Dolphin for Long Context π¬
So essentially,
Dolphin architecture helps models with long contexts understanding
Paper: Dolphin: Long Context as a New Modality for Energy-Efficient On-Device Language Models (13 Pages)
Github: https://huggingface.co/NexaAIDev/Dolphin
Researchers from NexaAI, through this paper, are balancing the need to process long contexts with the limitations of energy consumption and processing speed on mobile devices.
Hmm..Whatβs the background?
While on-device models offer benefits such as enhanced privacy, reduced latency, and offline functionality, they struggle with the demands of complex language processing, especially when handling long contexts.
Processing long contexts can quickly drain battery life, a critical concern for mobile device users and time it takes to process extended input sequences.
Ok, So what is proposed in the research paper?
Here's a breakdown of how Dolphin works:
The paper proposes treating long context as a separate modality, similar to how images are handled in vision-language models
Decoder-Decoder Architecture
0.5B parameter model, distills the long context into a compact representation
7B parameter decoder focuses on understanding the user's query and generating the response
To represent the long context information efficiently, the researchers introduce "memory tokens"
Whatβs next?
The researchers remark that these could directions for future work:
Exploring Other Modalities and Domains
Further Architecture Optimizations and Adaptations
So essentially,
Dolphin architecture helps models with long contexts understanding
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