Empower-functions, SOTA OSS function calling LLM

Hacker News

Function calling is a crucial feature for language models because it allows them to access real-time data and interact with external tools, making them far more versatile and powerful. The problem? Right now, if you want function calling, you're stuck using GPT-4. While it's an amazing model, it's also pretty expensive, which isn't ideal if you're trying to keep costs down. There are a few open-source models out there, but none of them are robust enough for real-world applications. So, we decided to change that. We've built a new LLM that supports function calling, and we are open-sourcing it. We've benchmarked our model against many other alternatives, and we're confident that it’s the best open-source LLM for function calling. Plus, it’s much cheaper than GPT-4, making it accessible to a wider range of users. Let us know if you have any questions or feedback. We're excited to see what you build with this new LLM!

Visit product View on Hacker NewsMay 21, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, new · Missing: mac, agents, macos

93%93% 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: supports · Missing: reddit linkedin, podcasting, created

87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist

68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: plus, users · Missing: platform, intuitive, reviews

56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal

42%42% 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, 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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