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Stanford Alpaca is an open-source project that demonstrates the capabilities of an instruction-following LLaMA model. Developed by a team of researchers at Stanford University, Alpaca is designed to understand and execute tasks based on user instructions. The project provides a dataset, data generation process, and fine-tuning code for reproducibility.

TypeInstruction-Following LLaMA Model

Alpaca - Resources

Resources to get started with Stanford Alpaca:

  • Stanford Alpaca GitHub Repository
  • Release Blog Post Detailed information about the Alpaca model, its potential harm and limitations, and the team's thought process behind releasing a reproducible model.

Alpaca - General Information

  • Fine-tuned from a 7B LLaMA model on 52K instruction-following data
  • Built using techniques from the Self-Instruct paper with modifications
  • Capable of understanding and executing tasks based on user instructions
  • Provides a dataset, data generation process, and fine-tuning code for reproducibility

Alpaca - Setup

Setup instructions for Stanford Alpaca:

  • Data Generation Process Instructions on generating the instruction-following dataset used for fine-tuning the Alpaca model.
  • Fine-tuning A guide to fine-tune LLaMA and OPT models using the provided dataset and code.
  • Recovering Alpaca Weights Instructions for recovering the Alpaca-7B weights from the released weight diff.

Alpaca Hackathon projects

Discover innovative solutions crafted with Alpaca, developed by our community members during our engaging hackathons.

a Role-Playing Novel Game

a Role-Playing Novel Game

We are a team dedicated to creating immersive and interactive gaming experiences. Our latest project is a text-based role-playing novel game that allows users to interact with the game's characters and environment, shaping their own unique storylines. Our game aims to offer a rich narrative experience, and we believe that the Claude-100K model API can be a valuable tool in achieving this goal. We plan to use the model API to generate dynamic and engaging text-based interactions and narratives. This includes but is not limited to: Character Interactions: Utilizing the model API, we aim to create non-player characters (NPCs) that can carry out complex and engaging conversations with the player. These interactions will be context-aware and responsive to the player's inputs. Narrative Generation: The API will also be used to generate a diverse range of narratives, including descriptions of environments, events, and plot developments, based on the player's decisions and actions within the game. Decision Making: We also intend to use the API to help generate meaningful decision prompts for the player, providing a deeper level of immersion and engagement. Game Guidance: The API can be used to provide helpful tips and guidance to players, making the game more accessible to new players while also providing depth for experienced ones. By using the Claude-100K model API, we hope to provide a more immersive and engaging experience for our players, offering them a unique narrative journey in every playthrough. We are excited about the potential of this collaboration and look forward to the possibility of integrating the Claude-100K model into our game. Thank you for considering our application.