盘点开源“Copilot”，do it yourself

Lowin Li

盘点开源“Copilot”，do it yourself | Lowin Li

文章作者: Lowin Li · 2022-06-27 · via Lowin Li

盘点开源“Copilot”，do it by yourself
- 目录
- 1.背景
- 2.简述
- 3. 盘点开源代码生成模型
  - 3.1. 模型清单
  - 3.2. 模型测试
    - 3.2.1. Python语言代码生成测试1
    - 3.2.2. Python语言代码生成测试2
    - 3.2.3. Python语言代码生成测试3
    - 3.2.4.Vue.js语言代码生成测试4
    - 3.2.5.JavaScript语言代码生成测试5
  - 3.3.亮点
  - 3.4.结论
- 4.搭建私有化代码生成服务
  - 4.1. onnx量化压缩
    - 4.1.1. fastgpt安装方法
    - 4.1.2. fastgpt快速使用
  - 4.2. 私有化web服务
    - 4.2.1. docker-compose启动
    - 4.2.2. 测试
- 5.制作私有化Vscode插件
- 6.enjoy-coding
- 附录：
  - 1. 推理计算资源

1.背景

Github Copilot即将收费：
- Copilot 官方近期宣布结束技术预览，并将在 2022 年 8 月 22 日开始收费, 收费标准为每月 10 美元或者每年 100 美元。学生和热门开源项目维护者可以免费使用。
程序员已经离不开 Copilot：
- Github声称，当前网站上 1/3 的代码都是在 Copilot 工具下完成的。而笔者也是在使用了半年的 Copilot 后，已经很难离开它的帮助，它已帮我做了很多重复性的编程工作。
开源代码生成模型：
- Huggingface Model Hub社区有很多开源模型可以直接下载使用，其中不乏一些开源代码生成模型，那么为什么不可以do it yourself。
私有化部署一套”Copilot”:
- 如果我们使用开源的代码生成模型自己部署一个代码生成服务，再辅以编辑器/IDE 插件，就可以模拟 Copilot 为自己和同事做代码生成服务。而且还有以下优点：
  1. 免去连 Copilot 偶尔的网络不稳定问题
  2. 免去代码上传 Copilot 的安全问题
  3. 根据自己的编码习惯，已有代码，对开源模型进行二次训练，为自己定制更懂自己的模型

2.简述

在本博客中，我们先从用户的角度，盘点一下当前开源代码生成模型的生成效果；然后自己搭建代码生成服务，搭建 Vscode 插件，为自己提供私有化”Copilot”。

3. 盘点开源代码生成模型

3.1. 模型清单

编号	模型	参数量	贡献者	训练语料	链接	支持 transformers	Huggingface 社区月下载量（May 2022）
1	code-autocomplete-distilgpt2-python	81.91M	shibing624	Python	链接	✔️	37k
2	code-autocomplete-gpt2-base	124.44M	shibing624	Python	链接	✔️	129
3	CodeGPT-small-py-adaptedGPT2	124.44M	Microsoft	Python	链接	✔️	5.57k
4	CodeGPT-small-java-adaptedGPT2	124.44M	Microsoft	Java	链接	✔️	3.31k
5	incoder-6B	6.7B	Facebook	Python JavaScript	链接	✔️	782
6	incoder-1B	1B	Facebook	Python JavaScript	链接	✔️	4.53k
7	codegen-350M-mono	350M	Salesforce	Python	链接	❌	232
8	codegen-2B-mono	2B	Salesforce	Python	链接	❌	438
9	codegen-6B-mono	6B	Salesforce	Python	链接	❌	180
10	codegen-16B-mono	16B	Salesforce	Python	链接	❌	89
11	codegen-350M-multi	350M	Salesforce	multiple programming languages	链接	❌	160
12	codegen-2B-multi	2B	Salesforce	multiple programming languages	链接	❌	140
13	codegen-6B-multi	6B	Salesforce	multiple programming languages	链接	❌	100
14	codegen-16B-multi	16B	Salesforce	multiple programming languages	链接	❌	30
15	gpt-neo-125M-code-search-py	125.20M	Flax-community	Python	链接	✔️	817
16	gpt-neo-125M-code-clippy	125.20M	Flax-community	multiple programming languages	链接	✔️	397
17	GPT2-python-code-generator	124.44M	SIC98	Python	链接	✔️	815
18	codeparrot	1.5B	CodeParrot	Python	链接	✔️	304
19	codeparrot-small	110M	CodeParrot	Python	链接	✔️	88

