LLaVA-OneVision-1.5 is a series of fully open-source large multimodal models that achieve advanced performance at a lower cost by training on native resolution images. This model demonstrates excellent performance in multiple multimodal benchmark tests, surpassing competitors such as Qwen2.5-VL.

MultimodalTransformers

MobileLLM-R1 is an efficient inference model in the MobileLLM series, specifically optimized for mathematics, programming, and scientific problems. It achieves higher accuracy with a smaller parameter scale, featuring low training cost and high efficiency.

Natural Language ProcessingTransformersEnglish

ContentV is an efficient video generation model framework that achieves high-quality video generation with limited computing resources through a minimalist architecture, multi-stage training strategy, and cost-effective reinforcement learning framework.

Computer VisionDiffusers

Fathom-R1-14B is a project based on the R1-distilled-14B model, achieving o4-mini level mathematical reasoning ability under a 16K context with a low training cost of $499.

Natural Language ProcessingTransformers

Light-R1-32B is a math competition-specific model trained based on Qwen2.5-32B-Instruct, achieving performance surpassing DeepSeek-R1-Distill through curriculum-style SFT and DPO techniques, with a training cost of only $1000.

Natural Language ProcessingTransformers

Minitron-8B-Base is a large language model obtained by pruning Nemotron-4 15B, employing distillation and continuous training methods, saving 40 times the training tokens and 1.8 times the computational cost compared to training from scratch.

Natural Language ProcessingTransformersEnglish

TiC-CLIP is an improved vision-language model based on OpenCLIP, employing continual training strategies on time-series data to effectively reduce computational costs for model updates.

TiC-CLIP is a continually trained vision-language model that maintains synchronization with the latest data through temporally continuous training, avoiding the high costs of frequent retraining.

YOLOv10 is an efficient real-time object detection model without extra training costs. By optimizing architecture and training strategies, it improves detection accuracy while maintaining real-time performance.

JetMoE-8B is an efficient open-source large language model that surpasses LLaMA2-7B performance with a low training cost of $100,000, activating only 2.2 billion parameters during inference

Natural Language ProcessingTransformers

JetMoE-8B is an efficient open-source large language model that achieves performance comparable to LLaMA2-7B with a training cost of under $100,000, specifically designed for low-resource environments.

Natural Language ProcessingTransformers

Pixart-α is an efficient text-to-image generation model based on Transformer architecture, capable of generating high-quality 1024-pixel images at extremely low training costs

Computer VisionDiffusers

LongLoRA is an efficient fine-tuning method for large language models that can extend the context length of pre-trained models at a limited computational cost. This method significantly reduces the computational requirements for long-context training while maintaining model performance through shifted short attention and improved LoRA technology.

Natural Language ProcessingTransformers