62 lines
3.9 KiB
Markdown
62 lines
3.9 KiB
Markdown
# Finetuning LLAMA Using QLoRA (experimental support)
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This example demonstrates how to finetune a llama2-7b model using Big-LLM 4bit optimizations on [Intel CPUs](../README.md).
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## Distributed Training Guide
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1. Single node with single socket: [simple example](https://github.com/intel-analytics/BigDL/tree/main/python/llm/example/CPU/QLoRA-FineTuning#example-finetune-llama2-7b-using-qlora)
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or [alpaca example](https://github.com/intel-analytics/BigDL/tree/main/python/llm/example/CPU/QLoRA-FineTuning/alpaca-qlora)
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2. [Single node with multiple sockets](https://github.com/intel-analytics/BigDL/tree/main/python/llm/example/CPU/QLoRA-FineTuning/alpaca-qlora#guide-to-finetuning-qlora-on-one-node-with-multiple-sockets)
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3. [multiple nodes with multiple sockets](https://github.com/intel-analytics/BigDL/blob/main/docker/llm/finetune/qlora/cpu/kubernetes/README.md)
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## Example: Finetune llama2-7b using QLoRA
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This example is ported from [bnb-4bit-training](https://colab.research.google.com/drive/1VoYNfYDKcKRQRor98Zbf2-9VQTtGJ24k).
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### 1. Install
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```bash
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conda create -n llm python=3.9
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conda activate llm
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pip install --pre --upgrade bigdl-llm[all]
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pip install transformers==4.34.0
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pip install peft==0.5.0
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pip install datasets
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pip install accelerate==0.23.0
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pip install bitsandbytes scipy
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```
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### 2. Finetune model
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If the machine memory is not enough, you can try to set `use_gradient_checkpointing=True` in [here](https://github.com/intel-analytics/BigDL/blob/1747ffe60019567482b6976a24b05079274e7fc8/python/llm/example/CPU/QLoRA-FineTuning/qlora_finetuning_cpu.py#L53C6-L53C6). While gradient checkpointing may improve memory efficiency, it slows training by approximately 20%.
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We Recommend using micro_batch_size of 8 for better performance using 48cores in this example. You can refer to [this guide](https://huggingface.co/docs/transformers/perf_train_gpu_one) for more details.
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And remember to use `bigdl-llm-init` before you start finetuning, which can accelerate the job.
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```
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source bigdl-llm-init -t
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python ./qlora_finetuning_cpu.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --dataset DATASET
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```
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#### Sample Output
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```log
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{'loss': 2.0251, 'learning_rate': 0.0002, 'epoch': 0.02}
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{'loss': 1.2389, 'learning_rate': 0.00017777777777777779, 'epoch': 0.03}
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{'loss': 1.032, 'learning_rate': 0.00015555555555555556, 'epoch': 0.05}
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{'loss': 0.9141, 'learning_rate': 0.00013333333333333334, 'epoch': 0.06}
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{'loss': 0.8505, 'learning_rate': 0.00011111111111111112, 'epoch': 0.08}
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{'loss': 0.8713, 'learning_rate': 8.888888888888889e-05, 'epoch': 0.09}
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{'loss': 0.8635, 'learning_rate': 6.666666666666667e-05, 'epoch': 0.11}
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{'loss': 0.8853, 'learning_rate': 4.4444444444444447e-05, 'epoch': 0.12}
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{'loss': 0.859, 'learning_rate': 2.2222222222222223e-05, 'epoch': 0.14}
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{'loss': 0.8608, 'learning_rate': 0.0, 'epoch': 0.15}
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{'train_runtime': xxxx, 'train_samples_per_second': xxxx, 'train_steps_per_second': xxxx, 'train_loss': 1.0400420665740966, 'epoch': 0.15}
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100%|███████████████████████████████████████████████████████████████████████████████████| 200/200 [07:16<00:00, 2.18s/it]
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TrainOutput(global_step=200, training_loss=1.0400420665740966, metrics={'train_runtime': xxxx, 'train_samples_per_second': xxxx, 'train_steps_per_second': xxxx, 'train_loss': 1.0400420665740966, 'epoch': 0.15})
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```
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### 3. Merge the adapter into the original model
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Using the [export_merged_model.py](https://github.com/intel-analytics/BigDL/blob/main/python/llm/example/GPU/LLM-Finetuning/QLoRA/export_merged_model.py) to merge.
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```
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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
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```
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Then you can use `./outputs/checkpoint-200-merged` as a normal huggingface transformer model to do inference.
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