LLM Models · Open Source
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Description: The NVIDIA Qwen3-Next-80B-A3B-Instruct NVFP4 model is the quantized version of Alibaba's Qwen3-Next-80B-A3B-Instruct model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA…
Model Introduction We introduce LongCat-Flash-Lite, a non-thinking 68.5B parameter Mixture-of-Experts (MoE) model with approximately 3B activated parameters, supporting a 256k context length through the YaRN method. Building upon the LongCat-Flash architecture, LongCat-Flash-Lite…
LFM2.5 is a new family of hybrid models designed for on-device deployment . It builds on the LFM2 architecture with extended pre-training and reinforcement learning.
Model Introduction We introduce LongCat-Flash-Lite, a non-thinking 68.5B parameter Mixture-of-Experts (MoE) model with approximately 3B activated parameters, supporting a 256k context length through the YaRN method. Building upon the LongCat-Flash architecture, LongCat-Flash-Lite…
LongCat-Flash-Lite-Sparse is a non-thinking Mixture-of-Experts (MoE) model with 69B total parameters and approximately 3B activated parameters per token. Built on LongCat-Flash-Lite, it replaces dense MLA with LongCat Sparse Attention (LSA) and natively supports context lengths o…
Mistral Small 4 is the next major release in the Mistral Small family, unifying the capabilities of several flagship Mistral models into a single system. It combines strong reasoning from...
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Hugging Face GitHub Launch Blog Documentation License : Apache 2.0 Authors : Google DeepMind
--- pipeline tag: text-generation base model: google/diffusiongemma-26B-A4B-it license: apache-2.0 license name: apache-license-2.0 license link: https://ai.google.dev/gemma/apache 2 tags: - nvidia - ModelOpt - DiffusionGemma-26B-A4B-IT - quantized - NVFP4 - nvfp4 ---
Solar Open is Upstage's flagship 102B-parameter large language model, trained entirely from scratch and released under the Upstage Solar License (see LICENSE for details). As a Mixture-of-Experts (MoE) architecture, it delivers enterprise-grade performance in reasoning, instructi…
This repository presents post FP8 quantized Falcon-H1R-7B-FP8 via NVIDIA Model Optimizer, enabling efficient inference while preserving the strong reasoning introduced in the paper Falcon-H1R: Pushing the Reasoning Frontiers with a Hybrid Model for Efficient Test-Time Scaling.
The post-training data has a cutoff date of November 28, 2025\. The pre-training data has a cutoff date of June 25, 2025\.