phixtral-2x2_8
Maintainer: mlabonne
145
👁️
| Property | Value |
|---|---|
| Run this model | Run 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
phixtral-2x2_8 is a Mixture of Experts (MoE) model made with two microsoft/phi-2 models, inspired by the mistralai/Mixtral-8x7B-v0.1 architecture. It performs better than each individual expert model. The model was created by mlabonne.
Another similar MoE model is the phixtral-4x2_8, which uses four microsoft/phi-2 models instead of two.
Model inputs and outputs
phixtral-2x2_8 is a text-to-text model that can handle a variety of input formats, including question-answering, chatbot, and code generation. The model takes in raw text prompts and generates relevant output text.
Inputs
- Free-form text prompts for tasks like:
- Question-answering (e.g. "What is a Fermi paradox?")
- Chatbot conversations (e.g. "I'm struggling to focus while studying. Any suggestions?")
- Code generation (e.g. "def print_prime(n):")
Outputs
- Relevant text responses to the input prompts, ranging from short answers to longer generated text.
Capabilities
The phixtral-2x2_8 model has shown strong performance on benchmarks like AGIEval, GPT4All, TruthfulQA, and Bigbench, outperforming its individual expert models. It demonstrates capabilities in areas like language understanding, logical reasoning, and code generation.
What can I use it for?
Given its diverse capabilities, phixtral-2x2_8 could be useful for a variety of applications, such as:
- Building chatbots or virtual assistants that can engage in open-ended conversations
- Developing question-answering systems for educational or research purposes
- Automating code generation for prototyping or productivity tasks
Things to try
Some interesting things to explore with phixtral-2x2_8 could include:
- Experimenting with different prompting techniques to see how the model responds
- Comparing the model's performance to other language models on specific tasks
- Investigating ways to further fine-tune or adapt the model for specialized use cases
Overall, phixtral-2x2_8 is a capable and versatile model that could be a valuable tool for researchers and developers working on a variety of natural language processing and generation projects.
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
🔄
phixtral-4x2_8
204
The phixtral-4x2_8 is a Mixture of Experts (MoE) model made with four microsoft/phi-2 models, inspired by the mistralai/Mixtral-8x7B-v0.1 architecture. This model performs better than each individual expert. Model inputs and outputs The phixtral-4x2_8 model takes text inputs and generates text outputs. It is a generative language model capable of producing coherent and contextual responses to prompts. Inputs Text prompts that the model can use to generate relevant and meaningful output. Outputs Coherent and contextual text responses generated based on the input prompts. Capabilities The phixtral-4x2_8 model demonstrates improved performance compared to individual models like dolphin-2_6-phi-2, phi-2-dpo, and phi-2-coder on various benchmarks such as AGIEval, GPT4All, TruthfulQA, and Bigbench. What can I use it for? The phixtral-4x2_8 model can be used for a variety of text-to-text tasks, such as: General language understanding and generation Question answering Summarization Code generation Creative writing Its strong performance on various benchmarks suggests it could be a capable model for many natural language processing applications. Things to try You can try fine-tuning the phixtral-4x2_8 model on specific datasets or tasks to further improve its performance for your use case. The model's modular nature, with multiple experts, also provides an opportunity to explore different expert configurations and observe their impact on the model's capabilities.
Updated Invalid Date
🌀
phi-2
3.2K
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.
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-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