微调llama3大模型(2) - 使用ollama搭建chatbot

paulwong — mon, 08 jul 2024 11:48:00 gmt

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

ollama serve

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

paulwong 2024-07-08 19:48 发表评论

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微调llama3大模型(1) - 使用llama factory微调llama3大模型

paulwong — mon, 08 jul 2024 10:44:00 gmt

对于象meta的开源大模型，如llama3，由于都是用通用数据进行预训练，对想使用其模型的公司来说，可能会不适用，因为这大模型对公司的数据不熟悉，因此引入微调(fine-tunning)。

通过喂给大模型大量数据，1万条起步，使得大模型也能对公司的数据熟悉，进而用于各种对话场景。

1.克隆并安装llama factory库，install-llamafactory.sh

bin_path=$(cd `dirname $0`; pwd)
cd $bin_path/../
pwd
git clone --depth 1 https://github.com/hiyouga/llama-factory.git
cd llama-factory
pip install -e ".[torch,metrics,bitsandbytes,modelscope]"

2.设置环境变量

export use_modelscope_hub=1 #使用modelscop模型库，非huggingface的
export cuda_visible_devices=0 ＃设置使用gpu
export hf_endpoint=https://hf-mirror.com ＃设置huggingface的替代地址
export modelscope_cache=/root/autodl-tmp/models/modelscope ＃设置modelscope中的大模型保存路径
export llamafactory_home=/root/autodl-tmp/llama-factory

3.准备数据

#在data/dataset_info.json中加入此数据

"docker_command_nl": {
"hf_hub_url": "mattcoddity/dockernlcommands"
},

在data目录中加入训练数据，mattcoddity/dockernlcommands.json

数据格式为：

[
  {
    "input": "give me a list of containers that have the ubuntu image as their ancestor.",
    "instruction": "translate this sentence in docker command",
    "output": "docker ps --filter 'ancestor=ubuntu'"
  },

]

4.训练大模型

训练的参数文件：llama3_lora_sft_docker_command.yaml

### model
#md model id
model_name_or_path: llm-research/meta-llama-3-8b-instruct
#huggingface model id
#model_name_or_path: meta-llama/meta-llama-3-8b-instruct

### method
stage: sft
do_train: true
finetuning_type: lora
lora_target: all

### dataset
dataset: docker_command_nl
template: llama3
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16

### output
output_dir: /root/autodl-tmp/my-test/saves/llama3-8b/lora/sft/docker-commnad-nlp/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true

### train
per_device_train_batch_size: 4
gradient_accumulation_steps: 8
learning_rate: 1.0e-4
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: true
ddp_timeout: 180000000

### eval
val_size: 0.1
per_device_eval_batch_size: 1
eval_strategy: steps
eval_steps: 500

训练命令：lora-train-docker-command.sh

bin_path=$(cd `dirname $0`; pwd)
cd $bin_path/
pwd
cd $llamafactory_home
pwd
llamafactory-cli train $bin_path/conf/llama3_lora_sft_docker_command.yaml

执行此命令即可开始训练大模型。

5.合并大模型

合并用的参数文件，llama3_lora_export_docker_command.yaml

### model
#md model id
model_name_or_path: llm-research/meta-llama-3-8b-instruct
#huggingface model id
#model_name_or_path: meta-llama/meta-llama-3-8b-instruct

adapter_name_or_path: /root/autodl-tmp/my-test/saves/llama3-8b/lora/docker-commnad-nlp/sft
template: llama3
export_dir: /root/autodl-tmp/my-test/saves/llama3-8b/lora/docker-commnad-nlp/export
finetuning_type: lora
export_size: 2
export_device: gpu
export_legacy_format: false

合并命令，lora-export-docker-command.sh

bin_path=$(cd `dirname $0`; pwd)
cd $bin_path/
pwd
llamafactory-cli export conf/llama3_lora_export_docker_command.yaml

paulwong 2024-07-08 18:44 发表评论

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