vicuna-13b-v1.3
Maintainer: lmsys
190
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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 vicuna-13b-v1.3 model is a large language model developed by LMSYS that has been fine-tuned on user-shared conversations collected from ShareGPT. It is an auto-regressive language model based on the transformer architecture, built by fine-tuning the LLaMA model. This model is available in several variants, including vicuna-7b-v1.3, vicuna-13b-v1.1, vicuna-7b-v1.1, and vicuna-33b-v1.3, which differ in their size and training details.
Model inputs and outputs
The vicuna-13b-v1.3 model is a text-to-text model, taking in natural language text as input and generating natural language text as output. It can be used for a variety of tasks, such as question answering, text generation, and dialogue.
Inputs
- Natural language text prompts
Outputs
- Natural language text responses
Capabilities
The vicuna-13b-v1.3 model has been trained to engage in open-ended dialogue and assist with a wide range of tasks. It can answer questions, provide explanations, and generate creative content. The model has shown strong performance on various benchmarks and is particularly capable at understanding and responding to user instructions.
What can I use it for?
The primary use of the vicuna-13b-v1.3 model is for research on large language models and chatbots. The model is intended to be used by researchers and hobbyists in natural language processing, machine learning, and artificial intelligence. Potential use cases include building conversational AI assistants, language generation applications, and language understanding systems.
Things to try
Researchers and developers can experiment with the vicuna-13b-v1.3 model by integrating it into custom applications through the command line interface or API endpoints provided by the LMSYS team. The model can be used to prototype and test new ideas in the field of conversational AI, exploring its capabilities and limitations.
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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vicuna-13b-v1.1
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The vicuna-13b-v1.1 is a large language model developed by LMSYS, a leading AI research organization. It is a fine-tuned version of the LLaMA model, trained on user conversations collected from ShareGPT to create a versatile chat assistant. Similar Vicuna models are available in different sizes, such as the vicuna-7b-v1.3, vicuna-33b-v1.3, and vicuna-13b-v1.5-16k, which offer a range of model sizes and capabilities. Model inputs and outputs The vicuna-13b-v1.1 model is an auto-regressive language model, meaning it generates text one token at a time, conditioned on the previous tokens. The model takes in natural language text as input and outputs generated text, making it suitable for a wide range of natural language processing tasks. Inputs Natural language text prompts Outputs Coherent, contextually-appropriate text generated one token at a time Capabilities The vicuna-13b-v1.1 model is capable of engaging in open-ended dialogue, answering questions, summarizing text, and generating creative content like stories and poems. It has been trained to provide helpful, detailed, and polite responses, making it suitable for use as a conversational AI assistant. What can I use it for? The primary use case for the vicuna-13b-v1.1 model is 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 topics like language generation, question answering, and interactive AI assistants. Additionally, the model could be fine-tuned for specific applications like customer service chatbots, writing assistants, or educational tools. Things to try One interesting aspect of the vicuna-13b-v1.1 model is its ability to engage in multi-turn conversations and maintain context. Try prompting the model with a series of related questions or requests and observe how it carries the conversation forward, using relevant information from previous exchanges. You can also experiment with providing the model with specific instructions or personas to see how it adapts its language and behavior accordingly.
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vicuna-33b-v1.3
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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.
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