LLM在智能应用中的新突破：最新技术趋势解析

import torch
from transformers import CLIPProcessor, CLIPModel

# 加载CLIP模型和处理器
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")

# 加载图像并处理
image = Image.open("image.jpg")
inputs = processor(images=image, return_tensors="pt", padding=True, truncation=True)

# 图像编码
image_features = model.get_image_features(**inputs)

# 文本编码
text_inputs = processor(text=["a photo of a cat", "a photo of a dog"], return_tensors="pt", padding=True, truncation=True)
text_features = model.get_text_features(**text_inputs)

# 计算相似度
image_features = image_features / image_features.norm(dim=-1, keepdim=True)
text_features = text_features / text_features.norm(dim=-1, keepdim=True)
similarity = (image_features @ text_features.T).squeeze()

# 输出相似度最高的文本描述
best_match_index = torch.argmax(similarity)
print("Best match:", processor.tokenizer.decode(text_inputs.input_ids[best_match_index]))

from transformers import AutoModelForCausalLM, AutoTokenizer, prepare_optimizer_parameters
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training

# 加载预训练模型和tokenizer
model_name = "meta-llama/Llama-2-7b-hf"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# 准备模型进行k-bit训练
model = prepare_model_for_kbit_training(model)

# 配置LoRA
lora_config = LoraConfig(
    r=8,
    lora_alpha=32,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
    task_type="CAUSAL_LM"
)

# 获取Peft模型
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()

# 准备训练数据和优化器
# ...（此处省略数据准备和优化器设置的代码）

# 训练模型
# ...（此处省略训练过程的代码）

# 保存适配器
model.save_pretrained("llama-2-7b-lora")

from fastapi import FastAPI
from transformers import pipeline

# 创建FastAPI应用实例
app = FastAPI()

# 加载LLM管道
model_name = "gpt2"
generator = pipeline('text-generation', model=model_name)

@app.post("/generate/")
async def generate_text(prompt: str, max_length: int = 50):
    """
    生成文本的API接口
    """
    try:
        # 生成文本
        output = generator(prompt, max_length=max_length)
        return {"result": output[0]['generated_text']}
    except Exception as e:
        return {"error": str(e)}

# 启动服务
# 使用uvicorn运行：uvicorn main:app --reload

import numpy as np
from transformers import AutoTokenizer, AutoModel
import faiss

# 加载预训练模型和tokenizer
model_name = "sentence-transformers/all-MiniLM-L6-v2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

# 准备对话历史数据
dialogue_history = [
    "User: Hello! How are you? Assistant: I'm just a program, but thanks for asking!",
    "User: Can you help me with something? Assistant: Of course! What do you need help with?"
]

# 将对话历史转换为嵌入向量
def get_embedding(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
    outputs = model(**inputs)
    return outputs.last_hidden_state.mean(dim=1).detach().numpy()

embeddings = np.array([get_embedding(text) for text in dialogue_history])

# 构建FAISS索引
dimension = embeddings.shape[1]
index = faiss.IndexFlatL2(dimension)
index.add(embeddings)

# 检索相关历史
current_query = "User: What were we talking about earlier?"
current_embedding = get_embedding(current_query)
k = 1  # 检索最相关的1个历史
distances, indices = index.search(current_embedding, k)

# 输出检索到的历史
for idx in indices[0]:
    print(dialogue_history[idx])

import openai

# 设置API密钥
openai.api_key = "your-api-key"

# 使用GPT-4生成文本描述
response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are an image description generator."},
        {"role": "user", "content": "Generate a detailed description of a serene mountain landscape."}
    ]
)
text_description = response.choices[0].message['content']

# 使用Imagen 3根据文本描述生成图像
image_response = openai.Image.create(
    prompt=text_description,
    n=1,
    size="1024x1024"
)
image_url = image_response['data'][0]['url']

print(f"Generated image URL: {image_url}")