Lamini

The LLM tuning & inference platform for enterprises. Factual LLMs. Deployed anywhere.

[Lamini @LaminiAI]

🎯 Aiming for 90%+ accuracy on your Text-to-SQL agent, but can't get past 50%? With our proven methodology, our customers have cracked the code and hit 9s of accuracy!

We're spilling the tea 🍵 in our upcoming webinar. Bring your toughest Text-to-SQL questions—we’ve got answers! 💪

🎯 Build high-accuracy Text-to-SQL BI agents
📅 March 20, 2025
🕘 10:00 - 10:45 AM PT
🔗 Register here today: https://t.co/BhQbxOtIcf

[Lamini @LaminiAI]

Join us for a webinar on building Text-to-SQL BI agents. We’ll show how to finetune any open LLM to reach 90%+ accuracy. Register now https://t.co/0B73RnZWWI

🎯 Build high-accuracy Text-to-SQL BI agents
📅 March 20, 2025
🕘 10:00 - 10:45 AM PT

[Lamini @LaminiAI]

🙌Introducing Memory RAG—a simpler approach to RAG that leverages embed-time compute to create more intelligent, validated data representations. Build mini-agents with a simple prompt.

Get the paper: https://t.co/X0sdzAuX2m

[Lamini @LaminiAI]

Thanks @DeepLearningAI for featuring us in The Batch!
https://t.co/oQVE0GSNYz

[Lamini @LaminiAI]

@nooriefyi Couldn't agree more!

[Lamini @LaminiAI]

Have you seen our Classifier Agent Toolkit 😺 demo yet?

Learn how to use our SDK to build a highly accurate Classifier Agent for a customer service chatbot. The agent categorizes customer interactions by intent so it can respond appropriately. You can run multiple evaluations until you reach your desired level of accuracy.

https://t.co/ogIpBKFguR

[Sharon Zhou @realSharonZhou]

I'm so excited to launch @LaminiAI’s Classifier Agent Toolkit, aka. CAT! 🚀🐱 CAT hunts & tags the important signals 🐭 in a vast amount of data — so devs can easily create agentic classifiers.
❌ Manual data labeling
❌ Large, slow general LLM calls that can only handle 20-30 categories with mid accuracy
✅ CAT has helped our customers tag 2,000 pages across 1,000 categories in just 3.6 seconds with 99.9% accuracy. Dev time? A few hours to a few days. Hallucinations? Approaching zero. Meow.

Some common agentic classifiers with CAT:
◽️Customer service agents that extract user intent
◽️Finding high severity tickets, so your teams can prioritize urgent issues
◽️Triage legacy application code based on importance, to prioritize development
◽️Analyze sentiment in earnings calls, reviews, posts, surveys, etc.

More on it 👉 https://t.co/t7k5N56A7u
Demo from one of our amazing architects Scott https://t.co/bVtRuvUuy8

Happy holidays, hope you like our gift 🎁 Reach out anytime to fill our inbox with cheer at [email protected] (we read, we respond!) This was a huge effort by the entire Lamini team 🎀

[Lamini @LaminiAI]

@realSharonZhou 😻😻😻😻😻😻😻😻😻

[Lamini @LaminiAI]

🎁 Our new Classifier Agent Toolkit (CAT 🐱) is here! No more extensive manual data labeling or heavy ML systems.

😻 Build classifier agents that can quickly categorize large volumes of data at 95%+ accuracy / 400k token throughput in under 2 seconds.

Watch the demo and get the link to the docs and repo here: https://t.co/1u1SpHrgRJ

[Lamini @LaminiAI]

🙌 Our new Enterprise Guide to Fine-Tuning is out! If you can't get above 40-50% accuracy with RAG, fine-tuning might be the answer. Learn the basics of fine-tuning and specific applications and use cases. https://t.co/lAMmspVaD2

[Lamini @LaminiAI]

.@realSharonZhou recently spoke at @Aurecon's #ExemplarForum2024 on high-ROI use cases for LLMs and overcoming key challenges in AI deployment, including poor model quality, hallucinations, costs, and security. Watch the video here: https://t.co/N24bfOyJGb

[Lamini @LaminiAI]

🎉🎉🎉 Excited to announce our new pay-as-you-go offering, Lamini On-Demand. Get $300 in free credit to run your tuning and inference jobs on our high-performance GPU cluster. Happy tuning! https://t.co/77M1tMpS6U

[Lamini @LaminiAI]

@StanTechAddict @GregoryDiamos Great idea @StanTechAddict!

[Lamini @LaminiAI]

LLM inference frameworks have hit the “memory wall”, which is a hardware imposed speed limit on memory bound code. Is it possible to tear down the memory wall? @GregoryDiamos explains how it works in his new technical blog post.
https://t.co/hAgiZmYaQb

[Lamini @LaminiAI]

Go from AI novice to fine-tuning wiz with our Improving Accuracy of LLM Applications course with @DeepLearningAI + @asangani7. Here's one student's experience getting to 96% accuracy on factual data in just 3 iterations. https://t.co/jgA0F2OsBP

[Lamini @LaminiAI]

Vertical vs. horizontal AI use cases? GitHub Copilot started vertical and crossed over into horizontal applications. Low latency + accuracy were key! Thanks for the great discussion @gajenkandiah and @Hitachi!
https://t.co/eGNB0AohJ3

[Lamini @LaminiAI]

Like many startups, our tech is possible because of access to open source LLMs.

@realSharonZhou @matthew_d_white @starlordxie and @pentagoniac recently discussed the importance of an open ecosystem and implications of SB 1047.

Thanks to @AIatMeta and @cerebral_valley for hosting and bringing awareness to SB 1047!

[AI at Meta @AIatMeta]

🆕 New course on @DeepLearningAI: Improving Accuracy of LLM Applications ➡️ https://t.co/jp2pdiKCtt

Created in collaboration with DL, Meta & @LaminiAI, this free course covers topics like evaluation frameworks, instruction & memory fine-tuning, LoRA + training data generation.

[Supermicro @Supermicro]

🎉We're excited to share how @LaminiAI, a leading AI cloud service provider, is leveraging our Supermicro GPU Servers to support their large-scale #LLM tuning and generative #AI models. Read our Success Story: https://t.co/I0mQX5t0IE #GenAI #datacenter #GPU #supermicro

[Andrew Ng @AndrewYNg]

Learn a development pattern to systematically improve the accuracy and reliability of LLM applications in our new short course, Improving Accuracy of LLM Applications, built in partnership with @LaminiAI and @Meta, and taught by Lamini’s CEO @realSharonZhou, and Meta’s Senior Director of Partner Engineering, @asangani7. (Disclosure: I am an investor in Lamini.)

The path to tuning an LLM application can be complex. In this course, you'll learn a systematic sequence of steps for improving accuracy by reducing hallucinations:
- Create an evaluation dataset to measure model accuracy
- Add prompt engineering and self-reflection
- Fine-tune your model including "memory-tuning" which is a new method of embedding facts in an LLM

Using the Llama 3-8B parameter model, you will:
- Build a text-to-SQL agent with a custom schema and simulate situations where it hallucinates
- Understand the difference between instruction fine-tuning, which gives pre-trained LLMs instructions to follow, and memory fine-tuning
- See how Performance-Efficient Fine-tuning (PEFT) techniques like Low-Rank Adaptation (LoRA) reduce training time by 100x and Mixture of Memory Experts (MoME) reduces it even further

I appreciate Meta releasing the Llama's family of open models -- this course gives an example of the unique type of work that developers can do with such models.

Please sign up here: https://t.co/FITZFVlzNk