REPOGEO REPORT · LITE

yangjianxin1/Firefly

Default branch master · commit e22b406e · scanned 6/28/2026, 9:38:31 PM

GitHub: 6,644 stars · 585 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

35 /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

3 / 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 yangjianxin1/Firefly, 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 core project description to the top of the README

Why:

CURRENT

# Firefly: 一站式大模型训练工具

<div align="left">

[](./pics/wechat-group.jpeg)
[](https://huggingface.co/YeungNLP)

[//]: # ([![Generic badge]&#40;https://img.shields.io/badge/微信-Firefly-brightgreen?logo=wechat&#41;]&#40;./pics/wechat.jpeg&#41;)
</div>

欢迎加入Firefly大模型技术交流群，关注我们的公众号，点击加群按钮即可。

欢迎关注我们的知乎进行交流讨论：**红雨瓢泼**

## 项目简介
**Firefly** 是一个开源的大模型训练项目，支持对主流的大模型进行预训练、指令微调和DPO，包括但不限于Qwen2、Yi-1.5、Llama3、Gemma、Qwen1.5、MiniCPM、MiniCPM3、Llama、InternLM、Baichuan、ChatGLM、Yi、Deepseek、Qwen、Orion、Ziya、Xverse、Mistral、Mixtral-8x7B、Zephyr、Vicuna、Bloom等。

COPY-PASTE FIX

# Firefly: 一站式大模型训练工具

**Firefly** 是一个开源的大模型训练项目，专注于高效地对主流开源大模型进行预训练、指令微调和DPO，支持全量参数训练、LoRA、QLoRA等多种训练方式，并已在Open LLM Leaderboard上验证其有效性。

<div align="left">

[](./pics/wechat-group.jpeg)
[](https://huggingface.co/YeungNLP)

[//]: # ([![Generic badge]&#40;https://img.shields.io/badge/微信-Firefly-brightgreen?logo=wechat&#41;]&#40;./pics/wechat.jpeg&#41;)
</div>

欢迎加入Firefly大模型技术交流群，关注我们的公众号，点击加群按钮即可。

欢迎关注我们的知乎进行交流讨论：**红雨瓢泼**

## 项目简介

highlicense#2

Add a standard open-source LICENSE file

Why:

COPY-PASTE FIX

Add a LICENSE file (e.g., Apache-2.0, MIT) to the root of the repository to clearly state the project's licensing terms.

mediumhomepage#3
Add a homepage URL to the repository's 'About' section
Why:
COPY-PASTE FIX
```
Set the repository homepage to `https://huggingface.co/YeungNLP` or a dedicated project website.
```

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 yangjianxin1/Firefly

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

huggingface/transformers

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

huggingface/transformers · recommended 1×
huggingface/accelerate · recommended 1×
huggingface/peft · recommended 1×
Lightning-AI/pytorch-lightning · recommended 1×
microsoft/DeepSpeed · recommended 1×

CATEGORY QUERY
What are the best tools for efficiently fine-tuning multiple open-source large language models?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers (huggingface/transformers)
2. Hugging Face Accelerate (huggingface/accelerate)
3. Hugging Face PEFT (huggingface/peft)
4. PyTorch Lightning (Lightning-AI/pytorch-lightning)
5. DeepSpeed (microsoft/DeepSpeed)
6. Ludwig (ludwig-ai/ludwig)
7. OpenAI Triton (openai/triton)
8. Ray Train (ray-project/ray)
AI recommended 8 alternatives but never named yangjianxin1/Firefly. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How can I easily perform instruction fine-tuning or DPO on popular LLMs with LoRA?
you: not recommended
AI recommended (in order):
1. Hugging Face TRL
2. Axolotl
3. Unsloth
4. Alpaca-LoRA
5. Lit-GPT
AI recommended 5 alternatives but never named yangjianxin1/Firefly. 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 yangjianxin1/Firefly?
pass
AI named yangjianxin1/Firefly explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts yangjianxin1/Firefly in production, what risks or prerequisites should they evaluate first?
pass
AI named yangjianxin1/Firefly 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 yangjianxin1/Firefly solve, and who is the primary audience?
pass
AI named yangjianxin1/Firefly 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 yangjianxin1/Firefly. 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)

[![RepoGEO](https://repogeo.com/badge/yangjianxin1/Firefly.svg)](https://repogeo.com/en/r/yangjianxin1/Firefly)

HTML

<a href="https://repogeo.com/en/r/yangjianxin1/Firefly"><img src="https://repogeo.com/badge/yangjianxin1/Firefly.svg" alt="RepoGEO" /></a>

Pro

Subscribe to Pro for deep diagnoses

yangjianxin1/Firefly — 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