Publications By LightOn

In this blogpost we show how we can train a Mamba model interchangeably on both NVIDIA and AMD and we compare both training performance and convergence in both cases. This shows that our training stack is becoming more GPU-agnostic.

We present our explorations on training language models based on the new Mamba architecture, which deviates from the traditional Transformer architecture.

4th workshop on Neural Scaling Laws: Towards Maximally Beneficial AGI, NeurIPS 2022 – Machine Learning/NLP – LLMsAbstract not available.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.