RRepoGEO

REPOGEO REPORT · LITE

allenai/open-instruct

Default branch main · commit e91ada42 · scanned 5/18/2026, 2:31:48 AM

GitHub: 3,727 stars · 540 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 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's H1 and opening sentence to emphasize 'framework'

    Why:

    CURRENT
    # Training Open Instruction-Following Language Models

This repo serves as an open effort on instruction-tuning and post-training popular pretrained language models on publicly available datasets.
    COPY-PASTE FIX
    # Open-Instruct: A Unified Framework for Instruction-Tuning and Post-Training LLMs

This repository provides a comprehensive and standardized open-source framework for instruction-tuning and post-training popular pretrained language models on publicly available datasets.
  • mediumabout#2
    Improve the repository's 'About' description

    Why:

    CURRENT
    AllenAI's post-training codebase
    COPY-PASTE FIX
    A unified framework for instruction-tuning and post-training large language models on open datasets.

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
ray-project/ray
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 2×
  2. Hugging Face Transformers · recommended 1×
  3. PEFT (Parameter-Efficient Fine-tuning) · recommended 1×
  4. Lit-GPT · recommended 1×
  5. DeepSpeed · recommended 1×
  • CATEGORY QUERY
    What are robust frameworks for instruction-tuning large language models on diverse open datasets?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PEFT (Parameter-Efficient Fine-tuning)
    3. Lit-GPT
    4. DeepSpeed
    5. Axolotl
    6. TRL (Transformer Reinforcement Learning)
    7. OpenAssistant
    8. Open-Assistant Conversations Dataset (OASST1) Training Stack

    AI recommended 8 alternatives but never named allenai/open-instruct. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to apply advanced post-training techniques like preference optimization to language models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Hugging Face TRL (huggingface/trl)
    3. DeepSpeed (microsoft/DeepSpeed)
    4. Hugging Face Accelerate (huggingface/accelerate)
    5. PyTorch Lightning (Lightning-AI/lightning)
    6. Weights & Biases (W&B) (wandb/wandb)
    7. MLflow (mlflow/mlflow)
    8. TensorBoard (tensorflow/tensorboard)
    9. Ray (ray-project/ray)
    10. Ray RLlib (ray-project/ray)
    11. OpenAI API
    12. Azure OpenAI Service
    13. Label Studio (heartexlabs/label-studio)
    14. Argilla (argilla-io/argilla)

    AI recommended 14 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 named allenai/open-instruct explicitly

    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 named allenai/open-instruct explicitly

    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