Shikong/WeChatMsg
1
0
Fork 0
mirror of https://github.com/LC044/WeChatMsg synced 2025-02-21 01:52:35 +08:00
Code Issues Packages Projects Releases Wiki Activity

Merge pull request #478 from sanbei011/master

Browse Source 
补充AI部署文档:增加Qwen2-0.5B模型进行微调,可免费部署在Modelspace创空间
This commit is contained in:
SiYuan 2024-06-15 11:03:47 +08:00 committed by GitHub
commit 8e33a2db57
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
8 changed files with 878 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

BIN
MemoAI/img/img3.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 359 KiB

BIN
MemoAI/img/img4.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 58 KiB

186
MemoAI/qwen2-0.5b/app.py Normal file
View File

@ -0,0 +1,186 @@
import os
import copy
import random
import threading
import subprocess
import gradio as gr
from typing import List, Optional, Tuple, Dict
os.system("pip uninstall -y tensorflow tensorflow-estimator tensorflow-io-gcs-filesystem")
os.environ["LANG"] = "C"
os.environ["LC_ALL"] = "C"
default_system = '你是一个微信聊天机器人'
from dashinfer.helper import EngineHelper, ConfigManager
log_lock = threading.Lock()
config_file = "di_config.json"
config = ConfigManager.get_config_from_json(config_file)
def download_model(model_id, revision, source="modelscope"):
print(f"Downloading model {model_id} (revision: {revision}) from {source}")
if source == "modelscope":
from modelscope import snapshot_download
model_dir = snapshot_download(model_id, revision=revision)
elif source == "huggingface":
from huggingface_hub import snapshot_download
model_dir = snapshot_download(repo_id=model_id)
else:
raise ValueError("Unknown source")
print(f"Save model to path {model_dir}")
return model_dir
cmd = f"pip show dashinfer | grep 'Location' | cut -d ' ' -f 2"
package_location = subprocess.run(cmd,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
shell=True,
text=True)
package_location = package_location.stdout.strip()
os.environ["AS_DAEMON_PATH"] = package_location + "/dashinfer/allspark/bin"
os.environ["AS_NUMA_NUM"] = str(len(config["device_ids"]))
os.environ["AS_NUMA_OFFSET"] = str(config["device_ids"][0])
## download original model
## download model from modelscope
original_model = {
"source": "modelscope",
"model_id": config["model_space"] + config["model_name"],
"revision": "master",
"model_path": ""
}
original_model["model_path"] = download_model(original_model["model_id"],
original_model["revision"],
original_model["source"])
engine_helper = EngineHelper(config)
engine_helper.verbose = True
engine_helper.init_tokenizer(original_model["model_path"])
## convert huggingface model to dashinfer model
## only one conversion is required
engine_helper.convert_model(original_model["model_path"])
engine_helper.init_engine()
engine_max_batch = engine_helper.engine_config["engine_max_batch"]
###################################################
History = List[Tuple[str, str]]
Messages = List[Dict[str, str]]
class Role:
USER = 'user'
SYSTEM = 'system'
BOT = 'bot'
ASSISTANT = 'assistant'
ATTACHMENT = 'attachment'
def clear_session() -> History:
return '', []
def modify_system_session(system: str) -> str:
if system is None or len(system) == 0:
system = default_system
return system, system, []
def history_to_messages(history: History, system: str) -> Messages:
messages = [{'role': Role.SYSTEM, 'content': system}]
for h in history:
messages.append({'role': Role.USER, 'content': h[0]})
messages.append({'role': Role.ASSISTANT, 'content': h[1]})
return messages
def messages_to_history(messages: Messages) -> Tuple[str, History]:
assert messages[0]['role'] == Role.SYSTEM
system = messages[0]['content']
history = []
for q, r in zip(messages[1::2], messages[2::2]):
history.append([q['content'], r['content']])
return system, history
def message_to_prompt(messages: Messages) -> str:
prompt = ""
for item in messages:
im_start, im_end = "<|im_start|>", "<|im_end|>"
prompt += f"\n{im_start}{item['role']}\n{item['content']}{im_end}"
prompt += f"\n{im_start}assistant\n"
return prompt
def model_chat(query: Optional[str], history: Optional[History],
system: str) -> Tuple[str, str, History]:
if query is None:
query = ''
if history is None:
history = []
messages = history_to_messages(history, system)
messages.append({'role': Role.USER, 'content': query})
prompt = message_to_prompt(messages)
gen_cfg = copy.deepcopy(engine_helper.default_gen_cfg)
gen_cfg["max_length"] = 1024
gen_cfg["seed"] = random.randint(0, 10000)
request_list = engine_helper.create_request([prompt], [gen_cfg])
request = request_list[0]
gen = engine_helper.process_one_request_stream(request)
for response in gen:
role = Role.ASSISTANT
system, history = messages_to_history(messages + [{'role': role, 'content': response}])
yield '', history, system
json_str = engine_helper.convert_request_to_jsonstr(request)
log_lock.acquire()
try:
print(f"{json_str}\n")
finally:
log_lock.release()
###################################################
with gr.Blocks() as demo:
demo_title = "<center>微信的你</center>"
gr.Markdown(demo_title)
with gr.Row():
with gr.Column(scale=3):
system_input = gr.Textbox(value=default_system,
lines=1,
label='System')
with gr.Column(scale=1):
modify_system = gr.Button("🛠️ Set system prompt and clear history.", scale=2)
system_state = gr.Textbox(value=default_system, visible=False)
chatbot = gr.Chatbot(label=config["model_name"])
textbox = gr.Textbox(lines=2, label='Input')
with gr.Row():
clear_history = gr.Button("🧹清除历史记录")
sumbit = gr.Button("🚀和我聊天!")
sumbit.click(model_chat,
inputs=[textbox, chatbot, system_state],
outputs=[textbox, chatbot, system_input],
concurrency_limit=engine_max_batch)
clear_history.click(fn=clear_session,
inputs=[],
outputs=[textbox, chatbot],
concurrency_limit=engine_max_batch)
modify_system.click(fn=modify_system_session,
inputs=[system_input],
outputs=[system_state, system_input, chatbot],
concurrency_limit=engine_max_batch)
demo.queue(api_open=False).launch(height=800, share=False, server_name="127.0.0.1", server_port=7860)

