Building the future of Decision Intelligence.
Joined February 2023
- Tweets 29
- Following 3
- Followers 813
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10 Photos and videos
Nolano.ai retweeted
3 Aug 2025
🚀 New Paper: Scaling Laws and Efficient Inference for Ternary Language Models.
Thrilled to share that our work was presented at ACL 2025! We explore ternary LMs (TriLMs), studying their scaling laws and efficiency compared to traditional FloatLMs. 🧵
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3 Mar 2025
Our work has been accepted as a Spotlight (top 5.1%) at ICLR 2025.
3 Mar 2025
🎉 Thrilled to share that our paper "Surprising effectiveness of pretraining ternary language models at scale" earned a spotlight at #ICLR2024! We dive into Ternary Language Models (TriLMs), systematically studying their training feasibility and scaling laws against FloatLMs.
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18 Jul 2024
🚀 SpectraSuite of Ternary and FP16 LLMs 🚀
We’re thrilled to release the Spectra Suite of open ternary (TriLMs) and FP16 (FloatLMs) language models from 99M to 3.9B parameters. At billion parameter scale, TriLMs upto 10x smaller can match the performance of FloatLMs.
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Nolano.ai retweeted
22 Dec 2023
A little Xmas present 4 you!🎁🎄🎉 Excited for the first release of our open-source Robin vision-language models built by the team at @irinarish’s @cercaai lab @ @UMontreal as part of our INCITE project tinyurl.com/yc3jzudt. Blog/models/code: tinyurl.com/robinv10 🧵
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31 Oct 2023
We are pleased to introduce Hi-NOLIN, the best performing 9B Hindi-English Bilingual LLM.
Blog: blog.nolano.ai/Hi-NOLIN/
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31 Oct 2023
At 60% training completion, it is already outperforming BLOOM and Pythia 9B across most Hindi, English and Coding benchmarks and closes the gap to LLaMa on Coding and English tasks.
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31 Oct 2023
Help Guide Us What Open Source Model Should We Train Next! docs.google.com/forms/d/e/1F…
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27 Sep 2023
1/ Introducing LoRD: Low-Rank Decomposition of Monolingual Code LLMs for one-shot compression.
Paper: arxiv.org/pdf/2309.14021.pdf
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27 Sep 2023
7/ Our findings suggest that LoRD is a promising new paradigm for compressing LLMs, offering significant reductions in model parameters without sacrificing model quality or differentiability, and enabling faster inference on modern hardware.
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8/ LoRD provides a novel approach to LLM compression, maintaining full differentiability and trainability of parameters. It is efficient, compatible with existing methods, and holds immense potential for advancements in the field of monolingual code generation.
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