LLM Models

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.

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…

BFS-Prover-V2: Scaling up Multi-Turn Off-Policy RL and Multi-Agent Tree Search for LLM Step-Provers

Overview The model is the instruction-tuned version of rinna/youri-7b . It adopts a chat-style input format.

This is a Japanese finetuned model based on deepseek-ai/DeepSeek-R1-Distill-Qwen-14B.

We introduce an updated version of LongCat-Flash-Thinking, named LongCat-Flash-Thinking-2601, a powerful and efficient Large Reasoning Model (LRM) with 560 billion total parameters, built upon an innovative Mixture-of-Experts (MoE) architecture.

Authors : Yizhe Zhang, Navdeep Jaitly (Apple)

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.

Model Summary DiaFill is a Japanese dialogue script generation model designed to produce natural, spoken-style dialogue scripts rich in fillers and brief utternaces. Unlike typical assistant models that respond to users, this model is fine-tuned to generate a multi-turn dialogue…

Overview The model is the instruction-tuned version of rinna/nekomata-7b . It adopts the Alpaca input format.

A 15B-parameter hybrid reasoning model combining Transformer attention and Mamba State Space layers for high efficiency and scalability. Derived from Apriel-Nemotron-15B-Thinker through progressive distillation, Apriel-H1 replaces less critical attention layers with linear Mamba…

AHN: Artificial Hippocampus Networks for Efficient Long-Context Modeling