DeepSeek honest correct dropped a fresh open-supply multmodal AI mannequin, Janus-Pro-7B. It is MIT opensource license.
It’s multimodal (can generate photos) and beats OpenAI’s DALL-E 3 and Accumulate Diffusion across GenEval and DPG-Bench benchmarks.
This comes on top of the total R1 hype.
Right here is the hyperlink to the Deepseek Janus 7B Github.
NEWS: DeepSeek honest correct dropped ANOTHER open-supply AI mannequin, Janus-Pro-7B.
It is multimodal (can generate photos) and beats OpenAI’s DALL-E 3 and Accumulate Diffusion across GenEval and DPG-Bench benchmarks.
This comes on top of the total R1 hype. The 🐋 is cookin’ pic.twitter.com/yCmDQoke0f
— Rowan Cheung (@rowancheung) January 27, 2025
Right here is the Huggingface dwelling for DeepSeek Janus Pro 7B.
Janus-Pro is a new autoregressive framework that unifies multimodal working out and technology. It addresses the barriers of old approaches by decoupling visual encoding into separate pathways, while nonetheless utilizing a single, unified transformer architecture for processing. The decoupling no longer easiest alleviates the warfare between the visual encoder’s roles in working out and technology, nonetheless also enhances the framework’s flexibility. Janus-Pro surpasses old unified mannequin and matches or exceeds the performance of job-convey fashions. The simplicity, excessive flexibility, and effectiveness of Janus-Pro originate it a ambitious candidate for next-technology unified multimodal fashions.
Mannequin Summary
Janus-Pro is a unified working out and technology MLLM, which decouples visual encoding for multimodal working out and technology. Janus-Pro is constructed in step with the DeepSeek-LLM-1.5b-unhealthy/DeepSeek-LLM-7b-unhealthy.
For multimodal working out, it makes enlighten of the SigLIP-L as the vision encoder, which helps 384 x 384 image input. For image technology, Janus-Pro makes enlighten of the tokenizer from here with a downsample fee of 16.
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