Instructions to use GarvinBhati/gemma-4-31b-indic-rys with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use GarvinBhati/gemma-4-31b-indic-rys with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-4-31B-it") model = PeftModel.from_pretrained(base_model, "GarvinBhati/gemma-4-31b-indic-rys") - Notebooks
- Google Colab
- Kaggle
Download evaluation/macro_summary.csv from GarvinBhati/gemma-4-31b-indic-rys: direct link, hf CLI and curl.
- Browser
- Download file 454 Bytes
-
https://huggingface.co/GarvinBhati/gemma-4-31b-indic-rys/resolve/main/evaluation/macro_summary.csv
- Command line
-
hf download hf://GarvinBhati/gemma-4-31b-indic-rys/evaluation/macro_summary.csv
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curl -L -o macro_summary.csv https://huggingface.co/GarvinBhati/gemma-4-31b-indic-rys/resolve/main/evaluation/macro_summary.csv
454 Bytes
| evaluation_key,milu,indicparam,indicifeval,equal_benchmark_macro,delta_vs_stock,delta_vs_sft | |
| selected_sft,0.7638190954773869,0.44242424242424244,0.6611295681063123,0.6224576353359805,0.011020993082463182,0.0 | |
| sft_plus_gated,0.764321608040201,0.44393939393939397,0.6843853820598007,0.6308821280131319,0.019445485759614578,0.008424492677151396 | |
| stock_base,0.7587939698492462,0.4575757575757576,0.6179401993355482,0.6114366422535173,0.0,-0.011020993082463182 | |