Llm Action is live · free · last verified 15 Sep 2026 · viability monitor
Llm Action
知乎中文专栏「动手学大模型」：95篇大模型原理与工程实践长文教程
别把它当工具找——Llm Action 是知乎专栏，产出是文章。但作为免费中文资料，它把 MoE 稀疏推理、解码与惩罚参数实现、DeepSpeed Chat 的 RLHF 三阶段、SentencePiece 词表扩充、Triton 服务化串成一条从架构到部署的主干，代码和公式都在，按篇目顺序啃下来性价比很高。前提是你已经有 PyTorch 和 Transformer 底子。
Verdict generatedVerified 9/15/2026 · liveness 59/100 · cite: rightaichoice.com/tools/llm-action
- 有 PyTorch 与 Transformer 基础、正在转向大模型工程的开发者
- 做大模型微调或推理优化的算法工程师，需要中文版原理+代码对照
- 准备面试或做专题补课，想快速补齐 MoE、解码策略、RLHF 知识点的人
- 习惯用中文资料学习、但需要工程视角而非纯理论推导的技术人员
- 零机器学习基础的入门读者，篇目默认你懂 Transformer
- 只想调用现成 API 的产品或业务人员，这里没有可直接使用的工具
- 需要英文资料或不读中文的技术团队
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Skip Llm Action if you want a runnable tool or API rather than reading long-form Chinese articles on model architecture, decoding and RLHF.
Llm Action's pricing fits teams whose volume aligns with the published tiers. Compare against the alternatives listed below for stage-specific value.
In short
Llm Action — 知乎中文专栏「动手学大模型」：95篇大模型原理与工程实践长文教程. Best for 有 PyTorch 与 Transformer 基础、正在转向大模型工程的开发者, 做大模型微调或推理优化的算法工程师，需要中文版原理+代码对照, 准备面试或做专题补课，想快速补齐 MoE、解码策略、RLHF 知识点的人. Free to use.
What is Llm Action?
Llm Action — 知乎中文专栏「动手学大模型」：95篇大模型原理与工程实践长文教程. Best for 有 PyTorch 与 Transformer 基础、正在转向大模型工程的开发者, 做大模型微调或推理优化的算法工程师，需要中文版原理+代码对照, 准备面试或做专题补课，想快速补齐 MoE、解码策略、RLHF 知识点的人. Free to use.
Llm Action 是知乎上的中文技术专栏，专栏名「动手学大模型」，由作者「吃果冻不吐果冻皮」持续更新，目前收录 95 篇长文，累计约 9,692 次赞同、1,589 次订阅。它的读者不是产品用户，而是想把大模型从论文读进代码的开发者——内容以架构剖析、公式推导和可运行代码片段为主，按主题成体系地铺开。 内容大致分四块。架构线覆盖 DeepSeek 技术演进（含从 DeepSeek VL/VL2 到 DeepSeek OCR 的视觉语言模型路径）、Qwen 演进、LLaMA3 与 Baichuan-7B 的对比拆解。推理与压缩线讲 MoE 稀疏激活原理——为什么同等计算量下能装下更大参数量，文中以 DeepSeek-V2/V3/V4、Qwen3-MoE、GLM5、Kimi K3 为例，另有 DoubleSparse 稀疏化方案、蒸馏与量化。 生成控制线把贪心搜索、束搜索、Top-k、Top-p、温度采样、Min-P 采样逐个拆开，再配重复惩罚、频率惩罚、存在惩罚三种参数的机制说明与代码实现。工程落地线包括 DeepSpeed Chat 的一键式 RLHF 训练实践（SFT、奖励模型、PPO 三阶段）、SentencePiece 词表扩充、Triton 推理服务化框架入门。近期还新增了 SkillReducer 论文解读，讨论公开 Agent Skill 的 token 冗余问题——既费成本，也稀释模型注意力。 定位上，它是 Hugging Face 官方文档与论文原文的中文补充件，不是替代品：专栏提供中文讲解和工程视角，规格细节仍要回官方文档核对。免费订阅，无 API、无交互界面，价值全在篇目编排和代码可跑性上。
Key features
- 95 篇中文长文，覆盖大模型原理与工程实践
- MoE 稀疏激活推理原理剖析，含 DeepSeek-V2/V3/V4、Qwen3-MoE、GLM5、Kimi K3 案例
- DoubleSparse 等大模型稀疏化压缩方案讲解
- DeepSeek 视觉语言模型技术演进：从 DeepSeek VL/VL2 到 DeepSeek OCR
- Qwen、LLaMA3、Baichuan-7B 架构对比剖析
- 文本解码策略详解：贪心、束搜索、Top-k、Top-p、温度、Min-P
- 重复惩罚、频率惩罚、存在惩罚三种惩罚参数的机制与代码实现
- DeepSpeed Chat 一键式 RLHF 训练实践（SFT、奖励模型、PPO）
- SentencePiece 词表扩充工具使用教程
- Triton 模型推理服务化框架入门教程
- 模型量化与知识蒸馏等推理优化方法
- SkillReducer 论文解读：Agent Skill 的 token 冗余与注意力稀释问题
- 大量架构图、公式推导与可运行代码示例
- 知乎专栏免费订阅，新文章推送
Behind the verdict
