RRepoGEO

REPOGEO REPORT · LITE

radixark/miles

Default branch main · commit 7e03b728 · scanned 6/24/2026, 12:58:27 AM

GitHub: 1,606 stars · 274 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
35 /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
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 radixark/miles, 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:

    CURRENT
    (none)
    COPY-PASTE FIX
    reinforcement-learning, llm, vlm, large-language-models, vision-language-models, post-training, enterprise, deep-learning, machine-learning, ai-framework
  • highreadme#2
    Add a concise introductory paragraph to the README

    Why:

    CURRENT
    The README currently starts with a <div align="center"> containing headings.
    COPY-PASTE FIX
    Miles is an enterprise-facing reinforcement learning framework designed for the post-training of large language models (LLM) and vision-language models (VLM). It is forked from and co-evolving with the `slime` project, focusing on high-performance rollout, low-precision training, and production stability for large-scale AI deployments.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    https://radixark.com/miles

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 radixark/miles
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. TRL · recommended 1×
  3. Ray RLlib · recommended 1×
  4. Acme · recommended 1×
  5. OpenAI Baselines · recommended 1×
  • CATEGORY QUERY
    What are the best enterprise reinforcement learning frameworks for post-training large language models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. TRL
    3. Ray RLlib
    4. Acme
    5. OpenAI Baselines
    6. DeepSpeed-MII

    AI recommended 6 alternatives but never named radixark/miles. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to efficiently fine-tune large vision and language models with low-precision reinforcement learning?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PEFT (huggingface/peft)
    3. TRL (huggingface/trl)
    4. DeepSpeed (microsoft/DeepSpeed)
    5. Microsoft ONNX Runtime (microsoft/onnxruntime)
    6. PyTorch FSDP
    7. BitsAndBytes (TimDettmers/bitsandbytes)
    8. Jax (google/jax)
    9. Flax (google/flax)
    10. NVIDIA NeMo Framework (NVIDIA/NeMo)
    11. TensorFlow (tensorflow/tensorflow)
    12. KerasNLP (keras-team/keras-nlp)

    AI recommended 12 alternatives but never named radixark/miles. 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 radixark/miles?
    pass
    AI named radixark/miles explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of radixark/miles. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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radixark/miles — 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