LLM Models · Open Source
Browse the world's large language models. Compare parameters, benchmarks, VRAM and more.
Llama 3.1 Swallow is a series of large language models (8B, 70B) that were built by continual pre-training on the Meta Llama 3.1 models. Llama 3.1 Swallow enhanced the Japanese language capabilities of the original Llama 3.1 while retaining the English language capabilities. We u…
A 15B-parameter token-mixer supernet derived from Apriel-1.6 via stochastic distillation. Every decoder layer exposes four trained mixer options —Full Attention, Sliding Window Attention, Gated DeltaNet, and Kimi Delta Attention—enabling flexible architecture selection from a sin…
BFS-Prover-V2: Scaling up Multi-Turn Off-Policy RL and Multi-Agent Tree Search for LLM Step-Provers
AHN: Artificial Hippocampus Networks for Efficient Long-Context Modeling
Qwen3-Swallow v0.2 is a family of large language models available in 8B , 30B-A3B , and 32B parameter sizes. Built as bilingual Japanese-English models, they were developed through Continual Pre-Training (CPT), Supervised Fine-Tuning (SFT), and Reinforcement Learning with Verifia…
Llama3 Swallow - Built with Meta Llama 3
Llama3 Swallow - Built with Meta Llama 3
Our Swallow model has undergone continual pre-training from the Llama 2 family, primarily with the addition of Japanese language data. The tuned versions use supervised fine-tuning (SFT). Links to other models can be found in the index.
AHN: Artificial Hippocampus Networks for Efficient Long-Context Modeling
Model Introduction We introduce LongCat-Flash-Omni , a state-of-the-art open-source omni-modal model with 560 billion parameters (with 27B activated), excelling at real-time audio-visual interaction, which is attained by leveraging LongCat-Flash's high-performance Shortcut-connec…
Our Swallow model has undergone continual pre-training from the Llama 2 family, primarily with the addition of Japanese language data. The tuned versions use supervised fine-tuning (SFT). Links to other models can be found in the index.
NOTE: This "delta model" cannot be used directly. Users have to apply it on top of the original LLaMA weights to get actual vicuna-13b-finetuned-langchain-MRKL weights. See https://github.com/rinnakk/vicuna-13b-delta-finetuned-langchain-MRKL model-weights for instructions.