longchat-7b-16k
Maintainer: lmsys
49
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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
longchat-7b-16k is an open-source chatbot model developed by the LongChat team. It was created by fine-tuning the LLAMA-7B model on a dataset of 80K conversations collected from ShareGPT.com. The model uses the condensing rotary embedding technique, which is described in the LongChat blog post. Similar models include the longchat-13b-16k and the fastchat-t5-3b-v1.0, all of which were developed by the LongChat team.
Model inputs and outputs
The longchat-7b-16k model is a text-to-text model, meaning it takes text as input and generates text as output. The input can be a prompt or question, and the output is the model's response.
Inputs
- Text prompts or questions
Outputs
- Generated text responses
Capabilities
The longchat-7b-16k model is capable of engaging in open-ended conversations on a variety of topics. It can understand context and provide relevant and coherent responses based on the input. The model has been evaluated using the LongEval benchmark, which measures the model's ability to maintain context and provide informative responses.
What can I use it for?
The primary use case for longchat-7b-16k is research in natural language processing, machine learning, and artificial intelligence. Researchers in these fields may use the model to explore language understanding, generation, and dialogue systems. The model may also be useful for applications such as chatbots, virtual assistants, and text generation.
Things to try
Researchers can fine-tune the longchat-7b-16k model on their own datasets to adapt it for specific tasks or domains. The model can also be used in conjunction with other language models or components to create more sophisticated conversational systems. Developers may find the model useful for building chatbots or other interactive applications that require natural language understanding and generation.
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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