Instructions to use jschew39/GenerAd-AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use jschew39/GenerAd-AI with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("undefined") model.load_adapter("jschew39/GenerAd-AI", set_active=True) - Notebooks
- Google Colab
- Kaggle
| library_name: adapter-transformers | |
| license: bigscience-openrail-m | |
| datasets: | |
| - generativeaidemo/generadai-sample | |
| pipeline_tag: text-generation | |
| ## Training procedure | |
| The following `bitsandbytes` quantization config was used during training: | |
| - quant_method: bitsandbytes | |
| - load_in_8bit: True | |
| - load_in_4bit: False | |
| - llm_int8_threshold: 6.0 | |
| - llm_int8_skip_modules: None | |
| - llm_int8_enable_fp32_cpu_offload: False | |
| - llm_int8_has_fp16_weight: False | |
| - bnb_4bit_quant_type: fp4 | |
| - bnb_4bit_use_double_quant: False | |
| - bnb_4bit_compute_dtype: float32 | |
| ### Framework versions | |
| - PEFT 0.6.0.dev0 |