hckrnws
Gemini last models: temperature, top_p, and top_k are deprecated and ignored
(ai.google.dev)
135
46
by greatgib
by greatgib
- They might be dynamically adjusting these at inference time [1]. For example, start with a low temperature and generate samples with increasingly high temperatures until one of them passes some quality gate.
- They don't want you to fine-tune on high temperature completions (rejection fine-tuning). You could call this "rejection fine-tuning rejection".
[1] https://rlhfbook.com/c/09-rejection-sampling#related-best-of...
- SynthID hides the watermark in the sampling RNG. No randomness -> no watermark.
- If you want to distil on the model outputs, you want temp=0 outputs. No temp=0 -> worse distillation.
Certain open models have/had the temperature locked on the official APIs, I assume they just have sampling incompatible with static temperature or do some fancy speculative decoding. It's clearly not to hide anything, as the weights are open and there are always alternative providers.
"Please be deterministic".
Oh well. I might be to picky here, but how I see things, determinism cannot be improved or worsened, but achieved or not achieved. Or Partially archieved, when analyzing a system that has both components that are deterministic or non deterministic.
There are times to think in absolutes, and when talking about deterministic behavior of technical systems, this is one of them. Join the sith side, we have cookies, and when we say we have cookies, we do.
What if I want to do the other thing? When performing research with many sub agents, having a lot of diversity in the hypotheses is a big deal. If my 5 parallel sub agents all produce the same conclusion I might as well have only ran one.
The latest OAI models have done the same thing. I'm currently adding random variation to prompts to compensate for the lack of higher temperature sampling.
I don't know though and am not aware of any docs going into detail here. That being said, sampling is really cheap. So implementing it in hardware wouldn't be worth it.
https://gist.github.com/Hellisotherpeople/71ba712f9f899adcb0...
They are extremely confusing.
I speculate OP wanted to put focus on their chosen detail in the title...
Is this guaranteed to work any better than top_k or top_p? This just sounds like making a smaller version of a Agent.md doc.
temperature, top_p, and top_k are deprecated and ignored. In future model generations, supplying these parameters returns an HTTP 400 error. Remove these parameters from all requests.
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>I'm currently adding random variation to prompts
This is better than naive temperature tweaks, if done right.
A models output is not a single token, but a list with the probability for all the tokens that it knows, so we need to use a sampler to select the token that it's going to be the next token in the sentence. For example a simple greedy sampler will choose the token with the highest probability, but samplers normally pick a random token weighted by probability. A model usually knows about ~250 thousand tokens and the probability of some of these tokens are gonna be high, but the vast majority is close to but not actually 0% so there's a chance the sampler might pick some random token that doesn't make much sense, so we filter tokens.
top_k filters the tokens so that only the k top tokens are selected. So top_k=50 will filter those 250k tokens to only 50. This is assuming the list of tokens is sorted by probability.
top_p filters the top tokens until a percentage is accumulated. So if for example if you set the top_p to 0.6 and the model gave the top token a 0.5 (50%) probability and the second top token a 0.2, those 2 token accumulated to 0.7 which is greater than what you set it to (0.6) so no more tokens are selected. If this ran after top_k=50 it'll turn the list of 50 tokens into one of 2.
After each filter parameter is processed, the probability of the tokens is adjusted to sum to 1 (100%), Also note that order of operation here matters, i.e. top_p could be applied before top_k, but most providers follow what's on huggingface, I think I've only seen different implementation in certain local model hosting frameworks.
Our tricks didn't work very well. But I didn't manage to keep up well enough to learn what worked.
if don't have time for that, just know that these are technical parameters that affect how likely it is an llm will produce the same result after being asked the same question.
For the "last/latest", it is the result of trying to trim the title enough to enter in the size limited title box and then I didn't notice the typo.
At first I wanted to put the 3.5/3.6 model number because "latest" is not clear and time dependant but it was too long then.
Funnily, in Galician there is a distinction between "último" (most recent) and "derradeiro" (final). I would have guessed that it would be the same in Portuguese, given how closely related the two languages are.
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I don't quite understand the point about order of operations - does it do normalization after every such filter pass? why not leave it to the end?
also: what is top_a? I saw it being mentioned in the GP link
Only coincidentally. Sampling nonsense tokens will certainly degrade its performance and/or brick it, but it's also there to encourage diversity.
For example, imagine we have the following sentence:
> The color of this ball is ____
Now, what should the model predict for "____"? There isn't really a "correct" answer here. It can be "red", it can be "blue", it can be "green", or any other color. But it's definitely not going to be "ব্যথাя". LLMs output a probability distribution for the next token, so imagine this is the probability distribution that it outputs:
red -> 60%
green -> 19%
blue -> 19%
ব্যথাя -> 2%
> I don't quite understand the point about order of operations - does it do normalization after every such filter pass? why not leave it to the end?
Because you can technically compose multiple samplers at the same time in a pipeline, and in some cases their order can matter and give you a different result (or take less/more time to execute). To give you a generic example: imagine you have a list with numbers in random order. You can execute one of two operations on it: (1) sort it, (2) take the leading 10 numbers. If you first sort it and then take 10 leading numbers you'll get a different result than if you'd first took 10 leading numbers and then sorted them.
top_a is not very common and is better explained if I explain how the much more common min_p works. min_p filters out tokens below a certain threshold. The formula is <filter threshold> = <min_p> * <top token probability>. So if the top token has 0.5 probability, min_p = 0.1 would cut out tokens below 0.05. This is a tunable that lets you filter out other tokens depending on how confident the model is.
top_a is almost the same formula but you just square the <top token probability>. So <filter threshold> = <top_a> * <top token probability> ^ 2. This makes the filtering ramp up faster (cut out more tokens) if the model has a much more confident top choice, but keep more choices if the model is not so confident.
It would be a bit like opening a text editor and typing in that you want to increase the font size. Someone external to the text would have to come along and click the font options.