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@@ -16,7 +16,7 @@ This checkpoint (CodeGen-Mono 350M) was firstly initialized with *CodeGen-Multi
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  ## Training procedure
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  CodeGen was trained using cross-entropy loss to maximize the likelihood of sequential inputs.
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- The family of models are trained using 4 TPU-v4 chips by Google, leveraging data and model parallelism.
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  See Section 2.3 of the [paper](https://arxiv.org/abs/2203.13474) for more details.
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  ## Evaluation results
@@ -35,8 +35,8 @@ This model can be easily loaded using the `AutoModelForCausalLM` functionality:
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  ```python
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  from transformers import AutoTokenizer, AutoModelForCausalLM
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- tokenizer = AutoTokenizer.from_pretrained('Salesforce/codegen-350M-mono')
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- model = AutoModelForCausalLM.from_pretrained('Salesforce/codegen-350M-mono')
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  text = "def hello_world():"
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  input_ids = tokenizer(text, return_tensors="pt").input_ids
 
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  ## Training procedure
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  CodeGen was trained using cross-entropy loss to maximize the likelihood of sequential inputs.
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+ The family of models are trained using multiple TPU-v4-512 by Google, leveraging data and model parallelism.
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  See Section 2.3 of the [paper](https://arxiv.org/abs/2203.13474) for more details.
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  ## Evaluation results
 
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  ```python
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  from transformers import AutoTokenizer, AutoModelForCausalLM
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+ tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-mono")
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+ model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-350M-mono")
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  text = "def hello_world():"
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  input_ids = tokenizer(text, return_tensors="pt").input_ids