RRepoGEO

REPOGEO REPORT · LITE

huggingface/gpt-oss-recipes

Default branch main · commit 154b3fc1 · scanned 6/17/2026, 9:33:12 AM

GitHub: 506 stars · 53 forks

AI VISIBILITY SCORE
28 /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
2 / 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 huggingface/gpt-oss-recipes, 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 relevant topics to the repository

    Why:

    COPY-PASTE FIX
    gpt-oss, openai, large-language-models, llm, fine-tuning, inference-optimization, recipes, scripts, huggingface
  • highreadme#2
    Reposition the README's opening to emphasize its 'recipe' nature for specific models

    Why:

    CURRENT
    # OpenAI GPT-OSS Recipes

Collection of scripts demonstrating different optimization and fine-tuning techniques for OpenAI's GPT-OSS models (20B and 120B parameters).
    COPY-PASTE FIX
    # OpenAI GPT-OSS Recipes

This repository provides a practical collection of ready-to-use scripts and notebooks for optimizing inference and fine-tuning OpenAI's GPT-OSS models (20B and 120B parameters). It serves as a cookbook for applying advanced techniques like Tensor Parallelism, Flash Attention, and LoRA specifically to these large open-source models.
  • mediumhomepage#3
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://huggingface.co/blog/gpt-oss

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 huggingface/gpt-oss-recipes
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. bitsandbytes · recommended 2×
  3. xFormers · recommended 2×
  4. PEFT (Parameter-Efficient Fine-Tuning) · recommended 1×
  5. LoRA (Low-Rank Adaptation) · recommended 1×
  • CATEGORY QUERY
    How can I fine-tune large open-source generative models effectively?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PEFT (Parameter-Efficient Fine-Tuning)
    3. LoRA (Low-Rank Adaptation)
    4. QLoRA (Quantized LoRA)
    5. IA3
    6. Accelerate
    7. DeepSpeed
    8. PyTorch FSDP (Fully Sharded Data Parallel)
    9. bitsandbytes
    10. Unsloth
    11. Axolotl
    12. FlashAttention
    13. xFormers

    AI recommended 13 alternatives but never named huggingface/gpt-oss-recipes. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are best practices for optimizing inference of very large transformer models?
    you: not recommended
    AI recommended (in order):
    1. bitsandbytes
    2. NVIDIA TensorRT
    3. OpenVINO
    4. Hugging Face Optimum
    5. Knowledge Distillation Toolkit (KDT)
    6. ONNX Runtime
    7. TorchScript
    8. XLA (Accelerated Linear Algebra)
    9. FlashAttention / FlashAttention-2
    10. xFormers
    11. Hugging Face Transformers
    12. DeepMind's AlphaCode 2
    13. Google's Med-PaLM 2
    14. Triton Inference Server
    15. Ray Serve
    16. NVIDIA GPUs with CUDA/cuDNN
    17. TPUs (Google Cloud)
    18. Intel Gaudi / Habana Labs

    AI recommended 18 alternatives but never named huggingface/gpt-oss-recipes. 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 huggingface/gpt-oss-recipes?
    pass
    AI did not name huggingface/gpt-oss-recipes — 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 huggingface/gpt-oss-recipes in production, what risks or prerequisites should they evaluate first?
    pass
    AI named huggingface/gpt-oss-recipes 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 huggingface/gpt-oss-recipes solve, and who is the primary audience?
    pass
    AI named huggingface/gpt-oss-recipes explicitly

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

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huggingface/gpt-oss-recipes — 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