File size: 2,343 Bytes
8901161 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 |
---
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-2509-8bit
This model [mlx-community/Apertus-8B-Instruct-2509-8bit](https://huggingface.co/mlx-community/Apertus-8B-Instruct-2509-8bit) 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-2509-8bit")
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)
```
|