LLM Models · Open Source
Browse the world's large language models. Compare parameters, benchmarks, VRAM and more.
We conduct continual pre-training of google/gemma-2-2b on 80B tokens from a mixture of Japanese and English datasets. The continual pre-training improves the model's performance on Japanese tasks.
This is a 8bit quantized version of upstage/SOLAR-0-70b-16bit
--- license: apache-2.0 library name: transformers base model: - Qwen/Qwen2.5-7B pipeline tag: text-generation ---
AquilaMoE: Efficient Training for MoE Models with Scale-Up and Scale-Out Strategies Language Foundation Model & Software Team Beijing Academy of Artificial Intelligence (BAAI) [Paper(released soon)] [Code] [github]
BaichuanMed-OCR-7B is a model fine-tuned from the Qwen2.5-VL-7B-Instruct with our constructed and curated medical report datasets consists of medical report images and related questions and answers (QAs). It has been specifically adapted to perform Optical Character Recognition (…
This is a Japanese continually pre-trained model based on meta-llama/Meta-Llama-3.1-70B-Instruct.
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.
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.
WARNING: The checkpoints on this repo are not fully trained model. Evaluations of intermediary checkpoints and the final model will be added when conducted (see below).
Ring-lite is a lightweight, fully open-sourced MoE (Mixture of Experts) LLM designed for complex reasoning tasks. It is built upon the publicly available Ling-lite-1.5 model, which has 16.8B parameters with 2.75B activated parameters.. We use a joint training pipeline combining k…
