LLM Models
Browse the world's large language models. Compare parameters, benchmarks, VRAM and more.
AHN: Artificial Hippocampus Networks for Efficient Long-Context Modeling
🤗 Hugging Face 💻 Github Repository 📑 Technique Report 💬 Issues & Discussions
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).
AHN: Artificial Hippocampus Networks for Efficient Long-Context Modeling
🤗 Hugging Face 💻 Github Repository 📑 Technique Report 💬 Issues & Discussions
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).
AHN: Artificial Hippocampus Networks for Efficient Long-Context Modeling
From Inquiry to Decision: Building Trustworthy Medical AI
SEA-LION is a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for the Southeast Asia (SEA) region. The size of the models range from 3 billion to 7 billion parameters. This is the card for the SEA-LION 7B Instruct (Non-Commercial) model.
- Model creator: AI Singapore - Original model: Qwen-SEA-LION-v4-32B-IT
[!IMPORTANT] ❗ This repo requires the use of the macOS Sequoia (15) Developer Beta to utilize the latest and greatest CoreML has to offer! Sign up for the Apple Beta Software Program here to get access. Check out the companion blog post to learn more about what's new in iOS 18 &…