GigaChat3-10B-A1.8B is an efficient dialogue model in the GigaChat series. Based on the Mixture-of-Experts (MoE) architecture, it has a total of 10 billion parameters and 1.8 billion active parameters. It adopts the innovative Multi-head Latent Attention (MLA) and Multi-token Prediction (MTP) technologies, aiming to optimize inference throughput and generation speed. The model is trained on 20T tokens of diverse data and supports 10 languages including Chinese, suitable for dialogue scenarios requiring quick responses.

Natural Language ProcessingGgufMultiple Languages

Qwen3-Next-80B-A3B-Instruct-AWQ-4bit is an efficient version based on the Qwen3-Next-80B-A3B-Instruct model with 4-bit AWQ quantization. This model adopts a hybrid attention mechanism and a highly sparse mixture-of-experts (MoE) architecture, supporting a context length of up to 262K tokens. It significantly reduces the computational resource requirements while maintaining high performance.

Natural Language ProcessingTransformers

TSLAM-15B is a 15-billion parameter telecom-specific language model developed by NetoAI Solutions. It is based on the Mixture-of-Experts (MoE) architecture and optimized for telecom domain expertise, advanced reasoning, and action-oriented workflows.

Natural Language ProcessingSafetensors

Qwen3-14B-Base is the latest generation of large language models in the Tongyi series, offering a comprehensive range of dense models and mixture-of-experts (MoE) models, and achieving significant progress in training data, model architecture, and optimization techniques.

Natural Language ProcessingTransformers

Qwen3 is the latest generation of large language models in the Tongyi Qianwen series, offering both dense models and Mixture-of-Experts (MoE) architectures. Trained on massive datasets, Qwen3 achieves breakthrough progress in reasoning, instruction following, agent capabilities, and multilingual support.

Natural Language ProcessingGguf

Qwen3-14B-Base is the latest generation of the Tongyi series of large language models, providing a comprehensive set of dense and mixture-of-experts (MoE) models with significant improvements in training data, model architecture, and optimization techniques.

Natural Language ProcessingTransformers

Qwen3-8B-Base is the latest generation of large language models in the Tongyi series, offering a comprehensive set of dense and mixture-of-experts (MoE) models based on significant improvements in training data, model architecture, and optimization techniques.

Natural Language ProcessingTransformers

Qwen3-1.7B-Base is the latest generation of large language models in the Tongyi series, offering a range of dense models and mixture-of-experts (MoE) models. It has made significant improvements in training data, model architecture, and optimization techniques.

Natural Language ProcessingTransformers

LOLA is an ultra-large-scale multilingual large model based on the sparse Mixture-of-Experts (MoE) Transformer architecture, supporting over 160 languages, with competitive advantages in natural language generation and understanding tasks.

Natural Language ProcessingTransformersOther

Camelidae-8x7B is a mixture-of-experts model based on parameter-efficient sparse construction technology. It converts a dense model into a MoE architecture through QLoRA and Adapter technologies and performs excellently in multiple domains such as code and mathematics.

Natural Language ProcessingTransformersEnglish