codefuse-ai / codefuse-modelcache Goto Github PK
View Code? Open in Web Editor NEWA LLM semantic caching system aiming to enhance user experience by reducing response time via cached query-result pairs.
License: Other
A LLM semantic caching system aiming to enhance user experience by reducing response time via cached query-result pairs.
License: Other
现在有几个功能想请假大佬,我使用的CodeFuse-CodeLlama-34B-4bits的模型,对auto_gptq不熟悉,不知道怎么修改成的openai_api的功能,能不能给一个使用AutoTokenizer, AutoModelForCausalLM调模型的案例。
另外一个是,我提问是
你是一个数据分析师,请使用python进行数据分析。我已提供文件titanic.csv,文件地址是/mnt/bst/,请你分析一下这个文件,先读取这个文件,请写出python代码
回答:
generate text is 首先,我们需要导入必要的库.我们将使用pandas,matplotlib和seaborn库.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
然后,我们将读取文件.
df = pd.read_csv('/mnt/bst/titanic.csv')
接下来,我们将查看数据的前几行.
print(df.head())
然后,我们将查看数据的描述.
print(df.describe())
接下来,我们将查看数据的数据类型.
print(df.dtypes)
接下来,我们将查看数据中是否有缺失值.
print(df.isnull().sum())
接下来,我们将查看数据的唯一值.
print(df.nunique())
接下来,我们将查看数据的分布.
df.hist(bins=50, figsize=(20,15))
plt.show()
最后,我们将查看数据的相关性.
corr_matrix = df.corr()
sns.heatmap(corr_matrix, annot=True)
plt.show()
这些就是一些基本的数据分析步骤.你可以根据需要进行更多的数据分析.
回答是分段回答,这块我要怎么写提示词,形成一个整块的可马上执行的代码
非常感谢。
Is the project still being maintained, or are there any new plans for updates?
This issue is created to better track my PRs for Todo List [Rank ability]
Efficiently retrieving relevant results from large-scale datasets plays a crucial role in software development and Q&A tasks, including code recommendation and vulnerability code clone discovery. Existing search approaches suffer the difficulty of being scaled up, while maintaining the effectiveness of retrieved results.
In this paper, we propose a two-stage search framework to address these limitations and achieve both effectiveness and efficiency in the search Scenario. We will add ranking model to refine the order of data after embedding recall.
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