Model Card for CodeFuse-13B

[中文] [English]

Model Description

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).

Code Community

Homepage: ? https://github.com/codefuse-ai (Please give us your support with a Star? + Fork? + Watch?)

• If you wish to fine-tune the model yourself, you can visit ✨MFTCoder✨✨

• If you wish to deploy the model yourself, you can visit ✨FasterTransformer4CodeFuse✨✨

• If you wish to see a demo of the model, you can visit ✨CodeFuse Demo✨✨

Requirements

• Python 3.8 or above.
• PyTorch 1.12 or above, with a recommendation for 2.0 or above.
• Transformers 4.24.0 or above.
• It is advisable to use CUDA 11.4 or above (for GPU users and flash-attention users, this option should be considered).

Quickstart

import torch
from modelscope import AutoModelForCausalLM, AutoTokenizer, snapshot_download

model_dir = snapshot_download('codefuse-ai/CodeFuse-13B', revision='v1.0.0')
tokenizer = AutoTokenizer.from_pretrained(model_dir)
model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", torch_dtype=torch.float16).eval()

input_ids = tokenizer.encode("# language: Python\ndef quick_sort(array):\n", return_tensors="pt").to("cuda")
output_ids = model.generate(input_ids, max_new_tokens=200)

print(tokenizer.decode(output_ids[0]))

MD5

We notice that the file may be corrupted during transfer process. Please check MD5 value before use.

简介

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)。

代码社区

大本营： ? https://github.com/codefuse-ai （请支持我们的项目 Star? + Fork? + Watch?）

• 如果您想自己微调该模型，可以访问 ✨MFTCoder✨✨

• 如果您想自己部署该模型，可以访问 ✨FasterTransformer4CodeFuse✨✨

• 如果您想观看该模型示例，可以访问 ✨CodeFuse Demo✨✨

要求

• python 3.8及以上版本
• pytorch 1.12及以上版本，推荐2.0及以上版本
• transformers 4.24.0及以上版本
• 建议使用CUDA 11.4及以上（GPU用户、flash-attention用户等需考虑此选

快速使用

import torch
from modelscope import AutoModelForCausalLM, AutoTokenizer, snapshot_download

model_dir = snapshot_download('codefuse-ai/CodeFuse-13B', revision='v1.0.0')
tokenizer = AutoTokenizer.from_pretrained(model_dir)
model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", torch_dtype=torch.float16).eval()

input_ids = tokenizer.encode("# language: Python\ndef quick_sort(array):\n", return_tensors="pt").to("cuda")
output_ids = model.generate(input_ids, max_new_tokens=200)

print(tokenizer.decode(output_ids[0]))

MD5

我们发现模型文件可能会在传输过程中损坏，使用前请检查文件MD5值。