快速跑通openai微调模型

openai提供两种对话方式，一种是使用现成的模型，例如gpt-3.5-turbo、gpt-4，另一种就是在现有的基础上，通过投喂数据集来训练自己的模型，也就是Fine-tuning，微调OpenAI文本生成模型可以使它们在特定应用场景中更好地发挥作用，但需要投入时间和精力。

一些常见的微调可以改善结果的用例包括：

• 设置风格、语气、格式或其他定性方面
• 提高生成期望输出的可靠性
• 以特定方式处理许多边缘情况
• 执行难以用提示清楚表达的新技能或任务 本人是期望能够用AI代替人工客服，所以尝试了一下openai的微调模型，本职前端，文章中部分直接翻译的官网文档，其余话术仅供参考，不是专业人士，所以如果有什么概念或者术语说错了的欢迎指正。

准备数据集

“需要创建一组多样化的演示对话，这些对话类似于要求模型在生产中的推理时响应的对话。”

json
复制代码
{"messages": [{"role": "system", "content": "Marv is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "What's the capital of France?"}, {"role": "assistant", "content": "Paris, as if everyone doesn't know that already."}]}
{"messages": [{"role": "system", "content": "Marv is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "Who wrote 'Romeo and Juliet'?"}, {"role": "assistant", "content": "Oh, just some guy named William Shakespeare. Ever heard of him?"}]}
{"messages": [{"role": "system", "content": "Marv is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "How far is the Moon from Earth?"}, {"role": "assistant", "content": "Around 384,400 kilometers. Give or take a few, like that really matters."}]}

“要微调模型，您需要提供至少 10 个示例。我们通常会看到对 50 到 100 个训练示例进行微调会带来明显的改进，但正确的数量根据具体的用例而有很大差异。”

训练模型时需要上传两个.jsonl文件，一个训练集和一个校验集，其中训练集用于训练模型，验证集用于评估模型的性能和调整超参数。校验集数据条数一般是训练集的10%-30%。建议一开始就多准备一些数据，并把数据都清理一下，避免影响最后的结果。

校验

校验每条数据中的token数不超过限制，token数量的限制取决于您选择的型号。对于gpt-3.5-turbo-0125，最大上下文长度为 16,385，因此每个训练示例也限制为 16,385 个token。超过的部分会直接被截取。

附token校验、计数和计费的脚本：token_conter.py

python
复制代码
import json
import tiktoken
import numpy as np
from collections import defaultdict

# 从 jsonl 文件中读取消息列表
def read_messages_from_jsonl(jsonl_file):
    messages = []
    with open(jsonl_file, 'r', encoding='utf-8') as f:
        for line in f:
            message = json.loads(line.strip())
            messages.append(message)
    return messages

# 读取你的 jsonl 文件并将其转换为消息列表
jsonl_file = './Training.jsonl'  # 修改为你的 jsonl 文件路径
dataset = read_messages_from_jsonl(jsonl_file)

# Format error checks
format_errors = defaultdict(int)

for ex in dataset:
    if not isinstance(ex, dict):
        format_errors["data_type"] += 1
        continue

    messages = ex.get("messages", None)
    if not messages:
        format_errors["missing_messages_list"] += 1
        continue

    for message in messages:
        if "role" not in message or "content" not in message:
            format_errors["message_missing_key"] += 1

        if any(k not in ("role", "content", "name") for k in message):
            format_errors["message_unrecognized_key"] += 1

        if message.get("role", None) not in ("system", "user", "assistant"):
            format_errors["unrecognized_role"] += 1

        content = message.get("content", None)
        if not content or not isinstance(content, str):
            format_errors["missing_content"] += 1

    if not any(message.get("role", None) == "assistant" for message in messages):
        format_errors["example_missing_assistant_message"] += 1

if format_errors:
    print("Found errors:")
    for k, v in format_errors.items():
        print(f"{k}: {v}")
else:
    print("No errors found")

# Token counting functions
encoding = tiktoken.get_encoding("cl100k_base")

# not exact!
# simplified from https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb
def num_tokens_from_messages(messages, tokens_per_message=3, tokens_per_name=1):
    num_tokens = 0
    for message in messages:
        num_tokens += tokens_per_message
        for key, value in message.items():
            num_tokens += len(encoding.encode(value))
            if key == "name":
                num_tokens += tokens_per_name
    num_tokens += 3
    return num_tokens

def num_assistant_tokens_from_messages(messages):
    num_tokens = 0
    for message in messages:
        if message["role"] == "assistant":
            num_tokens += len(encoding.encode(message["content"]))
    return num_tokens

def print_distribution(values, name):
    print(f"n#### Distribution of {name}:")
    print(f"min / max: {min(values)}, {max(values)}")
    print(f"mean / median: {np.mean(values)}, {np.median(values)}")
    print(f"p5 / p95: {np.quantile(values, 0.1)}, {np.quantile(values, 0.9)}")

