gpt-academic-private / docker-compose.yml
binary-husky
加入LLAMA + 盘古 + RWKV本地模型
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【请在修改完参数后,删除此行】请在以下方案中选择一种,然后删除其他的方案,最后docker-compose up运行
## ===================================================
## 【方案一】 如果不需要运行本地模型(仅chatgpt类远程服务)
## ===================================================
version: '3'
services:
gpt_academic_nolocalllms:
image: fuqingxu/gpt_academic:no-local-llms
environment:
# 请查阅 `config.py` 以查看所有的配置信息
API_KEY: ' sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx,fkxxxxxx-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx '
USE_PROXY: ' True '
proxies: ' { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", } '
LLM_MODEL: ' gpt-3.5-turbo '
AVAIL_LLM_MODELS: ' ["gpt-3.5-turbo", "api2d-gpt-4"] '
DEFAULT_WORKER_NUM: ' 10 '
WEB_PORT: ' 22303 '
ADD_WAIFU: ' True '
AUTHENTICATION: ' [("username", "passwd"), ("username2", "passwd2")] '
# 与宿主的网络融合
network_mode: "host"
# 不使用代理网络拉取最新代码
command: >
bash -c " echo '[gpt-academic] 正在从github拉取最新代码...' &&
git checkout master --force &&
git remote set-url origin https://github.com/binary-husky/chatgpt_academic.git &&
git pull &&
python3 -u main.py"
### ===================================================
### 【方案二】 如果需要运行ChatGLM本地模型
### ===================================================
version: '3'
services:
gpt_academic_with_chatglm:
image: fuqingxu/gpt_academic:chatgpt-chatglm-newbing # [option 2] 如果需要运行ChatGLM本地模型
environment:
# 请查阅 `config.py` 以查看所有的配置信息
API_KEY: ' sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx,fkxxxxxx-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx '
USE_PROXY: ' True '
proxies: ' { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", } '
LLM_MODEL: ' gpt-3.5-turbo '
AVAIL_LLM_MODELS: ' ["gpt-3.5-turbo", "api2d-gpt-4", "chatglm"] '
LOCAL_MODEL_DEVICE: ' cuda '
DEFAULT_WORKER_NUM: ' 10 '
WEB_PORT: ' 12303 '
ADD_WAIFU: ' True '
AUTHENTICATION: ' [("username", "passwd"), ("username2", "passwd2")] '
# 显卡的使用,nvidia0指第0个GPU
runtime: nvidia
devices:
- /dev/nvidia0:/dev/nvidia0
# 与宿主的网络融合
network_mode: "host"
# 使用代理网络拉取最新代码
# command: >
# bash -c " echo '[gpt-academic] 正在从github拉取最新代码...' &&
# truncate -s -1 /etc/proxychains.conf &&
# echo \"socks5 127.0.0.1 10880\" >> /etc/proxychains.conf &&
# proxychains git pull &&
# python3 -u main.py "
# 不使用代理网络拉取最新代码
command: >
bash -c " echo '[gpt-academic] 正在从github拉取最新代码...' &&
git pull &&
python3 -u main.py"
### ===================================================
### 【方案三】 如果需要运行ChatGPT + LLAMA + 盘古 + RWKV本地模型
### ===================================================
version: '3'
services:
gpt_academic_with_rwkv:
image: fuqingxu/gpt_academic:jittorllms # [option 2] 如果需要运行ChatGPT + LLAMA + 盘古 + RWKV本地模型
environment:
# 请查阅 `config.py` 以查看所有的配置信息
API_KEY: ' sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx,fkxxxxxx-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx '
USE_PROXY: ' True '
proxies: ' { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", } '
LLM_MODEL: ' gpt-3.5-turbo '
AVAIL_LLM_MODELS: ' ["gpt-3.5-turbo", "api2d-gpt-4", "newbing", "jittorllms_rwkv", "jittorllms_llama", "jittorllms_pangualpha"] '
LOCAL_MODEL_DEVICE: ' cuda '
DEFAULT_WORKER_NUM: ' 10 '
WEB_PORT: ' 32303 '
ADD_WAIFU: ' True '
AUTHENTICATION: ' [("username", "passwd"), ("username2", "passwd2")] '
# 显卡的使用,nvidia0指第0个GPU
runtime: nvidia
devices:
- /dev/nvidia0:/dev/nvidia0
# 与宿主的网络融合
network_mode: "host"
# 使用代理网络拉取最新代码
# command: >
# bash -c " echo '[gpt-academic] 正在从github拉取最新代码...' &&
# truncate -s -1 /etc/proxychains.conf &&
# echo \"socks5 127.0.0.1 10880\" >> /etc/proxychains.conf &&
# proxychains git pull &&
# python3 -u main.py "
# 不使用代理网络拉取最新代码
command: >
bash -c " echo '[gpt-academic] 正在从github拉取最新代码...' &&
git pull &&
python3 -u main.py"