Instructions to use OpenNLG/OpenBA-V2-Based with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenNLG/OpenBA-V2-Based with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="OpenNLG/OpenBA-V2-Based", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OpenNLG/OpenBA-V2-Based", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_ffn_bias": false, | |
| "add_lm_head_bias": true, | |
| "add_qkv_bias": true, | |
| "architectures": [ | |
| "OpenBAForConditionalGeneration" | |
| ], | |
| "attention_dropout": 0.0, | |
| "auto_map": { | |
| "AutoConfig": "configuration_openba.OpenBAConfig", | |
| "AutoModel": "modeling_openba.OpenBAForConditionalGeneration", | |
| "AutoModelForCausalLM": "modeling_openba.OpenBAForConditionalGeneration", | |
| "AutoModelForSeq2SeqLM": "modeling_openba.OpenBAForConditionalGeneration" | |
| }, | |
| "decoder_max_seq_length": 1040, | |
| "decoder_start_token_id": 0, | |
| "eos_token_id": 1, | |
| "ffn_hidden_size": 6912, | |
| "hidden_dropout": 0.0, | |
| "hidden_size": 2560, | |
| "initializer_factor": 1.0, | |
| "is_encoder_decoder": true, | |
| "kv_channels": 128, | |
| "max_seq_length": 1040, | |
| "model_type": "openba", | |
| "num_decoder_layers": 24, | |
| "num_heads": 20, | |
| "num_layers": 8, | |
| "pad_token_id": 0, | |
| "tie_word_embeddings": false, | |
| "tokenizer_class": "OpenBATokenizer", | |
| "transformers_version": "4.32.0", | |
| "use_cache": true, | |
| "vocab_size": 250240 | |
| } | |