LLM Models
Browse the world's large language models. Compare parameters, benchmarks, VRAM and more.
Granite-SWASH-3B-a600M (Sliding Window Attention + Sinks Hybrid)
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Molmo2-ER (Embodied Reasoning) is a 4B vision–language model specialized for the embodied perception skills that downstream action models depend on: scene understanding, pixel-accurate pointing, multi-image and egocentric–exocentric correspondence, and video temporal reasoning.
Model Summary: Granite-3.0-1B-A400M-Base is a decoder-only language model to support a variety of text-to-text generation tasks. It is trained from scratch following a two-stage training strategy. In the first stage, it is trained on 8 trillion tokens sourced from diverse domains…
💻Github Repo • 🤔Reporting Issues • 📜Technical Report
[\[📂 GitHub\]](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…
The GLM family welcomes new members, the GLM-4-32B-0414 series models, featuring 32 billion parameters. Its performance is comparable to OpenAI’s GPT series and DeepSeek’s V3/R1 series. It also supports very user-friendly local deployment features. GLM-4-32B-Base-0414 was pre-tra…
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🐙 GitHub • 👾 Discord • 🐤 Twitter • 💬 WeChat 📝 Paper • 💪 Tech Blog • 🙌 FAQ • 📗 Learning Hub
🐙 GitHub • 👾 Discord • 🐤 Twitter • 💬 WeChat 📝 Paper • 💪 Tech Blog • 🙌 FAQ • 📗 Learning Hub
We expand on our Olmo model series by introducing Olmo Hybrid, a new 7B hybrid RNN model in the Olmo family. Olmo Hybrid dramatically outperforms Olmo 3 in final performance, consistently showing roughly 2x data efficiency on core evals over the course of our pretraining run. We…
LFM2.5 is a new family of hybrid models designed for on-device deployment . It builds on the LFM2 architecture with extended pre-training and reinforcement learning.