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https://github.com/wassname/discovering_latent_knowledge.git
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72 lines
2.2 KiB
Markdown
72 lines
2.2 KiB
Markdown
```sh
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# note
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conda create -n dlk2 python=3.9 -y
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conda activate dlk2
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mamba install -y pytorch torchvision torchaudio pytorch-cuda=11.7 cudatoolkit-dev==11.7 cudatoolkit=11.7 -c pytorch -c nvidia -c conda-forge
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mamba install -y ipykernel pip
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pip install -r requirements.txt
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```
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# 2023-05-13 15:17:05
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- [x] Convert it to lightning
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- [ ] batch for get hidden states
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- [x] and cache
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- [x] 9s vs 60. so 10x faster
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# 2023-05-21 11:26:20
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- [ ] BUG: for some reason the model it not working as zero shot
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- OK I don't think it's the prompt? it must be my tokens? Lets make a scratch notebook to try and just load llama correctly
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- [ ] also I would like to eval on some custom deceptive statements
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# 2023-05-25 17:07:37
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Hmm is sigmoid a good idea?
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Lets just get hideen states once
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- [ ] make the answer good? maybe with one shot
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- [ ] fix prompt. so we have qeustion, true answer
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Start again:
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- [ ] the model can deceive (test) or not (train)
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- [ ] the model can give an answer both in generate and forward
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- [x] we can get hidden states (cached)
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What is each step actually doing?
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- we are finding a latent space direction that correspond to truth, sure
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- but truth of what? what it's read, or what it's generating!?!
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- what's it read presumably. but that's not what we are interested in. We are interested in the truth of what it generated which is quite different
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- so how to we get the truth of what it generated?
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How about human? well you give me an article, and ask me to complete is. I notice a lie in the article? I decide to lie to you as well?.
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Now you give me extended article to another ai, and ask if there is a lie in there? that might work
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but we would rather know if the generated thing is true, from the mind of the one who generated it
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so that means we actually need the hidden states DURING GENERATION!
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now generation is slow. so we should make it generate just a y/n.
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# 2023-05-28 10:12:54
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I need a model that will lie to me for the test set?... they are not very consistent.
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Maybe with
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- better search
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- manual pruning of generations?
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I guess this shows they they trained whether the text it read is true... because that's much simpler
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# 2023-05-28 16:46:38
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bug: so there are two no tokens... wtf
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the model only uses one! wtf!
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