上篇已经合并出了训练好的大模型，现在要搭建起一套CHATBOT，使得这套大模型能有一个WEBUI用起来。
1.设置环境变量，ollama的模型保存路径，/etc/profile
export OLLAMA_MODELS=/root/autodl-tmp/models/ollama
2.克隆ollama代码
curl -fsSL https://ollama.com/install.sh | sh
3.启动ollama
4.建立ollama镜像的配置文件，Modelfile
# set the base model
FROM /root/.ollama/llamafactory-export/saves/llama3-8b/lora/docker-commnad-nlp/export
# set custom parameter values
PARAMETER temperature 1
PARAMETER num_keep 24
PARAMETER stop <|start_header_id|>
PARAMETER stop <|end_header_id|>
PARAMETER stop <|eot_id|>
PARAMETER stop <|reserved_special_token
# set the model template
TEMPLATE """
{{ if .System }}<|start_header_id|>system<|end_header_id|>
{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
{{ .Response }}<|eot_id|>
"""
# set the system message
SYSTEM You are llama3 from Meta, customized and hosted @ Paul Wong (http://paulwong88.tpddns.cn).
# set Chinese lora support
#ADAPTER /root/.ollama/models/lora/ggml-adapter-model.bin
建立镜像命令，create-ollama-image-docker-command-nlp.sh
BIN_PATH=$(cd `dirname $0`; pwd)
cd $BIN_PATH/
pwd
ollama create llama3-docker-commnad-nlp:paul -f Modelfile
5.运行大模型
llama3-docker-commnad-nlp:paul