LLaMA-Factory微调（sft）ChatGLM3-6B保姆教程

准备

1、下载

下载LLaMA-Factory
下载ChatGLM3-6B
下载ChatGLM3
windows下载CUDA ToolKit 12.1 （本人是在windows进行训练的，显卡GTX 1660 Ti）

CUDA安装完毕后，通过指令nvidia-smi查看
查看cuda安装后的信息

2、PyCharm打开LLaMA-Factory项目

1、选择下载目录：E:\llm-train\LLaMA-Factory，并打开
打开项目

2、创建新的python环境，这里使用conda创建一个python空环境，选择python3.10

3、安装依赖

参考LLaMA-Factory的依赖安装步骤

安装LLaMA-Factory依赖

(llm) PS E:\llm-train\LLaMA-Factory> pwd

Path
----
E:\llm-train\LLaMA-Factory


(llm) PS E:\llm-train\LLaMA-Factory> pip install -r requirements.txt
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Installing collected packages: sentencepiece, pytz, pydub, mpmath, ffmpy, xxhash, websockets, urllib3, tzdata, typing-extensions, tqdm, toolz, termcolor, sympy, sniffio, six, shtab, semantic-version, safetensors, rpds-py, re
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ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
pylint 2.17.1 requires tomlkit>=0.10.1, which is not installed.
Successfully installed accelerate-0.28.0 aiofiles-23.2.1 aiohttp-3.9.3 aiosignal-1.3.1 altair-5.2.0 annotated-types-0.6.0 anyio-4.3.0 async-timeout-4.0.3 attrs-23.2.0 bitsandbytes-0.43.0 certifi-2024.2.2 charset-normalizer-3
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0.1 referencing-0.34.0 regex-2023.12.25 requests-2.31.0 rich-13.7.1 rpds-py-0.18.0 safetensors-0.4.2 scipy-1.12.0 semantic-version-2.10.0 sentencepiece-0.2.0 shtab-1.7.1 six-1.16.0 sniffio-1.3.1 sse-starlette-2.0.0 starlette
-0.36.3 sympy-1.12 termcolor-2.4.0 tokenizers-0.15.2 toolz-0.12.1 torch-2.2.2 tqdm-4.66.2 transformers-4.39.2 trl-0.8.1 typing-extensions-4.10.0 tyro-0.7.3 tzdata-2024.1 urllib3-2.2.1 uvicorn-0.29.0 websockets-11.0.3 xxhash-
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(llm) PS E:\llm-train\LLaMA-Factory>

为了使用cuda（GPU）加速训练和推理，不安装则使用cpu。安装torch的cuda版本：pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

(llm) PS E:\llm-train\LLaMA-Factory> pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
Looking in indexes: https://download.pytorch.org/whl/cu121
Requirement already satisfied: torch in c:\users\administrator\miniconda3\envs\llm\lib\site-packages (2.2.2)
Collecting torchvision
  Using cached https://download.pytorch.org/whl/cu121/torchvision-0.17.2%2Bcu121-cp310-cp310-win_amd64.whl (5.7 MB)
Collecting torchaudio
  Using cached https://download.pytorch.org/whl/cu121/torchaudio-2.2.2%2Bcu121-cp310-cp310-win_amd64.whl (4.1 MB)
Requirement already satisfied: filelock in c:\users\administrator\miniconda3\envs\llm\lib\site-packages (from torch) (3.13.3)
Requirement already satisfied: typing-extensions>=4.8.0 in c:\users\administrator\miniconda3\envs\llm\lib\site-packages (from torch) (4.10.0)
Requirement already satisfied: sympy in c:\users\administrator\miniconda3\envs\llm\lib\site-packages (from torch) (1.12)
Requirement already satisfied: networkx in c:\users\administrator\miniconda3\envs\llm\lib\site-packages (from torch) (3.2.1)
Requirement already satisfied: jinja2 in c:\users\administrator\miniconda3\envs\llm\lib\site-packages (from torch) (3.1.3)
Requirement already satisfied: fsspec in c:\users\administrator\miniconda3\envs\llm\lib\site-packages (from torch) (2024.2.0)
Requirement already satisfied: numpy in c:\users\administrator\miniconda3\envs\llm\lib\site-packages (from torchvision) (1.26.4)
Collecting torch
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Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in c:\users\administrator\miniconda3\envs\llm\lib\site-packages (from torchvision) (10.2.0)
Requirement already satisfied: MarkupSafe>=2.0 in c:\users\administrator\miniconda3\envs\llm\lib\site-packages (from jinja2->torch) (2.1.5)
Requirement already satisfied: mpmath>=0.19 in c:\users\administrator\miniconda3\envs\llm\lib\site-packages (from sympy->torch) (1.3.0)
Installing collected packages: torch, torchvision, torchaudio
  Attempting uninstall: torch
    Found existing installation: torch 2.2.2
    Uninstalling torch-2.2.2:
      Successfully uninstalled torch-2.2.2
Successfully installed torch-2.2.2+cu121 torchaudio-2.2.2+cu121 torchvision-0.17.2+cu121
(llm) PS E:\llm-train\LLaMA-Factory>

