Salesforce
/

codegen2-3_7B_P

Text Generation

Inference Endpoints

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rooa commited on May 4, 2023

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3d287dd

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1 Parent(s): b333432

Update README.md

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@@ -23,8 +23,8 @@ This model can be easily loaded using the `AutoModelForCausalLM` functionality.
 For regular causal sampling, simply generate completions given the context:
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
-tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen2-3.7B")
-model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen2-3.7B", trust_remote_code=True, revision="main")
 text = "def hello_world():"
 input_ids = tokenizer(text, return_tensors="pt").input_ids
@@ -56,8 +56,8 @@ The final snippet looks as follows:
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
-tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen2-3.7B")
-model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen2-3.7B", trust_remote_code=True, revision="main")
 def format(prefix, suffix):

 For regular causal sampling, simply generate completions given the context:
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen2-3_7B")
+model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen2-3_7B", trust_remote_code=True, revision="main")
 text = "def hello_world():"
 input_ids = tokenizer(text, return_tensors="pt").input_ids
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen2-3_7B")
+model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen2-3_7B", trust_remote_code=True, revision="main")
 def format(prefix, suffix):