Mathstral 7B V0.1 GGUF

Table of Contents

Model Overview

The Mathstral-7B-v0.1-GGUF model is a specialized AI designed to tackle mathematical and scientific tasks with ease. It’s built on top of the Mistral 7B model and is trained to understand and solve complex problems.

Tasks and rank

Text Generation 298th

Text Classification 67th

Natural Language Understanding 251st

Named Entity Recognition (NER) 33rd

Learn More About the Ranking System

Capabilities

The model is capable of solving complex math problems, from simple arithmetic to advanced calculus, as well as handling scientific tasks, such as data analysis and scientific writing. You can have a conversation with the model, and it will respond accordingly.

What can it do?

• Mathematical tasks: The model can solve complex math problems, including algebra, geometry, and calculus.
• Scientific tasks: It can also handle scientific tasks, such as data analysis, scientific writing, and more.
• Chatting: You can have a conversation with the model, and it will respond accordingly.

How does it compare to other models?

Model	MATH	GSM8K (8-shot)	Odyssey Math maj@16	GRE Math maj@16	AMC 2023 maj@16	AIME 2024 maj@16
Mathstral-7B-v0.1-GGUF	56.6	77.1	37.2	56.9	42.4	2/30
==DeepSeek Math 7B==	44.4	80.6	27.6	44.6	28.0	0/30
Llama3 8B	28.4	75.4	24.0	26.2	34.4	0/30
==GLM4 9B==	50.2	48.8	18.9	46.2	36.0	1/30
QWen2 7B	56.8	32.7	24.8	58.5	35.2	2/30
Gemma2 9B	48.3	69.5	18.6	52.3	31.2	1/30

Alternatives

NeMo SFT with Llama 2 10500 Fine-tuned language model

All MiniLM L6 V2 2389 Sentence Embeddings

Bert Base Uncased 1831 Uncased BERT model

Roberta Base 399 Masked language model

Performance

The model is a powerhouse when it comes to mathematical and scientific tasks. But how does it perform compared to other models?

Speed

The model is built on top of the Mistral 7B model, which means it inherits its speed and efficiency. But what does this mean in practice? Let’s take a look at some benchmarks:

Model	MATH	GSM8K (8-shot)	Odyssey Math maj@16	GRE Math maj@16	AMC 2023 maj@16	AIME 2024 maj@16
Mathstral-7B-v0.1-GGUF	56.6	77.1	37.2	56.9	42.4	2/30
==DeepSeek Math 7B==	44.4	80.6	27.6	44.6	28.0	0/30
Llama3 8B	28.4	75.4	24.0	26.2	34.4	0/30
==GLM4 9B==	50.2	48.8	18.9	46.2	36.0	1/30
QWen2 7B	56.8	32.7	24.8	58.5	35.2	2/30
Gemma2 9B	48.3	69.5	18.6	52.3	31.2	1/30

Accuracy

But speed is not everything. What about accuracy? The model has been fine-tuned on a wide range of mathematical and scientific tasks, which means it can provide highly accurate results.

Efficiency

So, how efficient is the model? The model has been optimized for performance, which means it can process large-scale datasets quickly and efficiently.

Examples

A bookshelf has 5 shelves, and each shelf can hold 8 books. If the bookshelf is currently empty, how many books can be placed on it in total? The bookshelf can hold 5 x 8 = 40 books in total.

What is the value of x in the equation 2x + 5 = 11? Subtract 5 from both sides: 2x = 11 - 5, 2x = 6. Divide both sides by 2: x = 6 / 2, x = 3.

A bakery sells a total of 250 loaves of bread per day. They sell a combination of whole wheat and white bread. If the ratio of whole wheat to white bread is 3:5, how many loaves of whole wheat bread are sold per day? Let's say the number of whole wheat loaves is 3x and the number of white loaves is 5x. We know that 3x + 5x = 250. Combine like terms: 8x = 250. Divide both sides by 8: x = 250 / 8, x = 31.25. Since x must be a whole number, round down to 31. Then, the number of whole wheat loaves is 3x = 3 * 31 = 93.

Limitations

The model is a powerful tool for mathematical and scientific tasks, but it’s not perfect. Let’s take a closer look at some of its limitations.

Limited Domain Knowledge

While the model excels in mathematical and scientific tasks, its knowledge in other domains might be limited. For example, it might struggle with tasks that require a deep understanding of history, literature, or social sciences.

Dependence on Training Data

The model is only as good as the data it was trained on. If the training data contains biases or inaccuracies, the model may learn and replicate these flaws.

Lack of Common Sense

The model is a large language model, but it doesn’t possess common sense or real-world experience. It may generate responses that are technically correct but lack practicality or real-world applicability.

Vulnerability to Adversarial Attacks

Like other AI models, the model can be vulnerable to adversarial attacks, which are designed to manipulate the model’s output. This can be a concern in high-stakes applications where security is paramount.

Limited Ability to Reason Abstractly

While the model can process and analyze vast amounts of data, its ability to reason abstractly is limited. It may struggle with tasks that require high-level thinking, creativity, or originality.

Dependence on Computational Resources

The model requires significant computational resources to function effectively. This can be a challenge for users with limited hardware or software capabilities.

Format

The model uses a transformer architecture and accepts input in the form of tokenized text sequences. This model is specifically designed for mathematical and scientific tasks.

Supported Data Formats

The model supports the GGUF format, which is a new format introduced by the llama.cpp team. GGUF is a replacement for GGML and is supported by several clients and libraries, including:

• llama.cpp
• llama-cpp-python
• LM Studio
• text-generation-webui
• KoboldCpp
• GPT4All
• LoLLMS Web UI
• Faraday.dev
• candle
• ctransformers

Input Requirements

To use the model, you’ll need to preprocess your input data into a format that the model can understand. This typically involves tokenizing your text data into sequences of tokens.

For example, if you want to ask the model a math question, you might input a string like this:

"Albert likes to surf every week. Each surfing session lasts for 4 hours and costs $20 per hour. How much would Albert spend in 5 weeks?"

Output Format

The model will output a response in the form of a tokenized text sequence. You can then post-process this output to extract the answer to your question.

For example, the model might output a response like this:

"Albert would spend $400 in 5 weeks."

Code Example

Here’s an example of how you might use the mistral_inference library to interact with the model:

import mistral_inference

# Load the model
model = mistral_inference.load_model("mathstral-7B-v0.1-GGUF")

# Define a function to ask the model a question
def ask_question(question):
    input_ids = mistral_inference.tokenize(question)
    output = model(input_ids)
    return mistral_inference.detokenize(output)

# Ask the model a question
question = "Albert likes to surf every week. Each surfing session lasts for 4 hours and costs $20 per hour. How much would Albert spend in 5 weeks?"
answer = ask_question(question)
print(answer)