准备数据集

1、创建`LLaMA-Factory\data\chatglm3_zh.json`文件，拷贝一下内容。作为训练测试数据

[
  {
    "instruction": "",
    "input": "安妮",
    "output": "女仆。  精灵族\n声音温柔娇媚，嗲音。\n年龄：26岁"
  },
  {
    "instruction": "",
    "input": "奥利维亚",
    "output": "元气少女，中气十足。\n活泼可爱，心直口快。\n年龄：16岁  矮人族"
  },
  {
    "instruction": "",
    "input": "维京人甲-\n维京战士",
    "output": "野蛮无礼的维京人。\n市井气息，有些无赖。\n年龄：38岁。"
  },
  {
    "instruction": "",
    "input": "维京人乙-维京射手",
    "output": "龙套角色。\n女性\n维京人。"
  },
  {
    "instruction": "",
    "input": "英灵-维京战神",
    "output": "维京人首领。\n王者风范，霸气十足。\n声如洪钟，气势如虹。\n年龄：55岁"
  },
  {
    "instruction": "",
    "input": "魔武·凛冬刃",
    "output": "魔武。\n物品NPC"
  },
  {
    "instruction": "",
    "input": "战斗教官",
    "output": "人类。\n粗犷的女性战士，中性范。\n年龄：35岁"
  },
  {
    "instruction": "",
    "input": "雅妮拉",
    "output": "青年女性，声音甜美。\n骄横，喜欢冷嘲热讽。\n\n主角团之一。"
  },
  {
    "instruction": "",
    "input": "公墓守卫队长",
    "output": "龙套角色。\n男性，骑士团战士。"
  },
  {
    "instruction": "",
    "input": "邪恶魔法师/黑巫师",
    "output": "人类。\n丧失理智的人类法师。\n癫狂，变态。\n\n年龄：33岁"
  },
  {
    "instruction": "",
    "input": "魔物-赤炎妖",
    "output": "魔物。\n嗜血恐怖的妖魔。"
  },
  {
    "instruction": "",
    "input": "英灵-裁决骑士",
    "output": "逝去的英雄之魂。\n女性。\n言辞铿锵有力，正义之士。"
  },
  {
    "instruction": "",
    "input": "布鲁克",
    "output": "人类贵族公子，商人。\n20岁。\n天真傲慢，蛮横自大，骄傲易怒。但实际上非常弱鸡，未涉世事。\n毫无社会常识的公子哥。\n"
  },
  {
    "instruction": "",
    "input": "地精仆从",
    "output": "布鲁克的仆人。\n地精。\n胆小怕事，说话结巴。"
  },
  {
    "instruction": "",
    "input": "嚣张佣兵",
    "output": "雷根佣兵团的佣兵，狗仗人势。"
  },
  {
    "instruction": "",
    "input": "神秘女神",
    "output": "女神，端庄温柔，持稳宁静。\n不紧不慢，慢条斯理。\n年龄：20岁"
  },
  {
    "instruction": "",
    "input": "托托",
    "output": "酒馆老板，人类\n八面玲珑，诙谐幽默。\n言谈举止进退有度，侃侃而谈。\n年龄：45岁"
  },
  {
    "instruction": "",
    "input": "酒馆女仆-露娜",
    "output": "温柔可人的女仆。"
  },
  {
    "instruction": "",
    "input": "狡诈魔魂",
    "output": "人类。-灵体\n阴险狡诈的腹黑女性。\n得势时飞扬跋扈，弱势时楚楚可怜。\n年龄：29岁"
  },
  {
    "instruction": "",
    "input": "火元素",
