Instructions to use casualprogrammer/shadow_mbart with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use casualprogrammer/shadow_mbart with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/mbart-large-50") model = PeftModel.from_pretrained(base_model, "casualprogrammer/shadow_mbart") - Transformers
How to use casualprogrammer/shadow_mbart with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("casualprogrammer/shadow_mbart") model = AutoModelForSeq2SeqLM.from_pretrained("casualprogrammer/shadow_mbart", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download generation_config.json from casualprogrammer/shadow_mbart: direct link, hf CLI and curl.
- Browser
- Download file 255 Bytes
-
https://huggingface.co/casualprogrammer/shadow_mbart/resolve/main/generation_config.json
- Command line
-
hf download hf://casualprogrammer/shadow_mbart/generation_config.json
-
curl -L -o generation_config.json https://huggingface.co/casualprogrammer/shadow_mbart/resolve/main/generation_config.json
255 Bytes
| { | |
| "_from_model_config": true, | |
| "bos_token_id": 0, | |
| "decoder_start_token_id": 2, | |
| "early_stopping": true, | |
| "eos_token_id": 2, | |
| "forced_eos_token_id": 2, | |
| "max_length": 200, | |
| "num_beams": 5, | |
| "pad_token_id": 1, | |
| "transformers_version": "5.5.0" | |
| } | |