Llama3-DocChat-1.0-8B
Maintainer: cerebras
61
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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 Llama3-DocChat-1.0-8B model, developed by Cerebras, is an 8 billion parameter large language model built on top of the Llama 3 base. It is designed for document-based conversational question answering, building on insights from NVIDIA's ChatQA model series. Cerebras leveraged their expertise in LLM training and dataset curation to improve upon the limitations of the ChatQA datasets and training recipes. Additionally, they employed synthetic data generation to address gaps that could not be fully resolved with available real data.
Model inputs and outputs
Inputs
- Text: The model takes natural language text as input, which can include questions, instructions, or dialogue.
Outputs
- Text: The model generates relevant and coherent natural language responses to the input text.
Capabilities
The Llama3-DocChat-1.0-8B model excels at conversational question answering tasks, particularly when the context is provided in the form of documents. It can understand and respond to queries that require reasoning over the provided information, and it outperforms several popular models on relevant benchmarks.
What can I use it for?
The Llama3-DocChat-1.0-8B model can be used to build applications that involve document-based question answering, such as:
- Customer support: Enabling users to ask questions and get answers based on product manuals, FAQs, or other relevant documentation.
- Research assistance: Helping researchers find relevant information and answer questions based on a corpus of academic papers or reports.
- Intelligent search: Enhancing search experiences by providing direct answers to queries, rather than just a list of relevant documents.
Things to try
One interesting aspect of the Llama3-DocChat-1.0-8B model is its ability to handle multi-turn conversations. By leveraging the provided context, the model can engage in a back-and-forth dialogue, building upon previous exchanges to provide more comprehensive and relevant responses. Developers can explore ways to incorporate this capability into their applications to create more natural and helpful conversational experiences.
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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