RRepoGEO

REPOGEO REPORT · LITE

R6410418/Jackrong-llm-finetuning-guide

Default branch main · commit 08f1e123 · scanned 5/23/2026, 10:38:50 PM

GitHub: 1,261 stars · 214 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
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 R6410418/Jackrong-llm-finetuning-guide, 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
  • highabout#1
    Add a concise 'About' description

    Why:

    COPY-PASTE FIX
    An educational, end-to-end guide and pipeline for LLM fine-tuning, offering detailed theoretical explanations, data processing workflows, reproducible training pipelines (SFT), and practical deployment strategies for beginners and developers.
  • highreadme#2
    Refine README H1 subtitle to emphasize 'guide' over 'pipeline'

    Why:

    CURRENT
    **An Educational, End-to-End LLM Fine-Tuning Pipeline for Beginners and Developers**
    COPY-PASTE FIX
    **Your Educational, End-to-End Guide to Building LLM Fine-Tuning Pipelines for Beginners and Developers**
  • mediumtopics#3
    Add specific fine-tuning technique and educational topics

    Why:

    CURRENT
    dataset, deepseek, fine-tuning, guide, llama3, llm, machine-learning, nlp, openai, pytorch, qwen, unsloth
    COPY-PASTE FIX
    dataset, deepseek, fine-tuning, guide, llama3, llm, machine-learning, nlp, openai, pytorch, qwen, unsloth, tutorial, sft

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 R6410418/Jackrong-llm-finetuning-guide
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers Library
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers Library · recommended 1×
  2. Hugging Face PEFT (Parameter-Efficient Fine-Tuning) Library · recommended 1×
  3. Keras · recommended 1×
  4. PyTorch Lightning · recommended 1×
  5. Fast.ai · recommended 1×
  • CATEGORY QUERY
    How to fine-tune large language models from scratch for beginners?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. Hugging Face PEFT (Parameter-Efficient Fine-Tuning) Library
    3. Keras
    4. PyTorch Lightning
    5. Fast.ai
    6. DeepSpeed
    7. JAX/Flax

    AI recommended 7 alternatives but never named R6410418/Jackrong-llm-finetuning-guide. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best practices for building an LLM supervised fine-tuning pipeline?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Datasets (https://github.com/huggingface/datasets)
    2. Pandas (https://github.com/pandas-dev/pandas)
    3. OpenAI API
    4. Anthropic API
    5. Google Gemini API
    6. Cleanlab (https://github.com/cleanlab/cleanlab)
    7. Hugging Face Transformers (https://github.com/huggingface/transformers)
    8. PyTorch (https://github.com/pytorch/pytorch)
    9. TensorFlow (https://github.com/tensorflow/tensorflow)
    10. bitsandbytes (https://github.com/TimDettmers/bitsandbytes)
    11. Hugging Face PEFT (https://github.com/huggingface/peft)
    12. Hugging Face Accelerate (https://github.com/huggingface/accelerate)
    13. PyTorch Lightning (https://github.com/Lightning-AI/lightning)
    14. DeepSpeed (https://github.com/microsoft/DeepSpeed)
    15. FSDP
    16. Weights & Biases (W&B) (https://github.com/wandb/wandb)
    17. MLflow (https://github.com/mlflow/mlflow)
    18. TensorBoard (https://github.com/tensorflow/tensorboard)
    19. Hugging Face Evaluate (https://github.com/huggingface/evaluate)
    20. Hugging Face TGI (Text Generation Inference) (https://github.com/huggingface/text-generation-inference)
    21. vLLM (https://github.com/vllm-project/vllm)
    22. ONNX Runtime (https://github.com/microsoft/onnxruntime)
    23. NVIDIA Triton Inference Server (https://github.com/triton-inference-server/server)
    24. AWS SageMaker
    25. Google Cloud AI Platform
    26. Azure Machine Learning

    AI recommended 26 alternatives but never named R6410418/Jackrong-llm-finetuning-guide. 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 R6410418/Jackrong-llm-finetuning-guide?
    pass
    AI named R6410418/Jackrong-llm-finetuning-guide explicitly

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

  • If a team adopts R6410418/Jackrong-llm-finetuning-guide in production, what risks or prerequisites should they evaluate first?
    pass
    AI named R6410418/Jackrong-llm-finetuning-guide 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 R6410418/Jackrong-llm-finetuning-guide solve, and who is the primary audience?
    pass
    AI did not name R6410418/Jackrong-llm-finetuning-guide — 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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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite