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fix path #533

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fix path #533

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2 changes: 1 addition & 1 deletion projects/Llama/adapter/adapter_config.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@
hidden_size=4096,
initializer_range=0.02,
intermediate_size=11008,
max_position_embeddings=4096,
max_position_embeddings=2048,
num_attention_heads=32,
hidden_layers=32,
pretraining_tp=1,
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1 change: 1 addition & 0 deletions projects/Llama/adapter/adapter_sft.py
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Expand Up @@ -39,6 +39,7 @@

# model
cfg.use_cache = False
cfg.pretrained_model_path=pretrained_model_path
model = LazyCall(LlamaForCausalLM)(cfg=cfg)

# datasets
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2 changes: 1 addition & 1 deletion projects/Llama/configs/llama_config.py
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Expand Up @@ -12,7 +12,7 @@
hidden_size=4096,
initializer_range=0.02,
intermediate_size=11008,
max_position_embeddings=4096,
max_position_embeddings=2048,
num_attention_heads=32,
hidden_layers=32,
pretraining_tp=1,
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1 change: 1 addition & 0 deletions projects/Llama/configs/llama_sft.py
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Expand Up @@ -39,6 +39,7 @@
)

# model
cfg.pretrained_model_path = pretrained_model_path
model = LazyCall(LlamaForCausalLM)(cfg=cfg)

# datasets
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2 changes: 1 addition & 1 deletion projects/Llama/readme.md
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Expand Up @@ -33,7 +33,7 @@ bash tools/train.sh projects/Llama/adapter/train_net.py projects/Llama/adapter/a

## Evaluate

> set the eval parameters in `/data/home/xiezipeng/libai/projects/Llama/utils/eval_adapter.py`, and running:
> set the eval parameters in `libai/projects/Llama/utils/eval_adapter.py`, and running:
```python3
python projects/Llama/utils/eval_adapter.py
```
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5 changes: 5 additions & 0 deletions projects/Llama/utils/prepare_alpaca.py
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Expand Up @@ -13,7 +13,9 @@

from libai.config import instantiate
from libai.utils.logger import setup_logger
from libai.config import LazyCall
from projects.Llama.configs.llama_config import tokenization
from projects.Llama.tokenizer import LlamaTokenizer

logger = setup_logger()

Expand Down Expand Up @@ -46,6 +48,9 @@ def prepare(
data = json.load(file)

logger.info("Loading tokenizer...")
tokenization.tokenizer = LazyCall(LlamaTokenizer)(
pretrained_model_path=os.path.join(checkpoint_dir, "tokenizer.model")
)
tokenizer = instantiate(tokenization.tokenizer)

# Partition the dataset into train and test
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