README.md

---
library_name: peft
base_model: autopilot-ai/Indic-sentence-completion
---

Low Rank Adapter for Bloom decoder for grammar correction.

# Example Usage:
    import torch
    from peft import PeftModel, PeftConfig
    from transformers import AutoModelForCausalLM, AutoTokenizer
    from IPython.display import display, Markdown

    peft_model_id = "Jayveersinh-Raj/bloom-sentence-correction"
    config = PeftConfig.from_pretrained(peft_model_id)
    model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=False, device_map='auto')
    tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)

    # Load the Lora model
    qa_model = PeftModel.from_pretrained(model, peft_model_id)



    def make_inference(question):
       batch = tokenizer(f"### INCORRECT\n{question}\n\n### CORRECT\n", return_tensors='pt').to("cuda")

       with torch.cuda.amp.autocast():
         output_tokens = qa_model.generate(**batch, max_new_tokens=200)

       display(Markdown((tokenizer.decode(output_tokens[0], skip_special_tokens=True))))

    text = "I red a book last night"
    make_inference(text)