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Update colab examples #86

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merged 7 commits into from
Aug 22, 2024
Merged

Update colab examples #86

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023ff4b
update colab examples
wenxindongwork Aug 20, 2024
ed7f1dd
Merge branch 'huggingface:main' into update_example
wenxindongwork Aug 20, 2024
8df30be
udpate exmaples
wenxindongwork Aug 20, 2024
403bdd0
Merge branch 'update_example' of https://github.com/wenxindongwork/op…
wenxindongwork Aug 20, 2024
f40451d
update fsdp_v2. get_fsdp_training_args
wenxindongwork Aug 21, 2024
2cb9068
load model in bf16
wenxindongwork Aug 21, 2024
1b83380
make style
wenxindongwork Aug 22, 2024
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2 changes: 1 addition & 1 deletion examples/language-modeling/gemma_tuning.ipynb
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Expand Up @@ -249,7 +249,7 @@
"metadata": {},
"outputs": [],
"source": [
"from optimum.tpu import AutoModelForCausalLM\n",
"from transformers import AutoModelForCausalLM\n",
"model = AutoModelForCausalLM.from_pretrained(model_id, use_cache=False)"
]
},
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3 changes: 1 addition & 2 deletions examples/language-modeling/llama_tuning.md
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Expand Up @@ -47,8 +47,7 @@ Then, the tokenizer and model need to be loaded. We will choose [`meta-llama/Met

```python
import torch
from transformers import AutoTokenizer
from optimum.tpu import AutoModelForCausalLM
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "meta-llama/Meta-Llama-3-8B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
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6 changes: 4 additions & 2 deletions optimum/tpu/fsdp_v2.py
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Expand Up @@ -83,8 +83,9 @@ def get_fsdp_training_args(model: PreTrainedModel) -> Dict:
matched_model = False
if model_type == "gemma":
from .modeling_gemma import GemmaForCausalLM
from transformers import GemmaForCausalLM as HFGemmaForCausalLLM

if isinstance(model, GemmaForCausalLM):
if isinstance(model, GemmaForCausalLM) or isinstance(model, HFGemmaForCausalLLM):
logger = logging.get_logger(__name__)
from torch_xla import __version__ as xla_version
if xla_version == "2.3.0":
Expand All @@ -95,8 +96,9 @@ def get_fsdp_training_args(model: PreTrainedModel) -> Dict:
matched_model = True
elif model_type == "llama":
from .modeling_llama import LlamaForCausalLM
from transformers import LlamaForCausalLM as HFLlamaForCausalLLM

if isinstance(model, LlamaForCausalLM):
if isinstance(model, LlamaForCausalLM) or isinstance(model, HFLlamaForCausalLLM):
cls_to_wrap = "LlamaDecoderLayer"
matched_model = True

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