mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-11-04 18:02:19 +08:00
476 lines
13 KiB
Markdown
476 lines
13 KiB
Markdown
The [dataset_info.json](dataset_info.json) contains all available datasets. If you are using a custom dataset, please **make sure** to add a *dataset description* in `dataset_info.json` and specify `dataset: dataset_name` before training to use it.
|
|
|
|
The `dataset_info.json` file should be put in the `dataset_dir` directory. You can change `dataset_dir` to use another directory. The default value is `./data`.
|
|
|
|
Currently we support datasets in **alpaca** and **sharegpt** format. Allowed file types include json, jsonl, csv, parquet, arrow.
|
|
|
|
```json
|
|
"dataset_name": {
|
|
"hf_hub_url": "the name of the dataset repository on the Hugging Face hub. (if specified, ignore script_url, file_name and cloud_file_name)",
|
|
"ms_hub_url": "the name of the dataset repository on the Model Scope hub. (if specified, ignore script_url, file_name and cloud_file_name)",
|
|
"script_url": "the name of the directory containing a dataset loading script. (if specified, ignore file_name and cloud_file_name)",
|
|
"cloud_file_name": "the name of the dataset file in s3/gcs cloud storage. (if specified, ignore file_name)",
|
|
"file_name": "the name of the dataset folder or dataset file in this directory. (required if above are not specified)",
|
|
"formatting": "the format of the dataset. (optional, default: alpaca, can be chosen from {alpaca, sharegpt})",
|
|
"ranking": "whether the dataset is a preference dataset or not. (default: False)",
|
|
"subset": "the name of the subset. (optional, default: None)",
|
|
"split": "the name of dataset split to be used. (optional, default: train)",
|
|
"folder": "the name of the folder of the dataset repository on the Hugging Face hub. (optional, default: None)",
|
|
"num_samples": "the number of samples in the dataset to be used. (optional, default: None)",
|
|
"columns (optional)": {
|
|
"prompt": "the column name in the dataset containing the prompts. (default: instruction)",
|
|
"query": "the column name in the dataset containing the queries. (default: input)",
|
|
"response": "the column name in the dataset containing the responses. (default: output)",
|
|
"history": "the column name in the dataset containing the histories. (default: None)",
|
|
"messages": "the column name in the dataset containing the messages. (default: conversations)",
|
|
"system": "the column name in the dataset containing the system prompts. (default: None)",
|
|
"tools": "the column name in the dataset containing the tool description. (default: None)",
|
|
"images": "the column name in the dataset containing the image inputs. (default: None)",
|
|
"videos": "the column name in the dataset containing the videos inputs. (default: None)",
|
|
"audios": "the column name in the dataset containing the audios inputs. (default: None)",
|
|
"chosen": "the column name in the dataset containing the chosen answers. (default: None)",
|
|
"rejected": "the column name in the dataset containing the rejected answers. (default: None)",
|
|
"kto_tag": "the column name in the dataset containing the kto tags. (default: None)"
|
|
},
|
|
"tags (optional, used for the sharegpt format)": {
|
|
"role_tag": "the key in the message represents the identity. (default: from)",
|
|
"content_tag": "the key in the message represents the content. (default: value)",
|
|
"user_tag": "the value of the role_tag represents the user. (default: human)",
|
|
"assistant_tag": "the value of the role_tag represents the assistant. (default: gpt)",
|
|
"observation_tag": "the value of the role_tag represents the tool results. (default: observation)",
|
|
"function_tag": "the value of the role_tag represents the function call. (default: function_call)",
|
|
"system_tag": "the value of the role_tag represents the system prompt. (default: system, can override system column)"
|
|
}
|
|
}
|
|
```
|
|
|
|
## Alpaca Format
|
|
|
|
### Supervised Fine-Tuning Dataset
|
|
|
|
* [Example dataset](alpaca_en_demo.json)
|
|
|
|
In supervised fine-tuning, the `instruction` column will be concatenated with the `input` column and used as the user prompt, then the user prompt would be `instruction\ninput`. The `output` column represents the model response.
