With Sparkflows Generative-AI Platform, you can effortlessly build optimized Retrieval-Augmented Generation(RAG) applications by harnessing the power of LLM models leveraging the 400+ processors .
It facilitates the development of data science analytics, chatbots, and statistical analytics on private knowledge warehouses and diverse datasets.
The platform supports both closed-source models like GPT and open-source models like Llama 2, Falcon-40b among others from the Hugging Face repository.
Hugging Face Integration
LLM models from Hugging Face can handle various Natural Language tasks, including Text Classification, Text Generation, Token Classification, Question Answering, Sentence Similarity, Summarization, Zero-shot classification, Translation, and Fill-Mask.
The inferences occur offline to ensure data security and mitigate risks like prompt injection, jailbreaking, and prompt leaking without any external API calls.
Sparkflows seamlessly integrates with various GPT models (e.g., GPT-4, GPT-3.5-turbo, text-DaVinci-003, text-DaVinci-002, davinci, curie, Babbage, ada) via the OpenAI API. Users can utilize GPT models to query private Knowledge bases or GPT itself.
It allows pegging responses with ground truth to tailor results. GPT API usage includes creating chatbots, data analysis, and code interpretation, among other applications.
Sparkflows users can build workflows by providing prompts to perform various tasks.
Some of the tasks where the Sparkflows copilot empowers the users are - building data engineering pipelines, predictive modelling pipelines, compare models, data profiling, data quality and auto code generation for the pipelines.
Gen-AI Enabled Solutions
Our vertical solutions leverage cutting-edge LLM models, leading in generative AI technology, to achieve precise and efficient results.
Powered by these state-of-the-art models, we enable sentiment analysis, text mining, and extracting valuable information to interpret emotions and opinions in textual data. Our Generative-AI solutions cater to market analysis, brand sentiment, and product recommendation systems, optimizing dynamic pricing, customer segmentation, and personalized shopping experiences.
Fine Tune LLM
Sparkflows No code Studio facilitates fine-tuning of LLM on personal datasets to customize responses. Both open-source models built in-house or from Hugging Face or elsewhere can be incorporated and fine-tuned on private data.
Even huge models like Llama 2 (60 Billion parameters) and Falcon (40 Billion parameters) can be fine-tuned efficiently. The process is less computationally intensive than LLM training and can be accomplished on a commodity GPU in a few hours or even a CPU on a laptop.