Instructions to use iamsatyajit/trained-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use iamsatyajit/trained-model with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("tiiuae/falcon-7b") model = PeftModel.from_pretrained(base_model, "iamsatyajit/trained-model") - Notebooks
- Google Colab
- Kaggle
Download config.json from iamsatyajit/trained-model: direct link, hf CLI and curl.
- Browser
- Download file 410 Bytes
-
https://huggingface.co/iamsatyajit/trained-model/resolve/main/config.json
- Command line
-
hf download hf://iamsatyajit/trained-model/config.json
-
curl -L -o config.json https://huggingface.co/iamsatyajit/trained-model/resolve/main/config.json
410 Bytes
| { | |
| "base_model_name_or_path": "tiiuae/falcon-7b", | |
| "bias": "none", | |
| "fan_in_fan_out": false, | |
| "inference_mode": true, | |
| "init_lora_weights": true, | |
| "layers_pattern": null, | |
| "layers_to_transform": null, | |
| "lora_alpha": 32, | |
| "lora_dropout": 0.05, | |
| "modules_to_save": null, | |
| "peft_type": "LORA", | |
| "r": 16, | |
| "revision": null, | |
| "target_modules": [ | |
| "query_key_value" | |
| ], | |
| "task_type": "CAUSAL_LM" | |
| } |