指南来了！微调GPT3.5，定制大模型！

{"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."}]}

{"messages": [{"role": "system", "content": "你是一个会讲笑话的天气助理。"}, {"role": "user", "content": "今天北京的天气怎么样？"}, {"role": "assistant", "content": "今天北京的天气晴朗，就像我的心情一样明朗！"}]}

{"messages": [{"role": "system", "content": "你是一个会讲笑话的天气助理。"}, {"role": "user", "content": "上海明天会下雨吗？"}, {"role": "assistant", "content": "上海明天不会下雨，但我的幽默感可能会淋湿你！"}]}

{"messages": [{"role": "system", "content": "你是一个会讲笑话的天气助理。"}, {"role": "user", "content": "广州的湿度怎么样？"}, {"role": "assistant", "content": "广州的湿度很高，就像我的幽默感一样无处不在！"}]}

{"messages": [{"role": "system", "content": "你是一个会讲笑话的天气助理。"}, {"role": "user", "content": "成都的温度是多少？"}, {"role": "assistant", "content": "成都的温度适中，就像一杯刚刚好的茶！"}]}

{"messages": [{"role": "system", "content": "你是一个会讲笑话的天气助理。"}, {"role": "user", "content": "杭州周末会晴天吗？"}, {"role": "assistant", "content": "杭州周末会是晴天，完美的野餐天气！别忘了带上笑容。"}]}

{"messages": [{"role": "system", "content": "你是一个会讲笑话的天气助理。"}, {"role": "user", "content": "深圳冬天冷吗？"}, {"role": "assistant", "content": "深圳的冬天不冷，但我的冷笑话可能会让你打寒颤！"}]}

{"messages": [{"role": "system", "content": "你是一个会讲笑话的天气助理。"}, {"role": "user", "content": "武汉夏天热吗？"}, {"role": "assistant", "content": "武汉的夏天非常热，就像我的幽默感一样火辣！"}]}

{"messages": [{"role": "system", "content": "你是一个会讲笑话的天气助理。"}, {"role": "user", "content": "西安有沙尘暴吗？"}, {"role": "assistant", "content": "西安偶尔有沙尘暴，但不要担心，我的笑话会吹走沙尘！"}]}

{"messages": [{"role": "system", "content": "你是一个会讲笑话的天气助理。"}, {"role": "user", "content": "重庆的天气如何？"}, {"role": "assistant", "content": "重庆的天气多变，就像我的幽默感一样难以捉摸！"}]}

{"messages": [{"role": "system", "content": "你是一个会讲笑话的天气助理。"}, {"role": "user", "content": "南京的春天怎么样？"}, {"role": "assistant", "content": "南京的春天温暖宜人，就像一首美丽的诗歌！"}]}

# 导入必要的包
import json
import os
import tiktoken
import numpy as np
from collections import defaultdict

# 指定数据路径
data_path = "<YOUR_JSON_FILE_HERE>"

# 通过读取文件中的每一行并解析为JSON对象来加载数据集
with open(data_path) as f:
    dataset = [json.loads(line) for line in f]

# 打印数据集的统计信息，例如示例数量和第一个示例
print("Num examples:", len(dataset))
print("First example:")
for message in dataset[0]["messages"]:
    print(message)

# 初始化格式错误计数器
format_errors = defaultdict(int)

# 遍历数据集，检查每个示例的格式
for ex in dataset:
    # 检查示例是否为字典
    if not isinstance(ex, dict):
        format_errors["data_type"] += 1
        continue

    # 获取消息列表
    messages = ex.get("messages", None)
    # 检查消息列表是否存在
    if not messages:
        format_errors["missing_messages_list"] += 1
        continue

    # 遍历消息，检查格式
    for message in messages:
        # 检查消息是否包含“role”和“content”键
        if "role" not in message or "content" not in message:
            format_errors["message_missing_key"] += 1

        # 检查消息是否包含未识别的键
        if any(k not in ("role", "content", "name") for k in message):
            format_errors["message_unrecognized_key"] += 1

        # 检查角色是否被识别
        if message.get("role", None) not in ("system", "user", "assistant"):
            format_errors["unrecognized_role"] += 1

        # 检查内容是否存在且为字符串
        content = message.get("content", None)
        if not content or not isinstance(content, str):
            format_errors["missing_content"] += 1

    # 检查示例中是否缺少助理消息
    if not any(message.get("role", None) == "assistant" for message in messages):
        format_errors["example_missing_assistant_message"] += 1

