Create README.md

README.md

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+---
+license: other
+tasks:
+- code-generation
+---
+# Model Card for CodeFuse-13B
+
+![logo](LOGO.png)
+
+
+[[中文]](#chinese)    [[English]](#english)
+
+<a id="english"></a>
+
+
+## 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).
+
+
+## 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 transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained(("CodeFuse-13B-evol-instruct-4k"))
+model = AutoModelForCausalLM.from_pretrained(("CodeFuse-13B-evol-instruct-4k"), device_map="auto").half().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.
+
+| Model File                       | MD5 Value                        |
+|:---------------------------------|:--------------------------------:|
+| pytorch_model-00001-of-00006.bin | b79e4ccc93c40fa6113aaf6a434473d5 |
+| pytorch_model-00002-of-00006.bin | 5a82f19e3f62c693e41fe627084c722b |
+| pytorch_model-00003-of-00006.bin | d4b53c391a353d0fc0a1be1c913d5f04 |
+| pytorch_model-00004-of-00006.bin | f9e3dcdea13ff02f4e3aad4f9db7a33f |
+| pytorch_model-00005-of-00006.bin | 698a8f2f05723a572193733bce12eb93 |
+| pytorch_model-00006-of-00006.bin | 312439d0b810f1bb81034fe094ff84c7 |
+
+<a id="chinese"></a>
+
+## 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)。
+
+
+## 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 transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained(("CodeFuse-13B-evol-instruct-4k"))
+model = AutoModelForCausalLM.from_pretrained(("CodeFuse-13B-evol-instruct-4k"), device_map="auto").half().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。
+
+| 模型文件                           | MD5值                            |
+|:---------------------------------|:--------------------------------:|
+| pytorch_model-00001-of-00006.bin | b79e4ccc93c40fa6113aaf6a434473d5 |
+| pytorch_model-00002-of-00006.bin | 5a82f19e3f62c693e41fe627084c722b |
+| pytorch_model-00003-of-00006.bin | d4b53c391a353d0fc0a1be1c913d5f04 |
+| pytorch_model-00004-of-00006.bin | f9e3dcdea13ff02f4e3aad4f9db7a33f |
+| pytorch_model-00005-of-00006.bin | 698a8f2f05723a572193733bce12eb93 |
+| pytorch_model-00006-of-00006.bin | 312439d0b810f1bb81034fe094ff84c7 |