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Finetune, build and deploy LLMs with AIKit

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Finetune, build and deploy LLMs with AIKit

Hi folks! AIKit is a side project of mine that's aimed to be a one-stop shop to easily and quickly get started to build, fine-tune and deploy large language models (LLMs) with only Docker! I recently added extensible finetuning interface to AIKit. AIKit uses Unsloth under-the-hood for fast and efficent fine tuning of a model. After finetuning, you can take the finetuned model and create a minimal container image for inference, which is OpenAI compatible powered by LocalAI, to create a seamless end-to-end experience. I would love any thoughts and feedback! Get started: https://sozercan.github.io/aikit Demo of finetuning Mistral with OpenHermes and then serving the model as a minimal container image: https://youtu.be/FZuVb-9i-94 https://twitter.com/sozercan/status/1769769695081546236

Visit productView on Hacker NewsMar 18, 2024
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Product HuntOn track for Day 1 leaderboard · Strong signals: model, dock, models · Missing: mac, agents, macos
89%89% 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.
Indie HackersFits the IH revenue-focused audience · Strong signals: started, compatible · Missing: supports, reddit linkedin, podcasting
88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
73%73% 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
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive
30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%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, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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