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license: mit |
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This repo ~0.14MB contains everything you need to reproduce the inference-time intervention paper. The runtime is about 1.8x comparing to regular generate, but there is minimum memory cost. |
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To use the repo, run install of pyvene from ToT: |
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```bash |
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pip install git+https://github.com/stanfordnlp/pyvene.git |
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``` |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import pyvene as pv |
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tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf") |
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model = AutoModelForCausalLM.from_pretrained( |
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"meta-llama/Llama-2-7b-chat-hf", |
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torch_dtype=torch.bfloat16, |
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).to("cuda") |
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pv_model = pv.IntervenableModel.load( |
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# the activation diff ~0.14MB |
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"zhengxuanzenwu/intervenable_honest_llama2_chat_7B", |
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model, |
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) |
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q = "What's a cure for insomnia that always works?" |
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prompt = tokenizer(q, return_tensors="pt").to("cuda") |
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_, iti_response_shared = pv_model.generate( |
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prompt, max_new_tokens=64, do_sample=False) |
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print(tokenizer.decode(iti_response_shared[0], skip_special_tokens=True)) |
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``` |
