Users generally appreciate Lamini for its ease of use in training custom LLMs, highlighting its developer-friendly nature with features like rapid fine-tuning and structured data output integration. The support for open-source LLMs and compatibility with both NVIDIA and AMD hardware is seen as a major strength. However, there are mentions of high computational costs associated with training multiple LLMs, although solutions like PEFT are being offered to mitigate these concerns. Sentiment around pricing is not directly mentioned, but there is a free offering for small LLMs, which suggests some positive feedback. Overall, Lamini enjoys a solid reputation, especially among developers focused on efficient and scalable LLM deployment.
Mentions (30d)
0
Reviews
0
Platforms
2
Sentiment
4%
4 positive
Users generally appreciate Lamini for its ease of use in training custom LLMs, highlighting its developer-friendly nature with features like rapid fine-tuning and structured data output integration. The support for open-source LLMs and compatibility with both NVIDIA and AMD hardware is seen as a major strength. However, there are mentions of high computational costs associated with training multiple LLMs, although solutions like PEFT are being offered to mitigate these concerns. Sentiment around pricing is not directly mentioned, but there is a free offering for small LLMs, which suggests some positive feedback. Overall, Lamini enjoys a solid reputation, especially among developers focused on efficient and scalable LLM deployment.
Features
Use Cases
Industry
information technology & services
Employees
6
Funding Stage
Series A
Total Funding
$25.0M
🎉 Big secret! We’ve been running on @AMD Instinct™ GPUs in production for over a year. 🤝 Thrilled to now partner with AMD to offer GPU-rich enterprise LLMs! 🥳 LLM Superstation – combining Lamini'
🎉 Big secret! We’ve been running on @AMD Instinct™ GPUs in production for over a year. 🤝 Thrilled to now partner with AMD to offer GPU-rich enterprise LLMs! 🥳 LLM Superstation – combining Lamini's LLM infrastructure with AMD Instinct. 👉 Learn more: https://t.co/OC3Vo2Pxxr
View original🎯 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 ou
🎯 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!
View originalJoin 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 age
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
View original🙌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:
🙌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 https://t.co/N4dAqUIncF
View originalThanks @DeepLearningAI for featuring us in The Batch! https://t.co/oQVE0GSNYz
Thanks @DeepLearningAI for featuring us in The Batch! https://t.co/oQVE0GSNYz
View original@nooriefyi Couldn't agree more!
@nooriefyi Couldn't agree more!
View originalHave 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 interactio
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
View original@realSharonZhou 😻😻😻😻😻😻😻😻😻
@realSharonZhou 😻😻😻😻😻😻😻😻😻
View original🎁 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%+
🎁 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
View original🙌 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 c
🙌 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 https://t.co/RyCgzRohgw
View original.@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
.@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
View original🎉🎉🎉 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.
🎉🎉🎉 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
View original@StanTechAddict @GregoryDiamos Great idea @StanTechAddict!
@StanTechAddict @GregoryDiamos Great idea @StanTechAddict!
View originalLLM 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
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
View originalGo 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 i
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
View originalVertical 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
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
View originalKey features include: User-friendly interface for model fine-tuning, Support for multiple pre-trained models, Automated data preprocessing tools, Customizable training parameters, Real-time performance monitoring, Integration with popular ML frameworks, Version control for models and datasets, Collaboration tools for team projects.
Lamini is commonly used for: Fine-tuning language models for specific industries, Creating chatbots with domain-specific knowledge, Enhancing sentiment analysis for customer feedback, Developing recommendation systems for e-commerce, Improving image classification accuracy, Optimizing NLP tasks for legal document analysis.
Lamini integrates with: TensorFlow, PyTorch, Keras, Hugging Face Transformers, AWS S3, Google Cloud Storage, Azure Machine Learning, Jupyter Notebooks, Slack for team notifications, GitHub for version control.
Based on user reviews and social mentions, the most common pain points are: down, critical, breaking.
Based on 91 social mentions analyzed, 4% of sentiment is positive, 96% neutral, and 0% negative.
