Phi 1 5

🚀 Phi-1.5 Language Model

Phi-1.5 is a 1.3-billion parameter Transformer model, offering high performance in common sense, language understanding, and logical reasoning. It can handle various text generation tasks like writing poems, code, etc., and aims to assist the research community in studying language model safety.

🚀 Quick Start

Phi-1.5 has been integrated in the transformers version 4.37.0, please ensure that you are using a version equal or higher than it.

✨ Features

• High Performance: Demonstrates nearly state-of-the-art performance among models with less than 10 billion parameters in benchmarks testing common sense, language understanding, and logical reasoning.
• Diverse Text Generation: Can write poems, draft emails, create stories, summarize texts, write Python code (such as downloading a Hugging Face transformer model), etc.
• Research-Oriented: Intended to help the research community study the safety of language models, such as reducing toxicity and understanding societal biases.

📦 Installation

Phi-1.5 has been integrated in the transformers version 4.37.0, please ensure that you are using a version equal or higher than it.

💻 Usage Examples

Basic Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

torch.set_default_device("cuda")

model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1_5", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1_5")

inputs = tokenizer('''def print_prime(n):
   """
   Print all primes between 1 and n
   """''', return_tensors="pt", return_attention_mask=False)

outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)

Advanced Usage

QA Format

Write a detailed analogy between mathematics and a lighthouse.

Answer: Mathematics is like a lighthouse, guiding us through the vast ocean of numbers and calculations. Just as a lighthouse illuminates the darkness, mathematics provides us with a clear path to navigate through complex problems. It helps us make sense of the world around us, just like a lighthouse helps ships find their way home.

Chat Format

Alice: I don't know why, I'm struggling to maintain focus while studying. Any suggestions?

Bob: Have you tried using a timer? It can help you stay on track and avoid distractions.

Alice: That's a good idea. I'll give it a try.

Charlie: Another thing that can help is to break up your study sessions into smaller chunks. It's easier to concentrate on one thing at a time.

Alice: That makes sense. I'll try that too.

Bob: And don't forget to take breaks! It's important to give your brain a rest so you can come back to your studies with a fresh perspective.

Alice: Thanks for the advice, guys. I feel more motivated now.

Charlie: No problem, Alice. We're all in this together.

Bob: Yeah, and remember that it's okay to ask for help if you need it. We're here to support each other.

Code Format

def print_prime(n):
   """
   Print all primes between 1 and n
   """
   primes = []
   for num in range(2, n+1):
       is_prime = True
       for i in range(2, int(math.sqrt(num))+1):
           if num % i == 0:
               is_prime = False
               break
       if is_prime:
           primes.append(num)
   print(primes)

📚 Documentation

Intended Uses

Given the nature of the training data, Phi-1.5 is best suited for prompts using the QA format, the chat format, and the code format. Note that Phi-1.5, being a base model, often produces irrelevant text following the main answer.

Limitations of Phi-1.5

• Generate Inaccurate Code and Facts: The model often produces incorrect code snippets and statements. Users should treat these outputs as suggestions or starting points, not as definitive or accurate solutions.
• Limited Scope for code: If the model generates Python scripts that utilize uncommon packages or scripts in other languages, we strongly recommend users manually verify all API uses.
• Unreliable Responses to Instruction: The model has not undergone instruction fine-tuning. As a result, it may struggle or fail to adhere to intricate or nuanced instructions provided by users.
• Language Limitations: The model is primarily designed to understand standard English. Informal English, slang, or any other language outside of English might pose challenges to its comprehension, leading to potential misinterpretations or errors in response.
• Potential Societal Biases: Regardless of the safe data used for its training, the model is not entirely free from societal biases. There's a possibility it may generate content that mirrors these societal biases, particularly if prompted or instructed to do so. We urge users to be aware of this and to exercise caution and critical thinking when interpreting model outputs.
• Toxicity: Despite that the model is trained with carefully selected data, the model can still produce harmful content if explicitly prompted or instructed to do so. We chose to release the model for research purposes only -- We hope to help the open-source community develop the most effective ways to reduce the toxicity of a model directly after pretraining.

🔧 Technical Details

Training

Model

Software

• PyTorch
• DeepSpeed
• Flash-Attention

Citation

You can find the paper at https://arxiv.org/abs/2309.05463. Please cite as:

@article{textbooks2,
  title={Textbooks Are All You Need II: \textbf{phi-1.5} technical report},
  author={Li, Yuanzhi and Bubeck, S{\'e}bastien and Eldan, Ronen and Del Giorno, Allie and Gunasekar, Suriya and Lee, Yin Tat},
  journal={arXiv preprint arXiv:2309.05463},
  year={2023}
}

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft’s Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.

📄 License

The model is licensed under the MIT license.