A honeypot backend API based on fine-tuning LLaMA via LoRA.
This repo contains code for fine-tuning LLaMA-7B vai LoRA, in order to fit the requirements for honeypot backend API. We gathered thousands of response paired with commands, try to adjust the model into a Linux console. In addition, we designed a prompt template which supports history, thus the model could better support multi-round interaction.
1.Install dependencies.
pip install -r requirements.txt2.Prepare LLaMA model with HuggingFace type. Either from HuggingFace repo or converted locally from original LLaMA model through scripts provided by transformers.
[
{
"command": "pwd",
"history": [],
"output": "/home/user"
},
{
"command": "pwd",
"history": [["cd Document", ""]],
"output": "/home/user/Document"
}
]- Easy Example
python finetune.py \
--base_model /path/to/base/model \
--data_path /path/to/data/file \
--output_dir ./lora-honeypot- Hyper-parameters Example
python finetune.py \
--base_model /path/to/base/model \
--data_path /path/to/data/file \
--output_dir ./lora-honeypot \
--batch_size 128 \
--micro_batch_size 4 \
--num_epochs 3 \
--learning_rate 1e-4 \
--cutoff_len 512 \
--val_set_size 2000 \
--lora_rank 8 \
--lora_alpha 16 \
--lora_dropout 0.05 \
--lora_target_modules '[q_proj,v_proj]' \
--train_on_inputs \
--group_by_lengthpython api.py \
--base_model /path/to/base/model \
--lora_weights ./lora-honeypot- Easy Example
curl -X POST "http://127.0.0.1:8000" \
-H 'Content-Type: application/json' \
-d '{"command": "pwd", "history": []}'- Hyper-parameters Example
curl -X POST "http://127.0.0.1:8000" \
-H 'Content-Type: application/json' \
-d '{"command": "pwd", "history": [], "temperature": 0.4, "top_p": 0.75, "top_k": 40, "num_beams": 4, "max_new_tokens": 128}'