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@ -65,9 +65,9 @@ Regarding the above dataset, the `columns` in `dataset_info.json` should be:
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}
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```
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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.
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The `query` column will be concatenated with the `prompt` column and used as the user prompt, then the user prompt would be `prompt\nquery`. The `response` column represents the model response.
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The `system` column will be used as the system prompt in the template. 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**.
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The `system` column will be used as the system prompt. The `history` column is a list consisting string tuples representing prompt-response pairs in the history. Note that the responses in the history **will also be used for training**.
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For the pre-training datasets, only the `prompt` column will be used for training.
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@ -123,6 +123,6 @@ Regarding the above dataset, the `columns` in `dataset_info.json` should be:
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}
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```
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where the `messages` column should be a list whose length is even, and follow the `u/a/u/a/u/a` order.
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where the `messages` column should be a list following the `u/a/u/a/u/a` order.
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Pre-training datasets and preference datasets are incompatible with the sharegpt format yet.
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@ -65,9 +65,9 @@
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}
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```
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其中 `prompt` 和 `response` 列应当是非空的字符串,分别代表用户指令和模型回答。`query` 列的内容将会和 `prompt` 列拼接作为模型输入。
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其中 `query` 列对应的内容会与 `prompt` 列对应的内容拼接后作为用户指令,即用户指令为 `prompt\nquery`。`response` 列对应的内容为模型回答。
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`system` 为模板中的系统提示词。`history` 列是由多个字符串二元组构成的列表,分别代表历史消息中每轮的指令和回答。注意每轮的模型回答**均会被用于训练**。
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`system` 列对应的内容将被作为系统提示词。`history` 列是由多个字符串二元组构成的列表,分别代表历史消息中每轮的指令和回答。注意历史消息中的回答**也会被用于训练**。
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对于预训练数据集,仅 `prompt` 列中的内容会用于模型训练。
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@ -123,6 +123,6 @@
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}
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```
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其中 `messages` 列必须为偶数长度的列表,且符合 `用户/模型/用户/模型/用户/模型` 的顺序。
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其中 `messages` 列应当是一个列表,且符合 `用户/模型/用户/模型/用户/模型` 的顺序。
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预训练数据集和偏好数据集尚不支持 sharegpt 格式。
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@ -20,10 +20,14 @@ def convert_alpaca(examples: Dict[str, List[Any]], dataset_attr: "DatasetAttr")
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prompt.append({"role": Role.USER, "content": old_prompt})
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prompt.append({"role": Role.ASSISTANT, "content": old_response})
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instruction = examples[dataset_attr.prompt][i]
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content = []
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if dataset_attr.prompt and examples[dataset_attr.prompt][i]:
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content.append(examples[dataset_attr.prompt][i])
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if dataset_attr.query and examples[dataset_attr.query][i]:
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instruction += "\n" + examples[dataset_attr.query][i]
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prompt.append({"role": Role.USER, "content": instruction})
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content.append(examples[dataset_attr.query][i])
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prompt.append({"role": Role.USER, "content": "\n".join(content)})
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if dataset_attr.response and isinstance(examples[dataset_attr.response][i], list):
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response = [{"role": Role.ASSISTANT, "content": content} for content in examples[dataset_attr.response][i]]
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