REPOGEO REPORT · LITE

PhoebusSi/Alpaca-CoT

Default branch main · commit bd46c44e · scanned 5/21/2026, 3:33:10 AM

GitHub: 2,796 stars · 251 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 PhoebusSi/Alpaca-CoT, 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 README opening to emphasize "unified platform" and "tabular intelligence"

Why:

CURRENT

This is the repository for the `Alpaca-CoT` project, which aims to build an instruction finetuning (IFT) platform with extensive instruction collection (especially the CoT datasets) and a unified interface for various large language models and parameter-efficient methods.

COPY-PASTE FIX

Alpaca-CoT is a comprehensive instruction finetuning (IFT) platform offering a unified interface for diverse large language models, parameter-efficient methods, and extensive instruction collections, including Chain-of-Thought (CoT) datasets. It also features a dedicated branch for building Tabular LLMs to solve Table Intelligence Tasks.

hightopics#2

Add "instruction-tuning-platform" topic

Why:

CURRENT

alpaca, chatglm, chatgpt, cot, instruction-tuning, llama, llm, lora, moss, p-tuning, parameter-efficient, pytorch, tabul, tabular-data, tabular-model

COPY-PASTE FIX

alpaca, chatglm, chatgpt, cot, instruction-tuning, instruction-tuning-platform, llama, llm, lora, moss, p-tuning, parameter-efficient, pytorch, tabul, tabular-data, tabular-model

mediumhomepage#3
Add a homepage URL to the About section
Why:
COPY-PASTE FIX
```
https://huggingface.co/QingyiSi/Alpaca-CoT
```

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 PhoebusSi/Alpaca-CoT

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Hugging Face Transformers

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Hugging Face Transformers · recommended 2×
OpenAI API · recommended 2×
PEFT (Parameter-Efficient Fine-tuning) Library · recommended 1×
Ludwig · recommended 1×
DeepSpeed · recommended 1×

CATEGORY QUERY
Seeking a unified platform for instruction tuning large language models with various efficient methods.
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. PEFT (Parameter-Efficient Fine-tuning) Library
3. Ludwig
4. OpenAI API
5. DeepSpeed
6. LitGPT
7. Axolotl
AI recommended 7 alternatives but never named PhoebusSi/Alpaca-CoT. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for a robust framework to perform instruction tuning on language models for tabular intelligence.
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. LoRA
3. QLoRA
4. Ludwig (ludwig-ai/ludwig)
5. OpenAI API
6. GPT-3.5 Turbo
7. GPT-4
8. DeepSpeed (microsoft/DeepSpeed)
9. PyTorch Lightning
AI recommended 9 alternatives but never named PhoebusSi/Alpaca-CoT. 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 PhoebusSi/Alpaca-CoT?
pass
AI did not name PhoebusSi/Alpaca-CoT — 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 PhoebusSi/Alpaca-CoT in production, what risks or prerequisites should they evaluate first?
pass
AI named PhoebusSi/Alpaca-CoT 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 PhoebusSi/Alpaca-CoT solve, and who is the primary audience?
pass
AI named PhoebusSi/Alpaca-CoT 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 PhoebusSi/Alpaca-CoT. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

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HTML

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PhoebusSi/Alpaca-CoT — 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