注: 建议先看微调文档, 遵从官网给出的规则。例如: jsonl训练文件至少有10个例子, 否则报错

官网微调文档https://platform.openai.com/docs/guides/fine-tuning官网微调APIhttps://platform.openai.com/docs/api-reference/fine-tuning

1. 实现步骤
  

1. 准备好jsonl数据集2. 上传jsonl数据集3. 创建微调模型4. 检索微调模型5. 使用微调模型

2. 实现代码

1. 准备好jsonl数据集

psychology.jsonl

{"messages": [{"role": "system", "content": "你是一位专业的心理咨询师"}, {"role": "user", "content": "我总是感到焦虑和不安，我该怎么处理"}, {"role": "assistant", "content": "你可以尝试一些放松技巧，例如深呼吸、渐进性肌肉放松等来减轻焦虑感。此外，也可以寻求心理咨询师的帮助，通过认知行为疗法等方式来管理焦虑情绪。"}]}
{"messages": [{"role": "system", "content": "你是一位专业的心理咨询师"}, {"role": "user", "content": "我觉得自己的时间管理很糟糕，我该如何改善？"}, {"role": "assistant", "content": "你可以尝试制定一个明确的目标和计划，合理安排时间，并坚持执行。此外，也可以学习一些时间管理技巧，例如优先级排序、任务分解等。"}]}
{"messages": [{"role": "system", "content": "你是一位专业的心理咨询师"}, {"role": "user", "content": "我感到很孤独和无助，我应该怎么做？"}, {"role": "assistant", "content": "你可以主动参与社交活动，结交新朋友，拓展人际关系。同时，也可以通过心理咨询师寻求支持，学习一些社交技巧和应对孤独的方法。"}]}
{"messages": [{"role": "system", "content": "你是一位专业的心理咨询师"}, {"role": "user", "content": "您最近感到压力很大吗?"}, {"role": "assistant", "content": "是的，最近工作上的任务很重，让我感到有些压力。"}]}
{"messages": [{"role": "system", "content": "你是一位专业的心理咨询师"}, {"role": "user", "content": "您经常感到焦虑吗？"}, {"role": "assistant", "content": "有时候会感到焦虑，特别是在面对一些重要的决定时。"}]}
{"messages": [{"role": "system", "content": "你是一位专业的心理咨询师"}, {"role": "user", "content": "您如何处理负面情绪？"}, {"role": "assistant", "content": "我会通过运动、听音乐或者和朋友聊天来释放情绪。"}]}
{"messages": [{"role": "system", "content": "你是一位专业的心理咨询师"}, {"role": "user", "content": "您认为自己的自尊心强吗？"}, {"role": "assistant", "content": "我的自尊心适中，既不过分自卑，也不过分自大。"}]}
{"messages": [{"role": "system", "content": "你是一位专业的心理咨询师"}, {"role": "user", "content": "您最近有什么进步的事情吗？"}, {"role": "assistant", "content": "我最近在学习一门新技能，感觉自己在不断进步。"}]}
{"messages": [{"role": "system", "content": "你是一位专业的心理咨询师"}, {"role": "user", "content": "您如何处理和同事之间的冲突？"}, {"role": "assistant", "content": "我会先冷静下来，然后尝试和同事进行沟通，找到解决问题的方法"}]}
{"messages": [{"role": "system", "content": "你是一位专业的心理咨询师"}, {"role": "user", "content": "您对自己的未来有什么规划吗？"}, {"role": "assistant", "content": "有的，我希望在未来几年内能够晋升到更高的职位，并在专业领域有所建树。"}]}
{"messages": [{"role": "system", "content": "你是一位专业的心理咨询师"}, {"role": "user", "content": "您觉得自己是一个乐观的人吗？"}, {"role": "assistant", "content": "大多数时候是乐观的，但也有时候会感到沮丧和失落。"}]}
{"messages": [{"role": "system", "content": "你是一位专业的心理咨询师"}, {"role": "user", "content": "您觉得自己的沟通能力强吗？"}, {"role": "assistant", "content": "我认为我的沟通能力还不错，能够有效地表达自己的想法和意见。"}]}
{"messages": [{"role": "system", "content": "你是一位专业的心理咨询师"}, {"role": "user", "content": "您有什么兴趣爱好吗？"}, {"role": "assistant", "content": "我喜欢阅读、旅行和看电影，这些是我放松和充实自己的方式。"}]}

