VL
VLLM with JSON Guided Generation
Hacker NewsVLLM with JSON Guided Generation
Our project, Outlines, now offers guided/constrained generation (e.g. according to a JSON schema) via the VLLM library. My colleague, Rémi, created some patches that allow one to pass vLLM a JSON schema along with the prompt, which dramatically simplifies deployment of JSON-guided generation. He also added a new `serve` interface that puts it all together and makes serving such models a 2-3 line process. Check it out and tell us what you think!
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AppSumoStrong fit for a featured deal · Strong signals: interface · Missing: plus, platform, intuitive67%67% predicted probability of success on AppSumo, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting42%42% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue13%13% 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, crypto5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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