# Warnings and tokens counts
n_missing_system = 0
n_missing_user = 0
n_messages = []
convo_lens = []
assistant_message_lens = []

for ex in dataset:
    messages = ex["messages"]
    if not any(message["role"] == "system" for message in messages):
        n_missing_system += 1
    if not any(message["role"] == "user" for message in messages):
        n_missing_user += 1
    n_messages.append(len(messages))
    convo_lens.append(num_tokens_from_messages(messages))
    assistant_message_lens.append(num_assistant_tokens_from_messages(messages))

print("Num examples missing system message:", n_missing_system)
print("Num examples missing user message:", n_missing_user)
print_distribution(n_messages, "num_messages_per_example")
print_distribution(convo_lens, "num_total_tokens_per_example")
print_distribution(assistant_message_lens, "num_assistant_tokens_per_example")
n_too_long = sum(l > 4096 for l in convo_lens)
print(f"n{n_too_long} examples may be over the 4096 token limit, they will be truncated during fine-tuning")

# Pricing and default n_epochs estimate
MAX_TOKENS_PER_EXAMPLE = 4096

TARGET_EPOCHS = 3
MIN_TARGET_EXAMPLES = 100
MAX_TARGET_EXAMPLES = 25000
MIN_DEFAULT_EPOCHS = 1
MAX_DEFAULT_EPOCHS = 25

n_epochs = TARGET_EPOCHS
n_train_examples = len(dataset)
if n_train_examples * TARGET_EPOCHS < MIN_TARGET_EXAMPLES:
    n_epochs = min(MAX_DEFAULT_EPOCHS, MIN_TARGET_EXAMPLES // n_train_examples)
elif n_train_examples * TARGET_EPOCHS > MAX_TARGET_EXAMPLES:
    n_epochs = max(MIN_DEFAULT_EPOCHS, MAX_TARGET_EXAMPLES // n_train_examples)

n_billing_tokens_in_dataset = sum(min(MAX_TOKENS_PER_EXAMPLE, length) for length in convo_lens)
print(f"Dataset has ~{n_billing_tokens_in_dataset} tokens that will be charged for during training")
print(f"By default, you'll train for {n_epochs} epochs on this dataset")
print(f"By default, you'll be charged for ~{n_epochs * n_billing_tokens_in_dataset} tokens")
print("See pricing page to estimate total costs")

# base cost per 1k tokens * number of tokens in the input file * number of epochs trained
def calculate_price_per_epoch(token_price_per_k, num_tokens, num_epochs):
    # 计算每个 epoch 的价格
    price_per_epoch = token_price_per_k * (num_tokens / 1000) * num_epochs
    return price_per_epoch

# Token 价格（每个 token）
token_dollar_per_k = 0.0080 # $0.0080 / 1K tokens

# 计算总价格
total_dollar = calculate_price_per_epoch(token_dollar_per_k, n_billing_tokens_in_dataset, n_epochs)

print(f"Total price: $~{total_dollar}")

运行效果：

开始训练

可以使用图形化训练或者用代码创建，这里我选择了图形化训练

1.上传数据集

可以提前上传，也可以在创建训练任务时上传

2.创建训练任务

运行

中途忘截图了，需要等模型跑一会

成功之后就可以试用了

附：fine-turning.py

python
复制代码
from openai import OpenAI
import json
client = OpenAI()

# 创建
# client.fine_tuning.jobs.create(
#   training_file="file-krCDQKRmCzcYxiOAMxef69nK", 
#   model="gpt-3.5-turbo",
#   hyperparameters={
#     "n_epochs":3
#   }
# )

import openai

# 初始化对话
conversation = [
    {"role": "system", "content": "xxx"},
]

while True:
    # 获取用户输入
    user_input = input("用户：")
    
    # 添加用户输入到对话中
    conversation.append({"role": "user", "content": user_input})
    
    # 请求模型回复
    completion = client.chat.completions.create(
        model="ft:gpt-3.5-turbo-0125:xxx:xx:xxx", # 训练成功后的模型名称
        messages=conversation
    )
    
    # 获取模型回复
    response = completion.choices[0].message.content
    
    # 打印模型回复
    print("模型：", response)
    
    # 添加模型回复到对话中
    conversation.append({"role": "assistant", "content": response})

运行效果：

结尾

搞了两次感觉效果不是很理想，Training loss两次都很高，第一次以为是数据集给得太少了，200多条数据，且没有上传校验集，第二次增加了三倍，校验集也准备了，依旧很高，可能这种微调模型不太适用于我的使用场景（中文、客服）