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Last week, we open-sourced PremSQL, a local first library that created customized Text-to-SQL solutions. When deploying RAG-based services (whether on documents or databases) for enterprises, it becomes crucial that the underlined data is not exposed to third-party APIs. With PremSQL, you can use our ready-made pipelines or customize and create
SQL
PremSQL is a local-first, open-source Text-to-SQL solution that ensures data privacy and control by avoiding third-party models. With support for small language models, PremSQL simplifies natural language querying and provides autonomous, AI-driven data analysis.
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Explore the latest Text-to-SQL advancements in our new blog. We evaluate top models like GPT-4o and Llama using the newly released text2sql package on BIRDBench. Learn about Execution Accuracy, Valid Efficiency Score, and how open-source models are challenging closed-source alternatives.
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Text to SQL is a task that aims to convert natural language questions into corresponding SQL queries that can be executed in relational databases. Interestingly, this problem statement has been through research before the rise of Large Language Models.
We believe in giving back to the community. So today we introduce Prem Benchmarks. This is a fully open-source project with its primary objective being to benchmark popular LLM inference engines (currently 13 + engines) like vLLM, TensorRT LLM, HuggingFace Transformers, etc on different precisions like float32, float16, int4, and int8.
Welcome to our third series of serverless LLM deployments. In our first blog, we explored Modal Labs and deployed Mistral 7B serverless. We did the same with Google Gemma in our second blog with Beam cloud. In this blog, we are going to deploy the latest Mistral 7B v0.2
In our previous blog, we showed you how to deploy a Large Language Model like Mistral 7B going serverless using Modal Labs. Today's blog explores Beam Cloud which provides similar services. We'll demonstrate how to deploy the Google Gemma 2B model with HuggingFace and Beam Cloud.
Most of our blogs primarily delve deep into research topics. From Mixture of Experts, synthetic datasets, and Model merging to LLM Evaluations, our blog covers it all. This post is a bit different and includes a more hands-on approach to how we can deploy Large Language Models going fully serverless.
Mamba is a very recent deep-learning architecture and it is fundamentally different from transformer-based models. Mamba's performance is on par with the current Small/Large Language models like Phi-2, Mistral, Llama 2, etc. The Structured State Space for Sequence Models (S4), a State Space Model, is considered the
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The previous blog post describes how Small Language Models (a.k.a SLMs) hold potential in the generative AI space. We discussed the emergence of the idea of SLMs and how synthetic data can be a driving factor in producing powerful small language models (for example: Phi-2 by Microsoft). Just
LLMs
Large Language Models (LLMs) became very famous because of the rise of their emergent capabilities and the concept of “one model does it all”, where a single model can do all sorts of tasks like generating emails, and poems, summarizing long papers, generating code, etc. However, LLMs are also infamous
News
Very recently llm360.ai has released LLM360, one of the very first truly open-source models. Popular open-source models, such as Llama2, and Mistral, are not fully open-source. Only the model's weights are released. But just making the weights public does not solve all the problems. Training an LLM
