LLM Models
Browse the world's large language models. Compare parameters, benchmarks, VRAM and more.
BaichuanMed-OCR-72B is a model fine-tuned from the Qwen2.5-VL-72B-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…
One of the focus areas at Together Research is new architectures for long context, improved training, and inference performance over the Transformer architecture. Spinning out of a research program from our team and academic collaborators, with roots in signal processing-inspired…
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…
CAT-Paws is an agentic LLM that thinks in Japanese (e.g., reasoning trace is in Japanese). The model is based on Qwen3-Swallow-v0.2 which is a continual pretraining model based on Qwen3 to read and write fluently in Japanese.
Our Swallow-MS-7b-v0.1 model has undergone continual pre-training from the Mistral-7B-v0.1, primarily with the addition of Japanese language data.
1. Model Summary 2. Evaluation 3. Intended Use 4. Limitations 5. Security and Responsible Use 6. License 7. Citation
Overview The model is the instruction-tuned version of rinna/nekomata-14b . It adopts the Alpaca input format.
- Model creator: AI Singapore - Original model: Qwen-SEA-LION-v4-32B-IT
Our Swallow-MS-7b-v0.1 model has undergone continual pre-training from the Mistral-7B-v0.1, primarily with the addition of Japanese language data.
Introduction We are thrilled to introduce Seed-Coder, a powerful, transparent, and parameter-efficient family of open-source code models at the 8B scale, featuring base, instruct, and reasoning variants. Seed-Coder contributes to promote the evolution of open code models through…
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1. Model Summary 2. Use 3. Limitations 4. Training 5. Evaluation 7. Citation