@RefuelAI

Solve enterprise data tasks at superhuman accuracy. Acquired by @togethercompute

 San Francisco, CA
refuel.ai?utm_source=twitter&utm_medium=profile
Joined May 2021
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  • Following42
  • Followers549
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We have some big news to share today - @RefuelAI is joining @togethercompute to help accelerate the future of open source and enterprise AI! together.ai/blog/together-ai…

Together AI acquires Refuel.ai to unlock data for developers and businesses building production-g...

Together AI acquires Refuel.ai, bringing its data cleaning and structuring models, including Refuel LLM-2, into the Inference and Fine-tuning Platform.

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3/ By joining @togethercompute, we will bring Refuel's team, technology and mission to Together’s AI platform, and help accelerate the AI adoption journey of the next generation of developers and enterprises
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4/ To our customers: thank you for trusting us to solve your critical data problems and helping shape the journey. And to the Refuel team: you made this possible — every late night, every launch, every hard technical choice. We're proud of what we’ve built. Onwards!
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🚀 Big news: Together AI has acquired @RefuelAI! Refuel specializes in models and tools that turn messy, unstructured data into clean, structured input—exactly what teams need to build high-quality, production-grade AI applications. Details below 👇
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Data intelligence too cheap to meter RefuelLLM-2-mini (75.02%), our latest 1.5B param SLM, outperforms all comparable models including Phi-3.5 (65.3%), Qwen2.5 (67.62%), Gemma2 (64.52%), Llama3-3B (55.8%) and Llama3-1B (39.92%) across our benchmark of data processing tasks such as labeling, enrichment and structure extraction RefuelLLM-2-mini is a Qwen2-1.5B base model, trained on a corpus of 2750+ datasets spanning tasks such as classification, reading comprehension, structured attribute extraction and entity resolution, using the same recipe as other models in the Refuel-LLM family. It's fast! We’re open sourcing the model weights, available on @huggingface - huggingface.co/refuelai/Qwen… If you'd like to access models, along with fine tuning support, DM me or reach out to us: refuel.ai/get-started Grateful to our early customers for their partnership, and the entire @RefuelAI team for their hard work 🚀
Nihit Desai
@nihit_desai
8 May 2024
Thrilled to introduce RefuelLLM-2, our latest family of LLMs built for data labeling and enrichment tasks. RefuelLLM-2 (83.82%) outperforms GPT-4-Turbo (80.88%), Claude-3-Opus (79.19%), Llama3-70B (78.2%) and Gemini-1.5-Pro (74.59%) on a benchmark of ~30 data labeling tasks: RefuelLLM-2-small (79.67%), aka Llama-3-Refueled, outperforms all comparable LLMs including Claude3-Sonnet (70.99%), Haiku (69.23%) and GPT-3.5-Turbo (68.13%). We’re open sourcing the model: huggingface.co/refuelai/Llam… You can try out the models here and give us some feedback! labs.refuel.ai/playground. The code and data used for benchmarking the LLMs is available in our Autolabel library: github.com/refuel-ai/autolab… One more thing: RefuelLLM-2 family of models output much better calibrated confidence scores - a useful lever to reject, retry or ensemble low confidence outputs.
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In 2017, Netflix got rid of its “5 star” rating system in favor of a simple thumbs up and thumbs down approach. This decision fundamentally transformed their business. A 🧵- (1/6)
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(5/6) - These observations led to Netflix eventually switching to a thumbs up and thumbs down system. The byproduct? An almost 200% increase in ratings!
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(6/6) - While not every marketplace looks like Netflix, recommendations drive revenue and high-quality data drives good recommendations. If you're building a recommendations system and thinking about data quality and the role LLMs can play, we should chat!
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Thank you @databricks @DbrxMosaicAI for the keynote shoutout! Always great connecting with new and old friends at the @Data_AI_Summit
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We're kicking off the Data+AI Summit with the #MosaicX #Meetup: San Francisco Edition on Monday, June 10th. We're at the #Moscone Center South, 2nd floor, with over 1500 registrants and 39 speakers across four tracks. It's a "slightly" packed agenda with: ✅ Discussion panels on #Hardware, Build & Risks, Data Panel, and #VC Panel with a special session on #OLMo ✅ #Research track on importance of high quality #data, common challenges in #RAG development, #diffusion models, and more ✅ A use cases track on composable #CDP, building models, #multimodal, #agents, and more ✅ In the building LLMs track, we discuss the challenges, tools/techniques to build them, fast #LLM inference, and building #GenAI apps. While we are fully packed, if you are already registered for #DataAISummit, we will have a waitlist at the door. We have speakers from @databricks @DbrxMosaicAI @LaminiAI @Oracle @VoltronData @Replit @AiSquared_ @robusthq @gretel_ai @superannotate @EssenceVenture @AmplifyPartners @llama_index @QuotientAI @ActionIQinc @RefuelAI @OrbyAI @yousearchengine @NumbersStnAI @lancedb @huggingface mosaicx.events/events/june-1…
