之前测验了根据LLaMA运用LoRA进行参数高效微调，有被冷艳到。相对于full finetuning，运用LaRA明显提高了练习的速度。

尽管 LLaMA 在英文上具有强壮的零样本学习和搬迁才能，可是由于在预练习阶段 LLaMA 几乎没有见过中文语料。因而，它的中文才能很弱，即使对其进行有监督的微调，平等参数规模下，它的中文才能也是要弱于bloom-7b1、chatglm-6b等。

下面，我们来测验根据中英双语的对话言语模型ChatGLM-6B运用LoRA进行参数高效微调。

环境建立

基础环境配置如下：

  • 操作系统: CentOS 7
  • CPUs: 单个节点具有 1TB 内存的 Intel CPU，物理CPU个数为64，每颗CPU核数为16
  • GPUs: 8 卡 A800 80GB GPUs
  • Python: 3.10 (需求先升级OpenSSL到1.1.1t版别（点击下载OpenSSL），然后再编译装置Python)，点击下载Python
  • NVIDIA驱动程序版别: 515.65.01，根据不同类型挑选不同的驱动程序，点击下载。
  • CUDA工具包: 11.7，点击下载
  • NCCL: nccl_2.14.3-1+cuda11.7，点击下载
  • cuDNN: 8.8.1.3_cuda11，点击下载

上面的NVIDIA驱动、CUDA、Python等工具的装置就不一一赘述了。

创立虚拟环境并激活虚拟环境chatglm-lora-venv-py310-cu117：

cd /home/guodong.li/virtual-venv
virtualenv -p /usr/bin/python3.10 chatglm-lora-venv-py310-cu117
source /home/guodong.li/virtual-venv/chatglm-lora-venv-py310-cu117/bin/activate

离线装置PyTorch，点击下载对应cuda版别的torch和torchvision即可。

pip install torch-1.13.1+cu117-cp310-cp310-linux_x86_64.whl
pip install torchvision-0.14.1+cu117-cp310-cp310-linux_x86_64.whl

装置相关的库。

pip install -r requirements.txt

requirements.txt文件内容如下：

# int8
bitsandbytes==0.37.1
accelerate==0.17.1
# chatglm
protobuf>=3.19.5,<3.20.1
transformers==4.27.1
icetk
cpm_kernels==1.0.11
#torch>=1.13.1
tensorboard
datasets==2.10.1

装置PEFT，PEFT 是一个库（LoRA 是其支撑的技能之一，除此之外还有Prefix Tuning、P-Tuning、Prompt Tuning），能够让你运用各种根据 Transformer 结构的言语模型进行高效微调。

git clone https://github.com/huggingface/peft.git
cd peft/
git checkout e536616
pip install .

数据集准备

本文选用Stanford Alpaca中供给的alpaca_data.json指令数据集进行参数高效微调，可是在Alpaca-LoRA中说到该数据集存在一些噪声，因而，他们对该数据集做了清洗后得到了alpaca_data_cleaned.json文件。选用该数据集进行练习大概率会得到更好成果。

数据预处理

下载ChatGLM-Tuning代码，并切换到对应的commitid保证代码的一致性。

git clone https://github.com/mymusise/ChatGLM-Tuning.git
cd ChatGLM-Tuning
git checkout 9973930

转化alpaca数据集为jsonl格式，即每行一条json语料。

python cover_alpaca2jsonl.py \
  --data_path /data/nfs/guodong.li/data/alpaca_data_cleaned.json \
  --save_path /data/nfs/guodong.li/data/alpaca_data_cleaned.jsonl

