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architectures/incoder.txt
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@@ -11,4 +11,26 @@ During the training of InCoder, spans of code were randomly masked and moved to
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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.
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In the code generation demo we use InCoder 1.3B.
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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.
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In the code generation demo we use InCoder 1.3B.
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You can load the model and tokenizer directly from the `transformers`:
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```python
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from transformers import AutoTokenizer, AutoModelWithLMHead
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tokenizer = AutoTokenizer.from_pretrained("facebook/incoder-6B")
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model = AutoModelWithLMHead.from_pretrained("facebook/incoder-6B")
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inputs = tokenizer("def hello_world():", return_tensors="pt")
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outputs = model(**inputs)
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```
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Or you can use a pipeline
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```python
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from transformers import pipeline
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pipe = pipeline("text-generation", model="facebook/incoder-6B")
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outputs = pipe("def hello_world():")
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```
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