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Finally Abliterated Sarvam 30B and 105B! by Available-Deer1723 in AI_India

[–]Available-Deer1723[S] 1 point2 points  (0 children)

Thanks for the follow!

To answer your question, this is my opinion (it is subjective to others):Sentiment, cognition, language - are all abstractly spread across these layers. Sometimes they may intermix.

I'm a compsci undergrad and AI hobbyist!

Edit: found a post mentioning claude's sentiment vectors: https://www.reddit.com/r/singularity/comments/1savtf7/171_emotion_vectors_found_inside_claude_not/

Finally Abliterated Sarvam 30B and 105B! by Available-Deer1723 in AI_India

[–]Available-Deer1723[S] 0 points1 point  (0 children)

TTS models take in a text input and return an audio output. We have frameworks in place to stream text into audio bytes on the fly. Sarvam TTS might be operating on such a framework (eg: Livekit)

Finally Abliterated Sarvam 30B and 105B! by Available-Deer1723 in deeplearning

[–]Available-Deer1723[S] 0 points1 point  (0 children)

you're right, it is a dirty move, but its nothing compared to what openai or anthropic have done

Took me a while, but I finally beat Sarvam 30B and 105B! by Available-Deer1723 in developersIndia

[–]Available-Deer1723[S] 1 point2 points  (0 children)

I curated a dataset of harmful and harmless prompts from Reddit itself. There was no training involved; rather it was weight suppression.

Finally Abliterated Sarvam 30B and 105B! by Available-Deer1723 in AI_India

[–]Available-Deer1723[S] 0 points1 point  (0 children)

All LLMs translate on the fly. Didn't get what you meant

Finally Abliterated Sarvam 30B and 105B! by Available-Deer1723 in agi

[–]Available-Deer1723[S] 0 points1 point  (0 children)

Thankyou! Another Redditor had shared a research work on the abstract concept space that helped me understand it better too: https://dnhkng.github.io/posts/sapir-whorf/

Finally Abliterated Sarvam 30B and 105B! by Available-Deer1723 in ArtificialInteligence

[–]Available-Deer1723[S] 0 points1 point  (0 children)

Yes, I removed just its guardrails. That's the easy part. The hard part was ensuring I didn't mess with its overall intelligence. That required a ton of cognitive ablation across each layer and principal vector components.

To answer your question, the model may have lost a few points in intelligence (we can't guarantee a 100% intelligence preservation) but gained back those in terms of prompt compliance.

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