RRepoGEO

REPOGEO REPORT · LITE

HongriJiujiu/op_pangu

Default branch main · commit f39ae5c9 · scanned 6/17/2026, 1:29:37 PM

GitHub: 466 stars · 6 forks

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 HongriJiujiu/op_pangu, 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's opening to clarify research focus and de-emphasize base model

    Why:

    CURRENT
    # Silent Inconsistency in Data-Parallel Full Fine-Tuning
    ### Experimental Fine-Tuned Models (S1-1 / S1-2 / S1-3)
    This repository provides **three fully fine-tuned models** corresponding to the experimental settings in the paper:
    COPY-PASTE FIX
    # Silent Inconsistency in Data-Parallel Full Fine-Tuning: Research Artifacts and Models
    ### Experimental Fine-Tuned Models (S1-1 / S1-2 / S1-3)
    
    This repository provides **research artifacts and three fully fine-tuned models** for reproducing and analyzing the phenomenon of **worker-level optimization misalignment** under synchronous data-parallel (DDP) full-parameter fine-tuning. Our work, detailed in the paper:
    
    > **Silent Inconsistency in Data-Parallel Full Fine-Tuning: Diagnosing Worker-Level Optimization Misalignment**
    
    focuses on diagnosing this issue. While the models were fine-tuned using an OpenPangu base, the core contribution and focus of this repository are the *diagnostic methods and analysis of training inconsistencies*, not the OpenPangu model itself.
  • highabout#2
    Add a concise repository description

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    Research artifacts and fine-tuned models for diagnosing silent inconsistencies and worker-level optimization misalignment in data-parallel full fine-tuning.
  • highlicense#3
    Add a LICENSE file

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file in the repository root. A common choice for research code is the MIT License. For example, create a file named 'LICENSE' with the content:
    
    MIT License
    
    Copyright (c) [YEAR] [COPYRIGHT HOLDER NAME]
    
    Permission is hereby granted, free of charge, to any person obtaining a copy
    of this software and associated documentation files (the "Software"), to deal
    in the Software without restriction, including without limitation the rights
    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
    copies of the Software, and to permit persons to whom the Software is
    furnished to do so, subject to the following conditions:
    
    The above copyright notice and this permission notice shall be included in all
    copies or substantial portions of the Software.
    
    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
    SOFTWARE.

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 HongriJiujiu/op_pangu
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorBoard
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorBoard · recommended 1×
  2. Weights & Biases (W&B) · recommended 1×
  3. MLflow · recommended 1×
  4. Prometheus & Grafana · recommended 1×
  5. Ray Tune · recommended 1×
  • CATEGORY QUERY
    How can I diagnose silent inconsistencies in data-parallel full fine-tuning models?
    you: not recommended
    Show full AI answer
  • CATEGORY QUERY
    What tools help monitor worker-level optimization misalignment during distributed model training?
    you: not recommended
    AI recommended (in order):
    1. TensorBoard
    2. Weights & Biases (W&B)
    3. MLflow
    4. Prometheus & Grafana
    5. Ray Tune

    AI recommended 5 alternatives but never named HongriJiujiu/op_pangu. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 HongriJiujiu/op_pangu?
    pass
    AI named HongriJiujiu/op_pangu explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts HongriJiujiu/op_pangu in production, what risks or prerequisites should they evaluate first?
    pass
    AI named HongriJiujiu/op_pangu 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 HongriJiujiu/op_pangu solve, and who is the primary audience?
    pass
    AI named HongriJiujiu/op_pangu explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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HongriJiujiu/op_pangu — 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