检查成果：

> head -n10 /data/nfs/guodong.li/data/alpaca_data_cleaned.jsonl
> {"context": "Instruction: Give three tips for staying healthy.\nAnswer: ", "target": "1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule."}
> {"context": "Instruction: What are the three primary colors?\nAnswer: ", "target": "The three primary colors are red, blue, and yellow."}
> {"context": "Instruction: Describe the structure of an atom.\nAnswer: ", "target": "An atom is made up of a nucleus, which contains protons and neutrons, surrounded by electrons that travel in orbits around the nucleus. The protons and neutrons have a positive charge, while the electrons have a negative charge, resulting in an overall neutral atom. The number of each particle determines the atomic number and the type of atom."}
> {"context": "Instruction: How can we reduce air pollution?\nAnswer: ", "target": "There are a number of ways to reduce air pollution, such as shifting to renewable energy sources, encouraging the use of public transportation, prohibiting the burning of fossil fuels, implementing policies to reduce emissions from industrial sources, and implementing vehicle emissions standards. Additionally, individuals can do their part to reduce air pollution by reducing car use, avoiding burning materials such as wood, and changing to energy efficient appliances."}
> {"context": "Instruction: Pretend you are a project manager of a construction company. Describe a time when you had to make a difficult decision.\nAnswer: ", "target": "I had to make a difficult decision when I was working as a project manager at a construction company. I was in charge of a project that needed to be completed by a certain date in order to meet the client\u2019s expectations. However, due to unexpected delays, we were not able to meet the deadline and so I had to make a difficult decision. I decided to extend the deadline, but I had to stretch the team\u2019s resources even further and increase the budget. Although it was a risky decision, I ultimately decided to go ahead with it to ensure that the project was completed on time and that the client\u2019s expectations were met. The project was eventually successfully completed and this was seen as a testament to my leadership and decision-making abilities."}
> {"context": "Instruction: Identify the odd one out.\nInput: Twitter, Instagram, Telegram\nAnswer: ", "target": "Telegram"}
> {"context": "Instruction: Explain why the following fraction is equivalent to 1/4\nInput: 4/16\nAnswer: ", "target": "The fraction 4/16 is equivalent to 1/4 because both numerators and denominators are divisible by 4. Dividing both the top and bottom numbers by 4 yields the fraction 1/4."}
> {"context": "Instruction: Write a short story in third person narration about a protagonist who has to make an important career decision.\nAnswer: ", "target": "John was at a crossroads in his life. He had just graduated college and was now facing the big decision of what career to pursue. After much deliberation, he decided that he wanted to be an accountant and help the financially disadvantaged. He had always been good with numbers and enjoyed seeing the tangible results of his work. \n\nJohn enrolled in accounting courses and initially found it quite challenging. He had to learn multiple systems and regulations quickly, but he worked hard and eventually excelled in his studies. After a few years, John started working at an accounting firm in his city. He was eager to put his knowledge of taxes and accounting to use in a real-world setting.\n\nJohn loved his job, as it let him express his creativity in finding strategies to save his clients money. After a few years at the firm, he became a senior accountant and was asked to manage bigger and more challenging cases. He was now a respected figure in the financial industry, but he still remembers when he was just a recent college graduate, unsure of the direction in which his life would take him."}
> {"context": "Instruction: Render a 3D model of a house\nAnswer: ", "target": "<nooutput> This type of instruction cannot be fulfilled by a GPT model."}
> {"context": "Instruction: Evaluate this sentence for spelling and grammar mistakes\nInput: He finnished his meal and left the resturant\nAnswer: ", "target": "He finished his meal and left the restaurant."}

对预处理后的语料进行分词并保存。

# 预先创立输出分词后的目录
mkdir -p /data/nfs/guodong.li/data/alpaca_tokenize
python tokenize_dataset_rows.py \
\--jsonl_path  /data/nfs/guodong.li/data/alpaca_data_cleaned.jsonl  \
\--save_path /data/nfs/guodong.li/data/alpaca_tokenize \
\--max_seq_length 200 \
\--skip_overlength True

参数阐明：

  • –jsonl_path 微调的数据途径, 格式jsonl, 对每行的[‘context’]和[‘target’]字段进行encode
  • –save_path 输出途径
  • –max_seq_length 样本的最大长度