    "output": "元素生命。\n魔物。\n龙套角色。"
  },
  {
    "instruction": "",
    "input": "商会管事/商队运输理事",
    "output": "龙套角色，均为年轻女性。"
  },
  {
    "instruction": "",
    "input": "安吉尔",
    "output": "女骑士，中性声线。\n热诚坦率，刚正不阿。"
  },
  {
    "instruction": "",
    "input": "托克",
    "output": "龙套角色。\n地精商贩   地精种族\n年龄：30岁\n青年商贩，有一点怪物腔调。"
  },
  {
    "instruction": "",
    "input": "王国守卫/铁卫禁军",
    "output": "龙套角色，普通男性战士。"
  },
  {
    "instruction": "",
    "input": "爱德华",
    "output": "人类，狮心骑士团中的精英骑士\n青壮男性，中气十足，正义之士。\n年龄：35岁"
  },
  {
    "instruction": "",
    "input": "骑士团长",
    "output": "血族\n大家风范，中性风。\n雷厉风行，刚正不阿。\n年龄：38岁"
  },
  {
    "instruction": "",
    "input": "主教",
    "output": "人类\n圣光教会主教。\n传令官，声音清晰中正，官方强调。\n年龄：30岁"
  },
  {
    "instruction": "",
    "input": "女王",
    "output": "人类。\n年轻而阴郁的贵族少女。\n郁郁寡欢。\n\n年龄：15岁"
  },
  {
    "instruction": "",
    "input": "艾伦",
    "output": "人类佣兵。——剧情主角之一。\n20岁。\n青年才俊，隐藏身份-皇室王子。\n绝顶聪明、攻于算计、略显市侩、巧于辞令。\n言语之中，充满了少年英气，但偶尔会有些轻浮。\n\n人物参考：《雪中悍刀行》徐凤年\n"
  },
  {
    "instruction": "",
    "input": "雅各布",
    "output": "半神。\n狼人始祖。（中年男子）\n傲慢无礼，嚣张跋扈，却又不失大家风范。\n\n\n人物参考：网易阴阳师里的八岐大蛇。\n"
  },
  {
    "instruction": "",
    "input": "佣兵首领威尔曼",
    "output": "人类佣兵。\n35岁。\n中年男子，沉稳持重，波澜不惊。\n"
  },
  {
    "instruction": "",
    "input": "食人魔",
    "output": "食人怪物，半兽人腔调，残暴贪婪。\n年龄：150岁"
  },
  {
    "instruction": "",
    "input": "兽人首领",
    "output": "兽人首领。\n年龄未知。（相当于成年男性）\n蛮横残暴，对人类等弱小种族表示轻蔑和无视。"
  },
  {
    "instruction": "",
    "input": "霍华德",
    "output": "半神。\n血族始祖。（神魔大陆之中非常重要的种族）\n沉默寡言，沉稳持重，有一种成熟大叔的依赖感。\n\n例子：《秦时明月》中的盖聂。"
  },
  {
    "instruction": "",
    "input": "凯尼",
    "output": "骄横佣兵，满不在乎，阴险狡诈。"
  },
  {
    "instruction": "",
    "input": "西蒙斯",
    "output": "佣兵战士，中年男性，外强中干。"
  },
  {
    "instruction": "",
    "input": "迪比斯",
    "output": "魔法师佣兵，龙套角色。"
  },
  {
    "instruction": "",
    "input": "兰希亚",
    "output": "衰弱的女神，有气无力。\n气若游丝，声音迟缓。\n年龄：45岁"
  },
  {
    "instruction": "",
    "input": "树灵龙",
