Update README.md

README.md

@@ -20,7 +20,7 @@ Llama-3.1-Centaur-70B is a foundation model of cognition model that can predict
 
 Note that Centaur is trained on a data set in which human choices are encapsulated by "<<" and ">>" tokens. For optimal performance, it is recommended to adjust prompts accordingly.
 
-This is the low-rank adapter that runs with unsloth on a single 80GB GPU.
+The recommended usage is by loading the low-rank adapter using unsloth:
 
 ```python
 from unsloth import FastLanguageModel
@@ -32,9 +32,13 @@ model, tokenizer = FastLanguageModel.from_pretrained(
   dtype = None,
   load_in_4bit = True,
 )
+
+FastLanguageModel.for_inference(model)
 ```
 
-Alternatively,  you can also directly use  the (untested) [merged model](https://huggingface.co/marcelbinz/Llama-3.1-Centaur-70B).
+This requires 80 GB GPU memory.
+
+You can alternatively also directly use the less-tested [merged model](https://huggingface.co/marcelbinz/Llama-3.1-Centaur-70B).
 
 
 ### Licensing Information
@@ -43,6 +47,16 @@ Alternatively,  you can also directly use  the (untested) [merged model](https:/
 
 ### Citation Information
 
-Forthcoming.
+```
+@misc{binz2024centaurfoundationmodelhuman,
+      title={Centaur: a foundation model of human cognition}, 
+      author={Marcel Binz and Elif Akata and Matthias Bethge and Franziska Brändle and Fred Callaway and Julian Coda-Forno and Peter Dayan and Can Demircan and Maria K. Eckstein and Noémi Éltető and Thomas L. Griffiths and Susanne Haridi and Akshay K. Jagadish and Li Ji-An and Alexander Kipnis and Sreejan Kumar and Tobias Ludwig and Marvin Mathony and Marcelo Mattar and Alireza Modirshanechi and Surabhi S. Nath and Joshua C. Peterson and Milena Rmus and Evan M. Russek and Tankred Saanum and Natalia Scharfenberg and Johannes A. Schubert and Luca M. Schulze Buschoff and Nishad Singhi and Xin Sui and Mirko Thalmann and Fabian Theis and Vuong Truong and Vishaal Udandarao and Konstantinos Voudouris and Robert Wilson and Kristin Witte and Shuchen Wu and Dirk Wulff and Huadong Xiong and Eric Schulz},
+      year={2024},
+      eprint={2410.20268},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2410.20268}, 
+}
+```
 
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