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We're trending on @huggingface! Check out: huggingface.co/refuelai/Llam… Try out the model here: labs.refuel.ai/playground
Nihit Desai
@nihit_desai
8 May 2024
Thrilled to introduce RefuelLLM-2, our latest family of LLMs built for data labeling and enrichment tasks. RefuelLLM-2 (83.82%) outperforms GPT-4-Turbo (80.88%), Claude-3-Opus (79.19%), Llama3-70B (78.2%) and Gemini-1.5-Pro (74.59%) on a benchmark of ~30 data labeling tasks: RefuelLLM-2-small (79.67%), aka Llama-3-Refueled, outperforms all comparable LLMs including Claude3-Sonnet (70.99%), Haiku (69.23%) and GPT-3.5-Turbo (68.13%). We’re open sourcing the model: huggingface.co/refuelai/Llam… You can try out the models here and give us some feedback! labs.refuel.ai/playground. The code and data used for benchmarking the LLMs is available in our Autolabel library: github.com/refuel-ai/autolab… One more thing: RefuelLLM-2 family of models output much better calibrated confidence scores - a useful lever to reject, retry or ensemble low confidence outputs.
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We're thrilled to introduce RefuelLLM-2. Outperforms every single LLM available (GPT-4-Turbo, Claude Opus, Llama 3-70B, Gemini 1.5 Pro) on our benchmark of data labeling tasks. * Launch: refuel.ai/blog-posts/announc… * Playground: labs.refuel.ai/playground
Nihit Desai
@nihit_desai
8 May 2024
Thrilled to introduce RefuelLLM-2, our latest family of LLMs built for data labeling and enrichment tasks. RefuelLLM-2 (83.82%) outperforms GPT-4-Turbo (80.88%), Claude-3-Opus (79.19%), Llama3-70B (78.2%) and Gemini-1.5-Pro (74.59%) on a benchmark of ~30 data labeling tasks: RefuelLLM-2-small (79.67%), aka Llama-3-Refueled, outperforms all comparable LLMs including Claude3-Sonnet (70.99%), Haiku (69.23%) and GPT-3.5-Turbo (68.13%). We’re open sourcing the model: huggingface.co/refuelai/Llam… You can try out the models here and give us some feedback! labs.refuel.ai/playground. The code and data used for benchmarking the LLMs is available in our Autolabel library: github.com/refuel-ai/autolab… One more thing: RefuelLLM-2 family of models output much better calibrated confidence scores - a useful lever to reject, retry or ensemble low confidence outputs.
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Thrilled to introduce RefuelLLM-2, our latest family of LLMs built for data labeling and enrichment tasks. RefuelLLM-2 (83.82%) outperforms GPT-4-Turbo (80.88%), Claude-3-Opus (79.19%), Llama3-70B (78.2%) and Gemini-1.5-Pro (74.59%) on a benchmark of ~30 data labeling tasks: RefuelLLM-2-small (79.67%), aka Llama-3-Refueled, outperforms all comparable LLMs including Claude3-Sonnet (70.99%), Haiku (69.23%) and GPT-3.5-Turbo (68.13%). We’re open sourcing the model: huggingface.co/refuelai/Llam… You can try out the models here and give us some feedback! labs.refuel.ai/playground. The code and data used for benchmarking the LLMs is available in our Autolabel library: github.com/refuel-ai/autolab… One more thing: RefuelLLM-2 family of models output much better calibrated confidence scores - a useful lever to reject, retry or ensemble low confidence outputs.
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Better data = Better AI. In this episode of @software_daily I dive into why this is true, what makes it hard and how @RefuelAI is solving this at scale. Shoutout to @seanfalconer for hosting! 👇🚀
Software Engineering Daily
@software_daily
30 Apr 2024
Nihit Desai of @RefuelAI joins the show with @seanfalconer to talk about the platform, and how to manage data in the current AI era. Listen here: softwareengineeringdaily.com… @nihit_desai

Using LLMs for Training Data Preparation with Nihit Desai - Software Engineering Daily

Machine learning models learn patterns and relationships from data to make predictions or decisions. The quality of the data influences how well these models can represent and generalize from the...

softwareengineeringdaily.com
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Thank you @cerebral_valley for the feature!
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Thanks @ericdodds, @KostasPardalis and the team at @DataStackShow!
The Data Stack Show@DataStackShow
15 Feb 2024
🧠 What if humans could write instructions once and have machines do all of their data cleaning, labeling, and enriching for them? @refuelAI founder @rish_bhargava says this is the way And he joins us this week to tell us how LLMs make it possible: apple.co/48cO7Ni
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