微调Llama3实现在线搜索引擎和RAG检索增强生成功能
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_env
conda install pytorch-cuda=12.1 pytorch cudatoolkit xformers -c pytorch -c nvidia -c xformers
pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
pip install --no-deps trl peft accelerate bitsandbytes2.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/viewer
from unsloth import FastLanguageModel
import torch
from trl import SFTTrainer
from transformers import TrainingArguments
max_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/unsloth
model, 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, 128
target_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 optimized
bias = "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 context
random_state = 3407,
use_rslora = False, # We support rank stabilized LoRA
loftq_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_TOKEN
texts.append(text)
return { "text" : texts, }
pass
from 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
export TAVILY_API_KEY=tvly-xxxxxxxxxxx2.install tavily-python
pip install tavily-python3.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
原文地址：https://blog.csdn.net/m0_57057282/article/details/139089028
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