FTaaS Pipeline component for text generation

Version: 0.0.25

View in Studio: https://ml.azure.com/registries/azureml/components/oss_text_generation_pipeline/version/0.0.25

Compute parameters

Name	Description	Type	Default	Optional	Enum
instance_type_data_import	Instance type to be used for data_import component in case of virtual cluster compute, eg. Singularity.D8_v3. The parameter compute_data_import must be set to 'virtual cluster' for instance_type to be used	string	Singularity.D8_v3	True
instance_type_finetune	Instance type to be used for finetune component in case of virtual cluster compute, eg. Singularity.ND40_v2. The parameter compute_finetune must be set to 'virtual cluster' for instance_type to be used	string	Singularity.ND40_v2	True
number_of_gpu_to_use_finetuning	number of gpus to be used per node for finetuning, should be equal to number of gpu per node in the compute SKU used for finetune	integer	1	True

Continual-Finetuning model path

Name	Description	Type	Default	Optional	Enum
mlflow_model_path	MLflow model asset path. Special characters like \ and ' are invalid in the parameter value.	mlflow_model		False

Preprocessing parameters TODO remove text key if the format is made similar to openai

Name	Description	Type	Default	Optional	Enum
text_key	key for text in an example. format your data keeping in mind that text is concatenated with ground_truth while finetuning in the form - text + groundtruth. for eg. "text"="knock knock\n", "ground_truth"="who's there"; will be treated as "knock knock\nwho's there"	string		False
ground_truth_key	key for ground_truth in an example. we take separate column for ground_truth to enable use cases like summarization, translation, question_answering, etc. which can be repurposed in form of text-generation where both text and ground_truth are needed. This separation is useful for calculating metrics. for eg. "text"="Summarize this dialog:\n{input_dialogue}\nSummary:\n", "ground_truth"="{summary of the dialogue}"	string		True

Dataset path Parameters

Name	Description	Type	Default	Optional	Enum
train_file_path	Path to the registered training data asset. The supported data formats are jsonl, json, csv, tsv and parquet. Special characters like \ and ' are invalid in the parameter value.	uri_file		False
validation_file_path	Path to the registered validation data asset. The supported data formats are jsonl, json, csv, tsv and parquet. Special characters like \ and ' are invalid in the parameter value.	uri_file		True

Finetuning parameters Training parameters

Name	Description	Type	Default	Optional	Enum
max_seq_length	Default is 4096.	integer	4096	True
num_train_epochs	training epochs	integer	1	True
per_device_train_batch_size	Train batch size	integer	1	True
learning_rate	Start learning rate.	number	0.0003	True

Validation parameters

Name	Description	Type	Default	Optional	Enum
system_properties	Validation parameters propagated from pipeline.	string		True

Compute parameters

Name	Description	Type	Default	Optional	Enum
compute_data_import	compute to be used for model_import eg. provide 'FT-Cluster' if your compute is named 'FT-Cluster'. Special characters like \ and ' are invalid in the parameter value. If compute cluster name is provided, instance_type field will be ignored and the respective cluster will be used	string	virtual cluster	True
compute_finetune	compute to be used for finetune eg. provide 'FT-Cluster' if your compute is named 'FT-Cluster'. Special characters like \ and ' are invalid in the parameter value. If compute cluster name is provided, instance_type field will be ignored and the respective cluster will be used	string	virtual cluster	True

Model parameters

Name	Description	Type	Default	Optional	Enum
model_asset_id	Asset id of model	string		False

Model registration

Name	Description	Type	Default	Optional	Enum
registered_model_name	Name of the registered model	string		True

Name	Description	Type
output_model	Output dir to save the finetuned lora weights	uri_folder