LLM Models
Browse the world's large language models. Compare parameters, benchmarks, VRAM and more.
[\[📂 GitHub\]](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…
Hugging Face GitHub Launch Blog Documentation License : Apache 2.0 Authors : Google DeepMind
[\[📂 GitHub\]](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…
Today, we're announcing Qwen3-Coder-Next-FP8 , an open-weight language model designed specifically for coding agents and local development. It features the following key enhancements:
Today, we're announcing Qwen3-Coder-Next , an open-weight language model designed specifically for coding agents and local development. It features the following key enhancements:
Model Summary: Granite-4.1-30B is a 30B parameter long-context instruct model finetuned from Granite-4.1-30B-Base using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. Granite 4.1 models have gone through an i…
NVIDIA-Nemotron-Nano-VL-12B-V2-FP8 is the quantized version of the NVIDIA Nemotron Nano VL V2 model, which is an auto-regressive vision language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Nemotron Nano VL FP4 QAD mod…
--- --- Developers Granite Team, IBM Model Type Decoder-only Dense Transformer (Reasoning) Architecture GraniteForCausalLM Base Model Granite-4.1-3B-Base Parameters 3B Context Length Natively Supports 128K (Long-context extension to 512K) Precision bfloat16 Tested Languages Engli…
Mistral Large 3 2512 is Mistral’s most capable model to date, featuring a sparse mixture-of-experts architecture with 41B active parameters (675B total), and released under the Apache 2.0 license.
- 🚀 Online Demo : Explore Step3-VL-10B on Hugging Face Spaces ! - 📢 [Notice] FP8 Quantization Support : FP8 quantized weights are now available. (Download link) - 📢 [Notice] vLLM Support: vLLM integration is now officially supported! (PR 32329) - ✅ [Fixed] HF Inference: Resolved…
[!NOTE] Note: " -Paddle " models use PaddlePaddle weights, while " -PT " models use Transformer-style PyTorch weights.