52
MemoAI/qwen2-0.5b/di_config.json Normal file
View File

@ -0,0 +1,52 @@
{
"model_space": "YOUR-NAME-SPACE",
"model_name": "YOUR-MODEL-NAME",
"model_type": "Qwen_v20",
"model_path": "./dashinfer_models/",
"data_type": "float32",
"device_type": "CPU",
"device_ids": [
0
],
"multinode_mode": false,
"engine_config": {
"engine_max_length": 1024,
"engine_max_batch": 2,
"do_profiling": false,
"num_threads": 0,
"matmul_precision": "medium"
},
"generation_config": {
"temperature": 0.7,
"early_stopping": true,
"top_k": 20,
"top_p": 0.8,
"repetition_penalty": 1.05,
"presence_penalty": 0.0,
"min_length": 0,
"max_length": 8192,
"no_repeat_ngram_size": 0,
"eos_token_id": 151643,
"seed": 1234,
"stop_words_ids": [
[
151643
],
[
151644
],
[
151645
]
]
},
"convert_config": {
"do_dynamic_quantize_convert": false
},
"quantization_config": {
"activation_type": "bfloat16",
"weight_type": "uint8",
"SubChannel": true,
"GroupSize": 512
}
}

1
MemoAI/qwen2-0.5b/requirements.txt Normal file
View File

@ -0,0 +1 @@
dashinfer

419
MemoAI/qwen2-0.5b/train.ipynb Normal file
View File

@ -0,0 +1,419 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "de53995b-32ed-4722-8cac-ba104c8efacb",
"metadata": {},
"source": [
"# 导入环境"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "52fac949-4150-4091-b0c3-2968ab5e385c",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from datasets import Dataset\n",
"import pandas as pd\n",
"from transformers import AutoTokenizer, AutoModelForCausalLM, DataCollatorForSeq2Seq, TrainingArguments, Trainer, GenerationConfig"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e098d9eb",
"metadata": {
"ExecutionIndicator": {
"show": true
},
"tags": []
},
"outputs": [],
"source": [
"df = pd.read_json('train.json')\n",
"ds = Dataset.from_pandas(df)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8ac92d42-efae-49b1-a00e-ccaa75b98938",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"ds[:3]"
]
},
{
"cell_type": "markdown",
"id": "380d9f69-9e98-4d2d-b044-1d608a057b0b",
"metadata": {},
"source": [
"# 下载模型"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "312d6439-1932-44a3-b592-9adbdb7ab702",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from modelscope import snapshot_download\n",
"model_dir = snapshot_download('qwen/Qwen2-0.5B-Instruct', cache_dir='qwen2-0.5b/')"
]
},
{
"cell_type": "markdown",
"id": "51d05e5d-d14e-4f03-92be-9a9677d41918",
"metadata": {},
"source": [
"# 处理数据集"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "74ee5a67-2e55-4974-b90e-cbf492de500a",
"metadata": {
"ExecutionIndicator": {
"show": true
},
"tags": []
},
"outputs": [],
"source": [
"tokenizer = AutoTokenizer.from_pretrained('./qwen2-0.5b/qwen/Qwen2-0___5B-Instruct/', use_fast=False, trust_remote_code=True)\n",
"tokenizer"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2503a5fa-9621-4495-9035-8e7ef6525691",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def process_func(example):\n",
" MAX_LENGTH = 384 # Llama分词器会将一个中文字切分为多个token，因此需要放开一些最大长度，保证数据的完整性\n",
" input_ids, attention_mask, labels = [], [], []\n",
" instruction = tokenizer(f\"<|im_start|>system\\n现在你需要扮演我,和我的微信好友快乐聊天!<|im_end|>\\n<|im_start|>user\\n{example['instruction'] + example['input']}<|im_end|>\\n<|im_start|>assistant\\n\", add_special_tokens=False)\n",
" response = tokenizer(f\"{example['output']}\", add_special_tokens=False)\n",
" input_ids = instruction[\"input_ids\"] + response[\"input_ids\"] + [tokenizer.pad_token_id]\n",
" attention_mask = instruction[\"attention_mask\"] + response[\"attention_mask\"] + [1] # 因为eos token咱们也是要关注的所以 补充为1\n",
" labels = [-100] * len(instruction[\"input_ids\"]) + response[\"input_ids\"] + [tokenizer.pad_token_id] \n",
" if len(input_ids) > MAX_LENGTH: # 做一个截断\n",
" input_ids = input_ids[:MAX_LENGTH]\n",
" attention_mask = attention_mask[:MAX_LENGTH]\n",
" labels = labels[:MAX_LENGTH]\n",
" return {\n",
" \"input_ids\": input_ids,\n",
" \"attention_mask\": attention_mask,\n",
" \"labels\": labels\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "84f870d6-73a9-4b0f-8abf-687b32224ad8",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"tokenized_id = ds.map(process_func, remove_columns=ds.column_names)\n",
"tokenized_id"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1f7e15a0-4d9a-4935-9861-00cc472654b1",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"tokenizer.decode(tokenized_id[0]['input_ids'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "97f16f66-324a-454f-8cc3-ef23b100ecff",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"tokenizer.decode(list(filter(lambda x: x != -100, tokenized_id[1][\"labels\"])))"
]
},
{
"cell_type": "markdown",
"id": "424823a8-ed0d-4309-83c8-3f6b1cdf274c",