什么时候该选它：你手上有一个具体的工程问题——MoE 推理为什么在同等算力下能上更大参数、Top-p 和 Min-P 到底差在哪、三种惩罚参数怎么在代码里落地——想找一份带公式和代码的中文讲解先建立直觉。这种场景下专栏比啃论文快，比翻英文博客省力。 什么时候可以跳过：零机器学习基础的人读它会很吃力，篇目默认你知道 Transformer 是什么。只想调现成 API 的产品和业务同事也不必来，这里没有能直接用的东西。 最接近的替代是 Hugging Face 官方文档加论文原文。差别在于专栏给的是中文语境下的工程视角和串讲，官方文档给的是权威规格。实际用法是两边对照：先在专栏里把思路理顺，再回官方文档核对参数和接口。 需要留个心：这是个人专栏，更新节奏和覆盖范围跟着作者兴趣走，不是一个会被系统性维护的课程大纲。篇目多不等于每条线都完整，遇到没覆盖的主题别硬套。 免费、订阅即推送新文章，试错成本几乎为零——这也是我们建议先订阅翻几篇再决定要不要花时间的主要原因。
Models under the hood
as of 2026-09-08
Limitations
这是个人知乎专栏，不是产品：没有 API、没有可交互界面、没有课程体系与作业。篇目按主题聚合，并非严格递进的学习路径，缺少统一的实验环境说明。涉及具体模型版本与框架用法（如 DeepSpeed Chat、Triton）的篇章会随时间失效，需要对照官方文档核对当前接口。更新节奏由作者决定，不能当作有稳定交付承诺的学习资源依赖。
as of 2026-10-19
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Is Llm Action still active?
Llm Action is live · free · last verified 15 Sep 2026 · viability monitor.
Viability score
How well maintained and how widely used is Llm Action? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →What do users say about Llm Action?
What people actually say about Llm Action — is it worth it?
Our community sentiment research written Jul 3, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
How we verified this
Every listed fact about Llm Action— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 15 Sep 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified Llm Action 9 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed our verdict, description, who should skip it, features, who it suits, summary
- — changed use cases, who it suits, description, our verdict, integrations, limitations, switching in, switching out, models, who should skip it, pricing fit, setup time, summary, features
- — changed domain expiry
- — changed summary, our verdict, description, features
- — changed SSL certificate
- — changed setup time, pricing fit, our verdict, who should skip it
Showing the 6 most recent of 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Llm Action cost in 2026?
Llm Action: Free to use. Pricing last verified 15 Sep 2026.
Where the pricing makes sense
The company stage and team size where Llm Action's pricing actually pencils out — and where peers do it cheaper.
Llm Action's pricing fits teams whose volume aligns with the published tiers. Compare against the alternatives listed below for stage-specific value.
Has Llm Action's pricing changed?
No plan price for Llm Action has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Llm Action?
Pick Llm Action if you are 有 PyTorch 与 Transformer 基础、正在转向大模型工程的开发者, 做大模型微调或推理优化的算法工程师，需要中文版原理+代码对照, 准备面试或做专题补课，想快速补齐 MoE、解码策略、RLHF 知识点的人, 习惯用中文资料学习、但需要工程视角而非纯理论推导的技术人员.
Look elsewhere if you are 零机器学习基础的入门读者，篇目默认你懂 Transformer, 只想调用现成 API 的产品或业务人员，这里没有可直接使用的工具, 需要英文资料或不读中文的技术团队, 需要权威规格参数、要求内容被系统性维护和版本更新的团队.
Skip it if Skip Llm Action if you want a runnable tool or API rather than reading long-form Chinese articles on model architecture, decoding and RLHF.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Llm Action actually fits — and what changes day-one when you adopt it.
