Search Results for "mtp"

GLM-4.5 is a cutting-edge open-source large language model designed by Z.ai for intelligent agent applications. The flagship GLM-4.5 model has 355 billion total parameters with 32 billion active parameters, while the compact GLM-4.5-Air version offers 106 billion total parameters and 12 billion active parameters. Both models unify reasoning, coding, and intelligent agent capabilities, providing two modes: a thinking mode for complex reasoning and tool usage, and a non-thinking mode for...

Message Transport Protocol (MTP) implementation using XMPP for the Java Agent DEvelopment framework (JADE). Produced under the Google Summer of Code program with the help of Quantum Leap Interactive and Jabber, Inc.

...The model natively supports a context length of 262K tokens and can be extended up to 1 million tokens using RoPE scaling (YaRN), making it highly capable for processing large documents and extended conversations. Multi-Token Prediction (MTP) boosts both training and inference, while stability optimizations such as weight-decayed and zero-centered layernorm ensure robustness. Benchmarks show it performs comparably to larger models like Qwen3-235B on reasoning, coding, multilingual, and alignment tasks while requiring only a fraction of the training cost.

...It is the first model built on Tencent’s rebuilt training infrastructure and introduces significant improvements in context learning, software engineering, and tool-based task execution. The model features 295B total parameters with only 21B activated during inference, plus a dedicated 3.8B Multi-Token Prediction (MTP) layer that accelerates generation through speculative decoding. Architecturally, it uses 192 routed experts with top-8 activation, a dense-MoE hybrid design, and a native 256K-token context window. Hy3-preview is optimized for efficient deployment while maintaining strong benchmark performance across reasoning, coding, and agent evaluations. ...

NVIDIA Nemotron 3.5 Lightning 30B-A3B NVFP4 is an open large language model optimized for efficient autonomous agents, sub-agent deployments, and local inference. It uses a hybrid Mixture-of-Experts architecture combining Mamba-2, MoE, and selected attention layers, with 30B total parameters but only 3B active during inference. The model supports context windows up to 1 million tokens, enabling long-running workflows and large-context reasoning. Its NVFP4 quantization reduces deployment...

GigaChat 3 Ultra is a flagship instruct-model built on a custom Mixture-of-Experts architecture with 702B total and 36B active parameters. It leverages Multi-head Latent Attention to compress the KV cache into latent vectors, dramatically reducing memory demand and improving inference speed at scale. The model also employs Multi-Token Prediction, enabling multi-step token generation in a single pass for up to 40% faster output through speculative and parallel decoding techniques. Its...