Instructions to use Ruqiya/rs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ruqiya/rs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Ruqiya/rs", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Ruqiya/rs", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "/content/falcon-7b", | |
| "alibi": false, | |
| "apply_residual_connection_post_layernorm": false, | |
| "architectures": [ | |
| "RWModel" | |
| ], | |
| "attention_dropout": 0.0, | |
| "auto_map": { | |
| "AutoConfig": "configuration_RW.RWConfig", | |
| "AutoModel": "modelling_RW.RWModel", | |
| "AutoModelForCausalLM": "modelling_RW.RWForCausalLM", | |
| "AutoModelForQuestionAnswering": "modelling_RW.RWForQuestionAnswering", | |
| "AutoModelForSequenceClassification": "modelling_RW.RWForSequenceClassification", | |
| "AutoModelForTokenClassification": "modelling_RW.RWForTokenClassification" | |
| }, | |
| "bias": false, | |
| "bos_token_id": 11, | |
| "eos_token_id": 11, | |
| "hidden_dropout": 0.0, | |
| "hidden_size": 4544, | |
| "initializer_range": 0.02, | |
| "layer_norm_epsilon": 1e-05, | |
| "model_type": "RefinedWebModel", | |
| "multi_query": true, | |
| "n_head": 71, | |
| "n_layer": 32, | |
| "parallel_attn": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.30.2", | |
| "use_cache": true, | |
| "vocab_size": 65024 | |
| } | |