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
Download config.json from tattabio/gLM2_650M_bgc_decoder: direct link, hf CLI and curl.
- Browser
- Download file 494 Bytes
-
https://huggingface.co/tattabio/gLM2_650M_bgc_decoder/resolve/main/config.json
- Command line
-
hf download hf://tattabio/gLM2_650M_bgc_decoder/config.json
-
curl -L -o config.json https://huggingface.co/tattabio/gLM2_650M_bgc_decoder/resolve/main/config.json
494 Bytes
| { | |
| "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 | |
| } | |