11–20 of 28 posts
Eli5?
If I understand correctly, Hugging Face is exploring approaches to tuning the output quality of a given model by tuning how long to let it run. Normally when you run an LLM, you set your prompt and whatever tunable parameters, and the LLM software (eg. lamma.cpp) spits out tokens at whatever rate it can. If you want higher quality, you run a bigger model (though you're limited by the amount of memory you have availab…
Earlier quoted context omitted.
If I understand correctly, Hugging Face is exploring approaches to tuning the output quality of a given model by tuning how long to let it run. Normally when you run an LLM, you set your prompt and whatever tunable parameters, and the LLM software (eg. lamma.cpp) spits out tokens at whatever rate it can. If you want higher quality, you run a bigger model (though you're limited by the amount of memory you have availab…
So I can get LLM results from an SLM if I run it long enough?
Earlier quoted context omitted.
If I understand correctly, Hugging Face is exploring approaches to tuning the output quality of a given model by tuning how long to let it run. Normally when you run an LLM, you set your prompt and whatever tunable parameters, and the LLM software (eg. lamma.cpp) spits out tokens at whatever rate it can. If you want higher quality, you run a bigger model (though you're limited by the amount of memory you have availab…
So I can get LLM results from an SLM if I run it long enough?
1. the reason for generalizations like 'long enough' and 'think more' are apparently because the methods are somewhat obscure 2. those methods are being explored by hugging face to make them less obscure
am I getting that right? I have been struggling to see past the metaphors and understand exactly what additional computation is being done - and here I read its something like multiple guesses being fed back in and chosen among which means its just multiple inferences in series that are all related to solving 1 problem.
Full blog is here: https://huggingface.co/spaces/HuggingFaceH4/blogpost-scaling... Happy to answer any questions about these methods.
Full blog is here: https://huggingface.co/spaces/HuggingFaceH4/blogpost-scaling... Happy to answer any questions about these methods.
In the blog post, learned verifiers are mentioned. Are these learned offline using data, and is the intent to learn a scoring heuristic to help the search?
In other words: 1) sample step-by-step solutions from "base" model; 2) do it at non-zero temperature so that you can get multiple continuation from each solution-prefix; 3) use MATH-labels to decide if full solution (leaf/terminal node in MC rolloout) has reward `1` or `0`; 4) roll up these rewards to calculate reward-to-go for each intermediate step.
Yes, verifier trained in this manner can be used to score solution-prefixes (as a process verifier) or a full-solution (as an outcome verifier).
In the original paper (https://arxiv.org/abs/2408.03314) they fine-tune a fresh verifier. HF's replication uses an off-the-shelf verifier based on another paper: https://arxiv.org/abs/2312.08935
Full blog is here: https://huggingface.co/spaces/HuggingFaceH4/blogpost-scaling... Happy to answer any questions about these methods.
Great work! When I use models like o1, they work better than sonnet and 4o for tasks that require some thinking but the output is often very verbose. Is it possible to get the best of both worlds? The thinking takes place resulting in better performance but the output is straightforward to work with like with sonnet and 4o. Did you observe similar behaviour with the 1B and 3B models? How does the model behaviour chan…
Yes, the search process (beam-search of best-of-N) does produce verbose traces because there is branching involved when sampling "thoughts" from base model. These branched traces (including incomplete "abandoned" branches) can be shown to the user or hidden, if the approach is deployed as-is.
What's a point of such inference time compute if verifier is 8B model itself? Am I missing something?
In contrast, in the original paper, verifier is a fine-tune of the exact same base model which is used to sample step-by-step solutions (="solver").
Eli5?
1) make model output a full solution, step-by-step, then induce it to revise the solution - repeat this as many times as you have token-budget for. You can do this via prompting alone (see Reflexion for example), or you can fine-tune the model to do that. The paper explores fine-tuning of the base model to turn it into self-revision model.
2) sample step-by-step (one "thought"-sentence per line) solutions from the model, and do it at non-zero temperature to be able to sample multiple next-steps. Then use verifier model to choose between next-step candidates and prefer to continue the rollout of the more promising branches of "thoughts". There are many many methods of exploring such tree when you can score intermediate nodes (beam search is an almost 50 years old algorithm!).
Earlier quoted context omitted.
So I can get LLM results from an SLM if I run it long enough?
They show Llama 3.2 1B with chain-of-thought that outperforms Llama 3.1 8B and 3.2 3B that outperforms 3.1 70B. It’s less clear whether you actually inference time is faster for CoT 3B using 256x generations vs 70B if you have enough RAM. Basically a classical RAM/compute trade off
I think it *is* an unlock.