MetaMath-Mistral-7B
Maintainer: meta-math
88
📈
| 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 MetaMath-Mistral-7B is a large language model developed by the meta-math team. It is fully fine-tuned on the MetaMathQA datasets and based on the powerful Mistral-7B model. The model achieves a pass@1 score of 77.7% on the GSM8K benchmark, a significant improvement over the 66.5% achieved by the LLaMA-2-7B model.
Model inputs and outputs
Inputs
- Free-form text prompts that describe a task or question to be answered
Outputs
- Coherent text responses that complete the requested task or answer the given question
Capabilities
The MetaMath-Mistral-7B model is capable of performing a wide range of mathematical reasoning and problem-solving tasks, thanks to its fine-tuning on the MetaMathQA datasets. It can solve complex multi-step math word problems, perform symbolic math calculations, and engage in open-ended math discussions.
What can I use it for?
The MetaMath-Mistral-7B model would be well-suited for building educational and tutoring applications focused on math. It could power intelligent math assistants, math homework helpers, or math problem-solving tools. Potential use cases include:
- Providing step-by-step solutions and explanations for math word problems
- Assisting with symbolic math computations and derivations
- Engaging students in interactive math discussions and exercises
- Generating diverse math practice problems and worksheets
Things to try
One interesting aspect of the MetaMath-Mistral-7B model is its potential to be combined with other math-focused datasets and models. As mentioned, there is an Arithmo-Mistral-7B model that integrates the MetaMathQA dataset with the MathInstruct dataset, resulting in a powerful math-focused language model. Experimenting with different dataset combinations and fine-tuning approaches could lead to further improvements in the model's mathematical reasoning 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
⚙️
Mathstral-7B-v0.1
182
Mathstral-7B-v0.1 is a model specializing in mathematical and scientific tasks, based on the Mistral 7B model. As described in the official blog post, the Mathstral 7B model was trained to excel at a variety of math and science-related benchmarks. It outperforms other large language models of similar size on tasks like MATH, GSM8K, and AMC. Model inputs and outputs Mathstral-7B-v0.1 is a text-to-text model, meaning it takes natural language prompts as input and generates relevant text as output. The model can be used for a variety of mathematical and scientific tasks, such as solving word problems, explaining concepts, and generating proofs or derivations. Inputs Natural language prompts related to mathematical, scientific, or technical topics Outputs Relevant and coherent text responses, ranging from short explanations to multi-paragraph outputs Can generate step-by-step solutions, derivations, or proofs for mathematical and scientific problems Capabilities The Mathstral-7B-v0.1 model demonstrates strong performance on a wide range of mathematical and scientific benchmarks. It excels at tasks like solving complex word problems, explaining abstract concepts, and generating detailed technical responses. Compared to other large language models, Mathstral-7B-v0.1 shows a particular aptitude for tasks requiring rigorous reasoning and technical proficiency. What can I use it for? The Mathstral-7B-v0.1 model can be a valuable tool for a variety of applications, such as: Educational and tutorial content generation: The model can be used to create interactive lessons, step-by-step explanations, and practice problems for students learning mathematics, physics, or other technical subjects. Technical writing and documentation: Mathstral-7B-v0.1 can assist with generating clear and concise technical documentation, user manuals, and other written materials for scientific and engineering-focused products and services. Research and analysis support: The model can help researchers summarize findings, generate hypotheses, and communicate complex ideas more effectively. STEM-focused chatbots and virtual assistants: Mathstral-7B-v0.1 can power conversational interfaces that can answer questions, solve problems, and provide guidance on a wide range of technical topics. Things to try One interesting capability of the Mathstral-7B-v0.1 model is its ability to provide step-by-step solutions and explanations for complex math and science problems. Try prompting the model with a detailed word problem or a request to derive a specific mathematical formula - the model should be able to walk through the problem-solving process and clearly communicate the reasoning and steps involved. Another area to explore is the model's versatility in handling different representations of technical information. Try providing the model with a mix of natural language, equations, diagrams, and other formats, and see how it integrates these various inputs to generate comprehensive responses.
Updated Invalid Date
🌿
Arithmo-Mistral-7B
59
The Arithmo-Mistral-7B model is a fine-tuned version of the powerful Mistral-7B model, developed by Ashvini Kumar Jindal and Ankur Parikh. This model exhibits strong mathematical reasoning capabilities, outperforming existing 7B and 13B state-of-the-art mathematical reasoning models on the GSM8K and MATH benchmarks. In comparison, the MetaMath-Mistral-7B model is another fine-tuned Mistral-7B that focuses on the MetaMathQA dataset, achieving impressive results on mathematical reasoning tasks. Both the Arithmo-Mistral-7B and MetaMath-Mistral-7B models leverage the capabilities of the base Mistral-7B model to excel at mathematical problem-solving. Model inputs and outputs The Arithmo-Mistral-7B model is a text-to-text model, taking in mathematical questions or prompts as input and generating responses that reason through the problem and provide the answer. Inputs Mathematical word problems or questions expressed in natural language Outputs Step-by-step reasoning to solve the mathematical problem The final answer to the question In some cases, the model can also generate a Python program that, when executed, provides the answer to the problem Capabilities The Arithmo-Mistral-7B model demonstrates strong mathematical reasoning abilities, outperforming existing 7B and 13B models on the GSM8K and MATH benchmarks. It can tackle a wide range of mathematical problems, from arithmetic to algebra to geometry, and provide detailed reasoning and solutions. The model can also generate Python code to solve mathematical problems, showcasing its versatility and programming skills. What can I use it for? The Arithmo-Mistral-7B model can be a valuable tool for students, educators, and researchers working on mathematical problems and reasoning. It can be used to aid in homework and exam preparation, to generate practice problems, or to provide step-by-step explanations for complex mathematical concepts. Additionally, the model's ability to generate Python code could be leveraged in programming and computer science education, or in the development of mathematical tools and applications. Things to try One interesting aspect of the Arithmo-Mistral-7B model is its ability to not only solve mathematical problems, but to also provide step-by-step reasoning and generate Python code to solve the problems. Try prompting the model with a variety of mathematical word problems and observe how it tackles the problems, generates the reasoning, and produces the final answer. Experiment with different problem types and complexities to see the full extent of the model's capabilities.
