Open R1
A fully open reproduction of DeepSeek-R1. The goal of this repo is to build the missing pieces of the R1 pipeline such that everybody can reproduce and build on top of it.
Qwen2.5-Omni
We release Qwen2.5-Omni, the new flagship end-to-end multimodal model in the Qwen series. Designed for comprehensive multimodal perception, it seamlessly processes diverse inputs including text, images, audio, and video, while delivering real-time streaming responses through both text generation and natural speech synthesis.
QwQ
QwQ is the reasoning-specialized model within the Qwen series. Unlike traditional instruction-tuned models, QwQ leverages advanced reasoning and critical thinking abilities to achieve superior performance on downstream tasks, especially those involving complex problem-solving. Our latest release, QwQ-32B, is a mid-sized model that competes effectively with top-tier reasoning models like DeepSeek-R1 and o1-mini, delivering robust and competitive results.
Qwen2.5-VL
In the past five months since Qwen2-VL’s release, numerous developers have built new models on the Qwen2-VL vision-language models, providing us with valuable feedback. During this period, we focused on building more useful vision-language models. Today, we are excited to introduce the latest addition to the Qwen family: Qwen2.5-VL. Key Enhancements: Powerful Document Parsing Capabilities: Upgrade text recognition to omnidocument parsing, excelling in processing multi-scene, multilingual, and various built-in (handwriting, tables, charts, chemical formulas, and music sheets) documents.
DeepSeek-R1
We introduce DeepSeek’s first-generation reasoning models: DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrated remarkable performance on reasoning. DeepSeek-R1 incorporates cold-start data before RL, and achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks.
DeepSeek-V3
We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger performance.
Qwen2-VL
Qwen2-VL is the latest version of the vision language models in the Qwen model families. SoTA understanding of images of various resolution & ratio: Qwen2-VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc. Understanding videos of 20min+: with the online streaming capabilities, Qwen2-VL can understand videos over 20 minutes for high-quality video-based question answering, dialog, content creation, etc.
DeepSeek-Coder-V2
We present DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained from an intermediate checkpoint of DeepSeek-V2 with additional 6 trillion tokens. Through this continued pre-training, DeepSeek-Coder-V2 substantially enhances the coding and mathematical reasoning capabilities of DeepSeek-V2, while maintaining comparable performance in general language tasks.