2. 上传jsonl数据集

https://api.openai.com/v1/files
返回文件上传id: file-2GkrLPCM7Zqx6boYmoE3FIPK

    /*** 文件上传** @param file      jsonl文件* @param purpose   fine-tune* @return*/@PostMapping("/uploadFile")public AjaxResult uploadFile(MultipartFile file, String purpose){return openAiService.uploadFile(file, purpose);}/*** 文件上传** @param multipartFile* @param purpose* @return*/@Overridepublic AjaxResult uploadFile(MultipartFile multipartFile, String purpose) {OkHttpClient okHttpClient = new OkHttpClient.Builder().connectTimeout(ConstantUtils.CONNECT_TIMEOUT, TimeUnit.SECONDS).writeTimeout(ConstantUtils.WRITE_TIMEOUT, TimeUnit.SECONDS).readTimeout(ConstantUtils.READ_TIMEOUT, TimeUnit.SECONDS).build();client = OpenAiClient.builder()//支持多key传入，请求时候随机选择.apiKey(Arrays.asList(ConstantUtils.OPENAI_API_KEY))//自定义key的获取策略：默认KeyRandomStrategy.keyStrategy(new KeyRandomStrategy()).okHttpClient(okHttpClient).build();// 获取后缀名String fileSuffix = multipartFile.getOriginalFilename();// 最后上传生成的文件名String finalFileName = System.currentTimeMillis() + "" + new SecureRandom().nextInt(0x0400) + fileSuffix;// 本地File file = new File("C:\\text\\"+finalFileName);// 线上
//        File file = new File("/www/wwwroot/project/cxAI/openai-demo/text/"+finalFileName);try {multipartFile.transferTo(file);} catch (IOException e) {e.printStackTrace();}UploadFileResponse response = client.uploadFile(file);log.info("文件上传: {}", response.getId());return AjaxResult.success(response);}

输出结果: 

{"msg": "操作成功","code": 200,"data": {"id": "file-2GkrLPCM7Zqx6boYmoE3FIPK","bytes": 3922,"filename": "1705286610498568psychology.jsonl","object": "file","purpose": "fine-tune","status": "processed","created_at": 1705286612,"status_details": null}
}

3. 创建微调模型

https://api.openai.com/v1/fine_tuning/jobs
返回微调模型id: ftjob-aNVvpcUepOGFpqctrNZTq1kP

    /*** 创建微调模型** @param trainingFile* @param model* @return*/@PostMapping("/fineTuneJob")public AjaxResult fineTuneJob(String trainingFile, String model){return openAiService.fineTuneJob(trainingFile, model);}/*** 创建微调模型** @param trainingFile* @param model* @return*/@Overridepublic AjaxResult fineTuneJob(String trainingFile, String model) {OkHttpClient okHttpClient = new OkHttpClient.Builder().connectTimeout(ConstantUtils.CONNECT_TIMEOUT, TimeUnit.SECONDS).writeTimeout(ConstantUtils.WRITE_TIMEOUT, TimeUnit.SECONDS).readTimeout(ConstantUtils.READ_TIMEOUT, TimeUnit.SECONDS).build();client = OpenAiClient.builder()//支持多key传入，请求时候随机选择.apiKey(Arrays.asList(ConstantUtils.OPENAI_API_KEY))//自定义key的获取策略：默认KeyRandomStrategy.keyStrategy(new KeyRandomStrategy()).okHttpClient(okHttpClient).build();FineTuneJobResponse response = client.fineTuneJob(trainingFile);log.info("创建微调模型: {}", response.getId());return AjaxResult.success(response);}

输出结果: 

