OpenAI has recently released a UI interface for fine-tuning language models. In this tutorial, I will be using the OpenAI UI to create a fine-tuned GPT model. To follow along with this part, you must have an OpenAI account and key. ...
Fine-tuning is necessary when you want to adjust the style, tone, or format. It is used to improve reliability and accuracy, handle complex prompts, or perform a new task that the prompt engineer could not achieve. In this tutorial, we will fine-tune the GPT-4o Mini model to classify ...
对模型进行Finetune补充领域内知识，在RLHF给予Reward Model关于数据真实性更高的倾向性，通过Prompt引导大...
基于微调(finetuning)的技术,基于Prompt(提示)的技术,情景学习(in-context learning),从人类反馈中强化学习(RLHF)技术,逐步发展并最终促成了ChatGPT的诞生。 图2:ChatGPT的进步 1. 语言模型(LM,language model) 语言模型,就是人类自然语言的概率模型。人类自然语言是一个个句子,一个句子是一个自然语言符号序列x1...
(正课)【生成式AI】Finetuning vs. Prompting:对于大型语言模型的不同期待所衍生的两类使用方式 (3_3) 15:34 (延申)自督导式学习 (Self-supervised Learning) (二) – BERT简介 50:41 (延申)自督导式学习 (Self-supervised Learning) (四) – GPT的野望 17:04 (作业)HW3 - CNN- Image Classifi...
EMNLP 2022 Tutorial Modular and Parameter-Efficient Fine-Tuning for NLP Model 281 -- 3:39:32 App ACL 2023 Tutorial Retrieval-based Language Models and Applications 222 -- 1:01:42 App AI Safety, RLHF, and Self-Supervision 558 -- 22:38 App AlphaGeometry 成功解决 IMO 几何问题!Google DeepM...
这个地方需要注意一些细节,也就是https://github.com/microsoft/DeepSpeedExamples/blob/master/applications/DeepSpeed-Chat/training/step3_rlhf_finetuning/BenckmarkSetting.md 中提到的内容,我这里翻译一下。 正如上面截图中非常重要的细节指出的,进行公平的比较对于机器学习社区来说至关重要,特别是在基准测试中。例如...
MiniCPM-V 2.6 can be easily used in various ways: (1) llama.cpp and ollama support for efficient CPU inference on local devices, (2) int4 and GGUF format quantized models in 16 sizes, (3) vLLM support for high-throughput and memory-efficient inference, (4) fine-tuning on new domain...
More Information about Text2SQL finetune DB-GPT-PluginsDB-GPT Plugins that can run Auto-GPT plugin directly GPT-VisVisualization protocol Install Usage Tutorial Features At present, we have introduced several key features to showcase our current capabilities: ...
私有部署+微调可能能解决大部分前面提到的问题。可能是有钱大公司用Model instance和fine-tuning,小公司独立开发者用Langchain等框架。更未来OpenAI的LLM服务能力外溢,可能不需要Prompt了,甚至把Langchain的功能都能包括了,LLM应用的开发接入也许只需要一个接口调用。