update architecture

architectures/incoder.txt

@@ -1,4 +1,4 @@
-[InCoder](https://huggingface.co/facebook/incoder-6B) uses a decoder-only Transformer with Causal Masking objective, to train a left-to-right language model to fill in masked token segments. 
+[InCoder](https://huggingface.co/facebook/incoder-6B) uses a decoder-only Transformer with Causal Masking objective, to train a left-to-right language model to fill in masked token segments, with a context length of 2048.
 
 |Model | # parameters |
 |   -   |   -  | 
@@ -11,7 +11,7 @@ During the training of InCoder, spans of code were randomly masked and moved to
 
 So in addition to program synthesis (via left-to-right generation), InCoder can also perform editing (via infilling). The model gives promising results in some zero-shot code infilling tasks such as type prediction, variable re-naming and comment generation.
 
-In the code generation demo we use InCoder 1.3B.
+In the code generation demo, at the end of the blog, we use InCoder 1.3B.
 
 You can load the model and tokenizer directly from [`transformers`](https://huggingface.co/docs/transformers/index):