phi-2
Maintainer: microsoft
3.2K
🌀
| Property | Value |
|---|---|
| Model Link | View on HuggingFace |
| API Spec | View on HuggingFace |
| Github Link | No Github link provided |
| Paper Link | No paper link provided |
Create account to get full access
Model Overview
The phi-2 is a 2.7 billion parameter Transformer model developed by Microsoft. It was trained on an augmented version of the same data sources used for the Phi-1.5 model, including additional NLP synthetic texts and filtered websites. The model has demonstrated near state-of-the-art performance on benchmarks testing common sense, language understanding, and logical reasoning, among models with less than 13 billion parameters.
Similar models in the Phi family include the Phi-1.5 and Phi-3-mini-4k-instruct. The Phi-1.5 model has 1.3 billion parameters and was trained on a subset of the Phi-2 data sources. The Phi-3-mini-4k-instruct is a 3.8 billion parameter model that has been fine-tuned for instruction following and safety.
Model Inputs and Outputs
The phi-2 model takes text as input and generates text as output. It is designed to handle prompts in a variety of formats, including question-answering (QA), chat-style conversations, and code generation.
Inputs
- Text prompts: The model can accept freeform text prompts, such as questions, statements, or instructions.
Outputs
- Generated text: The model produces text continuations in response to the input prompt, with capabilities spanning tasks like answering questions, engaging in dialogues, and generating code.
Capabilities
The phi-2 model has shown impressive performance on a range of natural language understanding and reasoning tasks. It can provide detailed analogies, maintain coherent conversations, and generate working code snippets. The model's strength lies in its ability to understand context and formulate concise, relevant responses.
What can I use it for?
The phi-2 model is well-suited for research projects and applications that require a capable, open-source language model. Potential use cases include virtual assistants, dialogue systems, code generation tools, and educational applications. Due to the model's strong reasoning abilities, it could also be valuable for tasks like question-answering, logical inference, and common sense reasoning.
Things to try
One interesting aspect of the phi-2 model is its attention overflow issue when used in FP16 mode. Users can experiment with enabling or disabling autocast on the PhiAttention.forward() function to see if it resolves any performance issues. Additionally, the model's capabilities in handling different input formats, such as QA, chat, and code, make it a versatile tool for exploring language model applications across a variety of domains.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
🌀
phi-2
51
The phi-2 model is a Transformer with 2.7 billion parameters, developed by the mlx-community. It was trained using the same data sources as the Phi-1.5 model, with an additional new data source consisting of various NLP synthetic texts and filtered websites. When assessed against benchmarks testing common sense, language understanding, and logical reasoning, Phi-2 showcased nearly state-of-the-art performance among models with less than 13 billion parameters. Unlike models fine-tuned through reinforcement learning from human feedback, Phi-2 has not undergone this process. The goal in creating this open-source model is to provide the research community with a non-restricted small model to explore vital safety challenges, such as reducing toxicity, understanding societal biases, and enhancing controllability. Model Inputs and Outputs The phi-2 model can accept text inputs and generate text outputs. It is particularly well-suited for prompts using the QA format, the chat format, and the code format. Inputs Text**: The model can accept various types of text inputs, such as questions, instructions, or prompts. Outputs Text**: The model generates fluent text responses based on the provided input. Capabilities The phi-2 model has demonstrated strong performance on benchmarks testing common sense, language understanding, and logical reasoning. It can be used to generate high-quality text in a variety of formats, including question-answering, chatbot conversations, and code generation. What Can I Use It For? The phi-2 model is intended for research purposes only. It can be a useful tool for exploring safety challenges in language models, such as reducing toxicity and understanding societal biases. Researchers can use the model to investigate ways to enhance controllability and align large language models with human preferences. Things to Try One interesting thing to try with the phi-2 model is to experiment with different input formats and prompts to see how it responds. For example, you could try providing the model with a QA-style prompt, a chat-style prompt, and a code generation prompt to compare its performance across different use cases. Another idea is to explore the model's capabilities in generating text that is aligned with human values and preferences, and to investigate ways to further enhance its safety and controllability. The open-source nature of the phi-2 model makes it a valuable resource for the research community to advance the field of safe and responsible AI development.
