Update README.md

README.md

@@ -19,19 +19,33 @@ Model | #Params | d_model | layers | lm loss uniref-100
 [Large](https://huggingface.co/lightonai/RITA_l)| 680M | 1536 | 24 | 1.82
 [XLarge](https://huggingface.co/lightonai/RITA_xl)| 1.2B | 2048 | 24 | 1.70 
 
+For full results see our preprint: https://arxiv.org/abs/2205.05789
 
-# Usage 
-
+## Usage 
 Instantiate a model like so:
-
-    from transformers import AutoModel, AutoModelForCausalLM
-    model = AutoModelForCausalLM.from_pretrained("lightonai/RITA_s, trust_remote_code=True")
-    tokenizer = AutoTokenizer.from_pretrained("lightonai/RITA_s")
-
-for generation use we support pipelines:
-   
-   
-    rita_gen = pipeline('text-generation', model=model, tokenizer = tokenizer)
-    sequences = rita_gen("MAB", max_length=20, do_sample=True, top_k=950, repetition_penalty=1.2, num_return_sequences=2, eos_token_id=2)
-    for seq in sequences:
-        print(f"seq: {seq['generated_text'].replace(' ', '')}")
+``` python
+from transformers import AutoModel, AutoModelForCausalLM
+model = AutoModelForCausalLM.from_pretrained("lightonai/RITA_s, trust_remote_code=True")
+tokenizer = AutoTokenizer.from_pretrained("lightonai/RITA_s")
+```
+for generation we support pipelines:
+``` python
+from transformers import pipeline
+rita_gen = pipeline('text-generation', model=model, tokenizer=tokenizer)
+sequences = rita_gen("MAB", max_length=20, do_sample=True, top_k=950, repetition_penalty=1.2, 
+                     num_return_sequences=2, eos_token_id=2)
+for seq in sequences:
+    print(f"seq: {seq['generated_text'].replace(' ', '')}")
+```
+Or see `example.py`
+
+## How to cite    
+
+    @misc{RITA2022,
+      doi = {10.48550/ARXIV.2205.05789},
+      url = {https://arxiv.org/abs/2205.05789},
+      author = {Hesslow, Daniel and Zanichelli, Niccoló and Notin, Pascal and Poli, Iacopo and Marks, Debora},
+      title = {RITA: a Study on Scaling Up Generative Protein Sequence Models},
+      publisher = {arXiv},
+      year = {2022},
+    }