RRepoGEO

REPOGEO REPORT · LITE

mistralai/mistral-finetune

Default branch main · commit 0eb00045 · scanned 6/24/2026, 11:37:58 AM

GitHub: 3,093 stars · 317 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 mistralai/mistral-finetune, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Add a concise description to the repo's 'About' section

    Why:

    COPY-PASTE FIX
    Archived: A lightweight, memory-efficient codebase for LoRA fine-tuning of Mistral models, optimized for multi-GPU single-node setups. No longer actively maintained.
  • mediumreadme#2
    Clarify the README's opening sentence to reflect its archived status and specific focus

    Why:

    CURRENT
    `mistral-finetune` is a light-weight codebase that enables memory-efficient and performant finetuning of Mistral's models.
    COPY-PASTE FIX
    This archived repository provides a light-weight codebase for memory-efficient and performant LoRA finetuning *specifically* of Mistral's models.

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 mistralai/mistral-finetune
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LoRA
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LoRA · recommended 1×
  2. huggingface/peft · recommended 1×
  3. QLoRA · recommended 1×
  4. microsoft/DeepSpeed · recommended 1×
  5. Dao-AILab/flash-attention · recommended 1×
  • CATEGORY QUERY
    How to efficiently fine-tune large language models on a single GPU?
    you: not recommended
    AI recommended (in order):
    1. LoRA
    2. Hugging Face `peft` (huggingface/peft)
    3. QLoRA
    4. DeepSpeed ZeRO (microsoft/DeepSpeed)
    5. FlashAttention (Dao-AILab/flash-attention)
    6. bitsandbytes (TimDettmers/bitsandbytes)
    7. PyTorch FSDP (pytorch/pytorch)

    AI recommended 7 alternatives but never named mistralai/mistral-finetune. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best tools for LoRA fine-tuning of open-source LLMs?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PEFT
    3. Axolotl
    4. Unsloth
    5. Lit-GPT
    6. bitsandbytes

    AI recommended 6 alternatives but never named mistralai/mistral-finetune. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 mistralai/mistral-finetune?
    pass
    AI named mistralai/mistral-finetune explicitly

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

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

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

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mistralai/mistral-finetune — 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