Vicuna-7B

Vicuna-7B-v1.5 is a chat assistant model designed for research in natural language processing, machine learning, and artificial intelligence. It is fine-tuned from Llama 2 using user-shared conversations collected from ShareGPT, offering researchers and hobbyists a powerful tool for exploring large language models and chatbots.

Key features:
- Fine-tuned Architecture: Based on Llama 2, utilizing the auto-regressive language model with transformer architecture.
- Research-oriented: Specifically designed for academic and experimental purposes in AI-related fields.
- Open-source Availability: Accessible through GitHub repository for collaborative development and research.
- Diverse Interfaces: Supports both command-line interface and API access for flexible integration.

How it works:
1. Access the model through the FastChat CLI or API interfaces.
2. Input text or queries for processing.
3. Receive generated responses based on the model's training.
4. Analyze outputs for research purposes or integrate into experimental applications.

Integrations:
OpenAI API, Huggingface API

Use of AI:
Vicuna-7B-v1.5 employs supervised instruction fine-tuning on a large dataset of user-shared conversations. This approach allows the model to generate human-like responses in a chat format, making it suitable for research on conversational AI and language understanding.

AI foundation model:
The foundation of Vicuna-7B-v1.5 is the Llama 2 model, which provides a strong base for natural language processing tasks. The fine-tuning process using ShareGPT conversations enhances its ability to engage in more specific and contextually relevant dialogues.

Target users:
- Researchers in natural language processing
- Machine learning enthusiasts
- Artificial intelligence developers
- Hobbyists exploring large language models

How to access:
Users can access Vicuna-7B-v1.5 through the FastChat command-line interface available on GitHub. Additionally, the model can be integrated using OpenAI and Huggingface APIs for more advanced applications and research projects.

Training details:
The model was trained on approximately 125,000 conversations collected from ShareGPT.com. Detailed information about the training process is available in the appendix of the associated research paper.

Evaluation methods:
- Standard benchmarks
- Human preference assessments
- LLM-as-a-judge methodologies

Licensing:
Vicuna-7B-v1.5 is released under the Llama 2 Community License Agreement, which governs its use and distribution.