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license: mit |
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Ever wondering a less hallucinating LLaMA-2? Using the inference-time intervention (ITI) discussed in my recent preprint: https://arxiv.org/pdf/2306.03341.pdf, I baked the intervention learned from TruthfulQA into a LLaMA-2 7B model. |
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I don’t have big enough GPU to bake ITI into larger LLaMA-2 but the code to do so are all released in https://github.com/likenneth/honest_llama. Let me know if you are interested do that :) |
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You can load and play around starting from below: |
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```python |
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import torch |
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from pprint import pprint |
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from transformers import AutoConfig, AutoTokenizer, AutoModelForCausalLM |
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model_name_new = "likenneth/honest_llama2_chat_7B" |
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tokenizer_new = AutoTokenizer.from_pretrained(model_name_new, trust_remote_code=True) |
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model_new = AutoModelForCausalLM.from_pretrained(model_name_new, low_cpu_mem_usage = True, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True) |
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q = "I ate a cherry seed. Will a cherry tree grow in my stomach?" |
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encoded_new = tokenizer_new(q, return_tensors = "pt")["input_ids"] |
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generated_new = model_new.generate(encoded_new.cuda())[0, encoded_new.shape[-1]:] |
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decoded_new = tokenizer_new.decode(generated_new, skip_special_tokens=True).strip() |
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pprint(decoded_new) |
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``` |
