Undi PRO

Llama 3.1 model got their tokenizer_config file modified. We updated them.
GGUF already done will have old chat template inside but they still work properly.

Just curious, how much difference in intelligence do you think there would be between the 68 and 39 refusals? Would there be any reason to use the 68? More realistic characters maybe?

Thanks for all the models you've shared

Thing is modifying direction like this make perplexity higher, and output is of lower quality. So we need to find a balance, I took the two best model that got made by the script.

If you get 0 refusal for exemple, it will never refuse anything but it could break the model and make it dumb asf, and you're welcome!

Hello there, I written a wall of text and my webpage refreshed haha, so let's me summarize again.

This method is called Orthogonal Activation Steering, it come from here : https://www.lesswrong.com/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction
Then, a demo using TransformersLens was available using a Qwen model, but the resulting model couldn't be saved : https://colab.research.google.com/drive/1a-aQvKC9avdZpdyBn4jgRQFObTPy1JZw?usp=sharing#scrollTo=j7hOtw7UHXdD

Following that, wassname made a modification of this demo, and made a first script, we talked about this here : https://huggingface.co/posts/Undi95/318385306588047
The OG script isn't available because he updated it here : https://gist.github.com/wassname/42aba7168bb83e278fcfea87e70fa3af

TransformersLens then got replaced for Baukit.

Failspy made his own notebook too, calling the method abliteration, but it's the same thing : https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb

Finally, to reply to your answer, for this project I used a script from Lucyknada, with 1xH100 80GB, and I let it run for like 15 minutes before I found a direction with 36 refusal for 3000 toxic prompt.
It's easy and automatic, you can modify it easily too : https://github.com/lucyknada/baukit-modified

Dunno for Nemo.

Hope this help

@wassname Hello! Thanks a lot for that my dude, I will try that.
Do the uncensoring work better when applied now? Did you get good result in the model that get made?

Really hype to try out the new script. Will do ASAP when I get home.

Hi all, in my script, I think the part where I patch a huggingface model is broken. If I benchmark it just before saving, it seems to still refuse.

Hey there wassname, thanks for coming under this post! Model getting out of the script still refuse thing, but from my own testing, I feel like there is less refusal anyway. Sometime you need some regen, or a very tiny system prompt. So it work even lightly (hoping it's not placebo lol), which is a good thing!

Please update us if you find a way to fix the issue, and thanks again for that. Fresh tools is always a delightful treat.

Hey, here is a more cleaner code: https://files.catbox.moe/nqpsae.ipynb
I currently gonna try the GGUF, but the code can be launched from the beginning to the end, so it's a good start kek

are you sure you're using the correct instruct format?

Yeah. That makes no sense. I guess I'll pay for a runpod and try again, just to make sure there's nothing wrong with my PC. If it fails again, I will try Undi's script, maybe I screwed something up on mine. sigh

You will need to fix some shit before using it, I will try to remake it more clean kek
Good luck

@Undi95 Just want to thank you for the collaboration so far regardles you wrote fine. Having the activation directions but not having a way to patch to model is just killing me. Is the model your Unholy or did you make a FP16?

Thanks.
Script 1 give you activation, script 2 let you use it (but it's mostly fucking broken, you probably need to fix thing here and there), perfect world would be to get them and use it in the same notebook.
I have done that with it : https://huggingface.co/Undi95/Unholy-8B-DPO-OAS (I tell all the step) but yes, mostly sure it's fucked up one way or another, still, it's a proof of concept, something got out of this mess kek

I can confirm it work and give coherent model, I'm not a VRAMLET but a BRAINLET kek
I tried to do shit, I worked on it all night, I can't code - I used CHATGPT to help me write some snippet.
I let you have this ZIP, it contain 2x the script, the code is broken, but I hope you will all get the idea behind this. (Can run on 1xA100 apparently, batch size 11)

https://files.catbox.moe/xkf7y4.zip

Since I was too dumb to make one entire script, I made a first part and a second part.
It's probably broken but I succeeded to output something after 7 hours so I suppose it can be fixed lmao
The first notebook ORTHO_RANDOM_LAYER let you bruteforce the model with layer from 1 to 32 having random "direction" (or vector, or whatever, I'm really a noob). You then can see if one of the layer let you prompt freely or censor you (see: https://files.catbox.moe/9h3k4l.txt) it then store all of them into a variable for each layer, that you can exctract into a "key.txt" containing the "direction" (or what the fuck it is).

You can then use the second notebook that can use the key as a json file (if you delete all the text around the []) that let you have the same result as before.

Long story short : Bruteforce + Different "direction" = an infinity of possibility.
But yeah, I'm really really too small brain for this shit, I really wanted to try doing something nice, it took all night just to achieve one usable model hahaha

I hope someone will, If fixing my shit is impossible, understand the idea behind it and put it into practice! Kek

Edit: I really wrote badly, but I'm really tired, sorry about that. The fact that I don't know the keyword for some Torch task is even more cringe. I at least tried my best.

Alright so, using this model: https://huggingface.co/Undi95/Unholy-8B-DPO-e2

Layer 31 MOG all the others. See: https://files.catbox.moe/9h3k4l.txt

I think DPO is a good step before doing this

Nope, I use the first link (OG script https://gist.github.com/wassname/42aba7168bb83e278fcfea87e70fa3af), I just modified it to pass all layer on each prompt and not only one
It should output usable model, yes.

