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RichardErkhov/
mistralai_-_Mathstral-7B-v0.1-gguf

Name: mistralai_-_Mathstral-7B-v0.1-gguf
Availability: InStock

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Mathstral-7B-v0.1 - GGUF

Model creator: https://huggingface.co/mistralai/
Original model: https://huggingface.co/mistralai/Mathstral-7B-v0.1/

Name	Quant method	Size
Mathstral-7B-v0.1.Q2_K.gguf	Q2_K	2.54GB
Mathstral-7B-v0.1.IQ3_XS.gguf	IQ3_XS	2.82GB
Mathstral-7B-v0.1.IQ3_S.gguf	IQ3_S	2.97GB
Mathstral-7B-v0.1.Q3_K_S.gguf	Q3_K_S	2.95GB
Mathstral-7B-v0.1.IQ3_M.gguf	IQ3_M	3.06GB
Mathstral-7B-v0.1.Q3_K.gguf	Q3_K	3.28GB
Mathstral-7B-v0.1.Q3_K_M.gguf	Q3_K_M	3.28GB
Mathstral-7B-v0.1.Q3_K_L.gguf	Q3_K_L	3.56GB
Mathstral-7B-v0.1.IQ4_XS.gguf	IQ4_XS	3.68GB
Mathstral-7B-v0.1.Q4_0.gguf	Q4_0	3.83GB
Mathstral-7B-v0.1.IQ4_NL.gguf	IQ4_NL	3.87GB
Mathstral-7B-v0.1.Q4_K_S.gguf	Q4_K_S	3.86GB
Mathstral-7B-v0.1.Q4_K.gguf	Q4_K	4.07GB
Mathstral-7B-v0.1.Q4_K_M.gguf	Q4_K_M	4.07GB
Mathstral-7B-v0.1.Q4_1.gguf	Q4_1	4.24GB
Mathstral-7B-v0.1.Q5_0.gguf	Q5_0	4.66GB
Mathstral-7B-v0.1.Q5_K_S.gguf	Q5_K_S	4.66GB
Mathstral-7B-v0.1.Q5_K.gguf	Q5_K	4.78GB
Mathstral-7B-v0.1.Q5_K_M.gguf	Q5_K_M	4.78GB
Mathstral-7B-v0.1.Q5_1.gguf	Q5_1	5.07GB
Mathstral-7B-v0.1.Q6_K.gguf	Q6_K	5.54GB
Mathstral-7B-v0.1.Q8_0.gguf	Q8_0	7.17GB

Original model description:

license: apache-2.0

extra_gated_description: If you want to learn more about how we process your personal data, please read our Privacy Policy.

Model Card for Mathstral-7b-v0.1

Mathstral 7B is a model specializing in mathematical and scientific tasks, based on Mistral 7B. You can read more in the official blog post.

Installation

It is recommended to use mistralai/Mathstral-7b-v0.1 with mistral-inference

pip install mistral_inference>=1.2.0

Download

from huggingface_hub import snapshot_download
from pathlib import Path

mistral_models_path = Path.home().joinpath('mistral_models', 'Mathstral-7b-v0.1')
mistral_models_path.mkdir(parents=True, exist_ok=True)

snapshot_download(repo_id="mistralai/Mathstral-7b-v0.1", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path)

Chat

After installing mistral_inference, a mistral-demo CLI command should be available in your environment.

mistral-chat $HOME/mistral_models/Mathstral-7b-v0.1 --instruct --max_tokens 256

You can then start chatting with the model, e.g. prompt it with something like:

"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?"

Usage in `transformers`

To use this model within the transformers library, install the latest release with pip install --upgrade transformers and run, for instance:

from transformers import pipeline
import torch

checkpoint = "mistralai/Mathstral-7b-v0.1"
pipe = pipeline("text-generation", checkpoint, device_map="auto", torch_dtype=torch.bfloat16)

prompt = [{"role": "user", "content": "What are the roots of unity?"}]
out = pipe(prompt, max_new_tokens = 512)

print(out[0]['generated_text'][-1])
>>> "{'role': 'assistant', 'content': ' The roots of unity are the complex numbers that satisfy the equation $z^n = 1$, where $n$ is a positive integer. These roots are evenly spaced around the unit circle in the complex plane, and they have a variety of interesting properties and applications in mathematics and physics.'}"

You can also manually tokenize the input and generate text from the model, rather than using the higher-level pipeline:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

checkpoint = "mistralai/Mathstral-7b-v0.1"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", torch_dtype=torch.bfloat16)

prompt = [{"role": "user", "content": "What are the roots of unity?"}]
tokenized_prompt = tokenizer.apply_chat_template(prompt, add_generation_prompt=True, return_dict=True, return_tensors="pt").to(model.device)

out = model.generate(**tokenized_prompt, max_new_tokens=512)
tokenizer.decode(out[0])
>>> '<s>[INST] What are the roots of unity?[/INST] The roots of unity are the complex numbers that satisfy the equation $z^n = 1$, where $n$ is a positive integer. These roots are evenly spaced around the unit circle in the complex plane, and they have a variety of interesting properties and applications in mathematics and physics.</s>'

Evaluation

We evaluate Mathstral 7B and open-weight models of the similar size on industry-standard benchmarks.

Benchmarks	MATH	GSM8K (8-shot)	Odyssey Math maj@16	GRE Math maj@16	AMC 2023 maj@16	AIME 2024 maj@16
Mathstral 7B	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

The Mistral AI Team

Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Alok Kothari, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Augustin Garreau, Austin Birky, Bam4d, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Carole Rambaud, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gaspard Blanchet, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Hichem Sattouf, Ian Mack, Jean-Malo Delignon, Jessica Chudnovsky, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickaël Seznec, Nicolas Schuhl, Niklas Muhs, Olivier de Garrigues, Patrick von Platen, Paul Jacob, Pauline Buche, Pavan Kumar Reddy, Perry Savas, Pierre Stock, Romain Sauvestre, Sagar Vaze, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibault Schueller, Thibaut Lavril, Thomas Wang, Théophile Gervet, Timothée Lacroix, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall

RichardErkhov/mistralai_-_Mathstral-7B-v0.1-gguf