vrfai/LFM2.5-2.6B-FP8
Text Generation • 3B • Updated • 2
Hung Ho
hungho77
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96% Correct Next Token Prediction, with No DNN, no Training, auto-distilled model - https://mltblog.com/4urfvTB
Over the last 12 months, I’ve built a model to predict the next token and to suggest synonyms or related queries to a user prompt, with 100% correct predictions on the training set in one shot, without training or deep neural networks (DNNs). The same model is now integrated in some of the most recent LLM architectures, albeit with costly training via DNNs. My version does not need DNNs or training.
The purpose of this article is to provide validation to my deep neural network alternative in the context of LLMs. The new model is as a substitute to standard DNNs, with increased explainability and higher accuracy. It is designed for corporate corpuses. The end goal is to provide better accuracy at a much lower cost, while providing full control over all the components.
An interesting feature is auto-distillation, whereas the model self-identifies weights that do not contribute over time in 99.9% of user-generated prompts, and drop them, based on prompts from a large, specialized user base. The gain is most spectacular in open-weight LLMs applied to specialized contexts, whether based on DNNs or not.
Read article and download the free technical paper with NVIDIA case study, at https://mltblog.com/4urfvTB
Over the last 12 months, I’ve built a model to predict the next token and to suggest synonyms or related queries to a user prompt, with 100% correct predictions on the training set in one shot, without training or deep neural networks (DNNs). The same model is now integrated in some of the most recent LLM architectures, albeit with costly training via DNNs. My version does not need DNNs or training.
The purpose of this article is to provide validation to my deep neural network alternative in the context of LLMs. The new model is as a substitute to standard DNNs, with increased explainability and higher accuracy. It is designed for corporate corpuses. The end goal is to provide better accuracy at a much lower cost, while providing full control over all the components.
An interesting feature is auto-distillation, whereas the model self-identifies weights that do not contribute over time in 99.9% of user-generated prompts, and drop them, based on prompts from a large, specialized user base. The gain is most spectacular in open-weight LLMs applied to specialized contexts, whether based on DNNs or not.
Read article and download the free technical paper with NVIDIA case study, at https://mltblog.com/4urfvTB
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