REPOGEO REPORT · LITE

li-plus/chatglm.cpp

Default branch main · commit 60c89b7e · scanned 5/21/2026, 9:36:52 PM

GitHub: 2,961 stars · 328 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

28 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 warn · 0 fail

Objective metadata checks

AI knows your name

2 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface li-plus/chatglm.cpp, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition the README's opening statement to clarify its role

Why:

CURRENT

C++ implementation of ChatGLM-6B, ChatGLM2-6B, ChatGLM3 and GLM-4(V) for real-time chatting on your MacBook.

COPY-PASTE FIX

A C++ inference engine for ChatGLM, GLM-4(V), and CodeGeeX2 models, built on ggml and designed for efficient local execution, similar to llama.cpp.

mediumabout#2

Update the repository description to reinforce its positioning

Why:

CURRENT

C++ implementation of ChatGLM-6B & ChatGLM2-6B & ChatGLM3 & GLM4(V)

COPY-PASTE FIX

Efficient C++ inference engine for ChatGLM, GLM-4(V), and CodeGeeX2 models, built on ggml (like llama.cpp) for local CPU/GPU execution.

lowhomepage#3
Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIX
```
https://pypi.org/project/chatglm-cpp/
```

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface li-plus/chatglm.cpp

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

llama.cpp

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

llama.cpp · recommended 1×
Ollama · recommended 1×
Hugging Face transformers · recommended 1×
bitsandbytes · recommended 1×
Hugging Face Optimum · recommended 1×

CATEGORY QUERY
How can I run large language models efficiently on a local CPU with quantization?
you: not recommended
AI recommended (in order):
1. llama.cpp
2. Ollama
3. Hugging Face transformers
4. bitsandbytes
5. Hugging Face Optimum
6. ONNX
7. OpenVINO
8. ONNX Runtime
9. Intel OpenVINO Toolkit
10. GGML
11. GGUF
12. MLC LLM
AI recommended 12 alternatives but never named li-plus/chatglm.cpp. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for a C++ library to deploy quantized LLMs on diverse hardware platforms.
you: not recommended
AI recommended (in order):
1. llama.cpp (ggerganov/llama.cpp)
2. ONNX Runtime (microsoft/onnxruntime)
3. TensorRT
4. OpenVINO (openvinotoolkit/openvino)
5. Apache TVM (apache/tvm)
6. GGML (ggerganov/ggml)
AI recommended 6 alternatives but never named li-plus/chatglm.cpp. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
warn
Suggestion:
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of li-plus/chatglm.cpp?
pass
AI did not name li-plus/chatglm.cpp — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts li-plus/chatglm.cpp in production, what risks or prerequisites should they evaluate first?
pass
AI named li-plus/chatglm.cpp explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo li-plus/chatglm.cpp solve, and who is the primary audience?
pass
AI named li-plus/chatglm.cpp explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of li-plus/chatglm.cpp. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/li-plus/chatglm.cpp.svg)](https://repogeo.com/en/r/li-plus/chatglm.cpp)

HTML

<a href="https://repogeo.com/en/r/li-plus/chatglm.cpp"><img src="https://repogeo.com/badge/li-plus/chatglm.cpp.svg" alt="RepoGEO" /></a>

Pro

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li-plus/chatglm.cpp — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite