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[NLLB-MoE] model_type update for auto mapping (#22470)

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edit default model type and testing path set to hf-internal-testing
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Arthur 2023-03-30 15:36:07 +02:00 committed by GitHub
parent 11426641dc
commit 349e1242d9
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2 changed files with 4 additions and 4 deletions
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2
src/transformers/models/nllb_moe/configuration_nllb_moe.py
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@ -125,7 +125,7 @@ class NllbMoeConfig(PretrainedConfig):
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "nllb_moe"
model_type = "nllb-moe"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

6
tests/models/nllb_moe/test_modeling_nllb_moe.py
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@ -354,14 +354,14 @@ class NllbMoeModelIntegrationTests(unittest.TestCase):
@cached_property
def tokenizer(self):
return NllbTokenizer.from_pretrained("ArthurZ/random-nllb-moe-2-experts")
return NllbTokenizer.from_pretrained("hf-internal-testing/random-nllb-moe-2-experts")
@cached_property
def big_model(self):
return NllbMoeForConditionalGeneration.from_pretrained("facebook/nllb-moe-54b")
def inference_no_head(self):
model = NllbMoeModel.from_pretrained("ArthurZ/random-nllb-moe-2-experts").eval()
model = NllbMoeModel.from_pretrained("hf-internal-testing/random-nllb-moe-2-experts").eval()
with torch.no_grad():
output = model(**self.model_inputs)
# fmt: off
@ -382,7 +382,7 @@ class NllbMoeModelIntegrationTests(unittest.TestCase):
and `transformers` implementation of NLLB-MoE transformers. We only check the logits
of the second sample of the batch, as it is padded.
"""
model = NllbMoeForConditionalGeneration.from_pretrained("ArthurZ/random-nllb-moe-2-experts").eval()
model = NllbMoeForConditionalGeneration.from_pretrained("hf-internal-testing/random-nllb-moe-2-experts").eval()
with torch.no_grad():
output = model(**self.model_inputs)