REPOGEO REPORT · LITE
mlabonne/llm-datasets
Default branch main · commit 67c52949 · scanned 5/20/2026, 12:58:22 PM
GitHub: 4,585 stars · 381 forks
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.
2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).
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 mlabonne/llm-datasets, 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.
- highreadme#1Reposition the README's core value proposition
Why:
CURRENTThe README's first substantive content after social links is a small italicized description, followed by a section 'What is a good dataset?'.
COPY-PASTE FIXThis repository, mlabonne/llm-datasets, offers a comprehensive, curated collection of high-quality datasets and practical guidance specifically designed for Large Language Model (LLM) post-training. It serves as a valuable resource for LLM developers, researchers, and practitioners seeking optimal datasets and techniques for supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF).
- highlicense#2Add a LICENSE file to the repository
Why:
COPY-PASTE FIXCreate a `LICENSE` file in the repository root with a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that reflects the intended usage and contribution model for the curated list and tools.
- mediumtopics#3Refine repository topics for specificity
Why:
CURRENTdata, dataset, llm
COPY-PASTE FIXllm-datasets, fine-tuning, sft, rlhf, data-curation, llm-training, machine-learning-datasets, generative-ai
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.
- Hugging Face Datasets · recommended 1×
- Kaggle · recommended 1×
- Google Dataset Search · recommended 1×
- Papers With Code · recommended 1×
- OpenAI's GPT-3/GPT-4 Training Data · recommended 1×
- CATEGORY QUERYWhere can I find diverse, high-quality datasets for fine-tuning large language models?you: not recommendedAI recommended (in order):
- Hugging Face Datasets
- Kaggle
- Google Dataset Search
- Papers With Code
- OpenAI's GPT-3/GPT-4 Training Data
- Common Crawl
- arXiv
- ACL Anthology
AI recommended 8 alternatives but never named mlabonne/llm-datasets. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhat resources help curate and improve datasets for effective large language model post-training?you: not recommendedAI recommended (in order):
- Snorkel
- Argilla
- Label Studio
- Prodigy
- Cleanlab
- Weights & Biases
- Galileo
AI recommended 7 alternatives but never named mlabonne/llm-datasets. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesswarn
Suggestion:
- README presencepass
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 mlabonne/llm-datasets?passAI named mlabonne/llm-datasets explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts mlabonne/llm-datasets in production, what risks or prerequisites should they evaluate first?passAI named mlabonne/llm-datasets 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 mlabonne/llm-datasets solve, and who is the primary audience?passAI named mlabonne/llm-datasets explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
Embed your GEO score
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mlabonne/llm-datasets — 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