TL;DR:
- Together AI introduces StripedHyena-7B, a disruptive AI model.
- StripedHyena offers efficient alternatives to traditional Transformers.
- The release includes StripedHyena-Hessian-7B and StripedHyena-Nous-7B.
- StripedHyena excels in handling lengthy sequences and outperforms rivals in short-context tasks.
- It employs a hybrid technique with gated convolutions and attention mechanisms.
- StripedHyena achieves remarkable speed and memory efficiency.
- The future vision includes larger models and multi-modal support.
Main AI News:
Together AI is at the forefront of sequence modeling architecture with the introduction of StripedHyena models, revolutionizing the field and challenging conventional Transformers. With a focus on computational efficiency and enhanced performance, this release features the base model, StripedHyena-Hessian-7B (SH 7B), and the chat model, StripedHyena-Nous-7B (SH-N 7B).
These models draw upon key insights from previous sequence modeling architectures, including H3, Hyena, HyenaDNA, and Monarch Mixer, all developed just last year. The innovation behind StripedHyena lies in its ability to handle lengthy sequences during training, fine-tuning, and generation with remarkable speed and memory efficiency.
A Unique Hybrid Technique
StripedHyena sets itself apart by employing a hybrid technique that combines gated convolutions and attention mechanisms, referred to as Hyena operators. Notably, it marks the first alternative architecture that competes head-to-head with strong Transformer base models. In short-context tasks, including those on the OpenLLM leaderboard, StripedHyena outperforms competitors like Llama-2 7B, Yi 7B, and even the most robust Transformer alternatives such as RWKV 14B.
Assessing Performance
Researchers conducted extensive evaluations to gauge StripedHyena’s capabilities in handling short-context tasks and processing lengthy prompts. Perplexity scaling experiments using Project Gutenberg books revealed that the model’s perplexity either saturates at 32k or decreases beyond that point, a testament to its capacity to assimilate information from longer prompts.
Efficiency Through Hybrid Design
Efficiency is at the core of StripedHyena’s design, achieved through a unique hybrid structure that seamlessly integrates attention and gated convolutions into Hyena operators. Innovative grafting techniques were employed to optimize this hybrid design, allowing for architecture modification during training.
Impressive Speed and Memory Efficiency
One of the standout advantages of StripedHyena is its remarkable speed and memory efficiency across various tasks, including training, fine-tuning, and generating long sequences. It outperforms an optimized Transformer baseline, leveraging FlashAttention v2 and custom kernels, by a significant margin—over 30%, 50%, and 100% in end-to-end training on lines 32k, 64k, and 128k, respectively.
A Glimpse into the Future
As for what lies ahead, Together AI’s researchers have ambitious plans for StripedHyena models. Their vision includes creating larger models capable of handling even longer contexts and pushing the boundaries of information understanding. Furthermore, they aim to incorporate multi-modal support, enabling the model to process and comprehend data from diverse sources, including text and images. Ultimately, their goal is to continually enhance the performance of StripedHyena models, ensuring they operate with the utmost effectiveness and efficiency.
Conclusion:
The introduction of StripedHyena-7B by Together AI signifies a significant leap in AI model innovation. This hybrid approach, surpassing traditional Transformers, holds promise for industries demanding computational efficiency and performance. As StripedHyena continues to evolve with larger models, multi-modal capabilities, and improved performance, it is poised to make a lasting impact in the AI market, catering to diverse information processing needs.
