梯度
Gradient 允许通过简单的 Web API 对 LLM 进行微调并获取完成。
本笔记本介绍了如何将 Langchain 与 Gradient 一起使用。
导入
from langchain.chains import LLMChain
from langchain_community.llms import GradientLLM
from langchain_core.prompts import PromptTemplate
设置环境 API 密钥
确保从 Gradient AI 获取您的 API 密钥。您可以获得 10 美元的免费积分来测试和微调不同的模型。
import os
from getpass import getpass
if not os.environ.get("GRADIENT_ACCESS_TOKEN", None):
# Access token under https://auth.gradient.ai/select-workspace
os.environ["GRADIENT_ACCESS_TOKEN"] = getpass("gradient.ai access token:")
if not os.environ.get("GRADIENT_WORKSPACE_ID", None):
# `ID` listed in `$ gradient workspace list`
# also displayed after login at at https://auth.gradient.ai/select-workspace
os.environ["GRADIENT_WORKSPACE_ID"] = getpass("gradient.ai workspace id:")
可选：验证您的环境变量 GRADIENT_ACCESS_TOKEN 和 GRADIENT_WORKSPACE_ID 以获取当前部署的模型。使用 gradientai Python 包。
%pip install --upgrade --quiet gradientai
Requirement already satisfied: gradientai in /home/michi/.venv/lib/python3.10/site-packages (1.0.0)
Requirement already satisfied: aenum>=3.1.11 in /home/michi/.venv/lib/python3.10/site-packages (from gradientai) (3.1.15)
Requirement already satisfied: pydantic<2.0.0,>=1.10.5 in /home/michi/.venv/lib/python3.10/site-packages (from gradientai) (1.10.12)
Requirement already satisfied: python-dateutil>=2.8.2 in /home/michi/.venv/lib/python3.10/site-packages (from gradientai) (2.8.2)
Requirement already satisfied: urllib3>=1.25.3 in /home/michi/.venv/lib/python3.10/site-packages (from gradientai) (1.26.16)
Requirement already satisfied: typing-extensions>=4.2.0 in /home/michi/.venv/lib/python3.10/site-packages (from pydantic<2.0.0,>=1.10.5->gradientai) (4.5.0)
Requirement already satisfied: six>=1.5 in /home/michi/.venv/lib/python3.10/site-packages (from python-dateutil>=2.8.2->gradientai) (1.16.0)
import gradientai
client = gradientai.Gradient()
models = client.list_models(only_base=True)
for model in models:
print(model.id)
99148c6d-c2a0-4fbe-a4a7-e7c05bdb8a09_base_ml_model
f0b97d96-51a8-4040-8b22-7940ee1fa24e_base_ml_model
cc2dafce-9e6e-4a23-a918-cad6ba89e42e_base_ml_model
new_model = models[-1].create_model_adapter(name="my_model_adapter")
new_model.id, new_model.name
('674119b5-f19e-4856-add2-767ae7f7d7ef_model_adapter', 'my_model_adapter')
创建 Gradient 实例
您可以指定不同的参数，例如模型、生成的 max_tokens、温度等。
由于我们稍后想要微调我们的模型，因此我们选择具有 ID 674119b5-f19e-4856-add2-767ae7f7d7ef_model_adapter 的模型适配器，但您可以使用任何基本模型或可微调模型。
llm = GradientLLM(
# `ID` listed in `$ gradient model list`
model="674119b5-f19e-4856-add2-767ae7f7d7ef_model_adapter",
# # optional: set new credentials, they default to environment variables
# gradient_workspace_id=os.environ["GRADIENT_WORKSPACE_ID"],
# gradient_access_token=os.environ["GRADIENT_ACCESS_TOKEN"],
model_kwargs=dict(max_generated_token_count=128),
)
创建提示模板
我们将为问答创建一个提示模板。
template = """Question: {question}
Answer: """
prompt = PromptTemplate.from_template(template)
启动 LLMChain
llm_chain = LLMChain(prompt=prompt, llm=llm)
运行 LLMChain
提供一个问题并运行 LLMChain。
question = "What NFL team won the Super Bowl in 1994?"
llm_chain.run(question=question)
'\nThe San Francisco 49ers won the Super Bowl in 1994.'
通过微调来改进结果（可选）
好吧，那是错误的——旧金山 49 人队没有获胜。问题的正确答案应该是 达拉斯牛仔队！。
让我们通过使用提示模板对正确答案进行微调来提高获得正确答案的可能性。
dataset = [
{
"inputs": template.format(question="What NFL team won the Super Bowl in 1994?")
+ " The Dallas Cowboys!"
}
]
dataset
[{'inputs': 'Question: What NFL team won the Super Bowl in 1994?\n\nAnswer: The Dallas Cowboys!'}]
new_model.fine_tune(samples=dataset)
FineTuneResponse(number_of_trainable_tokens=27, sum_loss=78.17996)
# we can keep the llm_chain, as the registered model just got refreshed on the gradient.ai servers.
llm_chain.run(question=question)
'The Dallas Cowboys'