Mathstral 7B V0.1

🚀 Mathstral-7b-v0.1模型卡片

Mathstral 7B是一款专注于数学和科学任务的模型，它基于Mistral 7B开发。你可以在官方博客文章中了解更多信息。

🚀 快速开始

Mathstral 7B是基于Mistral 7B的模型，专注于解决数学和科学领域的任务。以下为你介绍该模型的使用方法。

📦 安装指南

建议将mistralai/Mathstral-7b-v0.1与mistral-inference结合使用。

pip install mistral_inference>=1.2.0

💻 使用示例

下载模型

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)

聊天交互

安装mistral_inference后，你的环境中会有一个mistral-demo命令行工具。

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

你可以开始与模型进行对话，例如向它提问：

"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?"

在transformers库中使用

要在transformers库中使用该模型，先使用pip install --upgrade transformers安装最新版本，然后运行以下代码：

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.'}"

你也可以手动对输入进行分词，并从模型生成文本，而不是使用高级的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>'

📚 详细文档

评估结果

我们在行业标准基准测试中对Mathstral 7B和类似规模的开放权重模型进行了评估。

开发团队

Mathstral 7B由Mistral AI团队开发，团队成员包括：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

📄 许可证

本项目采用Apache-2.0许可证。

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