    "output": "古龙级别。\n\n可类比《霍比特人》里的恶龙史矛革"
  },
  {
    "instruction": "",
    "input": "世界之母",
    "output": "世界之母，声音沉稳而空灵，声音可带回音。\n\n可参考FF14中的水晶之母，海德林。"
  },
  {
    "instruction": "",
    "input": "树灵精灵",
    "output": "较弱可爱，声线可低幼一些，软萌音。\n楚楚可怜。"
  },
  {
    "instruction": "",
    "input": "狮心骑士维达",
    "output": "外表为中老年骑士。\n老年人特有的亲和力。\n深沉慈祥，智者形象。"
  },
  {
    "instruction": "",
    "input": "亚杰拉",
    "output": "龙套角色。\n初级佣兵。\n少女。\n战斗力低下。"
  },
  {
    "instruction": "",
    "input": "查克·肖特奇",
    "output": "龙套角色。\n初级佣兵。\n青年男性。\n战斗力低下。"
  },
  {
    "instruction": "",
    "input": "布里克",
    "output": "人族，中年男性。\n狮心高阶骑士。"
  },
  {
    "instruction": "",
    "input": "雷切尔",
    "output": "人族，中年女性。\n狮心高阶骑士。"
  },
  {
    "instruction": "",
    "input": "戴安娜",
    "output": "人族，年轻女性。\n狮心高阶骑士。"
  },
  {
    "instruction": "",
    "input": "巴伦德",
    "output": "矮人，中年男性。\n狮心高阶骑士。"
  },
  {
    "instruction": "",
    "input": "龙十八",
    "output": "禅国青年才俊。\n好风雅，外在散漫放浪。\n实则极有"
  },
  {
    "instruction": "",
    "input": "禅国商贩",
    "output": "龙套角色。\n外表看上去老实巴交，实则内心满是算计的中年男子。\n"
  },
  {
    "instruction": "",
    "input": "琳达·安吉",
    "output": "龙套角色。\n禅国年轻女子。"
  },
  {
    "instruction": "",
    "input": "芙娜德",
    "output": "龙套角色。\n禅国年轻女子。"
  },
  {
    "instruction": "",
    "input": "闹事者",
    "output": "龙套角色。\n禅国年轻男子，脾气暴躁，对商会有很深的怨念。"
  },
  {
    "instruction": "",
    "input": "商会守卫",
    "output": "龙套角色。\n中年男性\n禅国商会的守卫。"
  },
  {
    "instruction": "",
    "input": "穷苦孩童",
    "output": "龙套角色。\n少年男孩。\n生活潦倒，贫穷可怜。"
  },
  {
    "instruction": "",
    "input": "龙神傀儡/龙神",
    "output": "禅国幕后的统治者。\n实际上本身是利用死者捏造的傀儡，替真正的龙神-古龙青办事。\n为人腹黑阴险，却又气度非凡。"
  },
  {
    "instruction": "",
    "input": "佛陀八",
    "output": "寿命不可知。\n龙十八以及数代会长的老师。\n暗中与龙神勾结，获得不死之身，震慑禅国的朝纲。\n常年居住于观星阁。"
  },
  {
    "instruction": "",
    "input": "古龙青",
    "output": "龙神真身，在漫长的岁月当中，生命力之间衰弱，依靠着吞噬活人续命。\n从曾经禅国的守护者，成了禅国逐渐毁灭的黑洞。"
  },
  {
    "instruction": "",
    "input": "祭天坛护院/祭天坛守卫",
    "output": "龙套角色。\n禅国祭天坛的守护者。"
  }
]

