环境准备:python11
https://platform.openai.com/docs/guides/fine-tuning/preparing-your-dataset
Python包安裝器:https://pip.pypa.io/en/stable/installation/
git命令行工具: https://git-scm.com/downloads
# 1、安装openai脚手架
pip install --upgrade openai
# 需要的依赖
pip3 install openpyxl
配置自己的 api_key (从openai后台获取)
export OPENAI_API_KEY="<OPENAI_API_KEY>"
# 2、准备训练的数据
prompt.xlsx
prompt | completion |
---|---|
你觉得orangbus怎么样 | 帅,非常帅 |
使用 OpenAI cli 格式化为jsonl的格式数据
openai tools fine_tunes.prepare_data -f prompt.xlsx
Based on the analysis we will perform the following actions:
- [Necessary] Your format `XLSX` will be converted to `JSONL`
- [Recommended] Lowercase all your data in column/key `completion` [Y/n]: y
- [Recommended] Add a suffix separator ` ->` to all prompts [Y/n]: n
- [Recommended] Add a suffix ending ` END` to all completions [Y/n]: n
- [Recommended] Add a whitespace character to the beginning of the completion [Y
/n]: n
Your data will be written to a new JSONL file. Proceed [Y/n]: y
Wrote modified file to `openai_data_prepared.jsonl`
Feel free to take a look!
Now use that file when fine-tuning:
> openai api fine_tunes.create -t "openai_data_prepared.jsonl"
Once your model starts training, it'll approximately take 3.13 minutes to train
a `curie` model, and less for `ada` and `babbage`. Queue will approximately take
half an hour per job ahead of you
生成的格式大概是这样的 (每一条数据独占一行)
{"prompt": "<prompt text>", "completion": "<ideal generated text>"}
{"prompt": "<prompt text>", "completion": "<ideal generated text>"}
{"prompt": "<prompt text>", "completion": "<ideal generated text>"}
...
# 3、创建模型
BASE_MODEL: ada
, babbage
, ``curiedavinci` (差别不大,只是价格越高效果越好,哈哈哈)
openai api fine_tunes.create -t prompt.xlsx -m ada
Upload progress: 100%|███████████████████| 34.1k/34.1k [00:00<00:00, 17.2Mit/s]
Uploaded file from openai_data_prepared.jsonl: file-aealtK98z24aSt59uJrzsNLj
Created fine-tune: ft-uSw1UeAYzFVbno8QfFE8xHxR
Streaming events until fine-tuning is complete...
(Ctrl-C will interrupt the stream, but not cancel the fine-tune)
[2023-04-25 11:27:42] Created fine-tune: ft-uSw1UeAYzFVbno8QfFE8xHxR
[2023-04-25 11:27:57] Fine-tune costs $0.26
[2023-04-25 11:27:58] Fine-tune enqueued. Queue number: 0
[2023-04-25 11:27:59] Fine-tune started
查询预训练模型列表
openai api fine_tunes.list
{
"data": [
{
"created_at": 1682393262,
"fine_tuned_model": "curie:ft--2023-04-25-03-29-51", # 这个是你模型的名称
"hyperparams": {
"batch_size": 1,
"learning_rate_multiplier": 0.1,
"n_epochs": 4,
"prompt_loss_weight": 0.01
},
"id": "ft-xR", // YOUR_FINE_TUNE_JOB_ID
"model": "curie",
"object": "fine-tune",
"organization_id": "",
"result_files": [
{
"bytes": 10355,
"created_at": 1682393391,
"filename": "compiled_results.csv",
"id": "file-puFju0lqFsxybd2WdOXwHcmp",
"object": "file",
"purpose": "fine-tune-results",
"status": "processed",
"status_details": null
}
],
"status": "succeeded",
"training_files": [
{
"bytes": 34078,
"created_at": 1682393261,
"filename": "openai_data_prepared.jsonl",
"id": "file-aealzsNLj",
"object": "file",
"purpose": "fine-tune",
"status": "processed",
"status_details": null
}
],
"updated_at": 1682393392,
"validation_files": []
}
],
"object": "list"
}
测试
curl https://api.openai.com/v1/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer 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" \
-d '{
"model": "curie:ft--2023-04-25-03-29-51",
"prompt": " 以2023选择昆明海亦丰中学高考复读怎么样为题,写一篇不低于1000字的文章,要求文章逻辑严谨",
"temperature": 0.7,
"max_tokens": 1000,
"top_p": 1,
"frequency_penalty": 0,
"presence_penalty": 0
}'