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‎README.md‎

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# aiXcoder-7B Code Large Language Model
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<p align="center">
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🏠 <a href="https://www.aixcoder.com/" target="_blank">Official website</a>｜🛠 <a href="https://marketplace.visualstudio.com/items?itemName=aixcoder-plugin.aixcoder" target="_blank">VS Code Plugin</a>｜🛠 <a href="https://plugins.jetbrains.com/plugin/13574-aixcoder-code-completer" target="_blank">Jetbrains Plugin</a>｜🤗 <a href="https://huggingface.co/aiXcoder/aiXcoder-7b" target="_blank">Model Weights</a>｜<a href="" target="_blank">WeChat</a>｜<a href="./assets/wechat_2.jpg" target="_blank">WeChat Official Account</a>
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🏠 <a href="https://www.aixcoder.com/" target="_blank">Official website</a>｜🛠 <a href="https://marketplace.visualstudio.com/items?itemName=aixcoder-plugin.aixcoder" target="_blank">VS Code Plugin</a>｜🛠 <a href="https://plugins.jetbrains.com/plugin/13574-aixcoder-code-completer" target="_blank">Jetbrains Plugin</a>｜🤗 <a href="https://huggingface.co/aiXcoder/aixcoder-7b-base" target="_blank">Model Weights</a>｜<a href="./assets/wechat_1.jpg" target="_blank">WeChat</a>｜<a href="./assets/wechat_2.jpg" target="_blank">WeChat Official Account</a>
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Welcome to the official repository of aiXcoder-7B Code Large Language Model. This model is designed to understand and generate code across multiple programming languages, offering state-of-the-art performance in code completion, comprehension, generation, and more tasks about programming languages.
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You can download the model weights from the following link:
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- [aiXcoder Base Download](https://huggingface.co/aiXcoder/aiXcoder-7b)
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- [aiXcoder Base Download](https://huggingface.co/aiXcoder/aixcoder-7b-base)
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- aiXcoder Instruct Download (Comming soon...)
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### Inference Example
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device = "cuda" # the device to load the model onto
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tokenizer = AutoTokenizer.from_pretrained("aiXcoder/aiXcoder-7b")
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model = AutoModelForCausalLM.from_pretrained("aiXcoder/aiXcoder-7b", torch_dtype=torch.bfloat16)
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tokenizer = AutoTokenizer.from_pretrained("aiXcoder/aixcoder-7b-base")
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model = AutoModelForCausalLM.from_pretrained("aiXcoder/aixcoder-7b-base", torch_dtype=torch.bfloat16)
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text = input_wrapper(

‎README_CN.md‎

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# aiXcoder-7B Code Large Language Model
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<p align="center">
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🏠 <a href="https://www.aixcoder.com/" target="_blank">官网</a>｜🛠 <a href="https://marketplace.visualstudio.com/items?itemName=aixcoder-plugin.aixcoder" target="_blank">VS Code 插件</a>｜🛠 <a href="https://plugins.jetbrains.com/plugin/13574-aixcoder-code-completer" target="_blank">Jetbrains 插件</a>｜🤗 <a href="https://huggingface.co/aiXcoder/aiXcoder-7b" target="_blank">模型下载</a>｜<a href="" target="_blank">技术交流群</a>｜<a href="./assets/wechat_2.jpg" target="_blank">公众号</a>
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🏠 <a href="https://www.aixcoder.com/" target="_blank">官网</a>｜🛠 <a href="https://marketplace.visualstudio.com/items?itemName=aixcoder-plugin.aixcoder" target="_blank">VS Code 插件</a>｜🛠 <a href="https://plugins.jetbrains.com/plugin/13574-aixcoder-code-completer" target="_blank">Jetbrains 插件</a>｜🤗 <a href="https://huggingface.co/aiXcoder/aixcoder-7b-base" target="_blank">模型下载</a>｜<a href="./assets/wechat_1.jpg" target="_blank">技术交流群</a>｜<a href="./assets/wechat_2.jpg" target="_blank">公众号</a>
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欢迎来到aiXcoder-7B代码大型语言模型的官方仓库。该模型旨在理解和生成多种编程语言中的代码，提供在代码完成、理解、生成以及更多关于编程语言的任务中的最先进性能。
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您能从以下地址下载模型：
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- [aiXcoder Base Download](https://huggingface.co/aiXcoder/aiXcoder-7b)
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- [aiXcoder Base Download](https://huggingface.co/aiXcoder/aixcoder-7b-base)
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- aiXcoder Instruct Download (Comming soon...)
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### 推理示例
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device = "cuda" # the device to load the model onto
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tokenizer = AutoTokenizer.from_pretrained("aiXcoder/aiXcoder-7b")
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model = AutoModelForCausalLM.from_pretrained("aiXcoder/aiXcoder-7b", torch_dtype=torch.bfloat16)
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tokenizer = AutoTokenizer.from_pretrained("aiXcoder/aixcoder-7b-base")
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model = AutoModelForCausalLM.from_pretrained("aiXcoder/aixcoder-7b-base", torch_dtype=torch.bfloat16)
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text = input_wrapper(

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‎sess_huggingface.py‎

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device = "cuda" # the device to load the model onto
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tokenizer = AutoTokenizer.from_pretrained("aiXcoder/aiXcoder-7b")
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model = AutoModelForCausalLM.from_pretrained("aiXcoder/aiXcoder-7b", torch_dtype=torch.bfloat16)
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tokenizer = AutoTokenizer.from_pretrained("aiXcoder/aixcoder-7b-base")
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model = AutoModelForCausalLM.from_pretrained("aiXcoder/aixcoder-7b-base", torch_dtype=torch.bfloat16)
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text = input_wrapper(

‎sess_megatron.py‎

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help="Start id for whitespace encoding",
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)
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group.add_argument("--model_dir", type=str, default="")
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group.add_argument("--model_name", type=str, default="aix3-7b")
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group.add_argument("--model_name", type=str, default="aix3-7b-base")
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return parser
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