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Vicuna-13B

by Various

We introduce Vicuna-13B, an open-source chatbot trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. Preliminary evaluation using GPT-4 as a judge s

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Vicuna-13B

Added 1 June 2026

Overview

Vicuna-13B is an open-source chatbot created by fine-tuning the LLaMA model on 70,000 user-shared conversations from ShareGPT. It provides a free, locally runnable alternative to proprietary chat assistants, with weights and code released publicly under a noncommercial license.

Best for

Best for
Researchers, hobbyists, and developers seeking a free, locally deployable chatbot for experimentation and noncommercial use.

Use cases

  • Running a conversational AI assistant on local hardware without cloud dependency
  • Experimenting with instruction-tuned language model research and fine-tuning
  • Building custom chatbot applications with open weights and community tooling

Notes

Vicuna-13B is an open-source chatbot created by fine-tuning the LLaMA model on 70,000 user-shared conversations from ShareGPT. It provides a free, locally runnable alternative to proprietary chat assistants, with weights and code released publicly under a noncommercial license.

Use cases

  • Running a conversational AI assistant on local hardware without cloud dependency
  • Experimenting with instruction-tuned language model research and fine-tuning
  • Building custom chatbot applications with open weights and community tooling

Pros

  • Open-source weights and code enable full transparency and customization
  • Trains on real user conversations, producing more natural dialogue than synthetic data models
  • Can run on consumer GPUs, lowering the barrier to deployment

Cons

  • Noncommercial license restricts use in for-profit or corporate products
  • Performance lags behind larger commercial models like GPT-3.5 or GPT-4
  • Dependent on user-shared conversation quality, which can introduce inconsistency or bias

Indexed from awesome-generative-ai and enriched against its public facts.

Pros

  • Open-source weights and code enable full transparency and customization
  • Trains on real user conversations, producing more natural dialogue than synthetic data models
  • Can run on consumer GPUs, lowering the barrier to deployment

Cons

  • Noncommercial license restricts use in for-profit or corporate products
  • Performance lags behind larger commercial models like GPT-3.5 or GPT-4
  • Dependent on user-shared conversation quality, which can introduce inconsistency or bias