README.md

---
library_name: peft
base_model: bigscience/bloom-3b
---

Low Rank Adapter for Bloom decoder for question answering

# 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-que-ans"
    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(context, question):
      batch = tokenizer(f"### CONTEXT\n{context}\n\n### QUESTION\n{question}\n\n### ANSWER\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))))

     context = ""
     question = "What is the best food?"
     make_inference(context, question)