RRepoGEO

REPOGEO REPORT · LITE

oumi-ai/oumi

Default branch main · commit 0558a7cd · scanned 5/17/2026, 7:46:25 AM

GitHub: 9,228 stars · 765 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
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 oumi-ai/oumi, 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 README H1 to specify 'platform' and 'AI agents'

    Why:

    CURRENT
    ### Everything you need to build state-of-the-art foundation models, end-to-end
    COPY-PASTE FIX
    ### Oumi: The End-to-End Platform for LLM/VLM Development and AI Agent Creation
  • mediumtopics#2
    Add topics that signal 'platform' and 'AI agents'

    Why:

    CURRENT
    dpo, evaluation, fine-tuning, gpt-oss, gpt-oss-120b, gpt-oss-20b, inference, llama, llms, sft, slms, vlms
    COPY-PASTE FIX
    dpo, evaluation, fine-tuning, gpt-oss, gpt-oss-120b, gpt-oss-20b, inference, llama, llms, sft, slms, vlms, llm-platform, ai-platform, mlops, ai-framework, ai-agents
  • lowabout#3
    Refine the repository description to emphasize 'platform' and 'AI agents'

    Why:

    CURRENT
    Easily fine-tune, evaluate and deploy Gemma 4, Qwen3.5, Qwen3.6, gpt-oss, DeepSeek-R1, or any open source LLM / VLM!
    COPY-PASTE FIX
    An end-to-end platform to easily fine-tune, evaluate, and deploy Gemma 4, Qwen3.5, Qwen3.6, gpt-oss, DeepSeek-R1, or any open source LLM / VLM, and build AI agents.

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 oumi-ai/oumi
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 4 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 4×
  2. huggingface/transformers · recommended 2×
  3. huggingface/peft · recommended 2×
  4. huggingface/trl · recommended 2×
  5. huggingface/evaluate · recommended 2×
  • CATEGORY QUERY
    What tools help fine-tune, evaluate, and deploy open-source large language models?
    you: not recommended
    AI recommended (in order):
    1. Transformers (huggingface/transformers)
    2. Accelerate (huggingface/accelerate)
    3. PEFT (huggingface/peft)
    4. TRL (huggingface/trl)
    5. Evaluate (huggingface/evaluate)
    6. Inference Endpoints
    7. AutoTrain
    8. MLflow (mlflow/mlflow)
    9. Weights & Biases (W&B) (wandb/wandb)
    10. Ray Core (ray-project/ray)
    11. Ray Train (ray-project/ray)
    12. Ray Serve (ray-project/ray)
    13. DeepSpeed (microsoft/DeepSpeed)
    14. Kubernetes (kubernetes/kubernetes)
    15. KServe (kserve/kserve)
    16. Seldon Core (SeldonIO/seldon-core)
    17. Triton Inference Server (triton-inference-server/server)

    AI recommended 17 alternatives but never named oumi-ai/oumi. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a platform for DPO and SFT of custom LLMs, including robust evaluation metrics.
    you: not recommended
    AI recommended (in order):
    1. Transformers (huggingface/transformers)
    2. TRL (huggingface/trl)
    3. PEFT (huggingface/peft)
    4. Datasets (huggingface/datasets)
    5. Evaluate (huggingface/evaluate)
    6. OpenAI API
    7. PyTorch (pytorch/pytorch)
    8. JAX (google/jax)
    9. PyTorch Lightning (Lightning-AI/lightning)
    10. JAX Flax (google/flax)
    11. Weights & Biases (W&B) (wandb/wandb)
    12. MLflow (mlflow/mlflow)
    13. Ray Train (ray-project/ray)
    14. DeepSpeed (microsoft/DeepSpeed)

    AI recommended 14 alternatives but never named oumi-ai/oumi. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 oumi-ai/oumi?
    pass
    AI named oumi-ai/oumi explicitly

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

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

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

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oumi-ai/oumi — 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