这里列举了已code作为关键字，在HuggingFace Model Hub搜索 text generation类型的模型，过滤掉月下载量在100以下且没有介绍的开源模型。
可见大家主要围绕Python语言做代码生成

3.2. 模型测试

下面尝试输入代码，分别测试代码生成模型都能输出什么，看看哪个预训练模型更懂我。

生成配置统一为：

model.generate(
    input_ids=input_ids,
    max_length=64 + input_ids.shape[1],
    temperature=1.0,
    top_k=5,
    top_p=0.95,
    repetition_penalty=1.0,
    do_sample=False,
    num_return_sequences=1,
    length_penalty=2.0,
    num_beam=2,
    early_stopping=True
)

3.2.1. Python语言代码生成测试1

输入原文

import torch
from torch import nn
import torch.nn.functional as F


class TextCNN(nn.Module):
    def __init__(self, vocab_size, embedding_dim, out_channels, hidden_size=128):
        super(TextCNN, self).__init__()
        c_i = 1
        kernel_num = 300
        kernel_sizes = [2, 3]
        self.embeddings = nn.Embedding(vocab_size, embedding_dim, padding_idx=1)
        self.convs = nn.ModuleList(
            [nn.Conv2d(c_i, kernel_num, (k, embedding_dim)) for k in kernel_sizes]
        )
        self.dropout = nn.Dropout(0.5)
        self.fc = nn.Linear(len(kernel_sizes) * kernel_num, out_channels)

    def forward(self, x, lengths, train=False):

1.code-autocomplete-distilgpt2-python

# type: Tuple[nn.ModuleList[nn.Module] = nn.ModuleList[nn.Module]

# type: nnumpy.Module = nnumpy.ModuleList[nn.ModuleList[nn.Module]

# type: nnumpy.

2.code-autocomplete-gpt2-base

def forward(x, y=None, y=None, y=None, y=None, y=None, x=None, y=None, y=None, y=None, y=None, y=None, y=None, y=None, y

3.CodeGPT-small-py-adaptedGPT2

"""Forward computation. Args: x: a Tensor lengths: a list of length-1 tensors train: whether to train the forward computation Returns: a Tensor """ if train: x = self.forward_train(x) return x  def __init__(self, *args,

5.incoder-6B

length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_length_

6.incoder-1B

:=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):=False):

7.codegen-350M-mono

x = self.embeddings(x)
x = x.unsqueeze(1)
x = [F.relu(conv(x)).squeeze(3) for conv in self.convs]
x = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in x]
x = [F.relu(i) for i in x]
x = [F.relu(i) for i in x]
x = [F.max_

8.codegen-2B-mono

x = self.embeddings(x)
x = x.permute(1, 0, 2)
x = self.dropout(x)
x = F.relu(x)
x = x.permute(1, 0, 2)
x = [F.max_pool1d(c(x), x.size(2)).squeeze(2) for c in self.convs]
x = torch.cat(x, 1)
x = self.dropout(x)
x =

9.codegen-6B-mono

x = self.embeddings(x)
x = x.transpose(1, 2)
x = [F.relu(conv(x)).squeeze(3) for conv in self.convs]
x = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in x]
x = torch.cat(x, 1)
x = self.dropout(x)
x = self.fc(x)
return x

11.codegen-350M-multi

    if train:
        return self.dropout(self.fc(x, lengths))
    else:
        return self.fc(x, lengths)

def forward_batch(self, x, lengths, batch_size):
    return self.forward(x, lengths, train=True)

def forward_batch_with_lengths(self, x, lengths, batch_size):
    return self.forward_batch(x, lengths, batch_size)

def forward_batch_with_lengths_and_batch_size(self, x

12.codegen-2B-multi

x = self.embeddings(x)
x = x.view(-1, x.size(0), x.size(1))
x = F.relu(self.dropout(self.convs(x)))
x = x.view(-1, x.size(0), x.size(1))
x = self.fc(x)
return x, lengths

