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Zyphra's Zamba2-VL introduces a hybrid Mamba2–Transformer architecture designed to significantly reduce latency in vision-language models without sacrificing accuracy. By interleaving state-space layers with selective attention blocks, the model maintains context retrieval capabilities while achieving an order-of-magnitude speedup in time-to-first-token compared to standard Transformer-based VLMs.

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Zyphra Release Zamba2-VL: Hybrid Mamba2–Transformer Vision-Language Models That Cut Time-to-First-Token by About an Order of Magnitude

MarkTechPost12 iunie 20266 min de cititArticolul și perspectiva sunt în engleză.

Zyphra has released Zamba2-VL, a family of open vision-language models at 1.2B, 2.7B, and 7B parameters. The models use a hybrid Mamba2 state-space and Transformer backbone, shipping under Apache 2.0. They stay competitive with comparable Transformer VLMs while cutting time-to-first-token by about an order of magnitude. The post Zyphra Release Zamba2-VL: Hybrid Mamba2–Transformer Vision-Language Models That Cut Time-to-First-Token by About an Order of Magnitude appeared first on MarkTechPost.

Zyphra has released Zamba2-VL, a family of open vision-language models. The release covers three sizes: 1.2B, 2.7B, and 7B parameters. Each model is built on the Zamba2 hybrid SSM–Transformer backbone. Vision-language models (VLMs) read images and text together. They answer questions about charts, documents, and photos. Most open VLMs use a dense Transformer as the language model. Zamba2-VL replaces that with a hybrid state-space design. The goal is competitive accuracy at lower latency. Zamba2-VL follows the now-standard LLaVA-style VLM template. A pre-trained vision encoder turns image patches into features.…

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