Phi-3-mini-4k-instruct-gguf

Maintainer: microsoft

Total Score

348

Last updated 5/28/2024

🔄

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

Create account to get full access

or

If you already have an account, we'll log you in

Model overview

The Phi-3-mini-4k-instruct is a 3.8 billion-parameter, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and filtered publicly available websites data with a focus on high-quality and reasoning dense properties. The model belongs to the Phi-3 family, with the Mini version available in two variants - 4K and 128K - which is the context length (in tokens) it can support. The Phi-3-mini-128k-instruct is a similar model with a 128K context length. Both models have undergone a post-training process that incorporates supervised fine-tuning and direct preference optimization to ensure precise instruction adherence and robust safety measures.

Model inputs and outputs

The Phi-3-mini-4k-instruct model takes text as input and generates text as output. The model is best suited for prompts using the chat format, where the user provides a prompt starting with <|user|> and the model generates the response after <|assistant|>.

Inputs

  • Text prompts, typically in a chat format like:
    <|user|>
    How to explain the Internet to a medieval knight?
    <|end|>
    

Outputs

  • Generated text responses, for example:
    <|assistant|>
    To explain the Internet to a medieval knight, you could say that it is a magical network of interconnected "talking scrolls" that allow people to share information and communicate across vast distances, almost as if by magic. Just as a messenger on horseback can carry news and messages between distant keeps, the Internet allows information to travel quickly between far-flung locations. However, instead of a single messenger, the Internet has millions of these "talking scrolls" all connected together, allowing information to flow freely between them.
    
    You could liken the different websites on the Internet to the various fiefs, manors, and castles of the realm, each with their own unique content and purpose. And just as a knight might travel between these different places to gather news and resources, a user on the Internet can navigate between websites to find the information they seek.
    
    Of course, the technology behind the Internet is far more advanced than anything a medieval knight would be familiar with. But hopefully this analogy helps convey the core concept of how this new magical network functions and allows for the rapid sharing of information across vast distances.
    <|end|>
    

Capabilities

The Phi-3-mini-4k-instruct model showcases robust and state-of-the-art performance on a variety of benchmarks testing common sense, language understanding, math, code, long context, and logical reasoning, particularly among models with less than 13 billion parameters. It demonstrates strong capabilities in areas like memory/compute constrained environments, latency-bound scenarios, and applications requiring strong reasoning skills.

What can I use it for?

The Phi-3-mini-4k-instruct model is intended for commercial and research use in English. It can be used as a building block for developing generative AI-powered features and applications, especially those with requirements around memory/compute constraints, low latency, or strong reasoning abilities. Some potential use cases include:

  • Language model-powered chatbots and virtual assistants
  • Content generation for education, journalism, or creative writing
  • Code generation and programming assistance tools
  • Reasoning-intensive applications like question-answering systems or intelligent tutoring systems

Things to try

One interesting aspect of the Phi-3-mini-4k-instruct model is its ability to engage in multi-turn, chat-like conversations using the provided chat format. This allows you to explore the model's conversational capabilities and see how it responds to follow-up questions or requests. Additionally, you can experiment with prompts that require strong reasoning skills, such as math problems or logic puzzles, to assess the model's capabilities in these areas.



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-3-mini-4k-instruct

microsoft

Total Score

603

The Phi-3-mini-4k-instruct is a compact, 3.8 billion parameter language model developed by Microsoft. It is part of the Phi-3 family of models, which includes both the 4K and 128K variants that differ in their maximum context length. This model was trained on a combination of synthetic data and filtered web data, with a focus on reasoning-dense content. When evaluated on benchmarks testing common sense, language understanding, math, code, long context, and logical reasoning, the Phi-3-mini-4k-instruct demonstrated robust and state-of-the-art performance among models with less than 13 billion parameters. The model has undergone a post-training process that incorporates both supervised fine-tuning and direct preference optimization for instruction following and safety. This aligns it with human preferences for helpfulness and safety. Similar models include the Phi-3-mini-4k-instruct and the Meta-Llama-3-8B-Instruct, which are also compact, instruction-tuned language models. Model inputs and outputs Inputs The Phi-3-mini-4k-instruct model accepts text as input. Outputs The model generates text, including natural language and code. Capabilities The Phi-3-mini-4k-instruct model can be used for a variety of language-related tasks, such as summarization, question answering, and code generation. It has demonstrated strong performance on benchmarks testing common sense, language understanding, math, code, and logical reasoning. The model's compact size and instruction-following capabilities make it suitable for use in memory and compute-constrained environments, as well as latency-bound scenarios. What can I use it for? The Phi-3-mini-4k-instruct model can be a valuable tool for researchers and developers working on language models and generative AI applications. Its strong performance on a range of tasks, coupled with its small footprint, makes it an attractive option for building AI-powered features in resource-constrained environments. Potential use cases include chatbots, question-answering systems, and code generation tools. Things to try One interesting aspect of the Phi-3-mini-4k-instruct model is its ability to reason about complex topics and provide step-by-step solutions. Try prompting the model with math or coding problems and see how it approaches the task. Additionally, the model's instruction-following capabilities could be explored by providing it with detailed prompts or templates for specific tasks, such as writing business emails or creating an outline for a research paper.

