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