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g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains

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Re: g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains

#34
post #32

o1’s innovation is not Chain-of-Thought. It’s teaching the model to do CoT well (from massive amounts of human feedback) instead of just pretending to. You’ll never get o1 performance just from prompt engineering.

Does o1 need some method to allow it to generate lengthy chains of thought, or does it just do it normally after being trained to do so?

If so, I imagine o1 clones could just be fine tunes of llamas initially.

Re: g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains

#35

This is not even remotely close and very silly. A ChainOfThought in a loop. TreeOfThoughts is a more sophisticated method, see - https://arxiv.org/pdf/2305.10601 The clue we all had with OpenAI for a long time that this was a search through a tree, they hired Noam Brown, and his past work all hinted towards that. Q , is obviously a search on a tree like A . So take something like CoT, build out a tree, search for the…

Reminder: you need to escape the * otherwise you end up with emphasis (italics here).

Re: g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains

#36

Earlier quoted context omitted.

these projects become way less fun when you introduce evals

yeah or a lot of people can just fake progress by attaching whatever viral tag onto their glue code. I mean to start with, unless you do a bit of fine-tuning + rlhf there's no way to do it o1-like.

no its a lot more than RLHF, I think they figured out a way to have the LLM actually actively plot out scenario trajectories via context window manipulation and then use some kind of adhoc reward shaping mechanism to get it to select the best path based on the user's profile in a way that gets the most likely to be "liked" scenario (context window state change up to some N number of tokens (seems like they've been looking at 50k total range as "best area" minus the 20k tokens for the reasoning tokens)

also I think they deliberate give you bad answers sometimes / a lot over the last year to build up advanced chains where the user is not getting what they want so you have to explain why. I started building up like 10 or so of these conversations where after like 100 messages it gets the right answer and it was like hmm, I wonder if they are using this.

just my rambles

Re: g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains

#38
post #33

Groq 2 isn't as open as groq 1 iirc. Still hoping we get at least open weights.

You're thinking of Grok, the model from xAI. This Groq is the inference hardware company with a cloud service.

Exhibit 5478 that Grok is infringing Groq's trademark and creating confusion in the mind of the customers.

Re: g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains

#39
post #19

Earlier quoted context omitted.

Just because Apple includes it in one of their prompts doesn't mean it improves performance.

It seems plausible that stressing the importance of the system prompt instructions might do something, but I don't see how telling the model not to hallucinate would work. How could the model know that its most likely prediction has gone off the rails, without any external point of reference?

I think it's more likely that it's included for liability reasons.

Re: g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains

#40

Earlier quoted context omitted.

Telling LLMs not to hallucinate in their prompt improves the output. https://arstechnica.com/gadgets/2024/08/do-not-hallucinate-t...

Just because Apple includes it in one of their prompts doesn't mean it improves performance.

I’ve had pretty good experience with it personally. It quite often just tells me it doesn’t know or isn’t sure instead of just making something up.
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