See details on theAutoCoder GitHub. Simple test script: model_path = "" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto") HumanEval = load_dataset("evalplus/humanevalplus") Input = "" # input your question her...
Then, directly test it by using EvalPlus GitHub (You don't need to use to use evalplus's evalplus.sanitize to post-process the code this time). Test on DS-1000. (Skip to Step 5, if you don't want to test its performance on benchmarks) python test_ds1000.py Your will get ...
AutoCoder 社区是一个致力于简化开发者代码开发流程,提升开发效率的社区,开发有 auto-coder.chat (lite/pro), byzerllm 等项目, autocoder-nano 是 AutoCoder 社区的全新成员,基于 auto-coder.chat 功能简化,可以理解成 auto-coder.chat 的轻量级版本。 nano/lite/pro 有什么区别? Pro:分布式架构,支持分布式部署模...
If you encounter any issues, have feature requests, or simply want to provide feedback, please open an issue on our GitHub repository. We'll be more than happy to help! (Written by Autocoder - To remove this watermark, please upgrade to autocoder pro j/k it's free I wrote this part)...
cd /opt/auto-code-rover conda activate auto-code-rover PYTHONPATH=. python app/main.py github-issue --output-dir output --setup-dir setup --model gpt-4o-2024-05-13 --model-temperature 0.2 --task-id <task id> --clone-link --commit-hash <any version that has the issue> --...
代码地址:https://github.com/bin123apple/AutoCoder 这篇论文主要试图解决两个问题: 纠正教师模型生成的错误知识:教师模型(如GPT-4 Turbo)在生成代码指令数据集时可能会引入错误。论文提出了一个新的大规模代码指令数据集注释方法,称为AIEV-INSTRUCT(Instruction Tuning with Agent-Interaction and Execution-Verified)...
代码地址:github.com/bin123apple/ 模型地址:Bin12345/AutoCoder_S_6.7B · Hugging Face 1. 引言 代码生成是现代软件开发的关键技术之一。它通过提升生产力、减少错误、标准化代码、加速原型开发以及支持复杂系统,极大地改善了开发效率和质量 。近年来,大规模语言模型(LLMs)在代码生成方面取得了显著进展,例如GPT-4...
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Github Copilot 本质还是IDE工具的衍生,是一个更加“智能”的代码提示,而其提供的Copilot Chat 则更加只是把一个聊天框做到IDE而已,和集成一个搜索框到IDE工具没有任何区别,然还是一个古典产品的思维在做的一个产品。 更细节的,我可以从三个维度做给大家做分析: 第一个维度是 Github Copilot 的定位,我一直是...
git clone https://github.com/bin123apple/AutoCoder conda create -n AutoCoder pythnotallow=3.11 conda activate AutoCoder pip install -r requirements.txt cd /Web_demo pip install -r requirements.txt python chatbot.py 1. 2. 3. 4. 5.