LLM Models
Browse the world's large language models. Compare parameters, benchmarks, VRAM and more.
Hy-Embodied-VLM-1.0 Efficient Physical-World Agents Tencent Robotics X × Hy Vision Team × Futian Laboratory
[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL) [\[📜 InternVL 1.0\]](https://huggingface.co/papers/2312.14238) [\[📜 InternVL 1.5\]](https://huggingface.co/papers/2404.16821) [\[📜 InternVL 2.5\]](https://huggingface.co/papers/2412.05271) [\[📜 InternVL2.5-MPO\]](https://huggi…
🤗 HuggingFace 🤖 ModelScope 🪡 AngelSlim
[!NOTE] Note: " -Paddle " models use PaddlePaddle weights, while " -PT " models use Transformer-style PyTorch weights.
Model description xGen-MM is a series of the latest foundational Large Multimodal Models (LMMs) developed by Salesforce AI Research. This series advances upon the successful designs of the BLIP series, incorporating fundamental enhancements that ensure a more robust and superior…
🤗 HuggingFace 🤖 ModelScope 🪡 AngelSlim
[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL) [\[📜 InternVL 1.0\]](https://huggingface.co/papers/2312.14238) [\[📜 InternVL 1.5\]](https://huggingface.co/papers/2404.16821) [\[📜 InternVL 2.5\]](https://huggingface.co/papers/2412.05271) [\[📜 InternVL2.5-MPO\]](https://huggi…
From Inquiry to Decision: Building Trustworthy Medical AI
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.
MiniCPM is an End-Size LLM developed by ModelBest Inc. and TsinghuaNLP, with only 2.4B parameters excluding embeddings. MiniCPM-2B-128k is a long context extension trial of MiniCPM-2B. To our best knowledge, MiniCPM-2B-128k is the first long context( =128k) SLM smaller than 3B。 I…
[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL) [\[📜 InternVL 1.0\]](https://huggingface.co/papers/2312.14238) [\[📜 InternVL 1.5\]](https://huggingface.co/papers/2404.16821) [\[📜 InternVL 2.5\]](https://huggingface.co/papers/2412.05271) [\[📜 InternVL2.5-MPO\]](https://huggi…
This is a 16k context version of SmolLM2-1.7B-Instruct, which originnaly only supported 8k context. We finetune the model on 15k samples consisting of a subset of SmolTalk, LongAlign and SeaLong datasets and increase RoPE from 100k to 500k. This improves the evaluation on HELMET…