StripedHyena-Hessian-7B
Maintainer: togethercomputer
60
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The StripedHyena-Hessian-7B (SH 7B) is a large language model developed by the team at Together Computer. It is a hybrid architecture that combines multi-head, grouped-query attention and gated convolutions arranged in "Hyena" blocks, which differs from traditional decoder-only Transformers. The model has extended context capabilities, allowing it to process longer prompts of up to 32k tokens. Compared to optimized Transformer architectures like LLaMA-2, the SH 7B model offers improvements in training and inference-optimal scaling laws.
The team at Together has also developed similar models like the StripedHyena-Nous-7B and the LLaMA-2-7B-32K, which share the core architectural innovations but are tailored for different use cases like chat and long-context QA/summarization.
Model inputs and outputs
Inputs
- Text prompt: The SH 7B model takes in a text prompt as input, which can be of up to 32k tokens in length.
Outputs
- Generated text: The model outputs generated text, continuing the input prompt. The length of the generated text can be controlled via parameters like
max_new_tokens.
Capabilities
The SH 7B model excels at tasks that require processing long contexts, such as multi-document question answering, long-form text summarization, and generation on extended prompts. Its hybrid architecture and constant memory decoding allow for low latency, faster decoding, and higher throughput compared to traditional Transformer models.
What can I use it for?
The SH 7B model is well-suited for research and development purposes, particularly in applications that involve long-form text processing. Potential use cases include:
- Content generation: The model can be used to generate long-form articles, stories, or other creative content by providing it with appropriate prompts.
- Question answering: The extended context capabilities of the SH 7B make it useful for multi-document question answering tasks, where the model needs to synthesize information from multiple sources to provide a comprehensive answer.
- Summarization: The model can be employed for long-form text summarization, condensing lengthy documents or collections of documents into concise summaries.
Things to try
One interesting aspect of the SH 7B model is its ability to process longer sequences of text, up to 32k tokens. This can be particularly useful for tasks that require integrating information from multiple sources or maintaining context over an extended period. Developers and researchers may want to experiment with prompts that leverage this capability, such as multi-step instructions, multi-document question answering, or generation of long-form creative content.
Another avenue to explore is the model's performance on specialized tasks or fine-tuning on domain-specific datasets. The team at Together has demonstrated the model's effectiveness on benchmark tasks, but there may be opportunities to further refine and adapt the model for more specific applications.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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