检查处理之后的成果：

> ls -al --block-size=K /data/nfs/guodong.li/data/alpaca_tokenize
total 15588K
drwxrwxr-x 1 nobody nobody     0K Apr 13 14:06 .
drwxr-xr-x 1 nobody nobody     0K Apr 13 14:06 ..
-rw-rw-r-- 1 nobody nobody 15578K Apr 13 14:06 data-00000-of-00001.arrow
-rw-rw-r-- 1 nobody nobody     1K Apr 13 14:06 dataset_info.json # 数据集信息文件
-rw-rw-r-- 1 nobody nobody     1K Apr 13 14:06 state.json

参数高效微调

单卡形式模型练习

修正finetune.py文件：

# TODO
# tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("/data/nfs/llm/model/chatglm-6b", trust_remote_code=True)
...
def main():
    ...
    # TODO
    """
    model = AutoModel.from_pretrained(
        "THUDM/chatglm-6b", load_in_8bit=True, trust_remote_code=True, device_map="auto"
    )
    """
    model = AutoModel.from_pretrained(
         "/data/nfs/llm/model/chatglm-6b", load_in_8bit=True, trust_remote_code=True, device_map="auto"
    )

运转指令：

python finetune.py \
    --dataset_path /data/nfs/guodong.li/data/alpaca_tokenize \
    --lora_rank 8 \
    --per_device_train_batch_size 6 \
    --gradient_accumulation_steps 1 \
    --max_steps 52000 \
    --save_steps 1000 \
    --save_total_limit 2 \
    --learning_rate 1e-4 \
    --fp16 \
    --remove_unused_columns false \
    --logging_steps 50 \
    --output_dir /home/guodong.li/data/chatglm-6b-lora

运转进程：

{'loss': 2.2081, 'learning_rate': 9.991153846153847e-05, 'epoch': 0.01}
...
{'loss': 1.7604, 'learning_rate': 9.904615384615386e-05, 'epoch': 0.06}
{'loss': 1.7521, 'learning_rate': 9.895e-05, 'epoch': 0.07}
  1%|█▌                         | 588/52000 [11:42<16:38:08,  1.16s/it]

形似很慢，测验增大batch_size和gradient_accumulation_steps来提高。

python finetune.py \
  --dataset_path /data/nfs/guodong.li/data/alpaca_tokenize \
  --lora_rank 8 \
  --per_device_train_batch_size 32 \
  --gradient_accumulation_steps 4 \
  --num_train_epochs 3 \
  --save_steps 1000 \
  --save_total_limit 2 \
  --learning_rate 1e-4 \
  --fp16 \
  --remove_unused_columns false \
  --logging_steps 50 \
  --output_dir /home/guodong.li/data/chatglm-6b-lora

运转进程：

0%|                     | 0/1167 [00:00<?, ?it/s]
{'loss': 2.142, 'learning_rate': 9.571550985432734e-05, 'epoch': 0.13}
8%|██████████▊         | 89/1167 [33:17<6:30:07, 21.71s/it]