要降低线上模型的部署成本，先读 MoE 推理原理和 DoubleSparse 两篇理解稀疏化的收益来源，再读量化与蒸馏相关篇目确定可落地的压缩路线。
Outcome: 拿到一份带公式和代码的压缩方案对照，能直接判断哪条路线适合自己当前的模型规模和硬件。
按 DeepSpeed Chat 实践篇把流程拆成 SFT、奖励模型、PPO 三阶段，对照文中步骤把自己的数据接入。
Outcome: 跑通一次完整的 RLHF 训练，并知道每个阶段常见的失败点在哪里。
读 Triton 保姆式教程入门，把训练好的模型按文中流程配置成推理服务，同时用解码策略那篇调好生成参数。
Outcome: 模型从训练脚本变成可被业务调用的服务，生成质量与延迟都可控。
Use cases
- 系统学习 MoE 架构与稀疏化推理原理
- 为自建模型挑选并实现合适的解码策略与惩罚参数
- 用 DeepSpeed Chat 跑通一次完整的 RLHF 训练流程
- 用 SentencePiece 为中文语料扩充词表
- 用 Triton 把训练好的模型部署成推理服务
- 对比 DeepSeek、Qwen、LLaMA3、Baichuan 的架构差异
Setup time & first value
How long it actually takes to get something useful out of Llm Action — broken out by persona, not the marketing-page minute.
没有安装步骤，打开知乎专栏即可阅读；按主题挑 3–5 篇精读约需半天到一天，通读 95 篇并按篇目动手复现代码通常要数周。
Switching to or from Llm Action
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From 零散 GitHub 教程：把专栏当作索引，先在架构和解码两块建立坐标系，再按项目需要跳读微调与部署章节
- →From 英文论文原文：先读对应中文篇目建立直觉，再回论文核对公式与实验设置
- ↗To Hugging Face 官方文档：涉及具体模型接口与版本号时，以官方文档为准
- ↗To 论文原文：需要复现实验或核对推导细节时，回到专栏中引用的 arXiv 论文
Tutorials & Learning
YouTube returned 6 videos for “Llm Action”, and we withheld 6: 6 did not mention Llm Action. We are showing none, because we could not prove any of them are about Llm Action.
Official links
What are the best Llm Action alternatives?
The closest alternatives to Llm Action we list are D2l Zh, Hands On Large Language Models and LLMs From Scratch. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| D2l Zh | Freemium | Pricing not published | — |
| Hands On Large Language Models | Paid | Pricing not published | — |
| LLMs From Scratch | Paid | Pricing not published | — |
Alternatives to Llm Action
View all
D2l Zh
《动手学深度学习》：每个小节都是可运行的 Jupyter 记事本，含 PyTorch、TensorFlow、PaddlePaddle、MXNet 四种框架实现的深度学习教科书。
Hands On Large Language Models
An illustrated O'Reilly guide by Jay Alammar and Maarten Grootendorst that teaches Python developers to build and refine large language models.
LLMs From Scratch
Learn to build a large language model from scratch in PyTorch — a code-first book that codes a GPT-2 class LLM line by line
Tools that pair well with Llm Action
Common stack mates teams adopt alongside Llm Action, with the specific reason each pairing earns its keep.
D2l Zh
《动手学深度学习》：每个小节都是可运行的 Jupyter 记事本，含 PyTorch、TensorFlow、PaddlePaddle、MXNet 四种框架实现的深度学习教科书。
Hands On Large Language Models
An illustrated O'Reilly guide by Jay Alammar and Maarten Grootendorst that teaches Python developers to build and refine large language models.
LLMs From Scratch
Learn to build a large language model from scratch in PyTorch — a code-first book that codes a GPT-2 class LLM line by line
Llm Action vs its closest rival
Llm Action's closest head-to-head is with Surge Ai — Do not choose between Llm Action and Surge AI — they serve completely different needs. Llm Action is a free Chinese technical blog for developers learning LLM engineering. Surge AI is a premium expert feedback platform for frontier AI labs aligning models. If you are an engineer building LLM projects, go with Llm Action. If you need expert human data for RLHF or rigorous benchmarks like Antidote, Surge AI is the choice. Verdict generated
Head-to-head comparisons
Llm Action vs Surge Ai
Do not choose between Llm Action and Surge AI — they serve completely different needs. Llm Action is a free Chinese technical blog for developers learning LLM engineering. Surge AI is a premium expert feedback platform for frontier AI labs aligning models. If you are an engineer building LLM projects, go with Llm Action. If you need expert human data for RLHF or rigorous benchmarks like Antidote, Surge AI is the choice.
GeneratedLlm Action vs Praktika
These tools are incomparable—Praktika is a language learning app for conversational practice, while Llm Action is a Chinese-language technical blog on LLM engineering. Choose based on your need: improve spoken fluency with AI tutors or deepen your understanding of LLM training and deployment.
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