LLM Models
Browse the world's large language models. Compare parameters, benchmarks, VRAM and more.
Model Card for Olmo-3.1-32B-Instruct-DPO
[!Note] This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc. In light of its parameter scale, the intende…
📣 Update [10-07-2025]: Added a default system prompt to the chat template to guide the model towards more professional, accurate, and safe responses.
LFM2 is a family of hybrid models designed for on-device deployment. LFM2-24B-A2B is the largest model in the family, scaling the architecture to 24 billion parameters while keeping inference efficient.
DeepHermes 3 Preview is the latest version of our flagship Hermes series of LLMs by Nous Research, and one of the first models in the world to unify Reasoning (long chains of thought that improve answer accuracy) and normal LLM response modes into one model. We have also improved…
A Pocket-Sized MLLM for Ultra-Efficient Image and Video Understanding on Your Phone
Model Summary: Granite-4.1-3B is a 3B parameter long-context instruct model finetuned from Granite-4.1-3B-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 impr…
A Pocket-Sized MLLM for Ultra-Efficient Image and Video Understanding on Your Phone
A GPT-4V Level MLLM for Single Image, Multi Image and Video on Your Phone
We introduce Olmo 3, a new family of 7B and 32B models both Instruct and Think variants. Long chain-of-thought thinking improves reasoning tasks like math and coding.
This repository hosts the bitsandbytes (NF4, 4-bit) quantized version of MiniCPM-V 4.6. For the original BF16 weights and the full model card, please refer to openbmb/MiniCPM-V-4.6.
This repository hosts the GPTQ (W4A16, GPTQModel) quantized version of MiniCPM-V 4.6 Thinking. For the original BF16 weights and the full model card, please refer to openbmb/MiniCPM-V-4.6-Thinking.