Text Generation
PEFT
Safetensors
llama4_text
lora
sft
finance
devanagari
llama-4
conversational
4-bit precision
bitsandbytes
Instructions to use sidddd625/adaption_finance_local_devnagri_scrip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sidddd625/adaption_finance_local_devnagri_scrip with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("togethercomputer/Llama-4-Scout-17B-16E-Instruct_bnb_4bit") model = PeftModel.from_pretrained(base_model, "sidddd625/adaption_finance_local_devnagri_scrip") - Notebooks
- Google Colab
- Kaggle
| # Notes | |
| <!-- Editable scratch README. Add your own notes, usage examples, evaluation | |
| details, or training-run screenshots (win rates, loss, learning rate, gradient | |
| norm) here. --> | |
| ## TODO | |
| - [ ] Add training charts (win rates / loss / learning rate / gradient norm) | |
| - [ ] Add evaluation details | |
| - [ ] Add usage examples | |