Instructions to use WesScivetti/GPT-BERT_Random_Seed2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WesScivetti/GPT-BERT_Random_Seed2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="WesScivetti/GPT-BERT_Random_Seed2", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("WesScivetti/GPT-BERT_Random_Seed2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from WesScivetti/GPT-BERT_Random_Seed2: direct link, hf CLI and curl.
- Browser
- Download file 645 Bytes
-
https://huggingface.co/WesScivetti/GPT-BERT_Random_Seed2/resolve/main/config.json
- Command line
-
hf download hf://WesScivetti/GPT-BERT_Random_Seed2/config.json
-
curl -L -o config.json https://huggingface.co/WesScivetti/GPT-BERT_Random_Seed2/resolve/main/config.json
645 Bytes
| { | |
| "attention_probs_dropout_prob": 0.1, | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "intermediate_size": 2560, | |
| "max_position_embeddings": 512, | |
| "position_bucket_size": 32, | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "vocab_size": 16384, | |
| "layer_norm_eps": 1e-05, | |
| "model_type": "gpt_bert", | |
| "architectures": [ | |
| "GPTBERTForMaskedLM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_gpt_bert.ModelConfig", | |
| "AutoModel": "modeling_gpt_bert_test.GPTBERT", | |
| "AutoModelForMaskedLM": "modeling_gpt_bert_test.GPTBERTForMaskedLM", | |
| "AutoModelForCausalLM": "modeling_gpt_bert_test.GPTBERTForCausalLM" | |
| } | |
| } | |