Instructions to use ashkab/output_dir with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ashkab/output_dir with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ashkab/output_dir", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download run3/config.json from ashkab/output_dir: direct link, hf CLI and curl.
- Browser
- Download file 693 Bytes
-
https://huggingface.co/ashkab/output_dir/resolve/main/run3/config.json
- Command line
-
hf download hf://ashkab/output_dir/run3/config.json
-
curl -L -o config.json https://huggingface.co/ashkab/output_dir/resolve/main/run3/config.json
693 Bytes
| {"alpha_pattern": {}, "auto_mapping": {"base_model_class": "WhisperForConditionalGeneration", "parent_library": "transformers.models.whisper.modeling_whisper"}, "base_model_name_or_path": "openai/whisper-small", "bias": "none", "fan_in_fan_out": false, "inference_mode": true, "init_lora_weights": true, "layer_replication": null, "layers_pattern": null, "layers_to_transform": null, "loftq_config": {}, "lora_alpha": 64, "lora_dropout": 0.5, "megatron_config": null, "megatron_core": "megatron.core", "modules_to_save": null, "peft_type": "LORA", "r": 32, "rank_pattern": {}, "revision": null, "target_modules": ["v_proj", "q_proj"], "task_type": null, "use_dora": false, "use_rslora": false} |