manually install a Conda package manager, and add the conda path to your PYTHONPATH for example, using export PYTHONPATH="/opt/conda/lib/python3.8/site-packages" if your Conda package manager was installed in manually install a Conda package manager, and add the conda path to your PYTHONPATH for example, using export PYTHONPATH="/opt/conda/lib/python3.8/site-packages" if your Conda package manager was installed in manually install a Conda package manager, and add the conda path to your PYTHONPATH for example, using export PYTHONPATH="/opt/conda/lib/python3.8/site-packages" if your Conda package manager was installed in

storing TFLite models. !mkdir -p 'tflite_models' def convert_and_eval(model, model_name, quantized_export, test_dataset_x, test_dataset_y): """Helper method to convert the given model to TFLite and eval Set up the converter. converter = tf.lite.TFLiteConverter.from_keras_model(model) if quantized_export: converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE, tf.lite.Optimize.OPTIMIZE_FOR_LATENCY] tflite'.format( model_name, ('quantized' if quantized_export else 'float')) print('Model Name: {}, Quantized: {}'.format(model_name, quantized_export)) print('Model Size: {:.2f} KB'.format(len(tflite_model_str)

scales。具体操作步骤如下：首先，在使用 AutoAWQ 运行 model.quantize() 时，请务必记得添加 export_compatible=True 参数，如下所示： ... model.quantize( tokenizer, quant_config=quant_config, export_compatible=True ) model.save_pretrained(quant_path) python src/export_model.py \ --model_name_or_path path_to_base_model \ --adapter_name_or_path path_to_adapter \ --template default \ --finetuning_type lora \ --export_dir path_to_export \ --export_size 2 2 \ --export_legacy_format False 结语 上述内容是使用 LLaMA-Factory 训练 Qwen 的最简单方法。欢迎通过查看官方仓库深入了解详细信息！ 1.13 Function Calling 在 Qwen-Agent 中，我们提供了一个专用封装器，旨在实现通过 dashscope API 与 OpenAI API 进行的函数调 用。 1.13. Function

install Miniconda and PyTorch yourself, you can use the global Miniconda and PyTorch installed at /export/data/miniconda3 . 1. Initialize Miniconda: 2. If you want to use PyTorch, activate the pytorch cssystem is ~/.cshrc_user , so you should write the content in ~/.tcshrc to ~/.cshrc_user : source "/export/data/miniconda3/etc/profile.d/conda.csh" conda activate pytorch conda activate tf2 python python_script

数据-模型-服务流水线 数据集 生成 数据 处理 模型 训练 参数 调优 模型 部署 识别 服务 使用 Flask 快速搭建 验证码识别服务 使用 Flask 启动 验证码识别服务 $ export FLASK_ENV=development && flask run --host=0.0.0.0 打开浏览器访问测试 URL（http://localhost:5000/ping） 访问

support common neural net layers in quantized mode. TFLite supports quantized models, by allowing export of models with 8-bit unsigned int weights, and having integration with libraries like GEMMLOWP and

update sudo apt-get -y install cuda 安装程序后，运行以下命令查看GPU： nvidia-smi 最后，将CUDA添加到库路径以帮助其他库找到它。 echo "export LD_LIBRARY_PATH=\${LD_LIBRARY_PATH}:/usr/local/cuda/lib64" >> ~/.bashrc 756 16. 附录：深度学习工具 16.3