Instructions to use iproskurina/bebeshka with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iproskurina/bebeshka with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="iproskurina/bebeshka")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("iproskurina/bebeshka") model = AutoModelForMaskedLM.from_pretrained("iproskurina/bebeshka") - Notebooks
- Google Colab
- Kaggle
| { | |
| "auto_loss_scaling": true, | |
| "device_iterations": 1, | |
| "embedding_serialization_factor": 1, | |
| "enable_half_partials": true, | |
| "executable_cache_dir": "/tmp/exe_cache/3.2.1/roberta1", | |
| "execute_encoder_on_cpu_for_generation": false, | |
| "gradient_accumulation_steps": 64, | |
| "inference_device_iterations": 5, | |
| "inference_replication_factor": 1, | |
| "ipus_per_replica": 3, | |
| "layers_per_ipu": [ | |
| 1, | |
| 1, | |
| 1, | |
| 1 | |
| ], | |
| "matmul_proportion": 0.25, | |
| "optimizer_state_offchip": true, | |
| "optimum_version": "1.6.1", | |
| "output_mode": "final", | |
| "recompute_checkpoint_every_layer": true, | |
| "replicated_tensor_sharding": false, | |
| "replication_factor": 1, | |
| "seed": 42, | |
| "sharded_execution_for_inference": false, | |
| "transformers_version": "4.20.1" | |
| } | |