Instructions to use zelaouene/QA_Lora_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use zelaouene/QA_Lora_Model with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-3b") model = PeftModel.from_pretrained(base_model, "zelaouene/QA_Lora_Model") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "bigscience/bloom-3b", | |
| "apply_residual_connection_post_layernorm": false, | |
| "architectures": [ | |
| "BloomForCausalLM" | |
| ], | |
| "attention_dropout": 0.0, | |
| "attention_softmax_in_fp32": true, | |
| "bias_dropout_fusion": true, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "hidden_dropout": 0.0, | |
| "hidden_size": 2560, | |
| "initializer_range": 0.02, | |
| "layer_norm_epsilon": 1e-05, | |
| "masked_softmax_fusion": true, | |
| "model_type": "bloom", | |
| "n_head": 32, | |
| "n_inner": null, | |
| "n_layer": 30, | |
| "offset_alibi": 100, | |
| "pad_token_id": 3, | |
| "pretraining_tp": 1, | |
| "skip_bias_add": true, | |
| "skip_bias_add_qkv": false, | |
| "slow_but_exact": false, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.36.0.dev0", | |
| "unk_token_id": 0, | |
| "use_cache": true, | |
| "vocab_size": 250880 | |
| } | |