"metadata": {},
"source": [
"# 创建模型"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "170764e5-d899-4ef4-8c53-36f6dec0d198",
"metadata": {
"ExecutionIndicator": {
"show": true
},
"tags": []
},
"outputs": [],
"source": [
"import torch\n",
"\n",
"model = AutoModelForCausalLM.from_pretrained('./qwen2-0.5b/qwen/Qwen2-0___5B-Instruct', device_map=\"auto\",torch_dtype=torch.bfloat16)\n",
"model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2323eac7-37d5-4288-8bc5-79fac7113402",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"model.enable_input_require_grads()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f808b05c-f2cb-48cf-a80d-0c42be6051c7",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"model.dtype"
]
},
{
"cell_type": "markdown",
"id": "13d71257-3c1c-4303-8ff8-af161ebc2cf1",
"metadata": {},
"source": [
"# lora "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2d304ae2-ab60-4080-a80d-19cac2e3ade3",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from peft import LoraConfig, TaskType, get_peft_model\n",
"\n",
"config = LoraConfig(\n",
" task_type=TaskType.CAUSAL_LM, \n",
" target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"],\n",
" inference_mode=False, # 训练模式\n",
" r=8, # Lora 秩\n",
" lora_alpha=32, # Lora alaph，具体作用参见 Lora 原理\n",
" lora_dropout=0.1# Dropout 比例\n",
")\n",
"config"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2c2489c5-eaab-4e1f-b06a-c3f914b4bf8e",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"model = get_peft_model(model, config)\n",
"config"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ebf5482b-fab9-4eb3-ad88-c116def4be12",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"model.print_trainable_parameters()"
]
},
{
"cell_type": "markdown",
"id": "ca055683-837f-4865-9c57-9164ba60c00f",
"metadata": {},
"source": [
"# 配置训练参数"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7e76bbff-15fd-4995-a61d-8364dc5e9ea0",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"args = TrainingArguments(\n",
" output_dir=\"./output/\",\n",
" per_device_train_batch_size=4,\n",
" gradient_accumulation_steps=4,\n",
" logging_steps=10,\n",
" num_train_epochs=3,\n",
" learning_rate=1e-4,\n",
" gradient_checkpointing=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f142cb9c-ad99-48e6-ba86-6df198f9ed96",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"trainer = Trainer(\n",
" model=model,\n",
" args=args,\n",
" train_dataset=tokenized_id,\n",
" data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer, padding=True),\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aec9bc36-b297-45af-99e1-d4c4d82be081",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"trainer.train()"
]
},
{
"cell_type": "markdown",
"id": "8abb2327-458e-4e96-ac98-2141b5b97c8e",
"metadata": {},
"source": [
"# 合并加载模型,这里的路径可能有点不太一样,lora_path填写为Output的最后的checkpoint"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bd2a415a-a9ad-49ea-877f-243558a83bfc",
"metadata": {},
"outputs": [],
"source": [
"from transformers import AutoModelForCausalLM, AutoTokenizer\n",
"import torch\n",
"from peft import PeftModel\n",
"\n",
"mode_path = './qwen2-0.5b/qwen/Qwen2-0___5B-Instruct'\n",
"lora_path = './output/checkpoint-10' #修改这里\n",
"# 加载tokenizer\n",
"tokenizer = AutoTokenizer.from_pretrained(mode_path, trust_remote_code=True)\n",
"\n",
"# 加载模型\n",
"model = AutoModelForCausalLM.from_pretrained(mode_path, device_map=\"auto\",torch_dtype=torch.bfloat16, trust_remote_code=True).eval()\n",
"\n",
"# 加载lora权重\n",
"model = PeftModel.from_pretrained(model, model_id=lora_path)\n",
"\n",
"prompt = \"在干啥呢?\"\n",
"inputs = tokenizer.apply_chat_template([{\"role\": \"user\", \"content\": \"现在你需要扮演我,和我的微信好友快乐聊天!\"},{\"role\": \"user\", \"content\": prompt}],\n",
" add_generation_prompt=True,\n",
" tokenize=True,\n",
" return_tensors=\"pt\",\n",
" return_dict=True\n",
" ).to('cuda')\n",
"\n",
"\n",
"gen_kwargs = {\"max_length\": 2500, \"do_sample\": True, \"top_k\": 1}\n",
"with torch.no_grad():\n",
" outputs = model.generate(**inputs, **gen_kwargs)\n",
" outputs = outputs[:, inputs['input_ids'].shape[1]:]\n",
" print(tokenizer.decode(outputs[0], skip_special_tokens=True))\n",
"\n",
"# 保存合并后的模型和tokenizer\n",
"save_directory = './model_merge'\n",
"\n",
"# 保存模型\n",
"\n",
"model.save_pretrained(save_directory)\n",
"\n",
"# 保存tokenizer\n",
"tokenizer.save_pretrained(save_directory)"
]
},
{
"cell_type": "markdown",
"id": "b67e5e0a-2566-4483-9bce-92b5be8b4b34",
"metadata": {},
"source": [
"# 然后把模型上传到modelscope开始下一步"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dafe4f24-af5c-407e-abbc-eefd9d44cb15",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

