---
license: mit
language:
- en
---

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

```python
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](https://huggingface.co/notoookay/ragler-llama2-7b-reranker).