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Here is a feature you won't get anywhere else: 1. First, you add access to every major LLM out there. 2. Second, you implement intelligent routing so the best model answers your question. If you are still paying for 3-4 different models, you are wasting your money. Check this out:

20,802 görüntüleme • 2 yıl önce •via X (Twitter)

10 Yorum

MLOps Guy profil fotoğrafı
MLOps Guy2 yıl önce

How is the best answer determined?

Axioma AI 🔺 profil fotoğrafı
Axioma AI 🔺2 yıl önce

Business model of Perplexity? 🤔🤔

coinwatch profil fotoğrafı
coinwatch2 yıl önce

concept is good, a lot of time it doesn't work and support is non-existent. is this a sponsored post?

Jovan Cicmil profil fotoğrafı
Jovan Cicmil2 yıl önce

I don't understand. How does the router decide which model to use for each question?

ChillyNovember🐧 profil fotoğrafı
ChillyNovember🐧2 yıl önce

So its like an LLM model aggregator. Curious how the algorithm decides on the model?

Mark R Pommrehn profil fotoğrafı
Mark R Pommrehn2 yıl önce

Yeah, love chatllm! And yes, excellent value for money as well as so many features and much flexibility!

___raf stahelin___ profil fotoğrafı
___raf stahelin___2 yıl önce

Is there perplexity?

GigaMira profil fotoğrafı
GigaMira2 yıl önce

This sounds like a game-changer for businesses looking to leverage AI technology. How does the intelligent routing feature work?

Faris Hassan profil fotoğrafı
Faris Hassan2 yıl önce

You mean we subscribe to ClaudeAI and call it a day

GPT.Biz profil fotoğrafı
GPT.Biz2 yıl önce

Sounds like a smart way to save both time and money! Definitely worth checking out.

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Production traffic is not uniform. You get a few requests that need your best model, but most are simple questions and lookups you can solve with cheaper, faster models. The most expensive mistake you can make today is sending every request to your strongest model. You need routing. Period. This is the simplest trick to improve the architecture of whatever you are building. Please, don't implement routing yourself. You don't have to. I'm currently working with TrueFoundry's Auto Routing. It reads each request, classifies it as simple, medium, or complex, and sends it to the model assigned to that tier. You have two choices: 1. Send every request to the free heuristic classifier to score signals such as technical vocabulary, code, prompt length, and multi-step reasoning. 2. Send the request to an LLM classifier when its difficulty requires a more nuanced judgment. The beauty of using routing is that nothing changes in your code. You still call a single endpoint model, but routing works behind the scenes to pair every request with the best possible model. TrueFoundry ran several experiments with two different setups: 1. Send every request to Claude Opus 2. Send every request to a router with Haiku, Sonnet, and Opus The first experiment ran 550 deterministically graded academic prompts through every setup. Auto Routing was 69% cheaper while retaining 98% of the baseline quality. The second experiment ran three production-shaped workloads through every setup, using user chats, developer chats, and long agent tasks. Auto Routing was 80% cheaper. Thanks to the TrueFoundry team for partnering with me on this post.

Santiago

15,543 görüntüleme • 26 gün önce

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.

Santiago

164,162 görüntüleme • 2 yıl önce