2、编辑`LLaMA-Factory\data\dataset_info.json`，添加测试数据集到配置文件

  ...
  "chatglm3_zh": {
    "file_name": "chatglm3_zh.json"
  },
  ...

在这里插入图片描述

训练微调

1、启动web版本的训练

(llm) PS E:\llm-train\LLaMA-Factory> set CUDA_VISIBLE_DEVICES=0
(llm) PS E:\llm-train\LLaMA-Factory> python src/train_web.py
Running on local URL:  http://0.0.0.0:7860

To create a public link, set `share=True` in `launch()`.

2、调整配置，浏览器打开：http://localhost:7860/

在这里插入图片描述

语言选择中文
模型名称：ChatGLM3-6B-Chat
前面从Hugging Face下载的ChatGLM3-6B模型本地路径
微调方法：lora
训练阶段：sft
数据集：上面新添加的测试数据集
训练轮数：200，因为数据量比较小，为了能表现效果，这里使用200轮

在这里插入图片描述
预览命令

CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
    --stage sft \
    --do_train True \
    --model_name_or_path E:\llm-train\chatglm3-6b \
    --finetuning_type lora \
    --template chatglm3 \
    --dataset_dir data \
    --dataset chatglm3_zh \
    --cutoff_len 1024 \
    --learning_rate 5e-05 \
    --num_train_epochs 200.0 \
    --max_samples 100000 \
    --per_device_train_batch_size 2 \
    --gradient_accumulation_steps 8 \
    --lr_scheduler_type cosine \
    --max_grad_norm 1.0 \
    --logging_steps 5 \
    --save_steps 100 \
    --warmup_steps 0 \
    --optim adamw_torch \
    --output_dir saves\ChatGLM3-6B-Chat\lora\train_2024-03-29-15-09-35 \
    --fp16 True \
    --lora_rank 8 \
    --lora_alpha 16 \
    --lora_dropout 0.1 \
    --lora_target query_key_value \
    --plot_loss True

在这里插入图片描述

3、开始训练

在这里插入图片描述

[INFO|parser.py:242] 2024-03-29 15:18:18,458 >> Process rank: 0, device: cuda:0, n_gpu: 1, distributed training: False, compute dtype: torch.float16

[INFO|tokenization_utils_base.py:2082] 2024-03-29 15:18:19,291 >> loading file tokenizer.model

[INFO|tokenization_utils_base.py:2082] 2024-03-29 15:18:19,291 >> loading file added_tokens.json

[INFO|tokenization_utils_base.py:2082] 2024-03-29 15:18:19,291 >> loading file special_tokens_map.json

[INFO|tokenization_utils_base.py:2082] 2024-03-29 15:18:19,291 >> loading file tokenizer_config.json

[INFO|tokenization_utils_base.py:2082] 2024-03-29 15:18:19,291 >> loading file tokenizer.json

[WARNING|tokenization_chatglm.py:164] 2024-03-29 15:18:19,334 >> Setting eos_token is not supported, use the default one.

[WARNING|tokenization_chatglm.py:160] 2024-03-29 15:18:19,334 >> Setting pad_token is not supported, use the default one.

[WARNING|tokenization_chatglm.py:156] 2024-03-29 15:18:19,334 >> Setting unk_token is not supported, use the default one.

[INFO|template.py:371] 2024-03-29 15:18:19,609 >> Add <|user|>,<|observation|> to stop words.

[INFO|template.py:378] 2024-03-29 15:18:19,609 >> Cannot add this chat template to tokenizer.

[INFO|loader.py:33] 2024-03-29 15:18:19,610 >> Loading dataset chatglm3_zh.json...

[WARNING|utils.py:31] 2024-03-29 15:18:19,610 >> Checksum failed: missing SHA-1 hash value in dataset_info.json.

[INFO|configuration_utils.py:724] 2024-03-29 15:18:21,595 >> loading configuration file E:\llm-train\chatglm3-6b\config.json

[INFO|configuration_utils.py:724] 2024-03-29 15:18:21,600 >> loading configuration file E:\llm-train\chatglm3-6b\config.json

[INFO|configuration_utils.py:789] 2024-03-29 15:18:21,601 >> Model config ChatGLMConfig { "_name_or_path": "E:\llm-train\chatglm3-6b", "add_bias_linear": false, "add_qkv_bias": true, "apply_query_key_layer_scaling": true, "apply_residual_connection_post_layernorm": false, "architectures": [ "ChatGLMModel" ], "attention_dropout": 0.0, "attention_softmax_in_fp32": true, "auto_map": { "AutoConfig": "configuration_chatglm.ChatGLMConfig", "AutoModel": "modeling_chatglm.ChatGLMForConditionalGeneration", "AutoModelForCausalLM": "modeling_chatglm.ChatGLMForConditionalGeneration", "AutoModelForSeq2SeqLM": "modeling_chatglm.ChatGLMForConditionalGeneration", "AutoModelForSequenceClassification": "modeling_chatglm.ChatGLMForSequenceClassification" }, "bias_dropout_fusion": true, "classifier_dropout": null, "eos_token_id": 2, "ffn_hidden_size": 13696, "fp32_residual_connection": false, "hidden_dropout": 0.0, "hidden_size": 4096, "kv_channels": 128, "layernorm_epsilon": 1e-05, "model_type": "chatglm", "multi_query_attention": true, "multi_query_group_num": 2, "num_attention_heads": 32, "num_layers": 28, "original_rope": true, "pad_token_id": 0, "padded_vocab_size": 65024, "post_layer_norm": true, "pre_seq_len": null, "prefix_projection": false, "quantization_bit": 0, "rmsnorm": true, "seq_length": 8192, "tie_word_embeddings": false, "torch_dtype": "float16", "transformers_version": "4.39.2", "use_cache": true, "vocab_size": 65024 }

