LangChain使用fine-tuned GPT-3.5
参考：
https://openai.com/blog/gpt-3-5-turbo-fine-tuning-and-api-updates
https://platform.openai.com/docs/guides/fine-tuning
https://qiita.com/MandoNarin/items/6fadb78f357c66e25502
事前准备
!pip install openai
!pip install tiktoken
!pip install langchain
import os
os.environ["OPENAI_API_KEY"] = YOUR_KEY
准备数据
数据文件 mydata.jsonl
{"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."}]}
官方只给了三行例子，但是会报错
# has 3 example(s), but must have at least 10 examples
我姑且复制了三遍
验证数据
https://github.com/openai/openai-cookbook/blob/main/examples/Chat_finetuning_data_prep.ipynb
直接用了上述链接里的代码
import json
import tiktoken # 为了计算token消耗
import numpy as np
from collections import defaultdict
载入数据集
# data_file_path为数据文件的地址
data_path = data_file_path
# 载入数据集
with open(data_path, 'r', encoding='utf-8') as f:
dataset = [json.loads(line) for line in f]
# 打印初始数据集信息
print("Num examples:", len(dataset))
print("First example:")
for message in dataset[0]["messages"]:
print(message)
检查格式是否有错
# 格式错误检查
format_errors = defaultdict(int)
for ex in dataset:
# 是否为已知类型
if not isinstance(ex, dict):
format_errors["data_type"] += 1
continue
messages = ex.get("messages", None)
# 检查数据集中是否每一个元素都包含"messages"键值
if not messages:
format_errors["missing_messages_list"] += 1
continue
for message in messages:
# 检查"messages"中是否包含"role"和"content"
if "role" not in message or "content" not in message:
format_errors["message_missing_key"] += 1
# 检查"messages"中是否有除了"role" "content"或者"name"之外的字段
if any(k not in ("role", "content", "name") for k in message):
format_errors["message_unrecognized_key"] += 1
# 检查"role"中是否有除了"system" "user"或者"assistant"之外的值
if message.get("role", None) not in ("system", "user", "assistant"):
format_errors["unrecognized_role"] += 1
content = message.get("content", None)
# 检查"content"是否为str类型
if not content or not isinstance(content, str):
format_errors["missing_content"] += 1
# 检查是否缺少"assistant"提示
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计数
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)}")
# 警告和token计数
# 警告内容：缺少角色为system和user的数据
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")
价格和默认n_epochs估计
# 价格和默认n_epochs估计
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")
创建 fine-tuning
上传文件
import openai
openai.api_key = os.getenv("OPENAI_API_KEY")
test_data_file_object = openai.File.create(
file=open(data_path, "rb"),
purpose='fine-tune'
)
创建 fine-tuning 任务
获取文件ID
file_id = test_data_file_object.id
创建 fine-tuning
job_response = openai.FineTuningJob.create(training_file=file_id, model="gpt-3.5-turbo")
# 可选选项
# 列出10个 fine-tuning 任务
# openai.FineTuningJob.list(limit=10)
# 检索 fine-tune 的状态
# openai.FineTuningJob.retrieve(file_id)
# 终止一个任务
# openai.FineTuningJob.cancel(file_id)
# 列出 fine-tuning 任务中最多10个事件
# openai.FineTuningJob.list_events(id=file_id, limit=10)
# 删除一个 fine-tuned 模型 (但该模型必须是你创建的)
# openai.Model.delete(file_id)
使用 fine-tuned 模型
job_id = job_response.id
response_retrieve = openai.FineTuningJob.retrieve(job_id)
fine_tuned_model = response_retrieve.fine_tuned_model
completion = openai.ChatCompletion.create(
model=fine_tuned_model,
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello!"}
]
)
print(completion.choices[0].message)
{
"role": "assistant",
"content": "Hi there! How can I assist you today?"
}
langchain使用fine-tuned模型
from langchain.chains import LLMChain
from langchain.chat_models import ChatOpenAI
from langchain.schema import SystemMessage
from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder
from langchain import chat_models
from langchain.memory import ConversationBufferMemory
prompt = ChatPromptTemplate.from_messages(
[
# system消息
SystemMessage(content="You are a helpful AI bot."),
# 历史记录（记忆）
MessagesPlaceholder(variable_name="chat_history"),
# 用户输入
HumanMessagePromptTemplate.from_template("Extract triplets from the following sentence:{human_input}"),
]
)
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
llm = chat_models.ChatOpenAI(model=fine_tuned_model, temperature=0)
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
memory=memory,
verbose=True
)
llm_chain.run("sam is a teacher")
> Entering new LLMChain chain...
Prompt after formatting:
System: You are a helpful AI bot.
Human: Extract triplets from the following sentence:sam is a teacher
> Finished chain.
- (sam, is, teacher)\n- (sam, a, teacher)
llm_chain.run("Tom is Sam's teacher")
> Entering new LLMChain chain...
Prompt after formatting:
System: You are a helpful AI bot.
Human: sam is a teacher
AI: - (sam, is, teacher)
- (sam, a, teacher)
Human: Extract triplets from the following sentence:Tom is Sam's teacher
> Finished chain.
- (Tom, is, teacher)\n- (Tom, is, Sam's teacher)\n- (Sam, 's, teacher)