StripedHyena-Nous-7B
Maintainer: togethercomputer
135
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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-Nous-7B (SH-N 7B) is a state-of-the-art chat model developed by Together Computer in collaboration with Nous Research. It is part of the StripedHyena model family, which uses a hybrid architecture of multi-head, grouped-query attention and gated convolutions arranged in Hyena blocks - a departure from traditional decoder-only Transformer models.
The StripedHyena models are designed to improve on Transformers in terms of long-context processing, training, and inference performance. Compared to optimized Transformer models like LLaMA-2, SH-N 7B offers constant memory decoding, lower latency, and faster throughput. It is also trained on sequences up to 32k tokens, allowing it to handle longer prompts than typical chatbots.
The model is similar in scale and capabilities to other open-source chatbots like Pythia-Chat-Base-7B and Nous-Hermes-13b, which are also fine-tuned on large instruction datasets to excel at open-ended dialogue and task completion.
Model inputs and outputs
Inputs
- Prompt: The text that the model is asked to continue or respond to.
Outputs
- Response: The model's generated text output, continuing or responding to the provided prompt.
Capabilities
The StripedHyena-Nous-7B model is designed for open-ended chat and task completion. It can engage in freeform dialogue, answer questions, summarize information, and complete a variety of other language-based tasks. Its long-context processing capabilities allow it to maintain coherence and memory over longer interactions.
What can I use it for?
The SH-N 7B model is well-suited for building chatbots, virtual assistants, and other conversational AI applications. Its strong performance on language tasks makes it applicable for use cases like customer service, tutoring, content generation, and research. The long-context abilities could also enable applications in areas like multi-document summarization and question answering.
Things to try
One interesting aspect of the SH-N 7B model is its hybrid architecture, which aims to improve on the limitations of standard Transformer models. You could experiment with prompts that require long-range reasoning or coherence to see how the model performs compared to other chatbots. Additionally, you could try fine-tuning the model on domain-specific datasets to enhance its capabilities for your particular use case.
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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