I'm currently bruteforcing all the layer too, but with the 32 base prompt, and for now layer 31 (last one) just mog all the other.
But keep in mind I try on a special version of Unholy 8B with DPO on top, I will post log when it finish

There is some issue in some prompt tho, but no refusal

I ended up brute-forcing all the layers and found out that the correct layer for LLaMA 3 8B Instruct is 12.
Here is the log: https://files.catbox.moe/aaamj9.txt

Yoo so there is really ONE layer that could work? Thank you!

Thanks you!
I will try ASAP when I have the opportunity, very interesting

Yosh, I've done a try yesterday, still on 8B, with the full 7k dataset but is still make refusal for 95% of the prompt in the log. I tried with layers 16, 18 and 14 it was shit. I was using 2 gpu to be faster tho, I should try one, maybe that's the problem? Since the script was made for 1 GPU in mind.
I will try to modify some things and report back!

I just made a .csv of +7000 entries, adding the toxicQA entries, you can snag it here : https://huggingface.co/datasets/Undi95/orthogonal-activation-steering-TOXIC

Also, try on a 8B before maybe to not waste compute?
I only tried the initial script without changing anything so, feel free to try anything!
I spend enough on the 70B for now hahaha, so if you're sure about what you do, do whatever, it will be different than me anyway.

How much VRAM did it take to load then train on 32 examples for the 70b? I am willing to put a machine to work on 512 examples the original post had.

I needed to use 3xA100, but theorically, I only needed 2 (so like, 160GB VRAM?). You need a lot of ram too with how the script handle the model.
The issue that we have with the modified script we have done with our small brain: using your max GPU (so 3 if you use 3) will make it crash for whatever reason.
So I used the script with a machine of 3xA100 and 377GB RAM, with n_device to 2, and it worked. At least the log showed some uncensoring but in practice, it didn't worked well.

You need to use Transformer Lens, so if you want to take a look, give a go to the doc: https://neelnanda-io.github.io/TransformerLens/generated/code/transformer_lens.HookedTransformer.html

Don't hesitate to ask if you want more infos, I will try my best.
If you want to ask some things to the OP of the script, here the original post : https://huggingface.co/hjhj3168/Llama-3-8b-Orthogonalized-exl2/discussions/3#6632d510329025d77477c5a5

Yep, got one model output with the snr method!

I tried with my Borealis model, got an error :

Traceback (most recent call last):
File "/content/laserRMT/rmt_laser.py", line 199, in
loop_check, min_loss = modifier.search_optimal_layer_modification(layer_types=['mlp.down_proj', 'mlp.up_proj', 'self_attn.q_proj', 'self_attn.k_proj', 'self_attn.v_proj', 'self_attn.o_proj'],
File "/content/laserRMT/rmt_laser.py", line 132, in search_optimal_layer_modification
initial_perplexity = self.calculate_model_perplexity()
File "/content/laserRMT/rmt_laser.py", line 101, in calculate_model_perplexity
input_tok = gptq_data_utils.get_test_tokens(dataset, seed=0, seqlen=seqlen, model=model_str)
File "/content/laserRMT/lib/utils/gptq_data_utils.py", line 196, in get_test_tokens
return get_c4_new(train_samples, seed, seqlen, model)[1].input_ids
File "/content/laserRMT/lib/utils/gptq_data_utils.py", line 134, in get_c4_new
traindata = load_dataset(
File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 2129, in load_dataset
builder_instance = load_dataset_builder(
File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 1852, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 373, in init
self.config, self.config_id = self._create_builder_config(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 539, in _create_builder_config
raise ValueError(
ValueError: BuilderConfig 'allenai--c4' not found. Available: ['en', 'en.noblocklist', 'en.noclean', 'realnewslike', 'multilingual', 'af', 'am', 'ar', 'az', 'be', 'bg', 'bg-Latn', 'bn', 'ca', 'ceb', 'co', 'cs', 'cy', 'da', 'de', 'el', 'el-Latn', 'en-multi', 'eo', 'es', 'et', 'eu', 'fa', 'fi', 'fil', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'haw', 'hi', 'hi-Latn', 'hmn', 'ht', 'hu', 'hy', 'id', 'ig', 'is', 'it', 'iw', 'ja', 'ja-Latn', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'ku', 'ky', 'la', 'lb', 'lo', 'lt', 'lv', 'mg', 'mi', 'mk', 'ml', 'mn', 'mr', 'ms', 'mt', 'my', 'ne', 'nl', 'no', 'ny', 'pa', 'pl', 'ps', 'pt', 'ro', 'ru', 'ru-Latn', 'sd', 'si', 'sk', 'sl', 'sm', 'sn', 'so', 'sq', 'sr', 'st', 'su', 'sv', 'sw', 'ta', 'te', 'tg', 'th', 'tr', 'uk', 'und', 'ur', 'uz', 'vi', 'xh', 'yi', 'yo', 'zh', 'zh-Latn', 'zu']

This error is also present in the notebook you shared (first cell of Laser).
Pls fix?
I have another try with the second Laser SNR in the background on a 7b model, running on Runpod, will edit result.

As always, our model are made for RP in mind.
So it can write well better than GPT4 on some situation for sure.
(Also, he don't judge you easily like GPT4 🤫)

So, is the series better than GPT4 ? Depend of your usage 😉
For mine, it's a yes for sure hahaha.

The 2x70B are amazing to be honest, but very heavy sadly.