速度提高上去了，可是还是在单卡形式下进行练习，下面测验运用数据并行技能来进一步提高练习速度。

数据并行形式模型练习

首要，复制finetune.py文件为finetune_dp.py。

cp finetune.py finetune_dp.py

然后，修正finetune_dp.py文件。

# TODO
# tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("/data/nfs/llm/model/chatglm-6b", trust_remote_code=True, revision="")
...
def main():
    writer = SummaryWriter()
    finetune_args, training_args = HfArgumentParser(
        (FinetuneArguments, TrainingArguments)
    ).parse_args_into_dataclasses()
    # init model
    # TODO
    """
    model = AutoModel.from_pretrained(
        "THUDM/chatglm-6b", load_in_8bit=True, trust_remote_code=True, device_map="auto"
    )
    """
    while True:
        try:
            model = AutoModel.from_pretrained("/data/nfs/llm/model/chatglm-6b", trust_remote_code=True, revision="")
            break
        except:
            pass
    model.gradient_checkpointing_enable()
    model.enable_input_require_grads()
    # TODO 
    #model.is_parallelizable = True
    #model.model_parallel = True
    model.lm_head = CastOutputToFloat(model.lm_head)
    model.config.use_cache = (
        False  # silence the warnings. Please re-enable for inference!
    )
    # setup peft
    peft_config = LoraConfig(
        task_type=TaskType.CAUSAL_LM,
        inference_mode=False,
        r=finetune_args.lora_rank,
        lora_alpha=32,
        lora_dropout=0.1,
    )
    model = get_peft_model(model, peft_config)
    # load dataset
    dataset = datasets.load_from_disk(finetune_args.dataset_path)
    print(f"\n{len(dataset)=}\n")
    training_args.ddp_find_unused_parameters=False
    # start train
    trainer = ModifiedTrainer(
        model=model,
        train_dataset=dataset,
        args=training_args,
        callbacks=[TensorBoardCallback(writer)],
        data_collator=data_collator,
    )
    trainer.train()
    writer.close()
    # save model
    model.save_pretrained(training_args.output_dir)

注意：

chatglm加载模型时会调用transformers/dynamic_module_utils.py文件下的get_class_in_module办法，而该办法在并发情况下会存在找不到文件的问题。本文在程序中加了个while True进行简略的容错处理，因而，呈现FileNotFoundError能够疏忽。

运转指令：

torchrun --nproc_per_node=4 --master_port=29005 finetune_dp.py \
  --dataset_path /data/nfs/guodong.li/data/alpaca_tokenize \
  --lora_rank 8 \
  --per_device_train_batch_size 40 \
  --gradient_accumulation_steps 4 \
  --num_train_epochs 3 \
  --save_steps 1000 \
  --save_total_limit 2 \
  --learning_rate 1e-4 \
  --fp16 \
  --remove_unused_columns false \
  --logging_steps 50 \
  --output_dir /home/guodong.li/data/chatglm-6b-lora

运转成果：

WARNING:torch.distributed.run:
*****************************************
Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
*****************************************
===================================BUG REPORT===================================
Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues
================================================================================
...
/home/guodong.li/virtual-venv/chatglm-lora-venv-py310-cu117/lib/python3.10/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/opt/rh/devtoolset-7/root/usr/lib/dyninst'), PosixPath('/opt/rh/devtoolset-9/root/usr/lib/dyninst')}
  warn(msg)
CUDA SETUP: CUDA runtime path found: /usr/local/cuda-11.7/lib64/libcudart.so
CUDA SETUP: Highest compute capability among GPUs detected: 8.0
CUDA SETUP: Detected CUDA version 117
CUDA SETUP: Loading binary /home/guodong.li/virtual-venv/chatglm-lora-venv-py310-cu117/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda117.so...
/home/guodong.li/virtual-venv/chatglm-lora-venv-py310-cu117/lib/python3.10/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/opt/rh/devtoolset-9/root/usr/lib/dyninst'), PosixPath('/opt/rh/devtoolset-7/root/usr/lib/dyninst')}
...
CUDA SETUP: CUDA runtime path found: /usr/local/cuda-11.7/lib64/libcudart.so
CUDA SETUP: Highest compute capability among GPUs detected: 8.0
CUDA SETUP: Detected CUDA version 117
CUDA SETUP: Loading binary /home/guodong.li/virtual-venv/chatglm-lora-venv-py310-cu117/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda117.so...
...
Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8/8 [00:16<00:00,  2.05s/it]
/home/guodong.li/virtual-venv/chatglm-lora-venv-py310-cu117/lib/python3.10/site-packages/peft/tuners/lora.py:191: UserWarning: fan_in_fan_out is set to True but the target module is not a Conv1D. Setting fan_in_fan_out to False.
  warnings.warn(
Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8/8 [00:17<00:00,  2.17s/it]
/home/guodong.li/virtual-venv/chatglm-lora-venv-py310-cu117/lib/python3.10/site-packages/peft/tuners/lora.py:191: UserWarning: fan_in_fan_out is set to True but the target module is not a Conv1D. Setting fan_in_fan_out to False.
  warnings.warn(
Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8/8 [00:20<00:00,  2.54s/it]
/home/guodong.li/virtual-venv/chatglm-lora-venv-py310-cu117/lib/python3.10/site-packages/peft/tuners/lora.py:191: UserWarning: fan_in_fan_out is set to True but the target module is not a Conv1D. Setting fan_in_fan_out to False.
  warnings.warn(
Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8/8 [00:21<00:00,  2.63s/it]
len(dataset)=49847
len(dataset)=49847
len(dataset)=49847
len(dataset)=49847
{'loss': 2.1301, 'learning_rate': 7.863247863247864e-05, 'epoch': 0.64}
{'loss': 1.8471, 'learning_rate': 5.726495726495726e-05, 'epoch': 1.28}
{'loss': 1.7966, 'learning_rate': 3.58974358974359e-05, 'epoch': 1.92}
{'loss': 1.7829, 'learning_rate': 1.4529914529914531e-05, 'epoch': 2.56}
{'train_runtime': 2654.3961, 'train_samples_per_second': 56.337, 'train_steps_per_second': 0.088, 'train_loss': 1.8721362872001452, 'epoch': 3.0}
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 234/234 [44:13<00:00, 11.34s/it]

