SNAI) is a partnership between\
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\ the two Swiss Federal Institutes of Technology, ETH Zurich and EPFL. \n\nBy using\
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\ the Apertus LLM you agree to indemnify, defend, and hold harmless ETH Zurich and\
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\ EPFL against any third-party claims arising from your use of Apertus LLM. \n\n\
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The training data and the Apertus LLM may contain or generate information that directly\
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\ or indirectly refers to an identifiable individual (Personal Data). You process\
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\ Personal Data as independent controller in accordance with applicable data protection\
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\ law. SNAI will regularly provide a file with hash values for download which you\
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\ can apply as an output filter to your use of our Apertus LLM. The file reflects\
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\ data protection deletion requests which have been addressed to SNAI as the developer\
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\ of the Apertus LLM. It allows you to remove Personal Data contained in the model\
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\ output. We strongly advise downloading and applying this output filter from SNAI\
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\ every six months following the release of the model. "
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extra_gated_fields:
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Your Name: text
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Country: country
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Affiliation: text
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geo: ip_location
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By clicking Submit below I accept the terms of use: checkbox
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extra_gated_button_content: Submit
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---
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# mlx-community/Apertus-8B-Instruct-2509-bf16
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This model [mlx-community/Apertus-8B-Instruct-2509-bf16](https://huggingface.co/mlx-community/Apertus-8B-Instruct-2509-bf16) was
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converted to MLX format from [swiss-ai/Apertus-8B-Instruct-2509](https://huggingface.co/swiss-ai/Apertus-8B-Instruct-2509)
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using mlx-lm version **0.27.0**.
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## Use with mlx
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```bash
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pip install mlx-lm
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```
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```python
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from mlx_lm import load, generate
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model, tokenizer = load("mlx-community/Apertus-8B-Instruct-2509-bf16")
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prompt = "hello"
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if tokenizer.chat_template is not None:
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messages = [{"role": "user", "content": prompt}]
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prompt = tokenizer.apply_chat_template(
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messages, add_generation_prompt=True
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)
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response = generate(model, tokenizer, prompt=prompt, verbose=True)
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```
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