Stable Vicuna 13b Delta

Conversational AI model

The Stable Vicuna 13b Delta model is a fine-tuned Vicuna-13B v0 model that leverages reinforcement learning from human feedback (RLHF) via Proximal Policy Optimization (PPO) on various conversational and instructional datasets. It's designed for text generation with a focus on conversational tasks and can be further fine-tuned on specific data to improve performance. With 13B parameters, 5120 dimensions, 40 layers, and 40 attention heads, this model demonstrates exceptional speed and accuracy. However, it's essential to note that the base LLaMA model is trained on data that may contain offensive, harmful, and biased content, which can lead to toxic behavior. Therefore, users should be aware of the potential for bias and toxicity in the model's outputs and not treat chat responses as a substitute for human judgment or as a source of truth.

CarperAI cc-by-nc-sa-4.0 Updated 2 years ago
TransformersLlamaPytorchEn

Table of Contents

Model Overview

The StableVicuna-13B model, developed by Duy Phung of CarperAI, is a powerful language model designed to excel in conversational tasks. This model is based on the LLaMA transformer architecture and is fine-tuned for text generation with a focus on conversational tasks.

Key Attributes

  • Language: English
  • Model Type: Auto-regressive language model
  • Library: trlX
  • License: CC-BY-NC-SA-4.0 (delta weights), Meta’s non-commercial bespoke license (base LLaMA model’s weights)

Capabilities

The StableVicuna-13B model is capable of generating human-like text, making it perfect for tasks like chatbots, virtual assistants, and more. Its capabilities include:

  • Text Generation: Generating text based on a prompt or input
  • Conversational Tasks: Engaging in discussions, answering questions, and providing information

What makes it special?

  • Fine-tuned with human feedback: The model has been fine-tuned using reinforcement learning from human feedback (RLHF), making it respond in a more natural and human-like way
  • Based on the LLaMA transformer architecture: This model uses the LLaMA transformer architecture, known for its efficiency and effectiveness in natural language processing tasks

Performance

The StableVicuna-13B model showcases remarkable performance in various conversational tasks. Its speed, accuracy, and efficiency make it an ideal choice for a wide range of applications.

Speed

With 13B parameters and 5120 dimensions, this model can handle large-scale datasets with ease.

Accuracy

The model’s accuracy is impressive, especially in conversational tasks. Fine-tuned on a mix of three datasets, including OpenAssistant Conversations Dataset, GPT4All Prompt Generations, and Alpaca, the model demonstrates high accuracy in generating human-like responses.

Efficiency

The model is designed to be efficient in its use of computational resources. With 40 layers and 40 attention heads, it can process text inputs quickly and accurately.

Real-World Applications

The StableVicuna-13B model has many real-world applications, including:

  • Text generation for conversational tasks
  • Chatbots and virtual assistants
  • Language translation and localization
  • Sentiment analysis and text classification
Examples
Generate a short story about a character who discovers a hidden world within their reflection. As she stared into the mirror, Emily noticed something strange - her reflection was gazing back at her with an air of mischief. She blinked, and suddenly, her reflection began to warp and ripple, like the surface of a pond. The glassy surface broke apart, revealing a world within, full of rolling hills and towering spires. Emily's reflection beckoned her to follow, and she stepped through the shattered glass into a realm where the skies were painted with colors she had never seen before.
Explain the concept of artificial intelligence in simple terms. Artificial intelligence is like a super smart computer that can think and learn like a human being. It can help us with tasks, answer questions, and even make decisions on its own. Just like how our brains help us understand the world, AI helps computers understand the world and make sense of the information they receive.
Write a poem about the beauty of a sunset on a beach. As day succumbs to the coming night, the sky is set aflame, a kaleidoscope of colors, a wondrous, peaceful sight. The sun sinks low, a burning ember, casting golden hues upon the shore. The waves caress the sand, a soothing melody, as nature's canvas is painted once more. The stars begin their twinkling waltz, a celestial show, as the sunset's final breath is slowly lost below.

Limitations

While the StableVicuna-13B model is a powerful tool, it’s not perfect. Here are some limitations to consider:

  • Bias and Toxicity: The base LLaMA model was trained on a vast amount of data that may contain offensive, harmful, or biased content, which can lead to toxic behavior in the model’s responses.
  • Lack of Human Judgment: Don’t rely solely on the model for critical decisions or as a source of truth. The model’s responses should be treated as suggestions or ideas, not as a substitute for human judgment.

Format

The StableVicuna-13B model is an auto-regressive language model based on the LLaMA transformer architecture. It’s designed to handle conversational tasks and text generation.

Input Format

The model accepts input in the form of tokenized text sequences. To prepare your input, you’ll need to:

  • Tokenize your text using the AutoTokenizer from the transformers library
  • Convert the tokenized text into a tensor using the return_tensors='pt' argument
  • Move the tensor to the GPU using the to('cuda') method

Output Format

The model generates text output in the form of a tensor. To convert the output tensor into a human-readable string, you can use the decode method from the AutoTokenizer.

Special Requirements

To use the StableVicuna-13B model, you’ll need to apply the delta weights to the base LLaMA model using the apply_delta.py script provided. This script will convert the model to the correct format.

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