351 lines
9.7 KiB
Markdown
351 lines
9.7 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.
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Currently we support datasets in **alpaca** and **sharegpt** format.
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```json
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"dataset_name": {
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"hf_hub_url": "the name of the dataset repository on the Hugging Face hub. (if specified, ignore script_url and file_name)",
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"ms_hub_url": "the name of the dataset repository on the Model Scope hub. (if specified, ignore script_url and file_name)",
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"script_url": "the name of the directory containing a dataset loading script. (if specified, ignore file_name)",
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"file_name": "the name of the dataset folder or dataset file in this directory. (required if above are not specified)",
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"formatting": "the format of the dataset. (optional, default: alpaca, can be chosen from {alpaca, sharegpt})",
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"ranking": "whether the dataset is a preference dataset or not. (default: False)",
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"subset": "the name of the subset. (optional, default: None)",
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"folder": "the name of the folder of the dataset repository on the Hugging Face hub. (optional, default: None)",
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"num_samples": "the number of samples in the dataset used for training. (optional, default: None)",
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"columns (optional)": {
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"prompt": "the column name in the dataset containing the prompts. (default: instruction)",
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"query": "the column name in the dataset containing the queries. (default: input)",
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"response": "the column name in the dataset containing the responses. (default: output)",
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"history": "the column name in the dataset containing the histories. (default: None)",
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"messages": "the column name in the dataset containing the messages. (default: conversations)",
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"system": "the column name in the dataset containing the system prompts. (default: None)",
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"tools": "the column name in the dataset containing the tool description. (default: None)",
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"images": "the column name in the dataset containing the image inputs. (default: None)",
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"chosen": "the column name in the dataset containing the chosen answers. (default: None)",
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"rejected": "the column name in the dataset containing the rejected answers. (default: None)",
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"kto_tag": "the column name in the dataset containing the kto tags. (default: None)"
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},
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"tags (optional, used for the sharegpt format)": {
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"role_tag": "the key in the message represents the identity. (default: from)",
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"content_tag": "the key in the message represents the content. (default: value)",
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"user_tag": "the value of the role_tag represents the user. (default: human)",
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"assistant_tag": "the value of the role_tag represents the assistant. (default: gpt)",
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"observation_tag": "the value of the role_tag represents the tool results. (default: observation)",
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"function_tag": "the value of the role_tag represents the function call. (default: function_call)",
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"system_tag": "the value of the role_tag represents the system prompt. (default: system, can override system column)"
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}
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}
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```
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## Alpaca Format
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### Supervised Fine-Tuning Dataset
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* [Example dataset](alpaca_en_demo.json)
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In supervised fine-tuning, the `instruction` column will be concatenated with the `input` column and used as the human prompt, then the human prompt would be `instruction\ninput`. The `output` column represents the model response.
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The `system` column will be used as the system prompt if specified.
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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.
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```json
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[
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{
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"instruction": "human instruction (required)",
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"input": "human input (optional)",
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"output": "model response (required)",
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"system": "system prompt (optional)",
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"history": [
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["human instruction in the first round (optional)", "model response in the first round (optional)"],
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["human instruction in the second round (optional)", "model response in the second round (optional)"]
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]
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}
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]
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```
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Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
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```json
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"dataset_name": {
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"file_name": "data.json",
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"columns": {
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"prompt": "instruction",
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"query": "input",
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"response": "output",
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"system": "system",
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"history": "history"
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}
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}
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```
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### Pre-training Dataset
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- [Example dataset](c4_demo.json)
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In pre-training, only the `text` column will be used for model learning.
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```json
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[
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{"text": "document"},
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{"text": "document"}
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]
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```
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Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
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```json
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"dataset_name": {
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"file_name": "data.json",
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"columns": {
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"prompt": "text"
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}
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}
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```
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### Preference Dataset
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Preference datasets are used for reward modeling, DPO training and ORPO training.
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It requires a better response in `chosen` column and a worse response in `rejected` column.