199
MemoAI/qwen2-0.5b/train.md Normal file
View File

@ -0,0 +1,199 @@
# Qwen2-0.B-Instruct 微信AI 微调
这个教程给大家提供一个 [nodebook](./train.ipynb) 文件，来让大家更好的学习。
## 模型下载
使用 modelscope 中的 snapshot_download 函数下载模型，第一个参数为模型名称，参数 cache_dir 为模型的下载路径。
```python
import torch
from modelscope import snapshot_download, AutoModel, AutoTokenizer
import os
model_dir = snapshot_download('qwen/Qwen2-7B-Instruct', cache_dir='/root/autodl-tmp', revision='master')
```
## 环境配置
在完成基本环境配置和本地模型部署的情况下，你还需要安装一些第三方库，可以使用以下命令：
```bash
python -m pip install --upgrade pip
# 更换 pypi 源加速库的安装
pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
pip install modelscope==1.9.5
pip install "transformers>=4.39.0"
pip install streamlit==1.24.0
pip install sentencepiece==0.1.99
pip install accelerate==0.27
pip install transformers_stream_generator==0.0.4
pip install datasets==2.18.0
pip install peft==0.10.0
```
LLM 的微调一般指指令微调过程。所谓指令微调，是说我们使用的微调数据形如：
```json
{
"instruction":"以下是你的好友在和你聊天,你需要和他聊天",
"input":"吃了吗?",
"output":"还在食堂"
}
```
其中，`instruction` 是用户指令，告知模型其需要完成的任务；`input` 是用户输入，是完成用户指令所必须的输入内容；`output` 是模型应该给出的输出。
## 数据格式化
`Lora` 训练的数据是需要经过格式化、编码之后再输入给模型进行训练的，如果是熟悉 `Pytorch` 模型训练流程的同学会知道，我们一般需要将输入文本编码为 input_ids，将输出文本编码为 `labels`，编码之后的结果都是多维的向量。我们首先定义一个预处理函数，这个函数用于对每一个样本，编码其输入、输出文本并返回一个编码后的字典：
```python
def process_func(example):
MAX_LENGTH = 384 # Llama分词器会将一个中文字切分为多个token，因此需要放开一些最大长度，保证数据的完整性
input_ids, attention_mask, labels = [], [], []
instruction = tokenizer(f"<|im_start|>system\n现在你要扮演皇帝身边的女人--甄嬛<|im_end|>\n<|im_start|>user\n{example['instruction'] + example['input']}<|im_end|>\n<|im_start|>assistant\n", add_special_tokens=False) # add_special_tokens 不在开头加 special_tokens
response = tokenizer(f"{example['output']}", add_special_tokens=False)
input_ids = instruction["input_ids"] + response["input_ids"] + [tokenizer.pad_token_id]
attention_mask = instruction["attention_mask"] + response["attention_mask"] + [1] # 因为eos token咱们也是要关注的所以 补充为1
labels = [-100] * len(instruction["input_ids"]) + response["input_ids"] + [tokenizer.pad_token_id]
if len(input_ids) > MAX_LENGTH: # 做一个截断
input_ids = input_ids[:MAX_LENGTH]
attention_mask = attention_mask[:MAX_LENGTH]
labels = labels[:MAX_LENGTH]
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"labels": labels
}
```
`Qwen2` 采用的`Prompt Template`格式如下：
```text
<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
你是谁？<|im_end|>
<|im_start|>assistant
我是一个有用的助手。<|im_end|>