13.codegen-6B-multi

x = self.embeddings(x)
x = torch.cat([x, lengths], 1)
x = self.dropout(x)
x = torch.cat([x, x], 1)
x = torch.cat([x, x], 1)
x = torch.cat([x, x], 1)
x = torch.cat([x, x], 1)
x = torch.cat([x, x], 1)
x = torch.cat([x, x], 1)
x = torch.cat

16.gpt-neo-125M-code-clippy

 * * * * * * * *
 * * * * * * * * * * *
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т
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17.GPT2-python-code-generator

x = self.embeddings(x)
x = self.fc(x)
x = self.dropout(x)
x = self.fc

18.codeparrot

x = self.embed(x)
x = self.dropout(x)
x = self.fc(x)
x = self.dropout(x)
x = self.fc(x)
x = self.dropout(x)
x = self.fc(x)

19.codeparrot-small

x = self.embeddings(x)
lengths = self.dropout(lengths)
x = self.fc(x)
lengths = self.dropout(lengths)
x = self.embeddings(x)
lengths = self.dropout(lengths)
x = self.dropout(x)

3.2.2. Python语言代码生成测试2

输入原文

import numpy as np


def softmax(x):
    """Compute softmax values for each sets of scores in x."""

1.code-autocomplete-distilgpt2-python

x = np.zeros((x.shape[0], x.shape[1]))
x[:, :, :] = np.zeros((x.shape[0], x.shape[1]))
x[:, :, :]

2.code-autocomplete-gpt2-base

x = np.zeros((x.shape[0], x.shape[1], x.shape[2]), dtype=np.float32)
x[:, :, :, :] = x[:, :, :, :] * x[:, :

3.CodeGPT-small-py-adaptedGPT2

"""Compute softmax values for each sets of scores in x.""" x = np.asarray(x) # x = np.asarray(x.T) # x = np.asarray(x.T) # x = np.asarray(x.T) # x = np

5.incoder-6B

6.incoder-1B

x."""Compute softmax values for each sets of scores in x."""
x."""Compute softmax values for each sets of scores in x."""
x."""Compute softmax values for each sets of scores in x."""
x."""Compute softmax values for each sets of scores in x."""
x."""Compute

7.codegen-350M-mono

e_x = np.exp(x - np.max(x))
return e_x / e_x.sum()

8.codegen-2B-mono

e_x = np.exp(x - np.max(x))
return e_x / e_x.sum(axis=0)  #

9.codegen-6B-mono

e_x = np.exp(x - np.max(x))
return e_x / e_x.sum()

11.codegen-350M-multi
```
return np.exp(x - np.max(x, axis=1))
```

12.codegen-2B-multi

e_x = np.exp(x - np.max(x))
return e_x / e_x.sum()

13.codegen-6B-multi
```
#
```

15.gpt-neo-125M-code-search-py

    if len(x) == 1:
        return np.sum(np.abs(x))
    else:
        return np.sum(np.abs(x))
def _get_scores(self,

16.gpt-neo-125M-code-clippy

def softmax(x):
def softmax(x):
def softmax(x):
def softmax(x):
def softmax(x):
def Softmax(x):
def Softmax(x):
def Softmax(x):
def Softmax(x):

17.GPT2-python-code-generator

    return np.sum(np.abs(x - np.log(x)) / np.log(x))


def softmax_log_prob(x):
    """Compute softmax values for each sets of scores in x."""
    return

18.codeparrot

#    return np.exp(x - np.max(x, axis=1, keepdims=True))
    return np.exp(x - np.max(x, axis=1, keepdims=True))


def softmax_cross_entropy(logits, labels):
    """Compute softmax cross entropy

19.codeparrot-small

#    x = np.asarray(x)
    x = np.asarray(x)
    x = x.reshape(-1, 1)
    x = x.reshape(-1, 1)
    x = x.reshape(-1, 1)
    x = x.reshape(-1, 1)