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```json
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[
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{
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"instruction": "human instruction (required)",
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"input": "human input (optional)",
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"chosen": "chosen answer (required)",
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"rejected": "rejected answer (required)"
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}
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]
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```
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Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
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```json
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"dataset_name": {
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"file_name": "data.json",
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"ranking": true,
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"columns": {
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"prompt": "instruction",
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"query": "input",
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"chosen": "chosen",
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"rejected": "rejected"
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}
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}
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```
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### KTO Dataset
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- [Example dataset](kto_en_demo.json)
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KTO datasets require a extra `kto_tag` column containing the boolean human feedback.
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```json
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[
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{
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"instruction": "human instruction (required)",
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"input": "human input (optional)",
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"output": "model response (required)",
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"kto_tag": "human feedback [true/false] (required)"
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}
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]
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```
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Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
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```json
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"dataset_name": {
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"file_name": "data.json",
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"columns": {
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"prompt": "instruction",
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"query": "input",
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"response": "output",
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"kto_tag": "kto_tag"
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}
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}
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```
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### Multimodal Dataset
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- [Example dataset](mllm_demo.json)
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Multimodal datasets require a `images` column containing the paths to the input images. Currently we only support one image.
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```json
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[
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{
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"instruction": "human instruction (required)",
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"input": "human input (optional)",
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"output": "model response (required)",
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"images": [
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"image path (required)"
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]
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}
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]
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```
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Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
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```json
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"dataset_name": {
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"file_name": "data.json",
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"columns": {
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"prompt": "instruction",
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"query": "input",
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"response": "output",
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"images": "images"
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}
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}
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```
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## Sharegpt Format
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### Supervised Fine-Tuning Dataset
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- [Example dataset](glaive_toolcall_en_demo.json)
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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.
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Note that the human and observation should appear in odd positions, while gpt and function should appear in even positions.
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```json
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[
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{
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"conversations": [
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{
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"from": "human",
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"value": "human instruction"
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},
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{
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"from": "function_call",
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"value": "tool arguments"
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},
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{
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"from": "observation",
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"value": "tool result"
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},
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{
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"from": "gpt",
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"value": "model response"
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}
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],
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"system": "system prompt (optional)",
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"tools": "tool description (optional)"
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}
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]
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```
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Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
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```json
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"dataset_name": {
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"file_name": "data.json",
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"formatting": "sharegpt",
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"columns": {
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"messages": "conversations",
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"system": "system",
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"tools": "tools"
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}
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}
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```
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### Preference Dataset
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- [Example dataset](dpo_en_demo.json)
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Preference datasets in sharegpt format also require a better message in `chosen` column and a worse message in `rejected` column.
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```json
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[
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{
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"conversations": [
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{
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"from": "human",
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"value": "human instruction"
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},
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{
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"from": "gpt",
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"value": "model response"
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},
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{
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"from": "human",
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"value": "human instruction"
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}
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],
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"chosen": {
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"from": "gpt",
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"value": "chosen answer (required)"
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},
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"rejected": {
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"from": "gpt",
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"value": "rejected answer (required)"
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}
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}
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]
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```
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Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
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```json
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"dataset_name": {
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"file_name": "data.json",
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"formatting": "sharegpt",
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"ranking": true,
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"columns": {
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"messages": "conversations",
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"chosen": "chosen",
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"rejected": "rejected"
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}
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}
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```
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### OpenAI Format
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The openai format is simply a special case of the sharegpt format, where the first message may be a system prompt.
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```json
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[
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{
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"messages": [
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{
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"role": "system",
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"content": "system prompt (optional)"
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},
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{
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"role": "user",
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"content": "human instruction"
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},
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{
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"role": "assistant",
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"content": "model response"
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}
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]
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}
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]
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```
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Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
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```json
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"dataset_name": {
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"file_name": "data.json",
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"formatting": "sharegpt",
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"columns": {
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"messages": "messages"
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},
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"tags": {
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"role_tag": "role",
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"content_tag": "content",
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"user_tag": "user",
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"assistant_tag": "assistant",
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"system_tag": "system"
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}
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}
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```
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The KTO datasets and multimodal datasets in sharegpt format are similar to the alpaca format.
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Pre-training datasets are **incompatible** with the sharegpt format.
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