RRepoGEO

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
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. huggingface/accelerate · recommended 1×
  3. huggingface/peft · recommended 1×
  4. Lightning-AI/pytorch-lightning · recommended 1×
  5. 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)
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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