|
|
|
|
For reasoning models, if the dataset contains chain-of-thought (CoT), the CoT needs to be placed in the model responses, such as `<think>cot</think>output`.
|
|
|
|
The `system` column will be used as the system prompt if specified.
|
|
|
|
The `history` column is a list consisting of string tuples representing prompt-response pairs in the history messages. Note that the responses in the history **will also be learned by the model** in supervised fine-tuning.
|
|
|
|
```json
|
|
[
|
|
{
|
|
"instruction": "user instruction (required)",
|
|
"input": "user input (optional)",
|
|
"output": "model response (required)",
|
|
"system": "system prompt (optional)",
|
|
"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 *dataset description* in `dataset_info.json` should be:
|
|
|
|
```json
|
|
"dataset_name": {
|
|
"file_name": "data.json",
|
|
"columns": {
|
|
"prompt": "instruction",
|
|
"query": "input",
|
|
"response": "output",
|
|
"system": "system",
|
|
"history": "history"
|
|
}
|
|
}
|
|
```
|
|
|
|
> [!TIP]
|
|
> If the model has reasoning capabilities (e.g. Qwen3) but the dataset does not contain chain-of-thought (CoT), LLaMA-Factory will automatically add empty CoT to the data. When `enable_thinking` is `True` (slow thinking, by default), the empty CoT will be added to the model responses and loss computation will be considered; otherwise (fast thinking), it will be added to the user prompts and loss computation will be ignored. Please keep the `enable_thinking` parameter consistent during training and inference.
|
|
>
|
|
> If you want to train data containing CoT with slow thinking and data without CoT with fast thinking, you can set `enable_thinking` to `None`. However, this feature is relatively complicated and should be used with caution.
|
|
|
|
### Pre-training Dataset
|
|
|
|
- [Example dataset](c4_demo.jsonl)
|
|
|
|
In pre-training, only the `text` column will be used for model learning.
|
|
|
|
```json
|
|
[
|
|
{"text": "document"},
|
|
{"text": "document"}
|
|
]
|
|
```
|
|
|
|
Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
|
|
|
|
```json
|
|
"dataset_name": {
|
|
"file_name": "data.json",
|
|
"columns": {
|
|
"prompt": "text"
|
|
}
|
|
}
|
|
```
|
|
|
|
### Preference Dataset
|
|
|
|
Preference datasets are used for reward modeling, DPO training, ORPO and SimPO training.
|
|
|
|
It requires a better response in `chosen` column and a worse response in `rejected` column.
|
|
|
|
```json
|
|
[
|
|
{
|
|
"instruction": "user instruction (required)",
|
|
"input": "user input (optional)",
|
|
"chosen": "chosen answer (required)",
|
|
"rejected": "rejected answer (required)"
|
|
}
|
|
]
|
|
```
|
|
|
|
Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
|
|
|
|
```json
|
|
"dataset_name": {
|
|
"file_name": "data.json",
|
|
"ranking": true,
|
|
"columns": {
|
|
"prompt": "instruction",
|
|
"query": "input",
|
|
"chosen": "chosen",
|
|
"rejected": "rejected"
|
|
}
|
|
}
|
|
```
|
|
|
|
### KTO Dataset
|
|
|
|
An additional column `kto_tag` is required. Please refer to the [sharegpt](#sharegpt-format) format for details.
|
|
|
|
### Multimodal Image Dataset
|
|
|
|
An additional column `images` is required. Please refer to the [sharegpt](#sharegpt-format) format for details.
|
|
|
|
### Multimodal Video Dataset
|
|
|
|
An additional column `videos` is required. Please refer to the [sharegpt](#sharegpt-format) format for details.
|
|
|
|
### Multimodal Audio Dataset
|
|
|
|
An additional column `audios` is required. Please refer to the [sharegpt](#sharegpt-format) format for details.