[INFO|modeling_utils.py:3280] 2024-03-29 15:18:21,772 >> loading weights file E:\llm-train\chatglm3-6b\model.safetensors.index.json

[INFO|modeling_utils.py:1417] 2024-03-29 15:18:21,774 >> Instantiating ChatGLMForConditionalGeneration model under default dtype torch.float16.

[INFO|configuration_utils.py:928] 2024-03-29 15:18:21,775 >> Generate config GenerationConfig { "eos_token_id": 2, "pad_token_id": 0 }

[INFO|modeling_utils.py:4024] 2024-03-29 15:18:37,990 >> All model checkpoint weights were used when initializing ChatGLMForConditionalGeneration.

[INFO|modeling_utils.py:4032] 2024-03-29 15:18:37,990 >> All the weights of ChatGLMForConditionalGeneration were initialized from the model checkpoint at E:\llm-train\chatglm3-6b. If your task is similar to the task the model of the checkpoint was trained on, you can already use ChatGLMForConditionalGeneration for predictions without further training.

[INFO|modeling_utils.py:3573] 2024-03-29 15:18:37,999 >> Generation config file not found, using a generation config created from the model config.

[INFO|patcher.py:259] 2024-03-29 15:18:38,004 >> Gradient checkpointing enabled.

[INFO|adapter.py:90] 2024-03-29 15:18:38,004 >> Fine-tuning method: LoRA

[INFO|loader.py:126] 2024-03-29 15:18:38,190 >> trainable params: 1949696 || all params: 6245533696 || trainable%: 0.0312

[INFO|trainer.py:607] 2024-03-29 15:18:38,204 >> Using auto half precision backend

[INFO|trainer.py:1969] 2024-03-29 15:18:38,350 >> ***** Running training *****

[INFO|trainer.py:1970] 2024-03-29 15:18:38,351 >> Num examples = 59

[INFO|trainer.py:1971] 2024-03-29 15:18:38,351 >> Num Epochs = 200

[INFO|trainer.py:1972] 2024-03-29 15:18:38,351 >> Instantaneous batch size per device = 2

[INFO|trainer.py:1975] 2024-03-29 15:18:38,351 >> Total train batch size (w. parallel, distributed & accumulation) = 16

[INFO|trainer.py:1976] 2024-03-29 15:18:38,351 >> Gradient Accumulation steps = 8

[INFO|trainer.py:1977] 2024-03-29 15:18:38,352 >> Total optimization steps = 600

[INFO|trainer.py:1978] 2024-03-29 15:18:38,354 >> Number of trainable parameters = 1,949,696

