Llama-2-7B-32K-Instruct
Maintainer: togethercomputer
160
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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
Llama-2-7B-32K-Instruct is an open-source, long-context chat model fine-tuned from Llama-2-7B-32K, over high-quality instruction and chat data. The model was built by togethercomputer using less than 200 lines of Python script and the Together API. This model extends the capabilities of Llama-2-7B-32K to handle longer context and focuses on few-shot instruction following.
Model inputs and outputs
Inputs
- Llama-2-7B-32K-Instruct takes text as input.
Outputs
- The model generates text outputs, including code.
Capabilities
Llama-2-7B-32K-Instruct can engage in long-form conversations and follow instructions effectively, leveraging the extended context length of 32,000 tokens. The model has demonstrated strong performance on tasks like multi-document question answering and long-form text summarization.
What can I use it for?
You can use Llama-2-7B-32K-Instruct for a variety of language understanding and generation tasks, such as:
- Building conversational AI assistants that can engage in multi-turn dialogues
- Summarizing long documents or articles
- Answering questions that require reasoning across multiple sources
- Generating code or technical content based on prompts
Things to try
One interesting aspect of this model is its ability to effectively leverage in-context examples to improve its few-shot performance on various tasks. You can experiment with providing relevant examples within the input prompt to see how the model's outputs adapt and improve.
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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