csimonmeunier/test-model

In recent years, pre-trained language models (PLMs) achieve the best performance on a wide range of natural language processing (NLP) tasks. While the first models were trained on general domain data, specialized ones have emerged to more effectively treat specific domains.

A Neural Machine Translation (NMT) model based on a custom Transformer (Encoder-Decoder) architecture, trained from scratch. This model is designed to translate English sentences into Hebrew using multilingual encoding and specialized layer configurations.

Duchifat-2.3-Instruct is a state-of-the-art, instruction-tuned Large Language Model developed by TopAI. As the flagship of the Duchifat series, this model represents a fundamental breakthrough in how Hebrew is processed, reasoned, and generated in the LLM era.

### Model Overview TabPFN-3 is a transformer-based foundation model that uses in-context-learning to solve tabular prediction problems in a forward pass. Inference code can be found at https://github.com/PriorLabs/TabPFN. More details can be found in the Model Report.

### Model Overview TabPFN-2.6 is a transformer-based foundation model that uses in-context-learning to solve tabular prediction problems in a forward pass. Inference code can be found at https://github.com/PriorLabs/tabPFN.

Welcome to the repository for Nidum-Limitless-Gemma-2B-GGUF, an advanced language model that provides unrestricted and versatile responses across a wide range of topics. This version is designed for maximum flexibility, allowing you to run it on both CPU and GPU.