Instructions to use tattabio/gLM2_650M_bgc_decoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tattabio/gLM2_650M_bgc_decoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="tattabio/gLM2_650M_bgc_decoder", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("tattabio/gLM2_650M_bgc_decoder", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 494 Bytes
18495f1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"architectures": [
"gLM2ForMaskedLM"
],
"auto_map": {
"AutoConfig": "configuration_glm2.gLM2Config",
"AutoModel": "modeling_glm2.gLM2Model",
"AutoModelForMaskedLM": "modeling_glm2.gLM2ForMaskedLM"
},
"depth": 33,
"dim": 1280,
"ffn_dim_multiplier": null,
"heads": 20,
"model_type": "gLM2",
"norm_eps": 1e-05,
"swiglu_multiple_of": 256,
"tie_word_embeddings": false,
"torch_dtype": "float32",
"transformers_version": "4.49.0",
"vocab_size": 2036
}
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