LLM Models
Browse the world's large language models. Compare parameters, benchmarks, VRAM and more.
SEA-LION is a collection of Large Language Models (LLMs) which have been pretrained and instruct-tuned for the Southeast Asia (SEA) region.
This is the merged weights of bunny-phi-2-siglip-lora.
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BLIP-2 model, leveraging Flan T5-xxl (a large language model). It was introduced in the paper BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models by Li et al. and first released in this repository.
LLaDA2.0-mini-CAP is an enhanced version of LLaDA2.0-mini that incorporates Confidence-Aware Parallel (CAP) Training for significantly improved inference efficiency. Built upon the 16B-A1B Mixture-of-Experts (MoE) diffusion architecture, this model achieves faster parallel decodi…
We introduce EXAONE Deep, which exhibits superior capabilities in various reasoning tasks including math and coding benchmarks, ranging from 2.4B to 32B parameters developed and released by LG AI Research. Evaluation results show that 1) EXAONE Deep 2.4B outperforms other models…
RedPajama-INCITE-7B-Base was developed by Together and leaders from the open-source AI community including Ontocord.ai, ETH DS3Lab, AAI CERC, Université de Montréal, MILA - Québec AI Institute, Stanford Center for Research on Foundation Models (CRFM), Stanford Hazy Research resea…
MathForm: Scaling Mathematical Autoformalization with Knowledge Retrieval and Verification-Guided Refinement
Try CoDA · Paper · Model Collection · GitHub Repository
Emu3: Next-Token Prediction is All You Need
Developed by : Upstage Backbone Model : LLaMA Variations : It has different model parameter sizes and sequence lengths: 30B/1024, 30B/2048, 65B/1024 Language(s) : English Library : HuggingFace Transformers License : This model is under a Non-commercial Bespoke License and governe…
Emu3: Next-Token Prediction is All You Need