微调Llama3实现在线搜索引擎和RAG检索增强生成功能

视频中所出现的代码 Tavily Search+RAG

微调Llama3实现在线搜索引擎和RAG检索增强生成功能！打造自己的perplexity和GPTs！用PDF实现本地知识库_哔哩哔哩_bilibili

一.准备工作

1.安装环境

conda create --name unsloth_env python=3.10
conda activate unsloth_envconda install pytorch-cuda=12.1 pytorch cudatoolkit xformers -c pytorch -c nvidia -c xformerspip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"pip install --no-deps trl peft accelerate bitsandbytes

 2.微调代码（要先登录一下）

huggingface-cli login

点击提示的网页获取token（注意要选择可写的）

#dataset https://huggingface.co/datasets/shibing624/alpaca-zh/viewerfrom unsloth import FastLanguageModel
import torchfrom trl import SFTTrainer
from transformers import TrainingArgumentsmax_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.# 4bit pre quantized models we support for 4x faster downloading + no OOMs.
fourbit_models = ["unsloth/mistral-7b-bnb-4bit","unsloth/mistral-7b-instruct-v0.2-bnb-4bit","unsloth/llama-2-7b-bnb-4bit","unsloth/gemma-7b-bnb-4bit","unsloth/gemma-7b-it-bnb-4bit", # Instruct version of Gemma 7b"unsloth/gemma-2b-bnb-4bit","unsloth/gemma-2b-it-bnb-4bit", # Instruct version of Gemma 2b"unsloth/llama-3-8b-bnb-4bit", # [NEW] 15 Trillion token Llama-3
] # More models at https://huggingface.co/unslothmodel, tokenizer = FastLanguageModel.from_pretrained(model_name = "unsloth/llama-3-8b-bnb-4bit",max_seq_length = max_seq_length,dtype = dtype,load_in_4bit = load_in_4bit,# token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)model = FastLanguageModel.get_peft_model(model,r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128target_modules = ["q_proj", "k_proj", "v_proj", "o_proj","gate_proj", "up_proj", "down_proj",],lora_alpha = 16,lora_dropout = 0, # Supports any, but = 0 is optimizedbias = "none",    # Supports any, but = "none" is optimized# [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long contextrandom_state = 3407,use_rslora = False,  # We support rank stabilized LoRAloftq_config = None, # And LoftQ
)alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.### Instruction:
{}### Input:
{}### Response:
{}"""EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN
def formatting_prompts_func(examples):instructions = examples["instruction"]inputs       = examples["input"]outputs      = examples["output"]texts = []for instruction, input, output in zip(instructions, inputs, outputs):# Must add EOS_TOKEN, otherwise your generation will go on forever!text = alpaca_prompt.format(instruction, input, output) + EOS_TOKENtexts.append(text)return { "text" : texts, }
passfrom datasets import load_dataset#file_path = "/home/Ubuntu/alpaca_gpt4_data_zh.json"#dataset = load_dataset("json", data_files={"train": file_path}, split="train")dataset = load_dataset("yahma/alpaca-cleaned", split = "train")dataset = dataset.map(formatting_prompts_func, batched = True,)trainer = SFTTrainer(model = model,tokenizer = tokenizer,train_dataset = dataset,dataset_text_field = "text",max_seq_length = max_seq_length,dataset_num_proc = 2,packing = False, # Can make training 5x faster for short sequences.args = TrainingArguments(per_device_train_batch_size = 2,gradient_accumulation_steps = 4,warmup_steps = 5,max_steps = 60,learning_rate = 2e-4,fp16 = not torch.cuda.is_bf16_supported(),bf16 = torch.cuda.is_bf16_supported(),logging_steps = 1,optim = "adamw_8bit",weight_decay = 0.01,lr_scheduler_type = "linear",seed = 3407,output_dir = "outputs",),
)trainer_stats = trainer.train()model.save_pretrained_gguf("llama3", tokenizer, quantization_method = "q4_k_m")
model.save_pretrained_gguf("llama3", tokenizer, quantization_method = "q8_0")
model.save_pretrained_gguf("llama3", tokenizer, quantization_method = "f16")#to hugging face
model.push_to_hub_gguf("leo009/llama3", tokenizer, quantization_method = "q4_k_m")
model.push_to_hub_gguf("leo009/llama3", tokenizer, quantization_method = "q8_0")
model.push_to_hub_gguf("leo009/llama3", tokenizer, quantization_method = "f16")

3.我们选择将hugging face上微调好的模型下载下来（https://huggingface.co/leo009/llama3/tree/main）

4.模型导入ollama

下载ollama

 导入ollama

FROM ./downloads/mistrallite.Q4_K_M.gguf
ollama create example -f Modelfile

二.实现在线搜索

1.获取Tavily AI API 

Tavily AI

export TAVILY_API_KEY=tvly-xxxxxxxxxxx

 2.安装对应的python库

install tavily-python

pip install phidata

pip install ollam

3.运行app.py

#app.py
import warnings# Suppress only the specific NotOpenSSLWarning
warnings.filterwarnings("ignore", message="urllib3 v2 only supports OpenSSL 1.1.1+")from phi.assistant import Assistant
from phi.llm.ollama import OllamaTools
from phi.tools.tavily import TavilyTools# 创建一个Assistant实例，配置其使用OllamaTools中的llama3模型，并整合Tavily工具
assistant = Assistant(llm=OllamaTools(model="mymodel3"),  # 使用OllamaTools的llama3模型tools=[TavilyTools()],show_tool_calls=True,  # 设置为True以展示工具调用信息
)# 使用助手实例输出请求的响应，并以Markdown格式展示结果
assistant.print_response("Search tavily for 'GPT-5'", markdown=True)

 三.实现RAG

1.git clone https://github.com/phidatahq/phidata.git

2.phidata---->cookbook---->llms--->ollama--->rag里面 有示例和教程

修改assigant.py中的14行代码，将llama3改为自己微调好的模型

另外需要注意的是！！！

要将自己的模型名称加入到app.py里面的数组里

streamlit  run  /home/cxh/phidata/cookbook/llms/ollama/rag/assistant.py