Instructions to use juliushanusch/ChronosBERT-Optimized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use juliushanusch/ChronosBERT-Optimized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="juliushanusch/ChronosBERT-Optimized")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("juliushanusch/ChronosBERT-Optimized") model = AutoModelForMaskedLM.from_pretrained("juliushanusch/ChronosBERT-Optimized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from juliushanusch/ChronosBERT-Optimized: direct link, hf CLI and curl.
- Browser
- Download file 1.31 kB
-
https://huggingface.co/juliushanusch/ChronosBERT-Optimized/resolve/main/config.json
- Command line
-
hf download hf://juliushanusch/ChronosBERT-Optimized/config.json
-
curl -L -o config.json https://huggingface.co/juliushanusch/ChronosBERT-Optimized/resolve/main/config.json
1.31 kB
| { | |
| "_name_or_path": "/data/horse/ws/juha972b-AION-BERT-Chronos/BERTi/chronos_models/chronos_-6611716250758806883/run-0/checkpoint-final", | |
| "architectures": [ | |
| "BertForMaskedLM" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 1, | |
| "chronos_config": { | |
| "bos_token_id": 1, | |
| "context_length": 512, | |
| "eos_token_id": 2, | |
| "mask_token_id": 3, | |
| "model_type": "mlm", | |
| "n_special_tokens": 4, | |
| "n_tokens": 4096, | |
| "num_samples": 20, | |
| "pad_token_id": 0, | |
| "prediction_length": 64, | |
| "temperature": 1.0, | |
| "tokenizer_class": "MeanScaleUniformBins", | |
| "tokenizer_kwargs": { | |
| "high_limit": 15.0, | |
| "low_limit": -15.0 | |
| }, | |
| "top_k": 50, | |
| "top_p": 1.0, | |
| "use_eos_token": true | |
| }, | |
| "classifier_dropout": null, | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 512, | |
| "initializer_range": 0.05, | |
| "intermediate_size": 2048, | |
| "layer_norm_eps": 1e-12, | |
| "mask_token_id": 3, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 8, | |
| "num_hidden_layers": 4, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.49.0", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 4096 | |
| } | |