ipex-llm/docker/llm/finetune/qlora/cpu/docker

Fine-tune LLM with BigDL LLM Container

The following shows how to fine-tune LLM with Quantization (QLoRA built on BigDL-LLM 4bit optimizations) in a docker environment, which is accelerated by Intel CPU.

1. Prepare Docker Image

You can download directly from Dockerhub like:

docker pull intelanalytics/bigdl-llm-finetune-qlora-cpu:2.4.0-SNAPSHOT

Or build the image from source:

export HTTP_PROXY=your_http_proxy
export HTTPS_PROXY=your_https_proxy

docker build \
  --build-arg http_proxy=${HTTP_PROXY} \
  --build-arg https_proxy=${HTTPS_PROXY} \
  -t intelanalytics/bigdl-llm-finetune-qlora-cpu:2.4.0-SNAPSHOT \
  -f ./Dockerfile .

2. Prepare Base Model, Data and Container

Here, we try to fine-tune a Llama2-7b with English Quotes dataset, and please download them and start a docker container with files mounted like below:

export BASE_MODE_PATH=your_downloaded_base_model_path
export DATA_PATH=your_downloaded_data_path
export HTTP_PROXY=your_http_proxy
export HTTPS_PROXY=your_https_proxy

docker run -itd \
   --net=host \
   --name=bigdl-llm-fintune-qlora-cpu \
   -e http_proxy=${HTTP_PROXY} \
   -e https_proxy=${HTTPS_PROXY} \
   -v $BASE_MODE_PATH:/model \
   -v $DATA_PATH:/data/english_quotes \
   intelanalytics/bigdl-llm-finetune-qlora-cpu:2.4.0-SNAPSHOT

The download and mount of base model and data to a docker container demonstrates a standard fine-tuning process. You can skip this step for a quick start, and in this way, the fine-tuning codes will automatically download the needed files:

export HTTP_PROXY=your_http_proxy
export HTTPS_PROXY=your_https_proxy

docker run -itd \
   --net=host \
   --name=bigdl-llm-fintune-qlora-cpu \
   -e http_proxy=${HTTP_PROXY} \
   -e https_proxy=${HTTPS_PROXY} \
   intelanalytics/bigdl-llm-finetune-qlora-cpu:2.4.0-SNAPSHOT

However, we do recommend you to handle them manually, because the automatical download can be blocked by Internet access and Huggingface authentication etc. according to different environment, and the manual method allows you to fine-tune in a custom way (with different base model and dataset).

3. Start Fine-Tuning

Enter the running container:

docker exec -it bigdl-llm-fintune-qlora-cpu bash

Then, start QLoRA fine-tuning: If the machine memory is not enough, you can try to set use_gradient_checkpointing=True.

And remember to use bigdl-llm-init before you start finetuning, which can accelerate the job.

source bigdl-llm-init -t
bash start-qlora-finetuning-on-cpu.sh

After minutes, it is expected to get results like:

{'loss': 2.256, 'learning_rate': 0.0002, 'epoch': 0.03}
{'loss': 1.8869, 'learning_rate': 0.00017777777777777779, 'epoch': 0.06}
{'loss': 1.5334, 'learning_rate': 0.00015555555555555556, 'epoch': 0.1}
{'loss': 1.4975, 'learning_rate': 0.00013333333333333334, 'epoch': 0.13}
{'loss': 1.3245, 'learning_rate': 0.00011111111111111112, 'epoch': 0.16}
{'loss': 1.2622, 'learning_rate': 8.888888888888889e-05, 'epoch': 0.19}
{'loss': 1.3944, 'learning_rate': 6.666666666666667e-05, 'epoch': 0.22}
{'loss': 1.2481, 'learning_rate': 4.4444444444444447e-05, 'epoch': 0.26}
{'loss': 1.3442, 'learning_rate': 2.2222222222222223e-05, 'epoch': 0.29}
{'loss': 1.3256, 'learning_rate': 0.0, 'epoch': 0.32}
{'train_runtime': xxx, 'train_samples_per_second': xxx, 'train_steps_per_second': xxx, 'train_loss': 1.5072882556915284, 'epoch': 0.32}
100%|██████████████████████████████████████████████████████████████████████████████████████| 200/200 [xx:xx<xx:xx,  xxxs/it]
TrainOutput(global_step=200, training_loss=1.5072882556915284, metrics={'train_runtime': xxx, 'train_samples_per_second': xxx, 'train_steps_per_second': xxx, 'train_loss': 1.5072882556915284, 'epoch': 0.32})

4. Merge the adapter into the original model

Using the export_merged_model.py to merge.

python ./export_merged_model.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --adapter_path ./outputs/checkpoint-200 --output_path ./outputs/checkpoint-200-merged

Then you can use ./outputs/checkpoint-200-merged as a normal huggingface transformer model to do inference.

5. Use BigDL-LLM to verify the fine-tuning effect

Train more steps and try input sentence like ['quote'] -> [?] to verify. For example, using “QLoRA fine-tuning using BigDL-LLM 4bit optimizations on Intel CPU is Efficient and convenient” ->: to inference. BigDL-LLM llama2 example link. Update the LLAMA2_PROMPT_FORMAT = "{prompt}".

python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt "“QLoRA fine-tuning using BigDL-LLM 4bit optimizations on Intel CPU is Efficient and convenient” ->:"  --n-predict 20

Sample Output

Base_model output

Inference time: xxx s
-------------------- Prompt --------------------
“QLoRA fine-tuning using BigDL-LLM 4bit optimizations on Intel CPU is Efficient and convenient” ->:
-------------------- Output --------------------
“QLoRA fine-tuning using BigDL-LLM 4bit optimizations on Intel CPU is Efficient and convenient” ->: 💻 Fine-tuning a language model on a powerful device like an Intel CPU

Merged_model output

Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Inference time: xxx s
-------------------- Prompt --------------------
“QLoRA fine-tuning using BigDL-LLM 4bit optimizations on Intel CPU is Efficient and convenient” ->:
-------------------- Output --------------------
“QLoRA fine-tuning using BigDL-LLM 4bit optimizations on Intel CPU is Efficient and convenient” ->: ['bigdl'] ['deep-learning'] ['distributed-computing'] ['intel'] ['optimization'] ['training'] ['training-speed']