Instructions to use CluelessNovice/demo_cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use CluelessNovice/demo_cls with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("westlake-repl/SaProt_35M_AF2") model = PeftModel.from_pretrained(base_model, "CluelessNovice/demo_cls") - Notebooks
- Google Colab
- Kaggle
File size: 679 Bytes
95a238a fa3e343 95a238a fa3e343 95a238a fa3e343 95a238a fa3e343 95a238a fa3e343 95a238a fa3e343 95a238a fa3e343 95a238a fa3e343 95a238a fa3e343 95a238a fa3e343 95a238a bd2e991 95a238a fa3e343 95a238a fa3e343 bd2e991 fa3e343 95a238a fa3e343 95a238a fa3e343 95a238a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 |
---
base_model: westlake-repl/SaProt_35M_AF2
library_name: peft
---
# Model Card for Model-demo-35M
This model is used for a demo classification task
## Task type
Protein-level Classification
## Model input type
SA Sequence
## Label meanings
0: A
1: B
2: C
3: D
## LoRA config
- **r:** 8
- **lora_dropout:** 0.0
- **lora_alpha:** 16
- **target_modules:** ['query', 'intermediate.dense', 'key', 'value', 'output.dense']
- **modules_to_save:** ['classifier']
## Training config
- **optimizer:**
- **class:** AdamW
- **betas:** (0.9, 0.98)
- **weight_decay:** 0.01
- **learning rate:** 0.001
- **epoch:** 1
- **batch size:** 2
- **precision:** 16-mixed
|