4、训练完毕

在这里插入图片描述
导出模型：需要设置训练的最后一个检查点。

(llm) PS E:\llm-train\LLaMA-Factory> python src/export_model.py --model_name_or_path E:\\llm-train\\chatglm3-6b --adapter_name_or_path E:\\llm-train\\LLaMA-Factory\\saves\\ChatGLM3-6B-Chat\\lora\\train_glm3\\checkpoint-200 -
-template default --finetuning_type lora --export_dir E:\\llm-train\\chatglm3-trained --export_size 2 --export_legacy_format False
[INFO|tokenization_utils_base.py:2082] 2024-03-30 17:18:44,968 >> loading file tokenizer.model
[INFO|tokenization_utils_base.py:2082] 2024-03-30 17:18:44,968 >> loading file added_tokens.json      
[INFO|tokenization_utils_base.py:2082] 2024-03-30 17:18:44,968 >> loading file special_tokens_map.json
[INFO|tokenization_utils_base.py:2082] 2024-03-30 17:18:44,969 >> loading file tokenizer_config.json  
[INFO|tokenization_utils_base.py:2082] 2024-03-30 17:18:44,969 >> loading file tokenizer.json         
Setting eos_token is not supported, use the default one.
Setting pad_token is not supported, use the default one.
Setting unk_token is not supported, use the default one.
[INFO|configuration_utils.py:724] 2024-03-30 17:18:45,127 >> loading configuration file E:\\llm-train\\chatglm3-6b\config.json
[INFO|configuration_utils.py:724] 2024-03-30 17:18:45,132 >> loading configuration file E:\\llm-train\\chatglm3-6b\config.json
[INFO|configuration_utils.py:789] 2024-03-30 17:18:45,133 >> Model config ChatGLMConfig {
  "_name_or_path": "E:\\\\llm-train\\\\chatglm3-6b",
  "add_bias_linear": false,
  "add_qkv_bias": true,
  "apply_query_key_layer_scaling": true,
  "apply_residual_connection_post_layernorm": false,
  "architectures": [
    "ChatGLMModel"
  ],
  "attention_dropout": 0.0,
  "attention_softmax_in_fp32": true,
  "auto_map": {
    "AutoConfig": "configuration_chatglm.ChatGLMConfig",
    "AutoModel": "modeling_chatglm.ChatGLMForConditionalGeneration",
    "AutoModelForCausalLM": "modeling_chatglm.ChatGLMForConditionalGeneration",
    "AutoModelForSeq2SeqLM": "modeling_chatglm.ChatGLMForConditionalGeneration",
    "AutoModelForSequenceClassification": "modeling_chatglm.ChatGLMForSequenceClassification"
  },
  "bias_dropout_fusion": true,
  "classifier_dropout": null,
  "eos_token_id": 2,
  "ffn_hidden_size": 13696,
  "fp32_residual_connection": false,
  "hidden_dropout": 0.0,
  "hidden_size": 4096,
  "kv_channels": 128,
  "layernorm_epsilon": 1e-05,
  "model_type": "chatglm",
  "multi_query_attention": true,
  "multi_query_group_num": 2,
  "num_attention_heads": 32,
  "num_layers": 28,
  "original_rope": true,
  "pad_token_id": 0,
  "padded_vocab_size": 65024,
  "post_layer_norm": true,
  "pre_seq_len": null,
  "prefix_projection": false,
  "quantization_bit": 0,
  "rmsnorm": true,
  "seq_length": 8192,
  "tie_word_embeddings": false,
  "torch_dtype": "float16",
  "transformers_version": "4.39.2",
  "use_cache": true,
  "vocab_size": 65024
}

03/30/2024 17:18:45 - INFO - llmtuner.model.patcher - Using KV cache for faster generation.
[INFO|modeling_utils.py:3280] 2024-03-30 17:18:45,287 >> loading weights file E:\\llm-train\\chatglm3-6b\model.safetensors.index.json
[INFO|modeling_utils.py:1417] 2024-03-30 17:18:45,288 >> Instantiating ChatGLMForConditionalGeneration model under default dtype torch.float16.
[INFO|configuration_utils.py:928] 2024-03-30 17:18:45,289 >> Generate config GenerationConfig {
  "eos_token_id": 2,
  "pad_token_id": 0
}

Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:04<00:00,  1.60it/s]
[INFO|modeling_utils.py:4024] 2024-03-30 17:18:49,890 >> All model checkpoint weights were used when initializing ChatGLMForConditionalGeneration.