Updated Invalid Date
🔄
Mistral-7B-Instruct-v0.1-GPTQ
73
The Mistral-7B-Instruct-v0.1-GPTQ is an AI model created by Mistral AI, with quantized versions provided by TheBloke. This model is derived from Mistral AI's larger Mistral 7B Instruct v0.1 model, and has been further optimized through GPTQ quantization to reduce memory usage and improve inference speed, while aiming to maintain high performance. Similar models available from TheBloke include the Mixtral-8x7B-Instruct-v0.1-GPTQ, which is an 8-expert version of the Mistral model, and the Mistral-7B-OpenOrca-GPTQ, which was fine-tuned by OpenOrca on top of the original Mistral 7B model. Model inputs and outputs Inputs Prompt**: A text prompt to be used as input for the model to generate a completion. Outputs Generated text**: The text completion generated by the model based on the provided prompt. Capabilities The Mistral-7B-Instruct-v0.1-GPTQ model is capable of generating high-quality, coherent text on a wide range of topics. It has been trained on a large corpus of internet data and can be used for tasks like open-ended text generation, summarization, and question answering. The model is particularly adept at following instructions and maintaining consistent context throughout the generated output. What can I use it for? The Mistral-7B-Instruct-v0.1-GPTQ model can be used for a variety of applications, such as: Creative writing assistance: Generate ideas, story plots, or entire narratives to help jumpstart the creative process. Chatbots and conversational AI: Use the model to power engaging, context-aware dialogues. Content generation: Create articles, blog posts, or other written content on demand. Question answering: Leverage the model's knowledge to provide informative responses to user queries. Things to try One interesting aspect of the Mistral-7B-Instruct-v0.1-GPTQ model is its ability to follow instructions and maintain context across multiple prompts. Try providing the model with a series of prompts that build upon each other, such as: "Write a short story about a talking llama." "Now, have the llama encounter a mysterious stranger in the woods." "The llama and the stranger decide to work together on a quest. What happens next?" By chaining these prompts together, you can see the model's capacity to understand and respond to the evolving narrative, creating a cohesive and engaging story.
Updated Invalid Date
❗
Mistral-7B-Instruct-v0.2-GPTQ
45
The Mistral-7B-Instruct-v0.2-GPTQ model is a version of the Mistral 7B Instruct model that has been quantized using GPTQ techniques. It was created by TheBloke, who has also produced several similar quantized models for the Mistral 7B Instruct and Mixtral 8x7B models. These quantized models provide more efficient inference by reducing the model size and memory requirements, while aiming to preserve as much quality as possible. Model inputs and outputs Inputs Prompt**: The model expects prompts to be formatted with the [INST] {prompt} [/INST] template. This signifies the beginning of an instruction which the model should try to follow. Outputs Generated text**: The model will generate text in response to the provided prompt, ending the output when it encounters the end-of-sentence token. Capabilities The Mistral-7B-Instruct-v0.2-GPTQ model is capable of performing a variety of language tasks such as answering questions, generating coherent text, and following instructions. It can be used for applications like dialogue systems, content generation, and text summarization. The model has been fine-tuned on a range of datasets to develop its instructional capabilities. What can I use it for? The Mistral-7B-Instruct-v0.2-GPTQ model could be useful for a variety of applications that require language understanding and generation, such as: Chatbots and virtual assistants**: The model's ability to follow instructions and engage in dialogue makes it well-suited for building conversational AI systems. Content creation**: The model can be used to generate text, stories, or other creative content. Question answering**: The model can be prompted to answer questions on a wide range of topics. Text summarization**: The model could be used to generate concise summaries of longer passages of text. Things to try Some interesting things to try with the Mistral-7B-Instruct-v0.2-GPTQ model include: Experimenting with different prompting strategies to see how the model responds to more open-ended or complex instructions. Combining the model with other techniques like few-shot learning or fine-tuning to further enhance its capabilities. Exploring the model's limits by pushing it to generate text on more specialized or technical topics. Analyzing the model's responses to better understand its strengths, weaknesses, and biases. Overall, the Mistral-7B-Instruct-v0.2-GPTQ model provides a powerful and versatile language generation capability that could be valuable for a wide range of applications.
Updated Invalid Date