README.md

Post Tuning Tools

For post tuning formats, we mainly consider 4 formats to support ModelScope-Swift and LLaMA-Factory.

• Swift's Messages format (Very similar to the LLaMA-Factory's ShareGPT format, with different key names):

{
  "messages": [
    {
      "role": "system",
      "content": "<system>"
    },
    {
      "role": "user",
      "content": "<query1>"
    },
    {
      "role": "assistant",
      "content": "<response1>"
    },
    {
      "role": "user",
      "content": "<query2>"
    },
    {
      "role": "assistant",
      "content": "<response2>"
    }
  ]
}

• Swift's ShareGPT format:

{
  "system": "<system>",
  "conversation": [
    {
      "human": "<query1>",
      "assistant": "<response1>"
    },
    {
      "human": "<query2>",
      "assistant": "<response2>"
    }
  ]
}

• Alpaca format (used in the same definition in Swift and LLaMA-Factory):

{
  "system": "<system>",
  "instruction": "<query-inst>",
  "input": "<query-input>",
  "output": "<response>"
}

• Swift's Query-Response format:

{
  "system": "<system>",
  "query": "<query2>",
  "response": "<response2>",
  "history": [
    [
      "<query1>",
      "<response1>"
    ]
  ]
}

In Data-Juicer, we pre-set fields to align with the last two formats (Alpaca and Query-Response), which serves as our intermediate format for post-tuning dialog datasets. Correspondingly, we provide several tools to convert datasets in other formats to the following DJ format and vice versa.

• DJ default format for post-tuning OPs:

{
  "system": "<system>",
  "instruction": "<query-inst>",
  "query": "<query2>",
  "response": "<response2>",
  "history": [
    [
      "<query1>",
      "<response1>"
    ]
  ]
}