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# Qwen2.5-Coder
## 论文
- https://arxiv.org/pdf/2410.13187
## 模型结构
aiXcoder-7B 是一个轻量级且高效的代码大语言模型,专为代码补全、生成和理解设计。支持多种编程语言,包括Python、Java、C/C++、JavaScript、TypeScript、HTML等,适用于多种开发场景。官方文档未完全公开其详细的模型结构超参数。可以参考huggingface上公布的配置参数。
## 算法原理
aiXcoder-7B 的性能归功于以下关键训练策略:
多目标训练(Multi-objective Training):
- Next-Token Prediction (NTP):模拟从头编写代码,预测下一个 token。
- Fill-in-the-Middle (FIM):训练模型基于前后上下文预测中间代码片段,适合修改现有代码。
- Structured Fill-in-the-Middle (SFIM):创新的训练目标,基于语法树选择完整代码片段(如函数、循环块)作为 middle,提高生成完整代码的能力。
数据采样策略:
- 考虑文件间关系(如文件内容相似性、依赖关系、路径相似性),增强跨文件上下文理解。
数据处理:
- 数据收集流程包括爬取、清洗、去重、代码质量检查和敏感信息移除,处理 2.8TB 自然语言数据和 3.5TB 源代码数据。
- 数据覆盖多种编程语言,确保多语言支持。
## 环境配置
### Docker(方法一)
[光源](https://www.sourcefind.cn/#/service-list)中拉取docker镜像:
```
docker pull image.sourcefind.cn:5000/dcu/admin/base/custom:vllm0.8.5-ubuntu22.04-dtk25.04-rc7-das1.5-py3.10-20250521-fixpy-rocblas0521-beta2
```
创建容器并挂载目录进行开发:
```
docker run -it --name {name} --shm-size=1024G --device=/dev/kfd --device=/dev/dri/ --privileged --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --ulimit memlock=-1:-1 --ipc=host --network host --group-add video -v /opt/hyhal:/opt/hyhal:ro -v {}:{} {docker_image} /bin/bash
# 修改1 {name} 需要改为自定义名称
# 修改2 {docker_image} 需要需要创建容器的对应镜像名称
# 修改3 -v 挂载路径到容器指定路径
pip install -r requirements.txt
```
### Dockerfile(方法二)
```
cd docker
docker build --no-cache -t qwen2_5_coder:1.0 .
docker run -it --name {name} --shm-size=1024G --device=/dev/kfd --device=/dev/dri/ --privileged --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --ulimit memlock=-1:-1 --ipc=host --network host --group-add video -v /opt/hyhal:/opt/hyhal:ro -v {}:{} {docker_image} /bin/bash
pip install -r requirements.txt
```
### Anaconda(方法三)
线上节点推荐使用conda进行环境配置。
创建python=3.10的conda环境并激活
```
conda create -n qwen2_5_coder python=3.10
conda activate qwen2_5_coder
```
关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.hpccube.com/tool/)开发者社区下载安装。
```
DTK驱动:dtk25.04
python:python3.10
pytorch:2.4.1
torchvision:0.19.1
sentencepiece>=0.2.0
```
安装其他依赖包
```
pip install -r requirements.txt
```
下载预训练权重文件:[HuggingFace下载](https://huggingface.co/aiXcoder/aixcoder-7b-base)
可以通过huggingface-cli下载,下载命令:
```
pip install -U huggingface_hub
export HF_ENDPOINT=https://hf-mirror.com
huggingface-cli download --resume-download aiXcoder/aixcoder-7b-base --local-dir aiXcoder/aixcoder-7b-base
```
## 数据集
微调数据集:[bigcode/the-stack-smol](bigcode/the-stack-smol)
可以通过huggingface-cli下载,下载命令:
```
huggingface-cli download --repo-type dataset --resume-download bigcode/the-stack-smol --local-dir bigcode/the-stack-smol
```
## 训练
```
# 注意:
accelerate launch finetune.py \
--model_id "aiXcoder/aixcoder-7b-base" \
--dataset_name "bigcode/the-stack-smol" \
--subset "data/rust" \
--dataset_text_field "content" \
--split "train" \
--max_seq_length 1024 \
--max_steps 100 \
--micro_batch_size 1 \
--gradient_accumulation_steps 8 \
--learning_rate 5e-6 \
--warmup_steps 20 \
--fim_rate 0.5 \
--num_proc "$(nproc)"
```
## 推理
```
# 注意:修改为自己的模型路径
python sess_huggingface.py
```
## result
<div align=center>
<img src="./assets/infer.png"/>
</div>
## 精度
DCU与GPU精度一致,推理框架:pytorch。
## 应用场景
### 算法类别
代码生成
### 热点应用行业
代码生成,代码修改,教育
## 其他预训练权重
Instruct系列模型
[Qwen2.5-Coder-0.5B-Instruct](http://113.200.138.88:18080/aimodels/qwen/Qwen2.5-Coder-0.5B-Instruct)
[Qwen2.5-Coder-1.5B-Instruct](http://113.200.138.88:18080/aimodels/qwen/Qwen2.5-Coder-1.5B-Instruct)
[Qwen2.5-Coder-3B-Instruct](http://113.200.138.88:18080/aimodels/qwen/Qwen2.5-Coder-3B-Instruct)
[Qwen2.5-Coder-7B-Instruct](http://113.200.138.88:18080/aimodels/qwen/Qwen2.5-Coder-7B-Instruct)
[Qwen2.5-Coder-14B-Instruct](http://113.200.138.88:18080/aimodels/qwen/Qwen2.5-Coder-14B-Instruct)
Base系列模型
[Qwen2.5-Coder-0.5B](http://113.200.138.88:18080/aimodels/qwen/Qwen2.5-Coder-0.5B)
[Qwen2.5-Coder-1.5B](http://113.200.138.88:18080/aimodels/qwen/Qwen2.5-Coder-1.5B)
[Qwen2.5-Coder-3B](http://113.200.138.88:18080/aimodels/qwen/Qwen2.5-Coder-3B)
[Qwen2.5-Coder-7B](http://113.200.138.88:18080/aimodels/qwen/Qwen2.5-Coder-7B)
[Qwen2.5-Coder-14B](http://113.200.138.88:18080/aimodels/qwen/Qwen2.5-Coder-14B)
[Qwen2.5-Coder-32B](http://113.200.138.88:18080/aimodels/qwen/Qwen2.5-Coder-32B)
## 源码仓库及问题反馈
[https://developer.sourcefind.cn/codes/dongchy920/qwen2.5_coder](https://developer.sourcefind.cn/codes/dongchy920/qwen2.5_coder)
## 参考资料
[https://github.com/QwenLM/Qwen2.5-Coder](https://github.com/QwenLM/Qwen2.5-Coder)
[https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct)
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Class 1:
----
c
cpp
c-sharp
go
javascript
java
kotlin
python
typescript
----
Class 2:
----
cmake
cuda
julia
markdown
makefile
mathematica
php
powershell
ruby
rust
shell
sql
swift
vue
React
----
Class 3:
----
lean
haskell
protocol-buffer
racket
scheme
dart
erlang
common-lisp
ada
dockerfile
css
clojure
elm
ocaml
perl
glsl
emacs-lisp
zig
json
agda
thrift
pascal
elixir
r
yaml
lua
fortran
restructuredtext
html
vhdl
verilog
scala
\ No newline at end of file
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