Read more

Updated Invalid Date

🧠

Phi-3-mini-128k-instruct

microsoft

Total Score

1.3K

The Phi-3-mini-128k-instruct is a 3.8 billion-parameter, lightweight, state-of-the-art open model trained using the Phi-3 datasets. This dataset includes both synthetic data and filtered publicly available website data, with an emphasis on high-quality and reasoning-dense properties. The model belongs to the Phi-3 family with the Mini version in two variants 4K and 128K, which is the context length (in tokens) that it can support. After initial training, the model underwent a post-training process that involved supervised fine-tuning and direct preference optimization to enhance its ability to follow instructions and adhere to safety measures. When evaluated against benchmarks that test common sense, language understanding, mathematics, coding, long-term context, and logical reasoning, the Phi-3 Mini-128K-Instruct demonstrated robust and state-of-the-art performance among models with fewer than 13 billion parameters. Model inputs and outputs Inputs Text prompts Outputs Generated text responses Capabilities The Phi-3-mini-128k-instruct model is designed to excel in memory/compute constrained environments, latency-bound scenarios, and tasks requiring strong reasoning skills, especially in areas like code, math, and logic. It can be used to accelerate research on language and multimodal models, serving as a building block for generative AI-powered features. What can I use it for? The Phi-3-mini-128k-instruct model is intended for commercial and research use in English. It can be particularly useful for applications that require efficient performance in resource-constrained settings or low-latency scenarios, such as mobile devices or edge computing environments. Given its strong reasoning capabilities, the model can be leveraged for tasks involving coding, mathematical reasoning, and logical problem-solving. Things to try One interesting aspect of the Phi-3-mini-128k-instruct model is its ability to perform well on benchmarks testing common sense, language understanding, and logical reasoning, even with a relatively small parameter count compared to larger language models. This suggests it could be a useful starting point for exploring ways to build efficient and capable AI assistants that can understand and reason about the world in a robust manner.

Read more

Updated Invalid Date

🔍

Phi-3-small-8k-instruct

microsoft

Total Score

105

The Phi-3-small-8k-instruct is a 7B parameter, lightweight, state-of-the-art open model from Microsoft. It is part of the Phi-3 family of models, which includes variants with different context lengths - 8K and 128K. The Phi-3 models are trained on a combination of synthetic data and filtered public websites, with a focus on high-quality and reasoning-dense properties. The Phi-3-small-8k-instruct model has undergone a post-training process that incorporates both supervised fine-tuning and direct preference optimization to enhance its ability to follow instructions and adhere to safety measures. When evaluated on benchmarks testing common sense, language understanding, math, code, long context, and logical reasoning, the model demonstrated robust and state-of-the-art performance among models of similar size. Model inputs and outputs Inputs Text prompts, best suited for the chat format Outputs Generated text responses to the input prompts Capabilities The Phi-3-small-8k-instruct model excels at tasks that require strong reasoning, such as math, coding, and logical analysis. It can provide detailed and coherent responses across a wide range of topics. What can I use it for? The Phi-3-small-8k-instruct model is intended for broad commercial and research use in English. It can be used in general-purpose AI systems and applications that require memory/compute constrained environments, low-latency scenarios, or robust reasoning capabilities. The model can accelerate research on language and multimodal models, and serve as a building block for generative AI-powered features. Things to try One interesting aspect of the Phi-3-small-8k-instruct model is its ability to provide step-by-step explanations and solutions for math and coding problems. You can try prompting the model with math equations or coding challenges and observe how it breaks down the problem and walks through the solution. Another interesting area to explore is the model's language understanding and common sense reasoning capabilities. You can provide it with prompts that require an understanding of the physical world, social norms, or abstract concepts, and see how it responds.

Read more

Updated Invalid Date

🛠️

Phi-3-small-128k-instruct

microsoft

Total Score

113

The Phi-3-small-128k-instruct is a 7B parameter, lightweight, state-of-the-art open model trained by Microsoft. It belongs to the Phi-3 family of models, which includes variants with different context lengths such as the Phi-3-small-8k-instruct and Phi-3-mini-128k-instruct. The model was trained on a combination of synthetic data and filtered publicly available websites, with a focus on high-quality and reasoning-dense properties. After initial training, the model underwent a post-training process that incorporated both supervised fine-tuning and direct preference optimization to enhance its ability to follow instructions and adhere to safety measures. When evaluated against benchmarks testing common sense, language understanding, math, code, long context and logical reasoning, the Phi-3-small-128k-instruct demonstrated robust and state-of-the-art performance among models of the same size and next size up. Model inputs and outputs Inputs Text**: The Phi-3-small-128k-instruct model is best suited for prompts using the chat format, where the input is provided as text. Outputs Generated text**: The model generates text in response to the input prompt. Capabilities The Phi-3-small-128k-instruct model showcases strong reasoning abilities, particularly in areas like code, math, and logic. It performs well on benchmarks evaluating common sense, language understanding, and logical reasoning. The model is also designed to be lightweight and efficient, making it suitable for memory/compute-constrained environments and latency-bound scenarios. What can I use it for? The Phi-3-small-128k-instruct model is intended for broad commercial and research use in English. It can be used as a building block for general-purpose AI systems and applications that require strong reasoning capabilities, such as: Memory/compute-constrained environments Latency-bound scenarios AI systems that need to excel at tasks like coding, math, and logical reasoning Microsoft has also released other models in the Phi-3 family, such as the Phi-3-mini-128k-instruct and Phi-3-medium-128k-instruct, which may be better suited for different use cases based on their size and capabilities. Things to try One interesting aspect of the Phi-3-small-128k-instruct model is its strong performance on benchmarks evaluating logical reasoning and math skills. Developers could explore using this model as a foundation for building AI systems that need to tackle complex logical or mathematical problems, such as automated theorem proving, symbolic reasoning, or advanced question-answering. Another area to explore is the model's ability to follow instructions and adhere to safety guidelines. Developers could investigate how the model's instruction-following and safety-conscious capabilities could be leveraged in applications that require reliable and trustworthy AI assistants, such as in customer service, education, or sensitive domains.

Read more

Updated Invalid Date