我看到比较好的对原始数据集介绍的文章来自https://github.com/Mcompetitions/M4-methods, 部分原因是因为M4是希腊的一个学术组织提供的,这个github链接应该和原始组织有关系。 Dataset/M4-info.csv,格式如下 ID (M4id), domain (category), frequency (Frequency), number of forecasts requested (Horizon), seaso...
./dataset/calibration_data.tar:添加了数据集的四张照片 config文件: { "model_type": "ONNX", "npu_mode": "NPU1", "quant": { "input_configs": [ { "tensor_name": "images", "calibration_dataset": "./dataset/calibration_data.tar", "calibration_size": 4, "calibration_mean": [0, 0,...
If you want to train with a custom dataset, you can refer tothis guideto organize your dataset format and specify--dataset <dataset_path>. The--model_authorand--model_nameparameters are only effective when the dataset includesswift/self-cognition. ...
This M4Raw dataset will be valuable for the development of advanced data-driven methods specifically for low-field MRI. It can also serve as a benchmark dataset for general MRI reconstruction algorithms.doi:10.1038/s41597-023-02181-4M. Lyu...
data_collator, train_dataset=train_dataset, eval_dataset=val_dataset, template=template, ) trainer.train() Inference: # Perform inference using the native PyTorch engine engine = PtEngine(model_id_or_path, adapters=[lora_checkpoint]) infer_request = InferRequest(messages=[{'role': 'user', '...
# val_dataset可选,如果不指定,则会从dataset中切出一部分数据集作为验证集 --dataset train.jsonl \ --val_dataset val.jsonl \ 自定义数据集支持json和jsonl样式。glm-4v-9b支持多轮对话,但总的对话轮次中需包含一张图片,支持传入本地路径或URL。以下是自定义数据集的示例: {"query": "55555", "respo...
DAIGT - M4 datasetNotebookInputOutputLogsComments (0)Output Data train.csv(143.26 MB) get_app chevron_right Unable to show preview Unexpected end of JSON input Outputmore_vert calendar_view_week train.csv Download notebook output navigate_nextminimize content_copyhelp...
Learn more OK, Got it.Sergio Henrique · 1y ago· 176 views arrow_drop_up8 Copy & Edit12 more_vert DAIGT - M4 datasetNotebookInputOutputLogsComments (0)comment 0 Comments Hotness
以上为整个数据预处理流程,在配置文件中使用dataset: adv_gen_train, adv_gen_dev配置即可在微调中使用广告文案生成数据集。 5.2 微调 在LLaMa Factory路径下新建examples/train_lora/glm4_9b_chat_lora_sft.yaml微调配置文件,微调配置文件如下: ### model ...
以上为整个数据预处理流程,在配置文件中使用dataset: adv_gen_train, adv_gen_dev配置即可在微调中使用广告文案生成数据集。 5.2 微调 在LLaMa Factory路径下新建examples/train_lora/glm4_9b_chat_lora_sft.yaml微调配置文件,微调配置文件如下: ### model ...