Update README.md

README.md

@@ -61,7 +61,7 @@ pip install -U transformers
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model_name = "swiss-ai/Apertus-70B-Instruct-2509"
+model_name = "adamo1139/Apertus-70B-Instruct-2509-ungated"
 device = "cuda"  # for GPU usage or "cpu" for CPU usage
 
 # load the tokenizer and the model
@@ -111,10 +111,10 @@ You can use vLLM and SGLang to deploy the model in an API compatible with OpenAI
 In this section, we report the evaluation results of Apertus model.
 
 ### Base Pre-Trained Model
-- see [Apertus_Tech_Report.pdf](https://huggingface.co/swiss-ai/Apertus-70B-2509/blob/main/Apertus_Tech_Report.pdf)
+- see [Apertus_Tech_Report.pdf](https://huggingface.co/adamo1139/Apertus-70B-Instruct-2509-ungated/blob/main/Apertus_Tech_Report.pdf)
 
 ### Instruction Model
-- see [Apertus_Tech_Report.pdf](https://huggingface.co/swiss-ai/Apertus-70B-2509/blob/main/Apertus_Tech_Report.pdf)
+- see [Apertus_Tech_Report.pdf](https://huggingface.co/adamo1139/Apertus-70B-Instruct-2509-ungated/blob/main/Apertus_Tech_Report.pdf)
 
 ## Training
 
@@ -144,8 +144,8 @@ Apertus can produce text on a variety of topics, but the generated content may n
 ## Legal Aspects
 
 #### EU AI Act Transparency Documentation and Code of Practice
-- [Apertus_EU_Public_Summary.pdf](https://huggingface.co/swiss-ai/Apertus-70B-2509/blob/main/Apertus_EU_Public_Summary.pdf)
-- [Apertus_EU_Code_of_Practice.pdf](https://huggingface.co/swiss-ai/Apertus-70B-2509/blob/main/Apertus_EU_Code_of_Practice.pdf)
+- [Apertus_EU_Public_Summary.pdf](https://huggingface.co/adamo1139/Apertus-70B-Instruct-2509-ungated/blob/main/Apertus_EU_Public_Summary.pdf)
+- [Apertus_EU_Code_of_Practice.pdf](https://huggingface.co/adamo1139/Apertus-70B-Instruct-2509-ungated/blob/main/Apertus_EU_Code_of_Practice.pdf)
 
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