[INFO|modeling_utils.py:4032] 2024-03-30 17:18:49,890 >> All the weights of ChatGLMForConditionalGeneration were initialized from the model checkpoint at E:\\llm-train\\chatglm3-6b.
If your task is similar to the task the model of the checkpoint was trained on, you can already use ChatGLMForConditionalGeneration for predictions without further training.
[INFO|modeling_utils.py:3573] 2024-03-30 17:18:49,895 >> Generation config file not found, using a generation config created from the model config.
WARNING:root:Some parameters are on the meta device device because they were offloaded to the cpu.
03/30/2024 17:18:49 - INFO - llmtuner.model.adapter - Fine-tuning method: LoRA
INFO:llmtuner.model.adapter:Fine-tuning method: LoRA
03/30/2024 17:18:53 - INFO - llmtuner.model.adapter - Merged 1 adapter(s).
INFO:llmtuner.model.adapter:Merged 1 adapter(s).
03/30/2024 17:18:53 - INFO - llmtuner.model.adapter - Loaded adapter(s): E:\\llm-train\\LLaMA-Factory\\saves\\ChatGLM3-6B-Chat\\lora\\train_glm3\\checkpoint-200
INFO:llmtuner.model.adapter:Loaded adapter(s): E:\\llm-train\\LLaMA-Factory\\saves\\ChatGLM3-6B-Chat\\lora\\train_glm3\\checkpoint-200
03/30/2024 17:18:53 - INFO - llmtuner.model.loader - all params: 6243584000
INFO:llmtuner.model.loader:all params: 6243584000
[INFO|configuration_utils.py:471] 2024-03-30 17:18:53,066 >> Configuration saved in E:\\llm-train\\chatglm3-trained\config.json
[INFO|configuration_utils.py:697] 2024-03-30 17:18:53,066 >> Configuration saved in E:\\llm-train\\chatglm3-trained\generation_config.json
[INFO|modeling_utils.py:2482] 2024-03-30 17:19:08,637 >> The model is bigger than the maximum size per checkpoint (2GB) and is going to be split in 7 checkpoint shards. You can find where each parameters has been saved in th
e index located at E:\\llm-train\\chatglm3-trained\model.safetensors.index.json.
[INFO|tokenization_utils_base.py:2502] 2024-03-30 17:19:08,642 >> tokenizer config file saved in E:\\llm-train\\chatglm3-trained\tokenizer_config.json
[INFO|tokenization_utils_base.py:2511] 2024-03-30 17:19:08,643 >> Special tokens file saved in E:\\llm-train\\chatglm3-trained\special_tokens_map.json
(llm) PS E:\llm-train\LLaMA-Factory>

测试模型

1、修改训练后的模型路径：MODEL_PATH = os.environ.get(‘MODEL_PATH’, ‘E:\llm-train\chatglm3-trained’)

2、运行：ChatGLM3\basic_demo\cli_demo.py

用户：>? 安妮
人类。
年龄大约二十岁。
性格开朗。
青年女子。
青年女优。
音色甜美的声音。
用户：>? 魔魂
ChatGLM：亡灵。
年龄大约三十岁。
性格狡猾。
亡灵法师。
膂力过人。
声音低沉。
用户：

在这里插入图片描述

LLaMA-Factory微调（sft）ChatGLM3-6B保姆教程

LLaMA-Factory微调（sft）ChatGLM3-6B保姆教程

准备

1、下载

2、PyCharm打开LLaMA-Factory项目

3、安装依赖

准备数据集

1、创建`LLaMA-Factory\data\chatglm3_zh.json`文件，拷贝一下内容。作为训练测试数据

2、编辑`LLaMA-Factory\data\dataset_info.json`，添加测试数据集到配置文件

训练微调

1、启动web版本的训练

2、调整配置，浏览器打开：http://localhost:7860/

3、开始训练

4、训练完毕

测试模型

1、修改训练后的模型路径：MODEL_PATH = os.environ.get(‘MODEL_PATH’, ‘E:\llm-train\chatglm3-trained’)

2、运行：ChatGLM3\basic_demo\cli_demo.py

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LLaMA-Factory微调（sft）ChatGLM3-6B保姆教程

LLaMA-Factory微调（sft）ChatGLM3-6B保姆教程

准备

1、下载

2、PyCharm打开LLaMA-Factory项目

3、安装依赖

准备数据集

1、创建LLaMA-Factory\data\chatglm3_zh.json文件，拷贝一下内容。作为训练测试数据

2、编辑LLaMA-Factory\data\dataset_info.json，添加测试数据集到配置文件

训练微调

1、启动web版本的训练

2、调整配置，浏览器打开：http://localhost:7860/

3、开始训练

4、训练完毕

测试模型

1、修改训练后的模型路径：MODEL_PATH = os.environ.get(‘MODEL_PATH’, ‘E:\llm-train\chatglm3-trained’)

2、运行：ChatGLM3\basic_demo\cli_demo.py

相关文章

1、创建`LLaMA-Factory\data\chatglm3_zh.json`文件，拷贝一下内容。作为训练测试数据

2、编辑`LLaMA-Factory\data\dataset_info.json`，添加测试数据集到配置文件