Fine-tuning理论上很复杂,但是OpenAI把这个功能完善到任何一个人看了就能做出来的程度。 我们先从原理入手,你看这张图,左边是Pre-trained LLM (预训练大模型模型),也就是像ChatGPT这样的模型;右边是Fine-tuned LLM (微调过的语言大模型),中间就是进行微调的过程,它需要我们提供一些「ChatGPT提供不了但是我们需要...
Prompt Engineering:包括 Prompt 优化以及增加 Few-shot learning,这是最简单的方式,不需要额外组件,也不用调整 LLM,只需控制 LLM 的输入即可。 RAG:通过检索的方式查找问题相关内容,并扩展到 LLM 的 Prompt 中,以供 LLM 参考。此优化需要引入一个检索系统,不过当前相关方案已经比较成熟,实施代价不高,比如 Milvus ...
传统离散prompt 直接将模板 T 的每个 token 映射为对应的embedding,而 P-Tuning 将模板 T 中的Pi(Pseudo Prompt)映射为一个可训练的参数 hi。 优化关键点在于,自然语言的hard prompt,替换为可训练的soft prompt;使用双向LSTM 对模板 T 中的 pseudo token 序列进行表征;引入少量自然语言提示的锚字符(Anchor)提升...
如上图所示,OpenAI把对于优化LLM返回结果分为两个方向,一个方向是横坐标系的对LLM 模型本身的优化,另一个是对你提供的Context的优化 在对LLM本身的优化上没啥可弄的,最后就只能走到Fine-tuning这一条路 在对本身的Context进行优化的方式,我们一般起手式是先prompt-engineering prompt-engineering不好使了,我们会借...
https://youtu.be/ahnGLM-RC1Y?si=ikmDvLPmsbObzeB_ 大模型在垂直领域应用的过程中,如何使用提示词工程、检索增强生成和微调技术来最大化模型的效果?Prompt Engineering vs RAG vs SFT科技 计算机技术 检索 RAG 微调 LLM 提示词 大家好我是爱因 发消息 ...
ai openai gpt finetune llm Updated May 18, 2024 TypeScript Kamleshpaul / chatgpt-fine-tune-client Star 3 Code Issues Pull requests ChatGPT fine-tune GUI just put opanai key in .env file then download sample finetune data then create your own then upload and train gui chatbot fine...
LLM-Tuning-Safety/LLMs-Finetuning-Safety Star262 Code Issues Pull requests We jailbreak GPT-3.5 Turbo’s safety guardrails by fine-tuning it on only 10 adversarially designed examples, at a cost of less than $0.20 via OpenAI’s APIs. ...
few-shot learning. Prompt tuning introduces AI-authoredsoft prompts: learnable vector embeddings that are concatenated to the user’s hard prompt. Rather than retraining the model, prompt tuning entails freezing model weights and instead trains the soft prompt itself. Fast and efficient, prompt ...
Prompting strategies included zero-shot, few-shot, chain-of-thought, and ensemble/self-consistency voting. OpenMedLM delivered OS SOTA results on three medical LLM benchmarks, surpassing previous best-performing OS models that leveraged costly and extensive fine-tuning. OpenMedLM displays the first...
theory that smaller LLMs are just as powerful as much larger ones when they’re trained to specialize in a very specific task. Developers can create these specialized models by fine-tuning lightweight, general purpose LLMs on their own datasets, and that’s exactly what OpenPipe’s ...