要在16卡服務器上使用最新版的CUDA和驅動訓練llama - 2 - 7b和llama - 2 - 70b模型，并生成訓練指標數據，你可以按照以下步驟進行：

1. 環境準備

確保你的服務器已經安裝了最新版的CUDA和驅動，并且安裝了必要的Python庫，如torch、transformers、datasets等。可以使用以下命令安裝：

pip install torch transformers datasets accelerate deepspeed

2. 代碼實現

import torch
from torch.utils.data import DataLoader
from transformers import (AutoModelForCausalLM,AutoTokenizer,TrainingArguments,Trainer,default_data_collator
)
from datasets import load_dataset
import time# 定義模型名稱
model_names = ["meta-llama/Llama-2-7b-hf", "meta-llama/Llama-2-70b-hf"]# 加載數據集
dataset = load_dataset("wikitext", "wikitext-2-raw-v1")for model_name in model_names:print(f"Training {model_name}...")# 加載模型和分詞器tokenizer = AutoTokenizer.from_pretrained(model_name)tokenizer.pad_token = tokenizer.eos_tokenmodel = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)# 預處理數據集def preprocess_function(examples):inputs = tokenizer(examples["text"], truncation=True, max_length=512, padding="max_length")return inputstokenized_dataset = dataset.map(preprocess_function, batched=True)# 定義訓練參數training_args = TrainingArguments(output_dir=f"./results/{model_name}",num_train_epochs=1,per_device_train_batch_size=4,gradient_accumulation_steps=1,fp16=True,logging_steps=10,save_steps=1000,evaluation_strategy="steps",eval_steps=500,warmup_steps=500,weight_decay=0.01,logging_dir=f"./logs/{model_name}",deepspeed="ds_config.json"  # 使用DeepSpeed進行分布式訓練)# 定義Trainertrainer = Trainer(model=model,args=training_args,train_dataset=tokenized_dataset["train"],eval_dataset=tokenized_dataset["validation"],data_collator=default_data_collator,)# 開始訓練并記錄時間start_time = time.time()trainer.train()end_time = time.time()# 計算訓練指標total_steps = trainer.state.global_steptotal_time = end_time - start_timethroughput = total_steps / total_timeprint(f"Model: {model_name}")print(f"Total steps: {total_steps}")print(f"Total time (s): {total_time}")print(f"Throughput (steps/s): {throughput}")

3. DeepSpeed配置文件（ds_config.json）

{"train_batch_size": 64,"optimizer": {"type": "Adam","params": {"lr": 0.0001,"betas": [0.9,0.999],"eps": 1e-8,"weight_decay": 0.01}},"fp16": {"enabled": true,"loss_scale": 0,"initial_scale_power": 16},"zero_optimization": {"stage": 2,"allgather_partitions": true,"allgather_bucket_size": 2e8,"overlap_comm": true,"reduce_scatter": true,"reduce_bucket_size": 2e8,"contiguous_gradients": true}
}

4. 運行代碼

將上述代碼保存為train_llama.py，并在終端中運行：

deepspeed --num_gpus 16 train_llama.py

注意事項

• 模型權限：Llama - 2系列模型需要在Hugging Face上申請訪問權限，確保你已經獲得了相應的權限。
• 硬件資源：llama - 2 - 70b模型非常大，需要足夠的顯存和內存資源。確保你的服務器能夠支持該模型的訓練。
• 數據處理：這里使用的是wikitext - 2 - raw - v1數據集，你可以根據需要替換為自己的數據集。