RRepoGEO

REPOGEO REPORT · LITE

bbruceyuan/LLMs-Zero-to-Hero

Default branch master · commit 93ca367f · scanned 5/18/2026, 10:27:45 PM

GitHub: 2,188 stars · 148 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
33 /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
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 bbruceyuan/LLMs-Zero-to-Hero, 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's opening paragraph to clearly state it's a comprehensive guide

    Why:

    CURRENT
    开个新坑，从无名小卒到大模型（LLM）大英雄~ 欢迎关注B站后续更新！！！
    COPY-PASTE FIX
    这是一个从零开始，体系化学习大模型（LLM）的完整教程/指南，旨在帮助你从基础概念到实际部署，成为LLM大师。
  • hightopics#2
    Add more explicit educational topics

    Why:

    CURRENT
    llm, llm-from-zero-to-hero, llm-zero-to-hero, llm101
    COPY-PASTE FIX
    llm, llm-from-zero-to-hero, llm-zero-to-hero, llm101, llm-course, llm-tutorial, llm-guide, deep-learning-course
  • mediumabout#3
    Update the repository description for clarity

    Why:

    CURRENT
    从无名小卒到大模型（LLM）大英雄~ 欢迎关注后续！！！
    COPY-PASTE FIX
    一个从零开始，体系化学习大模型（LLM）的完整教程，涵盖从基础到部署的全链路实践。

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 bbruceyuan/LLMs-Zero-to-Hero
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 3×
  2. apache/spark · recommended 2×
  3. huggingface/datasets · recommended 2×
  4. pytorch/pytorch · recommended 2×
  5. microsoft/DeepSpeed · recommended 2×
  • CATEGORY QUERY
    I need a complete guide to developing large language models from zero to deployment.
    you: not recommended
    AI recommended (in order):
    1. Common Crawl
    2. Wikipedia Dumps
    3. BooksCorpus
    4. Project Gutenberg
    5. arXiv
    6. GitHub Repositories
    7. Apache Spark (apache/spark)
    8. PySpark (apache/spark)
    9. Dask (dask/dask)
    10. Hugging Face `datasets` library (huggingface/datasets)
    11. NLTK (nltk/nltk)
    12. spaCy (explosion/spaCy)
    13. PyTorch (pytorch/pytorch)
    14. TensorFlow (tensorflow/tensorflow)
    15. Keras (keras-team/keras)
    16. JAX (google/jax)
    17. DeepSpeed (microsoft/DeepSpeed)
    18. FairScale (facebookresearch/fairscale)
    19. PyTorch FSDP (pytorch/pytorch)
    20. Hugging Face `transformers` library (huggingface/transformers)
    21. GPT
    22. BERT
    23. T5
    24. Llama
    25. Mistral
    26. Megatron-LM (NVIDIA/Megatron-LM)
    27. NVIDIA A100 GPUs
    28. NVIDIA H100 GPUs
    29. AWS
    30. GCP
    31. Azure
    32. Hugging Face `evaluate` library (huggingface/evaluate)
    33. EleutherAI's `lm-evaluation-harness` (EleutherAI/lm-evaluation-harness)
    34. Hugging Face `transformers` `Trainer` class (huggingface/transformers)
    35. LoRA
    36. QLoRA
    37. PEFT library (Hugging Face) (huggingface/peft)
    38. TRL (Transformer Reinforcement Learning) library (Hugging Face) (huggingface/trl)
    39. vLLM (vllm-project/vllm)
    40. TGI (Text Generation Inference) (Hugging Face) (huggingface/text-generation-inference)
    41. NVIDIA Triton Inference Server (triton-inference-server/server)
    42. TensorRT-LLM (NVIDIA) (NVIDIA/TensorRT-LLM)
    43. ONNX Runtime (microsoft/onnxruntime)
    44. FastAPI (tiangolo/fastapi)
    45. Flask (pallets/flask)
    46. Django (django/django)
    47. Docker (docker/docker-ce)
    48. Kubernetes (kubernetes/kubernetes)
    49. Prometheus (prometheus/prometheus)
    50. Grafana (grafana/grafana)
    51. OpenTelemetry (open-telemetry/opentelemetry-specification)
    52. Llama 2
    53. Mistral

    AI recommended 53 alternatives but never named bbruceyuan/LLMs-Zero-to-Hero. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking hands-on resources for training and fine-tuning custom LLMs, covering pre-training and SFT.
    you: not recommended
    AI recommended (in order):
    1. transformers library (huggingface/transformers)
    2. datasets library (huggingface/datasets)
    3. accelerate library (huggingface/accelerate)
    4. DeepSpeed (microsoft/DeepSpeed)
    5. PyTorch Lightning (Lightning-AI/lightning)
    6. Lit-GPT (Lightning-AI/lit-gpt)
    7. OpenAI Cookbook (openai/openai-cookbook)
    8. peft library (huggingface/peft)
    9. trl library (huggingface/trl)

    AI recommended 9 alternatives but never named bbruceyuan/LLMs-Zero-to-Hero. 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 bbruceyuan/LLMs-Zero-to-Hero?
    pass
    AI named bbruceyuan/LLMs-Zero-to-Hero explicitly

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

  • If a team adopts bbruceyuan/LLMs-Zero-to-Hero in production, what risks or prerequisites should they evaluate first?
    pass
    AI named bbruceyuan/LLMs-Zero-to-Hero 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 bbruceyuan/LLMs-Zero-to-Hero solve, and who is the primary audience?
    pass
    AI did not name bbruceyuan/LLMs-Zero-to-Hero — 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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bbruceyuan/LLMs-Zero-to-Hero — 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