From 65f1a07e88e63130aa3f5999cb6f5bac05c9f6a1 Mon Sep 17 00:00:00 2001 From: Phodal Huang Date: Fri, 23 Jun 2023 14:59:14 +0800 Subject: [PATCH] feat: update for content --- src/2-workflow-and-application-design.md | 13 +++++++++++++ src/3-engineering-finetune.md | 2 +- src/3-llm-ops.md | 13 +++---------- src/SUMMARY.md | 4 ++-- src/images/llm-sdlc-processes.png | Bin 0 -> 367234 bytes src/images/llmops.png | Bin 0 -> 319563 bytes 6 files changed, 19 insertions(+), 13 deletions(-) create mode 100644 src/images/llm-sdlc-processes.png create mode 100644 src/images/llmops.png diff --git a/src/2-workflow-and-application-design.md b/src/2-workflow-and-application-design.md index a13b3de..50326df 100644 --- a/src/2-workflow-and-application-design.md +++ b/src/2-workflow-and-application-design.md @@ -1,8 +1,21 @@ # Workflow + AI 2.0 +在当前,我们对于 LLM 是无法充分信任的,因为他 + ## 高质量流程 +基于我们对 ChatGPT 的使用经验,我们建议在使用 ChatGPT 之前,先考虑如何设计高质量的流程。这里的高质量流程,指的是: + +- 明确的目标和目的:高质量的流程应该有明确的目标和目的,确保流程的设计和执行都能够达成预期的结果。 +- 易于理解和操作:高质量的流程应该简单易懂,让执行者能够轻松理解并操作。流程的设计应该尽可能避免复杂的步骤和冗长的说明,以免造成执行者的困惑和错误。 +- 明确的责任和角色:高质量的流程应该明确各个执行者的责任和角色,避免执行者的混淆和错误。流程设计者应该明确各个角色的职责和权限,确保流程的顺利执行。 +- 可度量和评估:高质量的流程应该能够被度量和评估。流程设计者应该设计合适的指标和评估方式,以便对流程的执行效果进行评估和改进。 + +如下是我们对于 SDLC + LLM 的探索过程中的展示示例: + +![SDLC](images/llm-sdlc-processes.png) +将旅程拆得足够的详细,才能获得最好的效果。 ## ChatFlow 的诞生动机:人类设计高质量流程 + AI 完成细节 diff --git a/src/3-engineering-finetune.md b/src/3-engineering-finetune.md index 0c39326..73b6734 100644 --- a/src/3-engineering-finetune.md +++ b/src/3-engineering-finetune.md @@ -1,4 +1,4 @@ -# 工程化微调 +# 微调之前的准备 作为纯纯的软件工程师,我们发现:学懂基本的 AI Prompt 原理与模式,不懂 LLM (大语言模型)算法,你也可以轻易驾驭 LoRA (Low-Rank Adaptation of LLM,即插件式的微调)训练。 diff --git a/src/3-llm-ops.md b/src/3-llm-ops.md index 9eec565..511804e 100644 --- a/src/3-llm-ops.md +++ b/src/3-llm-ops.md @@ -1,4 +1,6 @@ -# LLMOps +# 微调的三阶六步:LLMOps + +![LLMOps](images/llmops.png) 在开源大语言模型(LLM)的加持之下,我们可以做到: @@ -11,15 +13,6 @@ 通过利用 LLM 的能力,我们可以更好地应对 API 设计中的挑战,实现数字化价值最大化和 API 成为数字化资产的目标。 -于是,我们针对 LLM 与 API 的结合应用进行了深入研究,而在这个过程中,发现了一些很有意思的范式。我们将适合于此的 API 模式称为 -LLM 模型友好的 API。其定义如下: - -**大语言模型(LLM)友好的 API 是指为了更好地适应和利用大型语言模型的能力,而设计的软件 API 规格,以方便于 LLM 的指令设计和工具化集成。这种 -API 的格式和输入输出应当与大型语言模型的输入输出格式相匹配,以便大型语言模型可以更好地理解和处理输入,并输出符合预期的结果。 -** - -简单来说,就是设计 LLM 友好的 API 格式,以用于 LLM 生成与识别,以方便后续的代码生成等环节。 - ## **模式:集体智慧飞轮** 结合我们对于 LLM 的理解与探索,结合 GitHub Copilot 的免费试验过程与逻辑实现。我们基于 ChatGLM 训练了我们的第一个架构辅助设计 diff --git a/src/SUMMARY.md b/src/SUMMARY.md index 34eb693..51cf1c2 100644 --- a/src/SUMMARY.md +++ b/src/SUMMARY.md @@ -23,9 +23,9 @@ # LLM 应用手册 - 微调 + LLMOps篇 -- [工程化微调](./3-engineering-finetune.md) +- [微调之前的准备](./3-engineering-finetune.md) +- [微调的三阶六步:LLMOps](./3-llm-ops.md) - [BizDevOps + 微调](./3-bizdevops-finetune.md) -- [LLMOps](./3-llm-ops.md) # LLM 应用手册 - 训练篇(TBD) diff --git a/src/images/llm-sdlc-processes.png b/src/images/llm-sdlc-processes.png new file mode 100644 index 0000000000000000000000000000000000000000..559abd195597f1033387a8ccb1a300c3fef92b4f GIT binary patch literal 367234 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