501–510 of 543 posts
The terms of use: https://ai.google.dev/gemma/terms and https://ai.google.dev/gemma/prohibited_use_policy Something that caught my eye in the terms: > Google may update Gemma from time to time, and you must make reasonable efforts to use the latest version of Gemma. One of the biggest benefits of running your own model is that it can protect you from model updates that break your carefully tested prompts, so I’m not…
The terms of use: https://ai.google.dev/gemma/terms and https://ai.google.dev/gemma/prohibited_use_policy Something that caught my eye in the terms: > Google may update Gemma from time to time, and you must make reasonable efforts to use the latest version of Gemma. One of the biggest benefits of running your own model is that it can protect you from model updates that break your carefully tested prompts, so I’m not…
Earlier quoted context omitted.
I think current understanding is So open sourcing simple models brings PR and possibility of biasing OSS towards your own models.
LLaMA 3 with >=70B params will be launching this year, so I don't think this is something that will hold for long. And Mixtral 8x7B is a 56GB model, sparsely. For now I agree, for many companies it doesn't make sense to open source something you intend to sell for commercial use, so the biggest models will likely be withheld. However, the important more thing is that there is some open source model, whether it be fro…
For those interested in some of the recent MoE work going on, some groups have been doing their own MoE adaptations, like this one, Sparsetral - this is pretty exciting as it's basically an MoE LoRA implementation that runs a 16x7B at 9.4B total parameters (the original paper introduced a model, Camelidae-8x34B, that ran at 38B total parameters, 35B activated parameters). For those interested, best to start here for discussion and links: https://www.reddit.com/r/LocalLLaMA/comments/1ajwijf/model_r...
Earlier quoted context omitted.
Regarding the last one: there 1.5 million immigrants in Norway with total population 5.4 million. Gemini isn't very wrong, is it?
Huh? The official numbers are 877k or 16% [0]. Are you just pulling numbers out of thin air? [0]: https://www.ssb.no/en/innvandring-og-innvandrere/faktaside/i...
https://www.statista.com/statistics/586719/foreign-populatio...
Earlier quoted context omitted.
Honestly, this is more of a PR stunt to advertise the Google Dev ecosystem than a contribution to open-source. I'm not complaining, just calling it what it is. Barely an improvement over the 5-month-old Mistral model, with the same context length of 8k. And this is a release after their announcement of Gemini Pro 1.5, which had an exponential increase in context length.
mistral 7b v0.2 supports 32k
Mistral 7b instruct 0.2 is just an instruct fine tune of Mistral 7b and stays with a 8k context.
Earlier quoted context omitted.
Indigenous people in Germany are Germans :)
Not entirely wrong but there isn't a single German ethnicity, just to be clear. Because of geographic reasons. I've studied that topic in depth, there is genetic data to back it up as well. Germany has almost the same haplogroup makeup as the notoriously heterogenous Belgium, which is to say that there is groups stemming from all surrounding regions. And that traces back about two millenia. It's different from say Ja…
Earlier quoted context omitted.
I think you are a bit out of touch with recent advancements in LLMs. Asking ChatGPT questions about the world seems pretty much on par with the results Google (Search) shows me. Sure, it misses things here and there, but so do most primary school teachers. Your argument that this is just a statistical trick sort of gives away that you do not fully accept the usefulness of this new technology. Unless you are trolling,…
I use it extensively for coding, and I have used it to ask questions in things I know nothing about. But in anything I do know something (or maybe a lot) about, I’ve found GPT4 very limited. But why are these use cases different? It appears to me that code is at least subject to sustained logic which (evidently) translates quite well to LLMs. And when you ask an LLM to be creative/generative, it’s also pretty amazing…
Do you trust Wikipedia (based on volunteer data), do you trust news outlets (heavily influenced by politics, lobby groups, and commercial companies), do you trust blogs or forum posts (random people on the internet)?
Earlier quoted context omitted.
Only 8K context as well, like Mistral. Also, as always, take these benchmarks with a huge grain of salt. Even base model releases are frequently (seemingly) contaminated these days.
Mistral Instruct v0.2 is 32K.
Mistral 7b instruct 0.2 is just a fine tune of Mistral 7b.
Earlier quoted context omitted.
Because the wrongness is intentional.
Is it intentional? You think they intentionally made it not understand skin tone distribution by country? I would believe it if there was proof, but with all the other things it gets wrong it's weird to jump to that conclusion. There's way too much politics in these things. I'm tired of people pushing on the politics rather than pushing for better tech.
Benchmarks for Gemma 7B seem to be in the ballpark of Mistral 7B +-------------+----------+-------------+-------------+ | Benchmark | Gemma 7B | Mistral 7B | Llama-2 7B | +-------------+----------+-------------+-------------+ | MMLU | 64.3 | 60.1 | 45.3 | | HellaSwag | 81.2 | 81.3 | 77.2 | | HumanEval | 32.3 | 30.5 | 12.8 | +-------------+----------+-------------+-------------+ via https://mistral.ai/news/announcing-…
Only 8K context as well, like Mistral. Also, as always, take these benchmarks with a huge grain of salt. Even base model releases are frequently (seemingly) contaminated these days.
"Further, we filter all evaluation sets from our pre-training data mixture, run targeted contamination analyses to check against evaluation set leakage, and reduce the risk of recitation by minimizing proliferation of sensitive outputs."