|
|
|
|
## Sharegpt Format
|
|
|
|
### Supervised Fine-Tuning Dataset
|
|
|
|
- [Example dataset](glaive_toolcall_en_demo.json)
|
|
|
|
Compared to the alpaca format, the sharegpt format allows the datasets have **more roles**, such as human, gpt, observation and function. They are presented in a list of objects in the `conversations` column.
|
|
|
|
Note that the human and observation should appear in odd positions, while gpt and function should appear in even positions. The gpt and function will be learned by the model.
|
|
|
|
```json
|
|
[
|
|
{
|
|
"conversations": [
|
|
{
|
|
"from": "human",
|
|
"value": "user instruction"
|
|
},
|
|
{
|
|
"from": "function_call",
|
|
"value": "tool arguments"
|
|
},
|
|
{
|
|
"from": "observation",
|
|
"value": "tool result"
|
|
},
|
|
{
|
|
"from": "gpt",
|
|
"value": "model response"
|
|
}
|
|
],
|
|
"system": "system prompt (optional)",
|
|
"tools": "tool description (optional)"
|
|
}
|
|
]
|
|
```
|
|
|
|
Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
|
|
|
|
```json
|
|
"dataset_name": {
|
|
"file_name": "data.json",
|
|
"formatting": "sharegpt",
|
|
"columns": {
|
|
"messages": "conversations",
|
|
"system": "system",
|
|
"tools": "tools"
|
|
}
|
|
}
|
|
```
|
|
|
|
### Pre-training Dataset
|
|
|
|
Not yet supported, please use the [alpaca](#alpaca-format) format.
|
|
|
|
### Preference Dataset
|
|
|
|
- [Example dataset](dpo_en_demo.json)
|
|
|
|
Preference datasets in sharegpt format also require a better message in `chosen` column and a worse message in `rejected` column.
|
|
|
|
```json
|
|
[
|
|
{
|
|
"conversations": [
|
|
{
|
|
"from": "human",
|
|
"value": "user instruction"
|
|
},
|
|
{
|
|
"from": "gpt",
|
|
"value": "model response"
|
|
},
|
|
{
|
|
"from": "human",
|
|
"value": "user instruction"
|
|
}
|
|
],
|
|
"chosen": {
|
|
"from": "gpt",
|
|
"value": "chosen answer (required)"
|
|
},
|
|
"rejected": {
|
|
"from": "gpt",
|
|
"value": "rejected answer (required)"
|
|
}
|
|
}
|
|
]
|
|
```
|
|
|
|
Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
|
|
|
|
```json
|
|
"dataset_name": {
|
|
"file_name": "data.json",
|
|
"formatting": "sharegpt",
|
|
"ranking": true,
|
|
"columns": {
|
|
"messages": "conversations",
|
|
"chosen": "chosen",
|
|
"rejected": "rejected"
|
|
}
|
|
}
|
|
```
|
|
|
|
### KTO Dataset
|
|
|
|
- [Example dataset](kto_en_demo.json)
|
|
|
|
KTO datasets require a extra `kto_tag` column containing the boolean human feedback.
|
|
|
|
```json
|
|
[
|
|
{
|
|
"conversations": [
|
|
{
|
|
"from": "human",
|
|
"value": "user instruction"
|
|
},
|
|
{
|
|
"from": "gpt",
|
|
"value": "model response"
|
|
}
|
|
],
|
|
"kto_tag": "human feedback [true/false] (required)"
|
|
}
|
|
]
|
|
```
|
|
|
|
Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
|
|
|
|
```json
|
|
"dataset_name": {
|
|
"file_name": "data.json",
|
|
"formatting": "sharegpt",
|
|
"columns": {
|
|
"messages": "conversations",
|
|
"kto_tag": "kto_tag"
|
|
}
|
|
}
|
|
```
|
|
|
|
### Multimodal Image Dataset
|
|
|
|
- [Example dataset](mllm_demo.json)
|
|
|
|
Multimodal image datasets require an `images` column containing the paths to the input images.