3.2.3. Python语言代码生成测试3

输入原文

from flask_jwt_extended import JWTManager
from flask_apscheduler import APScheduler
from flask import Flask
from utils import scheduler_build


jwt_app = Flask(__name__)
class SchedulerConfig(object):
    JOBS = [
        {
            "id": "scheduler_build",
            "func": scheduler_build,
            "trigger": "interval",
            "seconds": 10,
        }
    ]
    SCHEDULER_API_ENABLED = True

scheduler = APScheduler()

1.code-autocomplete-distilgpt2-python

#!/usr/bin/env python3

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this

2.code-autocomplete-gpt2-base




# Copyright (c = """
from __version__version__ = """
from __version__
from __version__ import __version__
from __version__ import __version__
from __version__ import __version__

from __version__ import __version__ import __version

3.CodeGPT-small-py-adaptedGPT2

# TODO: move to the front of the list of supported schemas. # TODO: move to the front of the list of supported schemas. # TODO: move to the front of the list of supported schemas. # TODO: move to the front of the list of supported schemas.

5.incoder-6B

scheduler.add_job(
    id="scheduler_build",
    func=scheduler_build,
    trigger="interval",
    seconds=10,
)
scheduler.start()
</code>

6.incoder-1B

7.codegen-350M-mono

jwt = JWTManager(jwt_app)
scheduler.init_app(jwt_app)
scheduler.start()

@jwt.token_in_blocklist_loader
def check_if_token_in_blocklist(jwt_header, jwt_payload):
    return jwt_payload['jti'] in scheduler.job_dict

8.codegen-2B-mono

scheduler.init_app(jwt_app)
scheduler.configure(SchedulerConfig)
jwt = JWTManager(jwt_app)

from app import routes, models, errors

9.codegen-6B-mono

scheduler.init_app(jwt_app)
scheduler.start()
jwt_app.config.from_object(SchedulerConfig())
jwt = JWTManager(jwt_app)

11.codegen-350M-multi

jwt_app.config.update(SCHEDULER_API_ENABLED=SCHEDULER_API_ENABLED)

jwt_app.config.update(SECRET_KEY='secret')

jwt_app.config.update(SECURITY_PASSWORD='password')

jwt_app.config.update(
    JWT_MANAGER=JWTManager(jwt_app.config['SECRET_KEY'],
                           jwt_app.config['SECURITY_PASSWORD']

12.codegen-2B-multi

scheduler.init_app(jwt_app, config=SchedulerConfig)

jwt_manager = JWTManager(jwt_app)

@jwt_app.route('/')
def index():
    return 'Hello World!'

@jwt_app.route('/login', methods=['POST'])

13.codegen-6B-multi

jwt_manager = JWTManager(jwt_app)
jwt_manager.init_app(jwt_app)
jwt_manager.init_scheduler(scheduler, SchedulerConfig.JOBS)

@jwt_manager.jwt_required
def index():
    return "Hello World"

if __name__ == "__main__":
    jwt_app.run(host='0.0.0.0', port=80)

15.gpt-neo-125M-code-search-py
```
scheduler.add_job(JWTManager(
```

16.gpt-neo-125M-code-clippy


j.�.
class
_apSCheduler_extended_
_apscheduler_extended_extended_extended_extended_extended_extended_extended_extended_extended_extended_extended_extended_extended_

17.GPT2-python-code-generator


class Scheduler(JWTManager):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.__class__ = Scheduler

18.codeparrot

scheduler.init_app(scheduler_app)
scheduler.add_job(
    "interval",
    seconds=10,
    id="scheduler_build",
    func=scheduler_build,
    trigger="interval",
)
scheduler.start()

app = Flask(__name__)
app.

19.codeparrot-small

scheduler.add_app(jwt_app)
scheduler.add_app(SchedulerConfig())

scheduler.start()
# -*- coding: utf-8 -*-
#
# Copyright (C) Pootle contributors.
#
# This file is a part of the Pootle project.

