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--- |
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license: other |
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--- |
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# Model Card for CodeFuse-13B-4K |
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[[中文]](#chinese) [[English]](#english) |
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<a id="english"></a> |
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## Model Description |
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CodeFuse-13B is a 13 billion parameter code generation model trained on the GPT-NeoX framework, capable of handling code sequences of up to 4096 characters. This model was pretrained on a dataset consisting of 1000B token code, Chinese, and English data, covering over 40 programming languages. To further enhance the effectiveness and quality of the generated code, the model was fine-tuned on the CodeFuse-Evol-instruction-66k dataset, enabling it to produce more accurate, efficient, and compliant code. Pass@1 achieved 37.1% on the HumanEval evaluation set(BeamSearch strategy, BeamSize=3). |
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## Requirements |
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* Python 3.8 or above. |
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* PyTorch 1.12 or above, with a recommendation for 2.0 or above. |
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* Transformers 4.24.0 or above. |
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* It is advisable to use CUDA 11.4 or above (for GPU users and flash-attention users, this option should be considered). |
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## Quickstart |
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``` |
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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", torch_dtype="auto", device_map="auto") |
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input_ids = encode("def quick_sort(array):\n", return_tensors="pt") |
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output_ids = model.generate(input_ids, max_new_tokens=200, num_beams=3, num_return_sequences=1, repetition_penalty=1.2) |
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print(tokenizer.decode(output_idss[0])) |
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``` |
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<a id="chinese"></a> |
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## 简介 |
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CodeFuse-13B是基于GPT-NeoX框架训练的13B参数代码生成模型，能够处理4096个字符的代码序列。该模型在1000B Token的代码、中文、英文数据数据集上进行预训练，覆盖超过40种编程语言。为了进一步提升生成代码的效果和质量，该模型还在CodeFuse-Evol-instruction-66k数据集上进行了微调，使得该模型能够生成更加准确、高效、符合要求的代码。在HumanEval评测集上Pass@1达到37.1%(采用BeamSearch解码，其中BeamSize=3)。 |
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## 要求 |
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* python 3.8及以上版本 |
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* pytorch 1.12及以上版本，推荐2.0及以上版本 |
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* transformers 4.24.0及以上版本 |
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* 建议使用CUDA 11.4及以上（GPU用户、flash-attention用户等需考虑此选 |
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## 快速使用 |
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``` |
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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", torch_dtype="auto", device_map="auto") |
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input_ids = encode("def quick_sort(array):\n", return_tensors="pt") |
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output_ids = model.generate(input_ids, max_new_tokens=200, num_beams=3, num_return_sequences=1, repetition_penalty=1.2) |
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print(tokenizer.decode(output_idss[0])) |
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``` |