|
|
|
|
The number of images should be identical to the `<image>` tokens in the conversations.
|
|
|
|
```json
|
|
[
|
|
{
|
|
"conversations": [
|
|
{
|
|
"from": "human",
|
|
"value": "<image>user instruction"
|
|
},
|
|
{
|
|
"from": "gpt",
|
|
"value": "model response"
|
|
}
|
|
],
|
|
"images": [
|
|
"image path (required)"
|
|
]
|
|
}
|
|
]
|
|
```
|
|
|
|
Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
|
|
|
|
```json
|
|
"dataset_name": {
|
|
"file_name": "data.json",
|
|
"formatting": "sharegpt",
|
|
"columns": {
|
|
"messages": "conversations",
|
|
"images": "images"
|
|
}
|
|
}
|
|
```
|
|
|
|
### Multimodal Video Dataset
|
|
|
|
- [Example dataset](mllm_video_demo.json)
|
|
|
|
Multimodal video datasets require a `videos` column containing the paths to the input videos.
|
|
|
|
The number of videos should be identical to the `<video>` tokens in the conversations.
|
|
|
|
```json
|
|
[
|
|
{
|
|
"conversations": [
|
|
{
|
|
"from": "human",
|
|
"value": "<video>user instruction"
|
|
},
|
|
{
|
|
"from": "gpt",
|
|
"value": "model response"
|
|
}
|
|
],
|
|
"videos": [
|
|
"video path (required)"
|
|
]
|
|
}
|
|
]
|
|
```
|
|
|
|
Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
|
|
|
|
```json
|
|
"dataset_name": {
|
|
"file_name": "data.json",
|
|
"formatting": "sharegpt",
|
|
"columns": {
|
|
"messages": "conversations",
|
|
"videos": "videos"
|
|
}
|
|
}
|
|
```
|
|
|
|
### Multimodal Audio Dataset
|
|
|
|
- [Example dataset](mllm_audio_demo.json)
|
|
|
|
Multimodal audio datasets require an `audios` column containing the paths to the input audios.
|
|
|
|
The number of audios should be identical to the `<audio>` tokens in the conversations.
|
|
|
|
```json
|
|
[
|
|
{
|
|
"conversations": [
|
|
{
|
|
"from": "human",
|
|
"value": "<audio>user instruction"
|
|
},
|
|
{
|
|
"from": "gpt",
|
|
"value": "model response"
|
|
}
|
|
],
|
|
"audios": [
|
|
"audio path (required)"
|
|
]
|
|
}
|
|
]
|
|
```
|
|
|
|
Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
|
|
|
|
```json
|
|
"dataset_name": {
|
|
"file_name": "data.json",
|
|
"formatting": "sharegpt",
|
|
"columns": {
|
|
"messages": "conversations",
|
|
"audios": "audios"
|
|
}
|
|
}
|
|
```
|
|
|
|
### OpenAI Format
|
|
|
|
The openai format is simply a special case of the sharegpt format, where the first message may be a system prompt.
|
|
|
|
```json
|
|
[
|
|
{
|
|
"messages": [
|
|
{
|
|
"role": "system",
|
|
"content": "system prompt (optional)"
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": "user instruction"
|
|
},
|
|
{
|
|
"role": "assistant",
|
|
"content": "model response"
|
|
}
|
|
]
|
|
}
|
|
]
|
|
```
|
|
|
|
Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
|
|
|
|
```json
|
|
"dataset_name": {
|
|
"file_name": "data.json",
|
|
"formatting": "sharegpt",
|
|
"columns": {
|
|
"messages": "messages"
|
|
},
|
|
"tags": {
|
|
"role_tag": "role",
|
|
"content_tag": "content",
|
|
"user_tag": "user",
|
|
"assistant_tag": "assistant",
|
|
"system_tag": "system"
|
|
}
|
|
}
|
|
```
|