3.2.4.Vue.js语言代码生成测试4

输入原文

<template>
  <div class="login-container">
    <el-form
      ref="loginForm"
      :model="loginForm"
      :rules="loginRules"
      class="login-form"
      auto-complete="on"
      label-position="left"
    >
      <div class="title-container">
        <h3 class="title">Scorpio</h3>
      </div>

      <el-form-item prop="username">
        <span class="svg-container">
          <svg-icon icon-class="user" />
        </span>
        <el-input

5.incoder-6B

    placeholder="user"
    id="user"
    />
</el-form-item>

<el-form-item prop="username">
    <span class="svg-container">
    <svg-icon icon-class="user" />
    </span>
    <el-input
    placeholder="user"
    id="user"
    />
</

6.incoder-1B

    ref="username"
    :model="username"
    :rules="usernameRules"
    class="username-input"
    auto-complete="on"
    label-position="left"
>
    <span class="title-container">
    <h3 class="title">Scorpio</h3>
    </span>

11.codegen-350M-multi

    ref="username"
    class="username"
    placeholder="Username"
    value="">
    <el-input-group placeholder="Username" />
</el-input>
</el-form-item>

<el-form-item prop="password">
<span class="svg-container">
    <svg-icon icon-class="password" />
</span>
<el-input
    ref="password"
    class="password"
    placeholder="Password"
    value="">

12.codegen-2B-multi

    ref="username"
    placeholder="Username"
    class="username"
    label="Username"
    auto-complete="off"
/>
</el-form-item>

<el-form-item prop="password">
<span class="svg-container">
    <svg-icon icon-class="lock" />
</span>
<el-input
    ref="password"
    placeholder="Password"
    class="password"
    label="Password"
    auto-complete="off"

13.codegen-6B-multi

ref="username"
v-model="loginForm.username"
placeholder="Username"
autofocus
:label="loginForm.username"
:rules="loginRules"
:error-message="loginForm.usernameError"
:validation-error="loginForm.usernameError"
:validation-error-message="loginForm.usernameErrorMessage"
:validation-error-class="loginForm.usernameErrorClass"
:validation-error-class-message="loginForm.usernameErrorMessage"

16.gpt-neo-125M-code-clippy

�у-�.л-�у
<div class="login-container">
<div>
<div class="login-container">
<div class="login-�">
<div class="login-�="login-container-container-container-container-

3.2.5.JavaScript语言代码生成测试5

输入原文

import { asyncRoutes, constantRoutes } from '@/router'


function hasPermission(roles, route) {

5.incoder-6B

6.incoder-1B

) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {) {

11.codegen-350M-multi

return roles.indexOf(route.permission) > -1
}

function getPermission(roles, route) {
  return roles.indexOf(route.permission) > -1
}

function hasRoute(roles, route) {
  return roles.indexOf(route.permission) > -1
}

function getRoute(roles, route) {
  return roles.indexOf(route.permission) > -1
}

function hasRoutePermission(roles, route, permission) {
  return

12.codegen-2B-multi

return roles.indexOf(route.permission) > -1
}

export default asyncRoutes(
  {
    [constantRoutes.LOGIN]: {
      path: '/login',
      component: require('./login'),
      permission: 'user',
      meta: {
        title: 'Login',
        description: 'Login to the system',
        icon: 'fa fa-sign-in'
      }
    },
    [constantRoutes.LOGOUT]: {
      path: '/logout

13.codegen-6B-multi

  if (!roles) {
    return true
  }
  return roles.indexOf(route.meta.roles) >= 0
}

function hasPermission(roles, route, permission) {
  if (!roles) {
    return true
  }
  return roles.indexOf(route.meta.roles) >= 0 && route.meta[permission]
}

function hasPermission(roles, route, permission, permissionValue) {
  if (!roles) {
    return true
  }
  return roles

16.gpt-neo-125M-code-clippy
```
return {
```

3.3.亮点

codegen-6B-mono在测试1中，完美的写出了TextCNN的网络结构

3.4.结论

模型大真的能力会更强，参考codegen-6B-mono的表现
Model Hub的下载量水分很大，参考code-autocomplete-distilgpt2-python的表现
分领域很有用，参考codegen-6B-mono相比codegen-6B-multi在Python问题上的表现
Sailesforce的codegen系列，比其他开源代码生成模型好了一个档次
开源代码生成模型都是围绕Python语言居多，偶尔有全栈语言