```
## 加载tokenizer和半精度模型
模型以半精度形式加载，如果你的显卡比较新的话，可以用`torch.bfolat`形式加载。对于自定义的模型一定要指定`trust_remote_code`参数为`True`。
```python
tokenizer = AutoTokenizer.from_pretrained('./qwen2-0.5b/qwen/Qwen2-0___5B-Instruct/', use_fast=False, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained('./qwen2-0.5b/qwen/Qwen2-0___5B-Instruct/', device_map="auto",torch_dtype=torch.bfloat16)
```
## 定义LoraConfig
`LoraConfig`这个类中可以设置很多参数，但主要的参数没多少，简单讲一讲，感兴趣的同学可以直接看源码。
- `task_type`：模型类型
- `target_modules`：需要训练的模型层的名字，主要就是`attention`部分的层，不同的模型对应的层的名字不同，可以传入数组，也可以字符串，也可以正则表达式。
- `r`：`lora`的秩，具体可以看`Lora`原理
- `lora_alpha`：`Lora alaph`，具体作用参见 `Lora` 原理
`Lora`的缩放是啥嘞？当然不是`r`（秩），这个缩放就是`lora_alpha/r`, 在这个`LoraConfig`中缩放就是4倍。
```python
config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
inference_mode=False, # 训练模式
r=8, # Lora 秩
lora_alpha=32, # Lora alaph，具体作用参见 Lora 原理
lora_dropout=0.1# Dropout 比例
)
```
## 自定义 TrainingArguments 参数
`TrainingArguments`这个类的源码也介绍了每个参数的具体作用，当然大家可以来自行探索，这里就简单说几个常用的。
- `output_dir`：模型的输出路径
- `per_device_train_batch_size`：顾名思义 `batch_size`
- `gradient_accumulation_steps`: 梯度累加，如果你的显存比较小，那可以把 `batch_size` 设置小一点，梯度累加增大一些。
- `logging_steps`：多少步，输出一次`log`
- `num_train_epochs`：顾名思义 `epoch`
- `gradient_checkpointing`：梯度检查，这个一旦开启，模型就必须执行`model.enable_input_require_grads()`，这个原理大家可以自行探索，这里就不细说了。
```python
args = TrainingArguments(
output_dir="./output",
per_device_train_batch_size=4,
gradient_accumulation_steps=4,
logging_steps=10,
num_train_epochs=3,
save_steps=100,
learning_rate=1e-4,
save_on_each_node=True,
gradient_checkpointing=True
)
```
## 使用 Trainer 训练
```python
trainer = Trainer(
model=model,
args=args,
train_dataset=tokenized_id,
data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer, padding=True),
)
trainer.train()
```
## 加载 lora 权重推理
训练好了之后可以使用如下方式加载`lora`权重进行推理：
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from peft import PeftModel
mode_path = './qwen2-0.5b/qwen/Qwen2-0___5B-Instruct/'
lora_path = 'lora_path'
# 加载tokenizer
tokenizer = AutoTokenizer.from_pretrained(mode_path)
# 加载模型
model = AutoModelForCausalLM.from_pretrained(mode_path, device_map="auto",torch_dtype=torch.bfloat16)
# 加载lora权重
model = PeftModel.from_pretrained(model, model_id=lora_path, config=config)
prompt = "你是谁？"
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer([text], return_tensors="pt").to('cuda')
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```

