AlekseyKorshuk/vicuna-7b
AlekseyKorshuk/vicuna-7b is a 7 billion parameter, auto-regressive language model based on the transformer architecture, fine-tuned by AlekseyKorshuk. This variant of Vicuna 7B was trained on ShareGPT data without the "ethics" filtering applied to the original model. It is primarily intended for research and hobbyist use in large language models and chatbots, offering an unfiltered conversational experience.
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Overview
AlekseyKorshuk/vicuna-7b is a 7 billion parameter, auto-regressive language model built on the transformer architecture. It is a fine-tuned version of the original Vicuna 7B model, developed by AlekseyKorshuk. The key differentiator of this model is its training approach: it was fine-tuned using user-shared conversations from ShareGPT data, specifically without the "ethics" filtering present in the original Vicuna release. This makes it an alternative for users seeking a less constrained conversational AI.
Key Capabilities
- Conversational AI: Designed for chatbot interactions, leveraging fine-tuning on diverse user-shared conversations.
- Unfiltered Responses: Provides an alternative to models with built-in ethical constraints, offering raw outputs based on its training data.
- Research and Development: Primarily intended for researchers and hobbyists exploring large language models and chatbot behavior without content moderation.
Training Details
- Base Model: Fine-tuned from the LLaMA architecture.
- Training Data: Utilizes 70,000 conversations collected from ShareGPT.com.
- Original Development: The base Vicuna model was developed by a team from UC Berkeley, CMU, Stanford, and UC San Diego.
Intended Use Cases
- LLM Research: Ideal for studying the behavior of language models when ethical filtering is absent.
- Chatbot Experimentation: Suitable for hobbyists and developers experimenting with chatbot responses and capabilities in a less restricted environment.