Updated Invalid Date
🌀
phi-2
132
phi-2 is a 2.7 billion parameter language model from Microsoft's Skunkworks AI team. It builds upon their previous phi-1.5 model, using the same data sources augmented with new synthetic data and filtered web content. When tested on benchmarks of common sense, language understanding, and logical reasoning, phi-2 demonstrated state-of-the-art performance among models under 13 billion parameters. Unlike phi-1.5, phi-2 has not been fine-tuned for instruction following or through reinforcement learning from human feedback. Instead, the goal is to provide the research community with a non-restricted small model to explore safety challenges like reducing toxicity, understanding biases, and enhancing controllability. Model inputs and outputs Inputs Text prompts in a variety of formats, including question-answer, chat, and code Outputs Generated text responses to the input prompts Capabilities phi-2 exhibits strong performance on language tasks like question answering, dialogue, and code generation. However, it may produce inaccurate statements or code snippets, so users should treat the outputs as starting points rather than definitive solutions. The model also struggles with adhering to complex instructions, as it has not been fine-tuned for this purpose. What can I use it for? As an open-source research model, phi-2 is intended for exploring model safety and capabilities, rather than direct deployment in production applications. Researchers can use it to study techniques for reducing toxicity, mitigating biases, and improving controllability of language models. Developers may also find it useful as a building block for prototyping conversational AI features, though they should be cautious about relying on the model's outputs without thorough verification. Things to try One interesting aspect of phi-2 is its ability to generate code in response to prompts. Developers can experiment with giving the model code-related prompts, such as asking it to write a function to solve a specific problem. However, they should be mindful of the model's limitations in this area and verify the generated code before using it.
Updated Invalid Date
➖
phi-1_5
1.3K
phi-1.5 is a 1.3 billion parameter Transformer language model developed by Microsoft. It was trained on the same data sources as the phi-1 model, with an additional synthetic NLP data source. The model demonstrates state-of-the-art performance on benchmarks testing common sense, language understanding, and logical reasoning, compared to other models under 10 billion parameters. Unlike phi-1, phi-1.5 was not fine-tuned for instruction following or through reinforcement learning from human feedback. Instead, the intention was to provide the research community with an open-source small model to explore vital safety challenges, such as reducing toxicity, understanding societal biases, and enhancing controllability. The model's training data was carefully curated to exclude generic web-crawl sources, which helps prevent direct exposure to potentially harmful online content. However, the model is still vulnerable to generating harmful content, and the researchers hope the model can help further study the safety of language models. Model inputs and outputs Inputs Text prompts in a variety of formats, including QA, chat, and code Outputs Generative text responses, such as poems, emails, stories, summaries, and Python code Capabilities phi-1.5 can perform a wide range of natural language generation tasks, including writing poems, drafting emails, creating stories, summarizing texts, and generating Python code. The model is particularly well-suited for prompts in the QA, chat, and code formats. What can I use it for? The phi-1.5 model can be useful for researchers and developers exploring language model safety challenges, such as reducing toxicity, understanding biases, and enhancing controllability. The model's open-source nature and relatively small size make it an accessible option for these types of investigations. Things to try One interesting aspect of phi-1.5 is its exclusion of generic web-crawl data sources during training, which aims to prevent direct exposure to potentially harmful online content. Researchers could explore how this design choice affects the model's behavior and safety compared to models trained on broader web data. Another area to investigate is the model's performance on prompts that require logical reasoning or common sense understanding, given its strong results on related benchmarks. Developers could experiment with using phi-1.5 for applications that rely on these cognitive capabilities.
Updated Invalid Date
📶
phi-1
194
The phi-1 language model is a 1.3 billion parameter Transformer developed by Microsoft. It was trained on a variety of data sources, including Python code, Q&A content, competition code, and synthetic text. While the model and dataset are relatively small compared to contemporary large language models, phi-1 has demonstrated over 50% accuracy on the HumanEval coding benchmark, an impressive result. The model's capabilities are similar to other specialized coding models like phi-1.5 and Phi-3-Mini-4K-Instruct, with a focus on generating and understanding Python code. However, phi-1 is a more lightweight and constrained model compared to these later iterations. Model inputs and outputs Inputs Code format**: The model is best suited for prompts that provide a code template with comments, like the example given in the description. Natural language**: The model can also handle more open-ended natural language prompts, though its capabilities may be more limited outside of the code domain. Outputs The model generates continuations of the provided code template, attempting to complete the function or script. For more open-ended prompts, the model will generate text responses, though these may not be as reliable or coherent as the code outputs. Capabilities phi-1 has shown strong performance on coding-related tasks, particularly in generating and reasoning about simple Python scripts. However, as a smaller and more constrained model, its capabilities outside of the coding domain are more limited compared to larger language models. What can I use it for? Given its specialized training, phi-1 is best suited for projects that involve generating or understanding Python code, such as: Automatically generating code snippets or simple scripts based on natural language descriptions Assisting with coding tasks by providing template code or completing partially written functions Supplementing other tools and workflows that involve Python programming Things to try One interesting experiment would be to provide the model with more complex coding prompts or challenges, beyond the simple example shown, to see how it handles more sophisticated programming tasks. Additionally, exploring the model's robustness and safety considerations, such as its ability to generate accurate and secure code, could yield valuable insights.
Updated Invalid Date