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A minimalist proxy for your local LLM cluster (~1100 lines)

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A minimalist proxy for your local LLM cluster (~1100 lines)

Hi all! I made this small proxy when I built a small GPU cluster at home and wanted to share it with friends while keeping speed limits and token accounting. I also put a lot of effort in making it work as quick as possible, and IMO the result is worth sharing! It is not trying to be LiteLLM: in fact, I wanted to make it opposite, as small as possible, with a small amount of dependencies and without cloud connections by default. Will be happy to hear your feedback :) PS also installable with pip install smol-llm-proxy

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Actual performance

2points
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
38%38% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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