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codefuse-ai
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CodeFuse-13B

Text Generation
Transformers
PyTorch
gpt_neox
text-generation-inference
Model card Files
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 Community
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codefuse-admin commited on
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0074412
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1 Parent(s): 8d41a21

Update README.md

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  1. README.md +4 -4
README.md CHANGED
@@ -30,8 +30,8 @@ CodeFuse-13B is a 13 billion parameter code generation model trained on the GPT-
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  import torch
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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- tokenizer = AutoTokenizer.from_pretrained(("CodeFuse-13B-evol-instruct-4k"))
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- model = AutoModelForCausalLM.from_pretrained(("CodeFuse-13B-evol-instruct-4k"), device_map="auto").half().eval()
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  input_ids = tokenizer.encode("# language: Python\ndef quick_sort(array):\n", return_tensors="pt").to("cuda")
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  output_ids = model.generate(input_ids, max_new_tokens=200)
@@ -70,8 +70,8 @@ CodeFuse-13B是基于GPT-NeoX框架训练的13B参数代码生成模型，能够
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  import torch
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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- tokenizer = AutoTokenizer.from_pretrained(("CodeFuse-13B-evol-instruct-4k"))
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- model = AutoModelForCausalLM.from_pretrained(("CodeFuse-13B-evol-instruct-4k"), device_map="auto").half().eval()
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  input_ids = tokenizer.encode("# language: Python\ndef quick_sort(array):\n", return_tensors="pt").to("cuda")
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  output_ids = model.generate(input_ids, max_new_tokens=200)
 
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  import torch
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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+ tokenizer = AutoTokenizer.from_pretrained(("CodeFuse-13B"))
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+ model = AutoModelForCausalLM.from_pretrained(("CodeFuse-13B"), device_map="auto").half().eval()
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  input_ids = tokenizer.encode("# language: Python\ndef quick_sort(array):\n", return_tensors="pt").to("cuda")
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  output_ids = model.generate(input_ids, max_new_tokens=200)
 
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  import torch
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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+ tokenizer = AutoTokenizer.from_pretrained(("CodeFuse-13B"))
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+ model = AutoModelForCausalLM.from_pretrained(("CodeFuse-13B"), device_map="auto").half().eval()
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  input_ids = tokenizer.encode("# language: Python\ndef quick_sort(array):\n", return_tensors="pt").to("cuda")
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  output_ids = model.generate(input_ids, max_new_tokens=200)