Junvate/LLM-Algorithm-Intern-Guide: A Feishu-Hosted Interview Notes Repository for LLM Algorithm Internships

What the repository actually contains, and who it is written for

The README opens by describing the project as notes collected while preparing for large model algorithm internship interviews, and states that every knowledge point was either asked in an interview or gathered from interview write-ups on Xiaohongshu. The stated audience is narrow: candidates for LLM algorithm intern roles, most plausibly in the Chinese hiring market, since the repository is written in Chinese and the notes live on Feishu.

The important structural fact is that the GitHub repository does not hold the notes. The README says all content is organized as Feishu documents and gives a single link. The only top-level entry in the repository is README.md. So the project is really two things: a table of contents with frequency and priority tags, and an external document that carries the explanations. Treating the repository as a study resource in itself will disappoint you. Treating it as a syllabus will not.

The six-chapter topic map and how it is tagged

The README lays out six chapters, each with checkbox items. Chapter one covers architecture and math: Transformer variants and attention mechanisms (MHA, MQA, GQA, MLA), the progression from absolute position encodings to RoPE, KV Cache calculation and PagedAttention, optimizers and loss functions, and precision formats from FP32 to BF16 with memory estimation. Chapter two covers RAG and agents, including chunking, vector retrieval, query rewriting, the Bi-Encoder versus Cross-Encoder distinction in reranking, the MCP protocol with a Python example, and tool calling. Chapter three is PEFT: LoRA initialization, QLoRA with NF4 quantization and double quantization, AdaLoRA, DoRA and P-Tuning selection, and catastrophic forgetting. Chapter four is alignment: PPO versus DPO versus GRPO, reward hacking and entropy collapse, why GRPO drops the critic network, and KL divergence. Chapter five reads DeepSeek-V3 and R1 and Qwen reports, covering MoE and auxiliary-loss-free load balancing, cold-start SFT, thinking budgets, and scaling laws. Chapter six compares training frameworks (LLaMA-Factory, DeepSpeed, VerL), lists LeetCode Hot 100, and promises hand-written multi-head attention and RoPE in Python.

Some entries carry tags such as High Frequency, Project Essential, New and Hot. Those tags are the author's own signal about what comes up often. They are not derived from any stated survey, and the README does not explain how the labels were assigned.

Getting to the notes: there is nothing to install

There is no package, no CLI and no build step. The repository is a README that links outward, so the first real use is cloning it for the checklist and opening the Feishu wiki for the content. The README gives the wiki URL directly in the title line and again in the navigation section.

git clone https://github.com/Junvate/LLM-Algorithm-Intern-Guide.git
cd LLM-Algorithm-Intern-Guide
ls

The directory listing shows README.md and nothing else. To reach the study material you follow the link the README repeats twice.

# the README points to this wiki for all notes
# https://my.feishu.cn/wiki/Ipm5woCF1i28pPkFqtMcAhQyndh

A practical first pass: open the wiki, then work through chapter four first if you are targeting reinforcement-learning-heavy teams, since that chapter carries the Hot tag on PPO versus DPO versus GRPO and explicitly ties it to DeepSeek-R1. Chapter five is the reading list for paper-based rounds. Neither the README nor the repository files describe what you should see on the Feishu page, so the state of that wiki is something you have to check yourself.

The dependency on Feishu is the main failure mode

Everything substantive sits behind one external link. If the wiki is set to private, moved, rate-limited, or blocked on your network, the repository degrades to a list of headings. The README does not describe an offline copy, an export, or a mirror of the notes, and there is no fallback in the repository itself. For a candidate studying on a locked-down corporate network or in a region where Feishu is unreachable, this is not a minor inconvenience; it removes the product.

A second limitation is verifiability. The README displays an MIT licence badge linking to a LICENSE file, but the repository's top-level entries contain only README.md, so that file is not present at the root as listed. The README also carries a PRs Welcome badge pointing at CONTRIBUTING.md, which is likewise not among the listed entries. Whether those files exist elsewhere in the tree cannot be confirmed from what is available. Anyone who needs a clear licence before reusing the notes should resolve that first rather than trusting the badge.

The last push was on 2026-03-28. The README says the notes are continuously updated, but the repository has not been pushed to in roughly six months, so the update signal is the Feishu wiki, not this repository.

How it differs from a self-contained study repository

A comparable project in this space is a repository that ships its notes as Markdown files and its code as runnable Python, so a clone gives you everything and git history gives you revisions. This project takes the opposite approach: it keeps the prose in a hosted document and uses GitHub as a pointer. The trade-off is real in both directions. Hosted documents are easier to edit on a phone and can embed images and tables without fighting Markdown, which fits the README's screenshots of the table of contents. Markdown in the repository is diffable, forkable, and survives the original author losing interest.

If your priority is reading curated explanations with the author's own emphasis, the Feishu route is fine. If your priority is owning a copy you can annotate, grep, and keep after the link changes, a repository that stores its content in-tree is the better fit, and this one is the wrong tool.

Maintenance, licence and what to verify before you commit study time

The repository is not archived. Its last push was on 2026-03-28, which is more than six months before today, so the GitHub side should be treated as dormant even though the README claims continuous updates. The practical maintenance cost for a user is zero: there is nothing to upgrade, no dependency to track, and no version to pin. The cost is instead a link-rot risk you cannot mitigate from inside the repository.

On licensing, the README shows an MIT badge. The repository listing does not include a LICENSE file, so the badge is the only evidence available here. MIT would permit reuse with attribution, but the notes themselves live on Feishu, and the README says nothing about the terms that apply to that document. If you intend to republish or adapt the material, confirm the licence file exists and check the wiki's own terms rather than assuming the badge covers both.

Editorial conclusion

Adopt it if you are preparing for a Chinese LLM algorithm internship and want a reading list that names the specific topics interviewers raise, from MLA and KV Cache math to GRPO and reward hacking. Do not adopt it if you need offline notes, a licence you can verify from the repository, or a self-contained codebase: the GitHub repository holds only README.md, and the README points to a Feishu wiki for the actual material. Before relying on it, open the Feishu link and confirm it is still publicly accessible, and check whether a LICENSE file exists despite the MIT badge.

Frequently asked questions

Official sources

1. Issues
2. Junvate/LLM-Algorithm-Intern-Guide on GitHub
3. Project website
4. README