随着深度学习技术的快速发展,大模型(如 GPT、LLaMA、Bloom 等)已经成为人工智能领域的核心驱动力。本篇博客将探讨大模型的发展趋势及其在医疗、金融、教育等行业的实际应用,并通过2个实战项目展示如何使用开源大模型构建问答系统。此外,我们还会分析大模型的前沿技术方向。
以下是大模型从早期到现在的关键里程碑:
大模型通常指的是大规模的人工智能模型,是一种基于深度学习技术,具有海量参数、强大的学习能力和泛化能力,能够处理和生成多种类型数据的人工智能模型。通常说的大模型的“大”的特点体现在:参数数量庞大、训练数据量大、计算资源需求高。
2020年,OpenAI公司推出了GPT-3,模型参数规模达到了1750亿,2023年3月发布的GPT-4的参数规模是GPT-3的10倍以上,
达到1.8万亿,2021年11月阿里推出的M6 模型的参数量达10万亿
更大的模型通常能够捕捉更复杂的模式,但对计算资源和训练数据的需求也更高。
多模态大模型(如 CLIP、Flamingo 和 Gato)能够同时处理文本、图像、音频等多种模态的数据,为跨领域的任务提供了统一的解决方案。
大模型在医疗领域的应用包括:
大模型在金融领域的应用包括:
大模型在教育领域的应用包括:
Python 版本 3.11.5
Torch版本:
torch 2.5.1+cu121
torchaudio 2.5.1+cu121
torchvision 0.20.1+cu121
我们将使用 Hugging Face 提供的开源大模型(如 LLaMA 或 Bloom)构建一个问答系统。该系统可以回答用户提出的问题,并支持上下文理解。
以下是项目的具体实现步骤:
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Step 1: 加载预训练模型和分词器
model_name = "bigscience/bloom-560m" # 使用较小版本的 Bloom 模型
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Step 2: 定义问答函数
def generate_answer(question):
inputs = tokenizer.encode(question, return_tensors="pt")
outputs = model.generate(inputs, max_length=100, num_return_sequences=1)
answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
return answer
# Step 3: 测试问答系统
questions = [
"What is the capital of France?",
"Explain the theory of relativity in simple terms.",
"How does a neural network work?"
]
for q in questions:
print(f"Question: {q}")
print(f"Answer: {generate_answer(q)}\n")
程序运行输出:
Question: What is the capital of France?
Answer: What is the capital of France?"
"It is Paris," said the Frenchman, with a smile.
"It is the capital of France?"
"Yes," said the Frenchman, with a smile.
"It is the capital of France?"
"Yes," said the Frenchman, with a smile.
"It is the capital of France?"
"Yes," said the Frenchman, with a smile.
"It is the capital of France?"
Question: Explain the theory of relativity in simple terms.
Answer: Explain the theory of relativity in simple terms. The theory of relativity is a theory of the nature of space-time. It is a theory of the nature of space-time. It is a theory of the nature of space-time. It is a theory of the nature of space-time. It is a theory of the nature of space-time. It is a theory of the nature of space-time. It is a theory of the nature of space-time. It is a theory of the nature of
Question: How does a neural network work?
Answer: How does a neural network work? The answer is that it does. The neural network is a mathematical model that can be used to predict the behavior of a system. The neural network is a mathematical model that can be used to predict the behavior of a system. The neural network is a mathematical model that can be used to predict the behavior of a system. The neural network is a mathematical model that can be used to predict the behavior of a system. The neural network is a mathematical model that can be
pip install transformers torch gradio
from transformers import AutoModelForCausalLM, AutoTokenizer
import gradio as gr
model_name = "bigscience/bloom-560m"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
def generate_answer(question):
inputs = tokenizer.encode(question, return_tensors="pt")
outputs = model.generate(inputs,
max_length=200,
temperature=0.7,
top_k=50)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
interface = gr.Interface(
fn=generate_answer,
inputs=gr.Textbox(label="输入问题"),
outputs=gr.Textbox(label="生成回答"),
title="Bloom问答系统"
)
interface.launch(server_port=7860)
CMD输出:
* Running on local URL: http://127.0.0.1:7860
To create a public link, set `share=True` in `launch()`.
在浏览器输入: http://127.0.0.1:7860, 运行图片如下:
因为该模型较小,因此回答效果一般,可以换成时下流行的大模型量化版。
技术预测:
开发者建议:
# 未来技能栈示例
skills = {
"基础能力": ["PyTorch", "分布式训练"],
"进阶方向": ["提示工程", "模型蒸馏"],
"伦理必修": ["AI安全", "公平性评估"]
}
为了应对大模型的高计算需求,研究人员正在探索以下技术:
随着大模型在敏感领域的广泛应用,数据隐私和安全成为重要议题。联邦学习 (Federated Learning) 和差分隐私 (Differential Privacy) 是当前的研究热点。
大模型的决策过程通常被认为是“黑箱”,这引发了对可解释性和伦理问题的关注。研究人员正在开发工具和方法来提高模型的透明度和公平性。
延伸阅读: