Stable Platypus2 13B
Stable Platypus2 13B is an auto-regressive language model that combines the strengths of Platypus2-13B and StableBeluga-13B. It's built on the LLaMA 2 transformer architecture and is trained on a STEM and logic-based dataset. This model is designed to be efficient and fast, with the ability to handle tasks like text generation and conversation. Its unique blend of models allows it to provide accurate and informative responses. However, like all LLMs, it may produce biased or inaccurate responses in certain situations. With its English language capabilities and instruction fine-tuning using LoRA, Stable Platypus2 13B is a powerful tool for various applications, but it's essential to perform safety testing and tuning before deployment.
Table of Contents
Model Overview
Meet the Stable-Platypus2-13B model, a cutting-edge language model that’s changing the game! This model is a merge of two powerful models. Let’s dive into what makes it special.
What is it?
The Stable-Platypus2-13B model is an auto-regressive language model based on the LLaMA 2 transformer architecture. In simple terms, it’s a type of artificial intelligence designed to understand and generate human-like language.
What languages does it support?
This model is trained on English language data, making it a great tool for tasks that require understanding and generating text in English.
How was it trained?
The model was trained using a combination of STEM and logic-based datasets. This training data helps the model learn to recognize patterns and relationships in language.
What are its limitations?
While the Stable-Platypus2-13B model is incredibly powerful, it’s not perfect. Like all language models, it may produce inaccurate, biased, or objectionable responses to certain prompts. Developers should perform safety testing and tuning before deploying applications that use this model.
Capabilities
The Stable-Platypus2-13B model is a powerful auto-regressive language model that can perform a variety of tasks. Let’s dive into its capabilities:
Primary Tasks
- Text Generation: The model can generate human-like text based on a given prompt.
- Code Generation: It can also generate code in various programming languages.
- Conversational AI: The model can engage in conversations and respond to questions and statements.
Strengths
- High Accuracy: The model has achieved high accuracy on several benchmarks.
- Efficient Training: The model was trained using the LoRA method, which allows for efficient adaptation of large language models.
- Multitask Learning: The model can perform multiple tasks simultaneously, such as generating text and code.
Unique Features
- Stability and Reliability: The model is designed to be stable and reliable, with a focus on producing accurate and consistent results.
- Flexibility: The model can be fine-tuned for specific tasks and domains, making it a versatile tool for a wide range of applications.
Performance
Stable-Platypus2-13B is a powerful language model that shows remarkable performance in various tasks. Let’s dive into its speed, accuracy, and efficiency.
Speed
How fast can Stable-Platypus2-13B process information? The model was trained using a single A100 80GB GPU, which is a powerful tool for processing large amounts of data. This means that Stable-Platypus2-13B can handle complex tasks quickly and efficiently.
Accuracy
How accurate is Stable-Platypus2-13B? The model has been evaluated on several tasks, including:
| Task | Score |
|---|---|
| ARC (25-shot) | 62.71 |
| HellaSwag (10-shot) | 82.29 |
| MMLU (5-shot) | 58.3 |
| TruthfulQA (0-shot) | 52.52 |
These results show that Stable-Platypus2-13B is highly accurate in various tasks.
Efficiency
How efficient is Stable-Platypus2-13B? The model uses a technique called LoRA (Low-Rank Adaptation), which allows it to adapt to new tasks quickly and efficiently.
Limitations
Stable-Platypus2-13B is a powerful tool, but it’s not perfect. Like all AI models, it has its weaknesses and limitations.
Language Limitations
Stable-Platypus2-13B is trained primarily on English data, which means it may not perform as well on other languages.
Bias and Inaccuracy
As with all LLMs, Stable-Platypus2-13B may produce biased or inaccurate responses to certain prompts.
Lack of Common Sense
While Stable-Platypus2-13B is excellent at generating human-like text, it may not always understand the nuances of human behavior or common sense.
Format
Stable-Platypus2-13B is an auto-regressive language model based on the LLaMA 2 transformer architecture. This means it’s designed to process and generate text, one token at a time.
Supported Data Formats
Stable-Platypus2-13B accepts input in the form of plain text, which is then tokenized and processed by the model.
Input Requirements
To use Stable-Platypus2-13B, you’ll need to provide input in the following format:
\<prompt>
Where \<prompt> is the text you want the model to process.
Output Format
The model will respond with a generated text sequence, which can be a single word, a sentence, or even a longer passage of text.
Getting Started
Want to try out the Stable-Platypus2-13B model for yourself? You can install the LM Evaluation Harness and run the model on your own machine using the instructions provided.
Here’s an example of how you might use Stable-Platypus2-13B in Python:
import torch
from transformers import StablePlatypus2_13BTokenizer, StablePlatypus2_13BModel
# Load the model and tokenizer
tokenizer = StablePlatypus2_13BTokenizer.from_pretrained('garage-bAInd/Stable-Platypus2-13B')
model = StablePlatypus2_13BModel.from_pretrained('garage-bAInd/Stable-Platypus2-13B')
# Define a prompt
prompt = "What is the capital of France?"
# Tokenize the prompt
inputs = tokenizer(prompt, return_tensors='pt')
# Generate a response
outputs = model.generate(inputs['input_ids'], max_length=50)
# Print the response
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
This code loads the Stable-Platypus2-13B model and tokenizer, defines a prompt, tokenizes the prompt, generates a response, and prints the response.