显存占用：

Thu Apr 13 20:20:05 2023
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 515.105.01   Driver Version: 515.105.01   CUDA Version: 11.7     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA A800 80G...  Off  | 00000000:34:00.0 Off |                    0 |
| N/A   67C    P0   271W / 300W |  33429MiB / 81920MiB |    100%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+
|   1  NVIDIA A800 80G...  Off  | 00000000:35:00.0 Off |                    0 |
| N/A   68C    P0   342W / 300W |  52621MiB / 81920MiB |    100%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+
|   2  NVIDIA A800 80G...  Off  | 00000000:36:00.0 Off |                    0 |
| N/A   68C    P0   241W / 300W |  75273MiB / 81920MiB |    100%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+
|   3  NVIDIA A800 80G...  Off  | 00000000:37:00.0 Off |                    0 |
| N/A   70C    P0   341W / 300W |  29897MiB / 81920MiB |    100%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A     55827      C   ...nv-py310-cu117/bin/python    33427MiB |
|    1   N/A  N/A     55828      C   ...nv-py310-cu117/bin/python    52619MiB |
|    2   N/A  N/A     55829      C   ...nv-py310-cu117/bin/python    75271MiB |
|    3   N/A  N/A     55830      C   ...nv-py310-cu117/bin/python    29895MiB |
+-----------------------------------------------------------------------------+

模型输出文件：

> ls -al
total 14368
drwxrwxr-x  3 guodong.li guodong.li       86 Apr 13 20:00 .
drwxrwxr-x 12 guodong.li guodong.li      206 Apr 13 11:13 ..
-rw-rw-r--  1 guodong.li guodong.li      425 Apr 13 20:00 adapter_config.json
-rw-rw-r--  1 guodong.li guodong.li 14700953 Apr 13 20:00 adapter_model.bin
drwxrwxr-x 13 guodong.li guodong.li     4096 Apr 13 19:15 runs

