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fix lisa finetune example (#12775)

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Yishuo Wang 2025-02-06 16:35:43 +08:00 committed by GitHub
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2 changed files with 14 additions and 15 deletions
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24
python/llm/example/GPU/LLM-Finetuning/LISA/README.md
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@ -13,10 +13,8 @@ conda create -n llm python=3.11
conda activate llm
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
pip install bitsandbytes==0.43.0
pip install datasets==2.18.0
pip install --upgrade transformers==4.36.0
pip install scipy fire
pip install transformers==4.45.0 "trl<0.12.0" datasets
pip install bitsandbytes==0.45.1 scipy fire
```
### 2. LISA Finetune
@ -51,23 +49,23 @@ Optional parameters for `lisa_finetuning.py`:
```log
......
{'loss': 1.8391, 'learning_rate': 1.9967238104745695e-05, 'epoch': 0.03}
{'loss': 1.8242, 'learning_rate': 1.9869167087338908e-05, 'epoch': 0.05}
{'loss': 1.8391, 'learning_rate': 1.9967238104745695e-05, 'epoch': 0.03}
{'loss': 1.8242, 'learning_rate': 1.9869167087338908e-05, 'epoch': 0.05}
5%|██████▉ | 20/388 [xx:xx<x:xx:xx, x.xxs/it]
Activating layers at indices: [10] for the next steps.
{'loss': 1.8128, 'learning_rate': 1.9706429546259592e-05, 'epoch': 0.08}
{'loss': 1.775, 'learning_rate': 1.9480091799562706e-05, 'epoch': 0.1}
{'loss': 1.8128, 'learning_rate': 1.9706429546259592e-05, 'epoch': 0.08}
{'loss': 1.775, 'learning_rate': 1.9480091799562706e-05, 'epoch': 0.1}
10%|██████████████ | 40/388 [xx:xx<xx:xx, x.xxs/it]
Activating layers at indices: [30] for the next steps.
{'loss': 1.7669, 'learning_rate': 1.9191636897958123e-05, 'epoch': 0.13}
{'loss': 1.7749, 'learning_rate': 1.8842954907300236e-05, 'epoch': 0.15}
{'loss': 1.7669, 'learning_rate': 1.9191636897958123e-05, 'epoch': 0.13}
{'loss': 1.7749, 'learning_rate': 1.8842954907300236e-05, 'epoch': 0.15}
15%|█████████████████████ | 60/388 [xx:xx<xx:xx, x.xxs/it]
Activating layers at indices: [26] for the next steps.
{'loss': 1.7735, 'learning_rate': 1.8436330524160048e-05, 'epoch': 0.18}
{'loss': 1.7199, 'learning_rate': 1.797442810562721e-05, 'epoch': 0.21}
{'loss': 1.7735, 'learning_rate': 1.8436330524160048e-05, 'epoch': 0.18}
{'loss': 1.7199, 'learning_rate': 1.797442810562721e-05, 'epoch': 0.21}
21%|████████████████████████████ | 80/388 [xx:xx<xx:xx, x.xxs/it]
Activating layers at indices: [17] for the next steps.
{'loss': 1.7328, 'learning_rate': 1.7460274211432463e-05, 'epoch': 0.23}
{'loss': 1.7328, 'learning_rate': 1.7460274211432463e-05, 'epoch': 0.23}
25%|█████████████████████████████████▋ | 96/388 [xx:xx<xx:xx, x.xxs/it]
......

5
python/llm/example/GPU/LLM-Finetuning/LISA/lisa_finetuning.py
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@ -90,9 +90,10 @@ def train(
model = AutoModelForCausalLM.from_pretrained(
base_model,
load_in_low_bit="bf16",
optimize_model=True,
optimize_model=False,
torch_dtype=torch.bfloat16,
trust_remote_code=True
trust_remote_code=True,
modules_to_not_convert=["lm_head"], # avoid optimize lm_head
)
model = model.to("xpu")