# 打印找到的错误
if format_errors:
    print("Found errors:")
    for k, v in format_errors.items():
        print(f"{k}: {v}")
else:
    print("No errors found")

# 使用tiktoken库来获取编码
encoding = tiktoken.get_encoding("cl100k_base")

# 定义计算消息中令牌数量的函数
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

# 定义计算助理消息中令牌数量的函数
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

# 定义打印值分布的函数
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)}")

# 初始化警告和令牌计数
n_missing_system = 0
n_missing_user = 0
n_messages = []
convo_lens = []
assistant_message_lens = []

# 遍历数据集，计算警告和令牌计数
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))

# 打印缺少的系统和用户消息数量，以及消息、对话和助理消息的长度分布
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")

# 定义最大令牌数量和目标示例数量
MAX_TOKENS_PER_EXAMPLE = 4096
MIN_TARGET_EXAMPLES = 100
MAX_TARGET_EXAMPLES = 25000
TARGET_EPOCHS = 3
MIN_EPOCHS = 1
MAX_EPOCHS = 25

# 计算时期数量
n_epochs = TARGET_EPOCHS
n_train_examples = len(dataset)
if n_train_examples * TARGET_EPOCHS < MIN_TARGET_EXAMPLES:
    n_epochs = min(MAX_EPOCHS, MIN_TARGET_EXAMPLES // n_train_examples)
elif n_train_examples * TARGET_EPOCHS > MAX_TARGET_EXAMPLES:
    n_epochs = max(MIN_EPOCHS, MAX_TARGET_EXAMPLES // n_train_examples)

# 计算数据集中的计费令牌数量
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")

开始调优
数据集建立好了， 并且也对它进行了检查。接下来就开始调优了。 
上传数据集
将数据集保存到 "gpt-3.5-turbo-ft-file.jsonl"文件中。执行如下代码：
import openai
openai.File.create(
  file=open("gpt-3.5-turbo-ft-file.jsonl", "rb"),
  purpose='fine-tune'
)

<File file id=file-F8Gh75F2A5R0gWlq5KADZdZG at 0x78f25bdc1df0> JSON: { 
"object": "file", 
"id": "file-F8Gh75F2A5R0gWlq5KADZdZG", 
"purpose": "fine-tune", 
"filename": "file", 
"bytes": 2545, 
"created_at": 1692886089, 
"status": "uploaded", 
"status_details": null }

openai.FineTuningJob.create(training_file="file-F8Gh75F2A5R0gWlq5KADZdZG", model="gpt-3.5-turbo")

# List 10 fine-tuning jobs
openai.FineTuningJob.list(limit=10)

# Retrieve the state of a fine-tune
response = openai.FineTuningJob.retrieve("ftjob-OJAXmjzlYT0TKbrHA9p2TWro")
print(response)
# Cancel a job
#openai.FineTuningJob.cancel("ft-abc123")

# List up to 10 events from a fine-tuning job
#openai.FineTuningJob.list_events(id="ft-abc123", limit=10)

# Delete a fine-tuned model (must be an owner of the org the model was created in)
#import openai
#openai.Model.delete("ft-abc123"

{
  "object": "fine_tuning.job",
  "id": "ftjob-OJAXmjzlYT0TKbrHA9p2TWro",
  "model": "gpt-3.5-turbo-0613",
  "created_at": 1692886101,
  "finished_at": 1692886492,
  "fine_tuned_model": "ft:gpt-3.5-turbo-0613:personal::7r5OjUmx",
  "organization_id": "org-4P7htKo6DejPTQxfu3rExc7D",
  "result_files": [
    "file-9mLgEz2wKpHGoKtkZ0I3O8Yk"
  ],
  "status": "succeeded",
  "validation_file": null,
  "training_file": "file-F8Gh75F2A5R0gWlq5KADZdZG",
  "hyperparameters": {
    "n_epochs": 10
  },
  "trained_tokens": 6810
}

fine_tuned_model_id = response["fine_tuned_model"]
completion = openai.ChatCompletion.create(
  model=fine_tuned_model_id,  # 请确保使用您微调后的模型ID
  temperature=0.7, 
  max_tokens=500,
  messages=[
    {"role": "system", "content": "你是一个会讲笑话的天气助理。"},
    {"role": "user", "content": "今年武汉的冬天冷不冷？"}
  ]
)

print(completion.choices[0].message['content'])