Fill-Mask
Transformers
PyTorch
Safetensors
gpt_bert
feature-extraction
gpt-bert
babylm
remote-code
custom_code
Instructions to use jumelet/gptbert-ell-100steps-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jumelet/gptbert-ell-100steps-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jumelet/gptbert-ell-100steps-small", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jumelet/gptbert-ell-100steps-small", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from jumelet/gptbert-ell-100steps-small: direct link, hf CLI and curl.
- Browser
- Download file 1 kB
-
https://huggingface.co/jumelet/gptbert-ell-100steps-small/resolve/main/config.json
- Command line
-
hf download hf://jumelet/gptbert-ell-100steps-small/config.json
-
curl -L -o config.json https://huggingface.co/jumelet/gptbert-ell-100steps-small/resolve/main/config.json
1 kB
| { | |
| "architectures": [ | |
| "GPTBertForMaskedLM", | |
| "GPTBertForCausalLM", | |
| "GPTBertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "auto_map": { | |
| "AutoConfig": "configuration_gpt_bert.GPTBertConfig", | |
| "AutoModel": "modeling_gpt_bert.GPTBertForMaskedLM", | |
| "AutoModelForCausalLM": "modeling_gpt_bert.GPTBertForCausalLM", | |
| "AutoModelForMaskedLM": "modeling_gpt_bert.GPTBertForMaskedLM", | |
| "AutoModelForSequenceClassification": "modeling_gpt_bert.GPTBertForSequenceClassification" | |
| }, | |
| "bos_token_id": 1, | |
| "classifier_dropout": 0.1, | |
| "classifier_layer_norm_eps": 1e-05, | |
| "eos_token_id": 2, | |
| "force_causal_mask": true, | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 384, | |
| "intermediate_size": 1280, | |
| "layer_norm_eps": 1e-05, | |
| "mask_token_id": 4, | |
| "max_position_embeddings": 512, | |
| "model_type": "gpt_bert", | |
| "num_attention_heads": 6, | |
| "num_hidden_layers": 12, | |
| "num_labels": 2, | |
| "pad_token_id": 3, | |
| "position_bucket_size": 32, | |
| "vocab_size": 8192 | |
| } | |