RRepoGEO

REPOGEO REPORT · LITE

allenai/open-instruct

Default branch main · commit 78d1e5aa · scanned 6/29/2026, 8:06:55 AM

GitHub: 3,776 stars · 554 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
22 /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
1 / 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 allenai/open-instruct, 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
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    instruction-tuning, large-language-models, llm-finetuning, preference-learning, dpo, ppo, reinforcement-learning, open-source-llm, allenai
  • mediumreadme#2
    Clarify README's opening sentence to emphasize 'framework' and 'open-source'

    Why:

    CURRENT
    This repo serves as an open effort on instruction-tuning and post-training popular pretrained language models on publicly available datasets.
    COPY-PASTE FIX
    This repository provides an open-source framework for instruction-tuning and post-training popular pretrained language models on publicly available datasets.
  • lowreadme#3
    Add a clear note about evaluation tools in the README

    Why:

    CURRENT
    We also support some evaluations natively in the codebase, but these are now unmaintained and instead we suggest using OLMES, which we used for TÜLU 3.
    COPY-PASTE FIX
    **Note on Evaluation:** While this codebase previously included native evaluation support, these are now unmaintained. We recommend using OLMES, which was utilized for TÜLU 3, for current evaluations.

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 allenai/open-instruct
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. DeepSpeed · recommended 1×
  3. PyTorch Lightning · recommended 1×
  4. Axolotl · recommended 1×
  5. TGI (Text Generation Inference) · recommended 1×
  • CATEGORY QUERY
    Looking for a framework to post-train language models using instruction datasets.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. DeepSpeed
    3. PyTorch Lightning
    4. Axolotl
    5. TGI (Text Generation Inference)

    AI recommended 5 alternatives but never named allenai/open-instruct. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective methods for aligning large language models with human preferences?
    you: not recommended
    AI recommended (in order):
    1. InstructGPT
    2. ChatGPT
    3. Sparrow
    4. trl
    5. Claude
    6. Hugging Face Transformers
    7. GPT-4
    8. Gemini
    9. Llama 2
    10. LangChain
    11. LlamaIndex

    AI recommended 11 alternatives but never named allenai/open-instruct. 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 allenai/open-instruct?
    pass
    AI did not name allenai/open-instruct — 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 allenai/open-instruct in production, what risks or prerequisites should they evaluate first?
    pass
    AI named allenai/open-instruct 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 allenai/open-instruct solve, and who is the primary audience?
    pass
    AI did not name allenai/open-instruct — 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?

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allenai/open-instruct — 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