GPT-NeoXT-Chat-Base-20B
Maintainer: togethercomputer
694
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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
GPT-NeoXT-Chat-Base-20B is a 20 billion parameter language model developed by Together Computer. It is based on EleutherAI's GPT-NeoX model and has been fine-tuned on over 43 million high-quality conversational instructions. The fine-tuning process focused on tasks such as question answering, classification, extraction, and summarization. Additionally, the model has undergone further fine-tuning on a small amount of feedback data to better adapt to human preferences in conversations.
Model Inputs and Outputs
Inputs
- Text prompt to generate a response from the model
Outputs
- Generated text continuation of the input prompt
Capabilities
GPT-NeoXT-Chat-Base-20B is capable of engaging in open-ended dialog, answering questions, and generating human-like text across a variety of topics. Its fine-tuning on conversational data allows it to produce more coherent and contextually appropriate responses compared to a general language model.
What Can I Use It For?
The GPT-NeoXT-Chat-Base-20B model can be used as a foundation for building conversational AI applications, such as chatbots, virtual assistants, and interactive educational tools. Its large size and specialized training make it well-suited for tasks that require in-depth understanding and generation of natural language.
You can fine-tune this model further on domain-specific data to create custom AI assistants for your business or organization. The OpenChatKit feedback app provided by the maintainers is a good starting point to experiment with the model's capabilities.
Things to Try
Try using the model to engage in open-ended dialog on a wide range of topics. Observe how it maintains context and coherence across multiple turns of conversation. You can also experiment with different prompting techniques, such as providing detailed instructions or personas, to see how the model adapts its responses accordingly.
Another interesting aspect to explore is the model's ability to perform tasks like question answering, text summarization, and content generation. Provide the model with appropriate prompts and evaluate the quality and relevance of its outputs.
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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