vicuna-33b-v1.3

Maintainer: lmsys

Total Score

285

Last updated 5/28/2024

๐Ÿงช

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

vicuna-33b-v1.3 is an open-source chatbot developed by the Vicuna team at LMSYS. It is an auto-regressive language model based on the transformer architecture, fine-tuned from the LLaMA model on user-shared conversations collected from ShareGPT. This model builds upon the capabilities of LLaMA with additional training to improve its conversational abilities.

Similar models include the vicuna-13b-v1.5-16K and stable-vicuna-13B-HF, which are also fine-tuned versions of LLaMA with different training data and techniques.

Model inputs and outputs

Inputs

  • Text prompts: The model takes text prompts as input, which can be questions, instructions, or conversational starters.

Outputs

  • Generated text: The model generates coherent and contextual text responses based on the input prompt. The responses aim to be helpful, detailed, and polite.

Capabilities

vicuna-33b-v1.3 is capable of engaging in open-ended conversations, answering questions, and providing informative responses on a wide range of topics. It demonstrates strong language understanding and generation abilities, with the potential to assist users with tasks such as research, analysis, and creative writing.

What can I use it for?

The primary intended use of vicuna-33b-v1.3 is for research on large language models and chatbots. Researchers and hobbyists in natural language processing, machine learning, and artificial intelligence can use this model to explore advancements in conversational AI. Additionally, the model could be fine-tuned or integrated into various applications that require natural language interactions, such as virtual assistants, customer service chatbots, or educational tools.

Things to try

One interesting aspect of vicuna-33b-v1.3 is its ability to engage in back-and-forth conversations, where it can understand and respond to context. Users can try asking follow-up questions or providing additional context to see how the model adapts its responses. Additionally, users can experiment with different prompting strategies, such as using specific instructions or framing the interaction as a collaborative task, to further explore the model's capabilities.



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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