Update README.md

README.md

@@ -36,7 +36,6 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 
 tokenizer = AutoTokenizer.from_pretrained('dicta-il/dictalm-7b-instruct')
-# If you don't have cuda installed, remove the `.cuda()` call at the end
 model = AutoModelForCausalLM.from_pretrained('dicta-il/dictalm-7b-instruct', trust_remote_code=True).cuda()
 
 model.eval()
@@ -56,6 +55,11 @@ with torch.inference_mode():
     print(tokenizer.batch_decode(model.generate(**kwargs), skip_special_tokens=True))
 ```
 
+
+There are many different parameters you can input into `kwargs` for different results (greedy, beamsearch, different samplign configurations, longer/shorter respones, etc.).
+
+You can view the full list of parameters you can pass to the `generate` function [here](https://huggingface.co/docs/transformers/v4.33.0/en/main_classes/text_generation#transformers.GenerationMixin.generate).
+
 ### Alternative ways to initialize the model:
 
 If you have multiple smaller GPUs, and the package `accelerate` is installed, you can initialize the model split across the devices:
@@ -74,12 +78,6 @@ model = AutoModelForCausalLM.from_pretrained('dicta-il/dictalm-7b-instruct', tru
 ```
 
 
-
-There are many different parameters you can input into `kwargs` for different results (greedy, beamsearch, different samplign configurations, longer/shorter respones, etc.).
-
-You can view the full list of parameters you can pass to the `generate` function [here](https://huggingface.co/docs/transformers/v4.33.0/en/main_classes/text_generation#transformers.GenerationMixin.generate).
-
-
 ## Citation
 
 If you use DictaLM in your research, please cite ```ADD CITATION HERE```