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architectures/incoder.txt

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 [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.
+<div align="center">
 
 |Model | # parameters |
 |   -   |   -  | 
 | Decoder |1.3B |
 | Decoder |6.7B |
 
+</div>
 
 [Causal Masking objective](https://arxiv.org/abs/2201.07520) is a hybrid approach of Causal and Masked language models, "it combines the benefit of per-token generation with optional bi-directionality specifically tailored to prompting".
-During the training of InCoder, spans of code were randomly masked and moved to the end of each file, which allows for bidirectional context. Figure 1 from InCoder [paper](https://arxiv.org/pdf/2204.05999.pdf) illustrates the training process.
+During the training of InCoder, spans of code were randomly masked and moved to the end of each file, which allows for bidirectional context. Figure below from InCoder [paper](https://arxiv.org/pdf/2204.05999.pdf) illustrates the training process.
+
+<p align="center">
+    <img src="https://huggingface.co/datasets/loubnabnl/repo-images/raw/main/incoder.png" alt="drawing" width="220"/>
+</p>
 
 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.