metadata
license: mit
This model is a fine-tuned version of Llama2-7B using the RAG-LER (Retrieval Augmented Generation with LM-Enhanced Re-ranker) framework, as described in our paper.
How to Get Started with the Model
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("notoookay/ragler-llama2-7b")
model = AutoModelForCausalLM.from_pretrained("notoookay/ragler-llama2-7b", torch_dtype=torch.bfloat16, device_map="auto")
# Example usage
input_text = "### Instruction:\nAnswer the following question.\n\n### Input:\nQuestion:\nWhat is the capital of France?\n\n### Response:\n"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0]))
The corresponding re-ranker supervised by this model can be found here.