至此，整个练习进程就完成了，接下来运用生成的模型进行推理。

模型推理

新增推理代码inference.py：

from transformers import AutoModel,AutoTokenizer
import torch
from peft import PeftModel
import json
from cover_alpaca2jsonl import format_example
device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
model = AutoModel.from_pretrained("/data/nfs/llm/model/chatglm-6b", trust_remote_code=True, load_in_8bit=True, device_map='auto', revision="")
tokenizer = AutoTokenizer.from_pretrained("/data/nfs/llm/model/chatglm-6b", trust_remote_code=True,  revision="")
model = PeftModel.from_pretrained(model, "/home/guodong.li/data/chatglm-6b-lora")
# TODO
instructions = json.load(open("/data/nfs/guodong.li/data/alpaca_data_cleaned.json"))
answers = []
with torch.no_grad():
    for idx, item in enumerate(instructions[:3]):
        feature = format_example(item)
        input_text = feature['context']
        ids = tokenizer.encode(input_text)
        input_ids = torch.LongTensor([ids])
        input_ids = input_ids.to(device)
        out = model.generate(
            input_ids=input_ids,
            max_length=150,
            do_sample=False,
            temperature=0
        )
        out_text = tokenizer.decode(out[0])
        answer = out_text.replace(input_text, "").replace("\nEND", "").strip()
        item['infer_answer'] = answer
        print(out_text)
        print(f"### {idx+1}.Answer:\n", item.get('output'), '\n\n')
        answers.append({'index': idx, **item})

运转指令：

CUDA_VISIBLE_DEVICES=0 python inference.py

运转成果：

> CUDA_VISIBLE_DEVICES=0 python inference.py
===================================BUG REPORT===================================
Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues
================================================================================
/home/guodong.li/virtual-venv/chatglm-lora-venv-py310-cu117/lib/python3.10/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/opt/rh/devtoolset-7/root/usr/lib/dyninst'), PosixPath('/opt/rh/devtoolset-9/root/usr/lib/dyninst')}
  warn(msg)
CUDA SETUP: CUDA runtime path found: /usr/local/cuda-11.7/lib64/libcudart.so
CUDA SETUP: Highest compute capability among GPUs detected: 8.0
CUDA SETUP: Detected CUDA version 117
CUDA SETUP: Loading binary /home/guodong.li/virtual-venv/chatglm-lora-venv-py310-cu117/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda117.so...
Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.
Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8/8 [00:11<00:00,  1.39s/it]
The dtype of attention mask (torch.int64) is not bool
Instruction: Give three tips for staying healthy.
Answer: Three tips for staying healthy include: 1) eating a balanced diet, 2) getting regular exercise, and 3) getting enough rest.
### 1.Answer:
 1.Eat a balanced diet and make sure to include plenty of fruits and vegetables.
2. Exercise regularly to keep your body active and strong.
3. Get enough sleep and maintain a consistent sleep schedule.
Instruction: What are the three primary colors?
Answer: The three primary colors are red, blue, and yellow.
### 2.Answer:
 The three primary colors are red, blue, and yellow.
Instruction: Describe the structure of an atom.
Answer: An atom is a small particle of matter that contains a core of positive charge, surrounded by a cloud of negative charge. The positive charge is caused by the presence of an electron cloud, which is surrounded by an electron cloud. The negative charge is caused by the presence of an electron cloud, which is surrounded by an electron cloud. The positive and negative charges are balanced by the presence of an equal number of protons and neutrons.
### 3.Answer:
 An atom is made up of a nucleus, which contains protons and neutrons, surrounded by electrons that travel in orbits around the nucleus. The protons and neutrons have a positive charge, while the electrons have a negative charge, resulting in an overall neutral atom. The number of each particle determines the atomic number and the type of atom.

其间：Answer: 是模型的输出，#### Answer: 是原答案。

结语

本文主要讲述了根据ChatGLM-6B运用LoRA进行参数高效微调以及运用练习好的模型对其进行推理，后续再根据ChatGLM-6B运用其他的参数高效微调技能。

参考文档：

  • Alpaca-LoRA
  • Stanford Alpaca
  • ChatGLM-Tuning

我叫果冻，如果觉得我的文章/回答能够帮助到你，期待你的点赞，祝好~~~