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Fine-Tuning Index of Open-Source LLMs vs. OpenAI

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Fine-Tuning Index of Open-Source LLMs vs. OpenAI

The research team at Predibase analyzed 700+ fine-tuning experiments using 13 of the most popular open-source models, GPT-3.5/4/4o, and 31 distinct datasets and tasks. We chose open-source models with a max of 7B parameters to ensure that any organization can train the models on low-end GPUs. For evaluation, we utilized task-specific metrics including accuracy, rouge, and HumanEval to assess performance. Key Takeaways - LoRA Fine-tuned models outperform GPT-4 on specialized tasks - LoRA Fine-tuned models are fast and cheap to train and serve, averaging less than $8 each - Specialized tasks are ideal for LoRA fine-tuning

Visit productView on Hacker NewsMay 21, 2024
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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, openai · Missing: mac, agents, macos
94%94% 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: para, including · Missing: supports, reddit linkedin, podcasting
73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: lua, ide, io · Missing: https docs, excited, just released
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: way, para · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
22%22% predicted probability of success on BetaList, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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