4.搭建私有化代码生成服务

4.1. onnx量化压缩

模型一般部署在cpu上运行，使用onnxruntime量化技术可以大幅提高模型运行提速
推荐使用fastgpt库对transformers的GPT模型进行onnx量化和加载
对于不支持transformers的codegen系列，fastgpt也有codegen例子做onnx量化和代码生成

4.1.1. fastgpt安装方法

pip install fastgpt

4.1.2. fastgpt快速使用

from transformers import AutoTokenizer
from fastgpt import CausalLMModelForOnnxGeneration
model = CausalLMModelForOnnxGeneration.from_pretrained("distilgpt2")
tokenizer = AutoTokenizer.from_pretrained("distilgpt2")

prompt_text = "Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken and written"
input_ids = tokenizer(
    prompt_text, return_tensors="pt", add_special_tokens=False
).input_ids

generated_ids = model.generate(   # 这里完全兼容transformers的generate函数
    input_ids,
    max_length=64 + input_ids.shape[1],
    decoder_start_token_id=tokenizer.cls_token_id,
    eos_token_id=tokenizer.sep_token_id,
    output_scores=True,
    temperature=1,
    repetition_penalty=1.0,
    top_k=50,
    top_p=0.9,
    do_sample=True,
    num_return_sequences=1,
    length_penalty=2.0,
    early_stopping=True,
)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
print("=" * 20)

4.2. 私有化web服务

在fastgpt仓库的codegen例子中已经把codegen-350M-mono和codegen-350M-multi进行onnx量化和打包镜像，并上传了dockerhub

4.2.1. docker-compose启动

version: "2.3"
services:
  fastgpt-codegen:
    container_name: fastgpt-codegen
    image: lowinli98/fastgpt-codegen:v0.0.7
    expose:
      - 7104
    ports:
      - "7104:7104"
    environment:
      - PORT=7104
      - GUNICORN_WORKER=1
      - GUNICORN_THREADS=1
    restart: always

4.2.2. 测试

codegen-350M-mono

curl --location --request POST 'http://172.16.104.29:7104/generate_mono' \
--header 'Content-Type: application/json' \
--data-raw '{
    "inputs": "def calculdate_mean(x, y): \n",
    "parameters": {
        "do_sample": true
    }
}'

codegen-350M-multi

curl --location --request POST 'http://172.16.104.29:7104/generate_multi' \
--header 'Content-Type: application/json' \
--data-raw '{
    "inputs": "def calculdate_mean(x, y): \n",
    "parameters": {
        "do_sample": true
    }
}'

5.制作私有化Vscode插件

详见适配最新vscode版本(1.68.1)的vscode插件

6.enjoy-coding

附录：

1. 推理计算资源

cpu: Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz

模型	硬盘占用	torch加载使用内存	测试1用时	测试2用时	测试3用时
codegen-16B-mono	30G	*	*	*	*
codegen-6B-mono	14G	40G	346s	96s	45s
codegen-2B-mono	614M	17G	38s	13s	38s
codegen-350M-mono	167M	3G	5s	5s	5s

此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。原文来自 — 版权归原作者所有。

推荐订阅源

Lowin Li

目录

1.背景

2.简述

3. 盘点开源代码生成模型

3.1. 模型清单

3.2. 模型测试

3.2.1. Python语言代码生成测试1

3.2.2. Python语言代码生成测试2

3.2.3. Python语言代码生成测试3

3.2.4.Vue.js语言代码生成测试4

3.2.5.JavaScript语言代码生成测试5

3.3.亮点

3.4.结论

4.搭建私有化代码生成服务

4.1. onnx量化压缩

4.1.1. fastgpt安装方法

4.1.2. fastgpt快速使用

4.2. 私有化web服务

4.2.1. docker-compose启动

4.2.2. 测试

5.制作私有化Vscode插件

6.enjoy-coding

附录：

1. 推理计算资源