LLM Models
Browse the world's large language models. Compare parameters, benchmarks, VRAM and more.
Gemma-2-Llama-Swallow series was built by continual pre-training on the gemma-2 models. Gemma 2 Swallow enhanced the Japanese language capabilities of the original Gemma 2 while retaining the English language capabilities. We use approximately 200 billion tokens that were sampled…
📖 Paper 🏠 Github 🤗 Spatial-SSRL-7B Model 🤗 Spatial-SSRL-3B Model 🤗 Spatial-SSRL-Qwen3VL-4B Model 🤗 Spatial-SSRL-81k Dataset 📰 Daily Paper
📃 License • 👨💻 Code • 🖥️ Demo • 📑 Technical Report • 📊 Benchmarks • 🚀 Getting Started
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]
Building the Next Generation of Open-Source and Bilingual LLMs
🚀 BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic Theorem Proving State-of-the-art tactic generation model in Lean4
Baichuan-M2-32B is Baichuan AI's medical-enhanced reasoning model, the second medical model released by Baichuan. Designed for real-world medical reasoning tasks, this model builds upon Qwen2.5-32B with an innovative Large Verifier System. Through domain-specific fine-tuning on r…
This model is an example of the Simple Self-Distillation (SimpleSD) method that improves code generation by fine-tuning a language model on its own sampled outputs—without rewards, verifiers, teacher models, or reinforcement learning. Please see the paper below for more informati…
💻Github Repo • 🤔Reporting Issues • 📜Technical Report
Our Swallow-MX-8x7b-NVE-v0.1 model has undergone continuous pre-training from the Mixtral-8x7B-Instruct-v0.1, primarily with the addition of Japanese language data.
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]
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 SEA-LION-v1-3B.