Update README.md

README.md

@@ -13,12 +13,11 @@ library_name: transformers
 <!-- Provide a quick summary of what the model is/does. -->
 
 AI Squared's `dlite-v1-124m` ([blog post](https://medium.com/ai-squared/introducing-dlite-a-lightweight-chatgpt-like-model-based-on-dolly-deaa49402a1f)) is a large language 
-model which is derived from OpenAI's smallest [GPT-2](https://huggingface.co/gpt2) model and fine-tuned on a single T4 GPU on a corpos of 50k records
+model which is derived from OpenAI's smallest [GPT-2](https://huggingface.co/gpt2) model and fine-tuned on a single T4 GPU on a corpus of 50k records
 ([Stanford Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html)) to help it exhibit chat-based capabilities.
 
 While `dlite-v1-124m` is **not a state-of-the-art model**, we believe that the level of interactivity that can be achieved on such a small model that is trained so cheaply
-is important to showcase, as it continues to demonstrate that creating powerful AI capabilities is much more accessible than previously thought. 
-## Model Details
+is important to showcase, as it continues to demonstrate that creating powerful AI capabilities may be much more accessible than previously thought. 
 
 
 ### Model Description
@@ -45,11 +44,16 @@ Just as with any other LLM, we advise users of this technology to exercise good
 
 ## Usage
 
+The code below shows how to use `dlite-v1-124m` in the way which it was trained.  While the model can be used "out of the box" using the
+`transformers` library, using the function defined below to create a response from the model will achieve better results.
+
 ### Load Model and Tokenizer from this Repository Using the `transformers` Package
 
 ```python
-from transfomrers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
 model_id = 'aisquared/dlite-v1-124m'
+
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id)
 ```