vicuna-13b-delta-v1.1
Maintainer: lmsys
411
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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
vicuna-13b-delta-v1.1 is a large language model developed by LMSYS. It is fine-tuned from the LLaMA model and trained on user-shared conversations collected from ShareGPT. This "delta model" cannot be used directly, but rather must be applied on top of the original LLaMA weights to get the actual Vicuna weights. Similar models include vicuna-13b-delta-v0, vicuna-7b-delta-v0, vicuna-13b-v1.1, and vicuna-7b-v1.3.
Model inputs and outputs
vicuna-13b-delta-v1.1 is an auto-regressive language model that takes in text and generates new text. It can be used for a variety of natural language processing tasks such as text generation, question answering, and conversational AI.
Inputs
- Text prompts
Outputs
- Generated text
Capabilities
vicuna-13b-delta-v1.1 has been trained to engage in open-ended dialogue and assist with a wide range of tasks. It demonstrates strong language understanding and generation capabilities, allowing it to provide informative and coherent responses. The model can be used for research on large language models and chatbots.
What can I use it for?
The primary use of vicuna-13b-delta-v1.1 is for research on large language models and chatbots. Researchers and hobbyists in natural language processing, machine learning, and artificial intelligence can use the model to explore advancements in these fields. To get started, users can access the model through the command line interface or APIs provided by the maintainer.
Things to try
Experiment with the model's language generation capabilities by providing it with a variety of prompts and observing the outputs. Assess the model's performance on natural language tasks and compare it to other language models. Explore ways to fine-tune or adapt the model for specific applications or domains.
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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