GigaChat 3.1 Lightning GigaChat 3.1 Lightning is the compact instruct model of the GigaChat 3.1 family. It is a Mixture of Experts (MoE) model with 10B total parameters and 1.8B active parameters, designed for fast multilingual assistant workloads, reasoning, code, function calling, and product style deployment. This repository contains the BF16 version of the model. The FP8 checkpoint is available at GigaChat3.1 10B A1.8B, and a GGUF version is available at GigaChat3.1 10B A1.8B GGUF. More details can be found in the Habr article. Model architecture GigaChat 3.1 Lightning uses a custom MoE architecture with the following key components. Mixture of Experts (MoE) The model has 10B total parameters with 1.8B active parameters at inference time. This allows it to scale model capacity aggressively while keeping the active compute budget much lower than that of an equally large dense model. Multi head Latent Attention (MLA) Instead of standard multi head attention, the model uses MLA, which compresses the KV cache into a latent representation. This reduces memory usage and improves inference throughput, especially in long context settings. Multi Token Prediction (MTP) The model is train…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy