[New Model]: support GTE NewModel

[noooop]

Summary

• support GTE NewModel
  • Alibaba-NLP/gte-multilingual-base
  • Alibaba-NLP/gte-base-en-v1.5
  • Alibaba-NLP/gte-large-en-v1.5
• validate models previously supported
  • BertModel
    • thenlper/gte-large
    • thenlper/gte-base
    • thenlper/gte-small
    • thenlper/gte-large-zh
    • thenlper/gte-base-zh
    • thenlper/gte-small-zh
  • Qwen2ForCausalLM
    • Alibaba-NLP/gte-Qwen2-1.5B-instruct
    • Alibaba-NLP/gte-Qwen2-7B-instruct
  • ModernBertModel
    • Alibaba-NLP/gte-modernbert-base

Details

GTE NewModel

GTE NewModel uses NTKScalingRotaryEmbedding(RotaryEmbedding extended with fixed and mixed NTK scaling. https://kexue.fm/archives/9706)

Alibaba-NLP/gte-base-en-v1.5: type_vocab_size == 0

The second-generation GTE model (mGTE-TRM) is named NewModel. The name NewModel is too generic, you should set --hf-overrides '{"architectures": ["GteNewModel"]}' to specify the use of the GteNewModel architecture.

numerical stability

BertModel require float32 to achieve relatively good numerical stability (<1e-4).

float16 thenlper/gte-large 0.7680763719749079 -0.23436936369131067 0.007572298762589869
float32 thenlper/gte-large 0.7680763719749079 -2.4026911306096466e-06 7.336910572172527e-06

although torch_dtype is float16 in config.json. weird.

Other models can also achieve good results with float16.

float16 Alibaba-NLP/gte-multilingual-base 0.7750746088095989 -2.6885162520251527e-05 1.1313339850093886e-05
float32 Alibaba-NLP/gte-multilingual-base 0.7750746088095989 -5.2213094295927576e-06 7.063984056561489e-06

float16 Alibaba-NLP/gte-modernbert-base 0.7481935698597016 -3.955676059219648e-05 1.221876070985973e-05
float32 Alibaba-NLP/gte-modernbert-base 0.7481935698597016 -1.3103018835503022e-05 5.7769652475943475e-06

Alibaba-NLP/gte-Qwen2-1.5B-instruct

Adding {"is_causal": True} can get results consistent with SentenceTransformer.

float16 Alibaba-NLP/gte-Qwen2-1.5B-instruct 0.7280529229028553 5.124313459714536e-05 1.6385524234026275e-05

Not adding {"is_causal": True} can get better results on STS12 dataset

float16 Alibaba-NLP/gte-Qwen2-1.5B-instruct 0.7280529229028553 0.030351551441818114 1.2688565748575559e-05

tests Involved

pytest tests/models/language/pooling/test_gte.py

Fix #13616

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[DarkLight1337]

Unblocking extended LM tests

[DarkLight1337]

Need to install MTEB for the tests

[noooop]

Need to install MTEB for the tests

Didn't you only test a few models before?

MTEB has not merged my PR. ┓( ´∀` )┏

[DarkLight1337]

I mean the CI fails because of this

[noooop]

What should I do? When testing only core_model before, we could skip test_models_mteb.

[DarkLight1337]

You can add pytest.skip to skip these models for now