{"msg": "操作成功","code": 200,"data": {"id": "ftjob-aNVvpcUepOGFpqctrNZTq1kP","created_at": 1705286939,"error": null,"fine_tuned_model": null,"finished_at": null,"hyperparameters": {"nepochs": "auto","batch_size": "auto","learning_rate_multiplier": "auto","n_epochs": "auto"},"model": "gpt-3.5-turbo-1106","object": "fine_tuning.job","organization_id": "org-alYnskSd8W2bhEJiu0XKoD3a","result_files": [],"status": "validating_files","trained_tokens": null,"training_file": "file-2GkrLPCM7Zqx6boYmoE3FIPK","validation_file": null,"data": null}
}

4. 检索微调模型

https://api.openai.com/v1/fine_tuning/jobs/{fine_tuning_job_id }
返回模型fine_tuned_model: ft:gpt-3.5-turbo-1106:0101-ii-50-5000-203::8h7Ohlvf

注: fine_tuned_model需要等待创建微调模型的返回 (openai文档概述: 开始微调工作后，可能需要一些时间才能完成。您的作业可能排在我们系统中的其他作业后面，训练模型可能需要几分钟或几小时，具体取决于模型和数据集大小。)

    /*** 检索微调模型** @param fineTuningJobId* @return*/@GetMapping("/retrieveFineTuningJob/{fineTuningJobId}")public AjaxResult RetrieveFineTuningJob(@PathVariable String fineTuningJobId){return openAiService.RetrieveFineTuningJob(fineTuningJobId);}/*** 检索微调模型** @param fineTuningJobId* @return*/@Overridepublic AjaxResult RetrieveFineTuningJob(String fineTuningJobId) {OkHttpClient okHttpClient = new OkHttpClient.Builder().connectTimeout(ConstantUtils.CONNECT_TIMEOUT, TimeUnit.SECONDS).writeTimeout(ConstantUtils.WRITE_TIMEOUT, TimeUnit.SECONDS).readTimeout(ConstantUtils.READ_TIMEOUT, TimeUnit.SECONDS).build();client = OpenAiClient.builder()//支持多key传入，请求时候随机选择.apiKey(Arrays.asList(ConstantUtils.OPENAI_API_KEY))//自定义key的获取策略：默认KeyRandomStrategy.keyStrategy(new KeyRandomStrategy()).okHttpClient(okHttpClient).build();FineTuneJobResponse response = client.retrieveFineTuneJob(fineTuningJobId);log.info("检索微调模型: {}", response.getModel());return AjaxResult.success(response);}

输出结果: 

{"msg": "操作成功","code": 200,"data": {"id": "ftjob-aNVvpcUepOGFpqctrNZTq1kP","created_at": 1705286939,"error": null,"fine_tuned_model": "ft:gpt-3.5-turbo-1106:0101-ii-50-5000-203::8h7Ohlvf","finished_at": 1705287231,"hyperparameters": {"nepochs": "7","batch_size": "1","learning_rate_multiplier": "2","n_epochs": "7"},"model": "gpt-3.5-turbo-1106","object": "fine_tuning.job","organization_id": "org-alYnskSd8W2bhEJiu0XKoD3a","result_files": ["file-cxmqzQjJbg9sIzXgwDkxG4nO"],"status": "succeeded","trained_tokens": 7672,"training_file": "file-2GkrLPCM7Zqx6boYmoE3FIPK","validation_file": null,"data": null}
}

5. 使用微调模型

https://api.openai.com/v1/chat/completions
和之前调用API一样, 把model设置成检索微调模型返回的fine_tuned_model

    /*** 使用微调模型** @param systemContent* @param userContent* @return*/@PostMapping("/fineTuneJobModelChat")public AjaxResult fineTuneJobModelChat(String systemContent, String userContent, String ftjob){return openAiService.fineTuneJobModelChat(systemContent, userContent, ftjob);}/*** 使用微调模型** @param systemContent* @param userContent* @param ftjob* @return*/@Overridepublic AjaxResult fineTuneJobModelChat(String systemContent, String userContent, String ftjob) {String text = this.getText(userContent, systemContent, true, ftjob);log.info("微调模型: {}", text);return AjaxResult.success(text);}

输出结果: 

以上已列出微调的实现流程以及核心代码, 相信小伙伴们可以轻松实现! 当然, 如需要完整代码的小伙伴可以留言或者私信我