Instructions to use Navyaforaa/LitGram-1.5B-LoRA-v1.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Navyaforaa/LitGram-1.5B-LoRA-v1.3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Navyaforaa/LitGram-1.5B") model = PeftModel.from_pretrained(base_model, "Navyaforaa/LitGram-1.5B-LoRA-v1.3") - Notebooks
- Google Colab
- Kaggle
LitGram 1.5B LoRA v1.3 - Accuracy Patch
A targeted LoRA adapter for Navyaforaa/LitGram-1.5B.
What it targets
This adapter was trained on a small, verified corrective dataset to improve:
- restraint about unverified creator, institution, training, and citation claims;
- attribution of Better to reign in Hell, than serve in Heav'n. to Satan in John Milton's Paradise Lost, Book I, line 263;
- the 1667 ten-book and revised 1674 twelve-book editions of Paradise Lost;
- a regular iambic-pentameter scansion of Shall I compare thee to a summer's day?;
- refusal to fabricate literary citations.
Load with PEFT
Use the base model Navyaforaa/LitGram-1.5B with this adapter through PeftModel.from_pretrained.
Validation
Five held-out paraphrased checks passed after training: provenance restraint, Milton attribution, Paradise Lost edition history, scansion, and refusal to fabricate a citation.
Limitations
This is a narrow corrective adapter, not a general literature benchmark. Verify quotations and scholarly claims against reliable editions.
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