`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. 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.
`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. For high-performance inference, an fp8 version of the model is available - GigaChat3.1-10B-A1.8B. bf16 version is also avaliable - GigaChat3.1-10B-A1.8B-bf16.
`GigaChat Audio 10B` is an audio-native LLM built on top of the GigaChat 3.1 Lightning text model. A Conformer speech encoder and a modality adapter feed audio embeddings directly into a Mixture-of-Experts decoder, so the model keeps the text quality of its base while adding speech understanding. Capabilities: audio question answering and classification, temporal grounding (localization in long audio, timestamped event descriptions, audio summarization with timestamps), tool-use, and text-only tasks. The temporal grounding skills are trained on TimeGround-1M — a purpose-built dataset of long-form audio paired with time-aligned annotations.
