Zyphra Release Zamba2-VL: Hybrid Mamba2–Transformer Vision-Language Models That Cut Time-to-First-Token by About an Order of Magnitude
Quick Answer
Zyphra has unveiled Zamba2-VL, a series of open vision-language models with 1.2B, 2.7B, and 7B parameters.
Quick Take
Zyphra has unveiled Zamba2-VL, a series of open vision-language models with 1.2B, 2.7B, and 7B parameters. These models utilize a hybrid Mamba2 and Transformer architecture, achieving a significant reduction in time-to-first-token by approximately an order of magnitude, while remaining competitive with existing Transformer VLMs.
Key Points
- Zamba2-VL models come in 1.2B, 2.7B, and 7B parameter sizes.
- The models are built on a hybrid Mamba2 state-space and Transformer backbone.
- They are released under the Apache 2.0 license.
- Zamba2-VL achieves a time-to-first-token reduction by about an order of magnitude.
- These models remain competitive with similar Transformer vision-language models.
Article Excerpt
From source RSS / original summaryZyphra 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.
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