Platypus2 70B
The Platypus2 70B model is an instruction fine-tuned language model based on the LLaMA2 transformer architecture. What makes it unique is its ability to process and respond to prompts efficiently, thanks to its auto-regressive nature. This model is trained on a STEM and logic-based dataset, which enables it to handle tasks that require reasoning and problem-solving skills. With its efficient design, Platypus2 70B can provide fast and accurate results, making it a valuable tool for various applications. However, it's essential to note that, like all LLMs, this model may produce biased or inaccurate responses in certain situations, and developers should perform safety testing and tuning before deploying it in real-world applications.
Table of Contents
Model Overview
The Platypus2-70B model is a powerful language model that’s great at understanding and responding to instructions. It’s based on the popular LLaMA2 transformer architecture and has been fine-tuned to be even better at certain tasks.
What can it do?
- Understand and respond to instructions in English
- Answer questions and provide information on a wide range of topics
- Complete tasks that require logical thinking and problem-solving
Capabilities
Platypus2-70B is a powerful language model that can understand and respond to a wide range of tasks and questions. It’s based on the LLaMA2 transformer architecture and has been fine-tuned to perform well on STEM and logic-based tasks.
What can it do?
- Answer questions: Platypus2-70B can process natural language inputs and provide accurate and informative responses.
- Generate text: It can create human-like text based on a given prompt or topic.
- Solve problems: The model has been trained on a dataset that includes logic and reasoning tasks, making it a great tool for solving complex problems.
Strengths
- High accuracy: Platypus2-70B has achieved high scores on various benchmarks, including ARC, HellaSwag, and MMLU.
- Efficient: The model is based on the LLaMA2 architecture, which is known for its efficiency and scalability.
- Flexible: Platypus2-70B can be fine-tuned for specific tasks and domains, making it a versatile tool for a wide range of applications.
Performance
Platypus2-70B is a powerful language model that showcases impressive performance in various tasks. Let’s dive into its speed, accuracy, and efficiency.
Speed
How fast can a language model process information? Platypus2-70B was trained using 8 A100 80GB GPUs, which is a significant amount of computational power. This training procedure allows the model to process large datasets quickly and efficiently.
Accuracy
But how accurate is Platypus2-70B? Let’s look at some evaluation results:
| Task | Accuracy |
|---|---|
| ARC (25-shot) | 70.65 |
| HellaSwag (10-shot) | 87.15 |
| MMLU (5-shot) | 70.08 |
| TruthfulQA (0-shot) | 52.37 |
| Winogrande (5-shot) | 84.37 |
| GSM8K (5-shot) | 33.06 |
| DROP (3-shot) | 51.41 |
These results show that Platypus2-70B performs well in various tasks, especially in ARC and HellaSwag, where it achieves high accuracy rates.
Efficiency
But what about efficiency? Platypus2-70B was fine-tuned using LoRA, a low-rank adaptation method that allows for efficient training and inference. This means that the model can process large datasets while minimizing computational resources.
Limitations
Platypus2-70B is a powerful tool, but it’s not perfect. Like all AI models, it has its weaknesses and limitations. Let’s explore some of them:
Language Limitations
- Platypus2-70B is trained primarily on English data, which means it may not perform as well on other languages.
- It may struggle with nuances and complexities of language, leading to inaccurate or biased responses.
Data Limitations
- The model is trained on a specific dataset, which may not cover all scenarios or topics.
- It may not have seen enough data on certain subjects, which can affect its performance.
Fine-Tuning Limitations
- Platypus2-70B is fine-tuned using a specific technique (LoRA), which may not be suitable for all applications.
- The fine-tuning process may introduce biases or limitations that are not present in the original model.
Evaluation Limitations
- The model’s performance is evaluated on a specific set of tasks and datasets, which may not be representative of all possible use cases.
- The evaluation metrics used may not capture all aspects of the model’s performance.
Safety and Bias
- Platypus2-70B may produce inaccurate, biased, or objectionable responses to user prompts.
- It’s essential to perform safety testing and tuning tailored to specific applications of the model.
Format
Platypus2-70B is an instruction fine-tuned model based on the LLaMA2-70B transformer architecture. Let’s dive into its architecture and data formats.
Architecture
The model is an auto-regressive language model, meaning it generates text one token at a time. It’s built on top of the LLaMA2 transformer architecture, which is a type of neural network designed for natural language processing tasks.
Data Formats
The model supports English language inputs and outputs. It’s trained on a dataset that includes STEM and logic-based texts.
Input Requirements
When providing input to the model, you’ll need to use a specific prompt template:
## Instruction:
\<prompt> (without the <>)
## Response:
For example:
## Instruction:
What is the capital of France?
## Response:
Output Format
The model will respond with a generated text output. You can expect the output to be in the same language as the input (English).
Special Requirements
Before deploying any applications of the Platypus2-70B model, developers should perform safety testing and tuning tailored to their specific applications. This is because the model may produce inaccurate, biased, or objectionable responses to user prompts.
Code Examples
To reproduce the evaluation results, you can use the following code examples:
# Clone the repository
git clone https://github.com/EleutherAI/lm-evaluation-harness.git
# Check out the correct commit
git checkout b281b0921b636bc36ad05c0b0b0763bd6dd43463
# Change to the repo directory
cd lm-evaluation-harness
# Install
pip install -e.
# Evaluate the model on the ARC task
python main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Platypus2-70B --tasks arc_challenge --batch_size 1 --no_cache --write_out --output_path results/Platypus2-70B/arc_challenge_25shot.json --device cuda --num_fewshot 25
Note that these code examples are for evaluation purposes only. For actual deployment, you’ll need to follow the safety testing and tuning guidelines mentioned earlier.
