phixtral-4x2_8
Maintainer: mlabonne
204
🔄
| 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
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.
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-2x2_8
145
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.
Updated Invalid Date
🛸
Beyonder-4x7B-v2
120
The Beyonder-4x7B-v2 is a Mixture of Experts (MoE) model created by mlabonne using the mergekit tool. It combines four base models: openchat/openchat-3.5-1210, beowolx/CodeNinja-1.0-OpenChat-7B, maywell/PiVoT-0.1-Starling-LM-RP, and WizardLM/WizardMath-7B-V1.1. This MoE architecture enables the model to leverage the strengths of these diverse base models, potentially leading to improved capabilities. Model inputs and outputs Inputs The recommended context length for Beyonder-4x7B-v2 is 8k. Outputs The model can generate natural language responses based on the provided input. Capabilities The Beyonder-4x7B-v2 model displays competitive performance on the Open LLM Leaderboard compared to the larger 8-expert Mixtral-8x7B-Instruct-v0.1 model, despite only having 4 experts. It also shows significant improvements over the individual expert models. Additionally, the Beyonder-4x7B-v2 performs very well on the Nous benchmark suite, coming close to the performance of the much larger 34B parameter Yi-34B fine-tuned model, while only using around 12B parameters. What can I use it for? The Beyonder-4x7B-v2 model can be used for a variety of natural language processing tasks, such as open-ended conversation, question answering, and task completion. Its strong performance on the Nous benchmark suggests it may be particularly well-suited for instruction following and reasoning tasks. Things to try Experiment with the model's capabilities by prompting it to complete a wide range of tasks, from creative writing to analytical problem-solving. Pay attention to how it handles different types of inputs and whether its responses demonstrate strong reasoning and language understanding abilities.
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
🧪
Mixtral-8x7B-v0.1-GPTQ
125
The Mixtral-8x7B-v0.1-GPTQ is a quantized version of the Mixtral 8X7B Large Language Model (LLM) created by Mistral AI_. This model is a pretrained generative Sparse Mixture of Experts that outperforms the Llama 2 70B model on most benchmarks. TheBloke has provided several quantized versions of this model for efficient GPU and CPU inference. Similar models available include the Mixtral-8x7B-v0.1-GGUF which uses the new GGUF format, and the Mixtral-8x7B-Instruct-v0.1-GGUF which is fine-tuned for instruction following. Model inputs and outputs Inputs Text prompt**: The model takes a text prompt as input and generates relevant text in response. Outputs Generated text**: The model outputs generated text that is relevant and coherent based on the input prompt. Capabilities The Mixtral-8x7B-v0.1-GPTQ model is a powerful generative language model capable of producing high-quality text on a wide range of topics. It can be used for tasks like open-ended text generation, summarization, question answering, and more. The model's Sparse Mixture of Experts architecture allows it to outperform the Llama 2 70B model on many benchmarks. What can I use it for? This model could be valuable for a variety of applications, such as: Content creation**: Generating articles, stories, scripts, or other long-form text content. Chatbots and virtual assistants**: Building conversational AI agents that can engage in natural language interactions. Query answering**: Providing informative and coherent responses to user questions on a wide range of subjects. Summarization**: Condensing long documents or articles into concise summaries. TheBloke has also provided quantized versions of this model optimized for efficient inference on both GPUs and CPUs, making it accessible for a wide range of deployment scenarios. Things to try One interesting aspect of the Mixtral-8x7B-v0.1-GPTQ model is its Sparse Mixture of Experts architecture. This allows the model to excel at a variety of tasks by combining the expertise of multiple sub-models. You could try prompting the model with a diverse set of topics and observe how it leverages this specialized knowledge to generate high-quality responses. Additionally, the quantized versions of this model provided by TheBloke offer the opportunity to experiment with efficient inference on different hardware setups, potentially unlocking new use cases where computational resources are constrained.
Updated Invalid Date