21
doc/ai-qwen/readme.md Normal file
View File

@ -0,0 +1,21 @@
**鉴于** 本仓库原来的训练模型 `Chatllm3-6b` 在低性能机器上部署比较困难,我在原基础上使用微型模型 `Qwen2-0.5b-Instruct` 模型完成模型训练到部署到免费
Modelspace 创空间中，比较简单，`并且可做到全程免费` 下面是流程：
***
# 第一步，[创建 Modelspace 免费 GPU](https://www.modelscope.cn/my/mynotebook/preset)
![GPU](/MemoAI/img/img3.png)
# 开始训练
**可以参照训练[模板](/MemoAI/qwen2-0.5b/train.md)**
<br>
把 `train.json` 上传,一步步点击即可<br>
最后把模型上传到 `Modelspace`
<br />
![训练](/MemoAI/img/img4.png)
# 部署到创空间
编辑 `di_config.json` 的一下两个字段
`model_space: YOUR-NAME-SPACE`
`model_name: YOUR-MODEL-NAME`
**然后把一下MemoAI/qwen2-0.5b的三个文件:`di_config.json`,`app.py`,`requirements.txt`上传到创空间,点击部署！**
**最后看看成品吧：**[成品](https://www.modelscope.cn/studios/sanbei101/qwen-haoran/summary)