vicuna-7b-v1.5
Maintainer: lmsys
240
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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-7b-v1.5 model is a chat assistant developed by LMSYS. It is an auto-regressive language model based on the transformer architecture, fine-tuned from Llama 2 on user-shared conversations collected from ShareGPT. The model aims to be useful for research on large language models and chatbots, with the primary intended users being researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
Similar models include the vicuna-33b-v1.3 which is also a Vicuna model fine-tuned from a larger LLaMA base, and the vicuna-13B-v1.5-16K-GGML which is a GGML version of the 13B Vicuna model, optimized for CPU and GPU inference.
Model inputs and outputs
Inputs
- Prompt: The model takes a free-form text prompt as input, which can be a question, instruction, or conversational message.
Outputs
- Text response: The model generates a coherent text response based on the input prompt. The response aims to be helpful, detailed, and polite.
Capabilities
The vicuna-7b-v1.5 model is capable of engaging in open-ended conversations on a wide range of topics. It can answer questions, provide explanations, and offer suggestions based on the input prompt. The model demonstrates strong performance on standard benchmarks, human preference tests, and LLM-as-a-judge evaluations, achieving around 90% of the quality of GPT-4 according to the Vicuna team.
What can I use it for?
The primary use case for the vicuna-7b-v1.5 model is research on large language models and chatbots. Researchers and hobbyists in natural language processing, machine learning, and artificial intelligence can experiment with the model, explore its capabilities, and use it as a starting point for further fine-tuning or development.
Things to try
One interesting aspect of the vicuna-7b-v1.5 model is its fine-tuning on user-shared conversations from ShareGPT. This means the model has been exposed to a diverse range of conversational styles and topics, which could allow it to engage in more natural and context-aware dialogue compared to models trained on more curated datasets. Experimenting with open-ended conversations on a variety of subjects could help uncover the model's strengths and limitations in real-world settings.
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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