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If you are using a custom dataset, please provide your dataset definition in the following format in `dataset_info.json`.
```json
"dataset_name": {
"hf_hub_url": "the name of the dataset repository on the Hugging Face hub. (if specified, ignore below 3 arguments)",
"script_url": "the name of the directory containing a dataset loading script. (if specified, ignore below 2 arguments)",
"file_name": "the name of the dataset file in the this directory. (required if above are not specified)",
"file_sha1": "the SHA-1 hash value of the dataset file. (optional, does not affect training)",
"subset": "the name of the subset. (optional, default: None)",
"ranking": "whether the dataset is a preference dataset or not. (default: false)",
"formatting": "the format of the dataset. (optional, default: alpaca, can be chosen from {alpaca, sharegpt})",
"columns": {
"prompt": "the column name in the dataset containing the prompts. (default: instruction, for alpaca)",
"query": "the column name in the dataset containing the queries. (default: input, for alpaca)",
"response": "the column name in the dataset containing the responses. (default: output, for alpaca)",
"history": "the column name in the dataset containing the histories. (default: None, for alpaca)",
"messages": "the column name in the dataset containing the messages. (default: conversations, for sharegpt)",
"role": "the key in the message represents the identity. (default: from, for sharegpt)",
"content": "the key in the message represents the content. (default: value, for sharegpt)"
}
}
```
Given above, you can use the custom dataset via specifying `--dataset dataset_name`.
Currently we support dataset in **alpaca** or **sharegpt** format, the dataset in alpaca format should follow the below format:
```json
[
{
"instruction": "user instruction (required)",
"input": "user input (optional)",
"output": "model response (required)",
"history": [
["user instruction in the first round (optional)", "model response in the first round (optional)"],
["user instruction in the second round (optional)", "model response in the second round (optional)"]
]
}
]
```
Regarding the above dataset, the `columns` in `dataset_info.json` should be:
```json
"dataset_name": {
"columns": {
"prompt": "instruction",
"query": "input",
"response": "output",
"history": "history"
}
}
```
where the `prompt` and `response` columns should contain non-empty values, represent instruction and response respectively. The `query` column will be concatenated with the `prompt` column and used as input for the model.
The `history` column is a list consisting string tuples representing query-response pairs in history. Note that the responses **in each round will be used for training**.
For the pre-training datasets, only the `prompt` column will be used for training.
For the preference datasets, the `response` column should be a string list whose length is 2, with the preferred answers appearing first, for example:
```json
{
"instruction": "user instruction",
"input": "user input",
"output": [
"chosen answer",
"rejected answer"
]
}
```
The dataset in sharegpt format should follow the below format:
```json
[
{
"conversations": [
{
"from": "human",
"value": "user instruction"
},
{
"from": "gpt",
"value": "model response"
}
]
}
]
```
Regarding the above dataset, the `columns` in `dataset_info.json` should be:
```json
"dataset_name": {
"columns": {
"messages": "conversations",
"role": "from",
"content": "value"
}
}
```
where the `messages` column should be a list whose length is even, and follow the `u/a/u/a/u/a` order.
Pre-training datasets and preference datasets are incompatible with the sharegpt format yet.
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