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Hello on behalf of the Gemma team! We are really excited to answer any questions you may have about our models. Opinions are our own and not of Google DeepMind.
Is there any truth behind this claim that folks who worked on Gemma have left Google? https://x.com/yar_vol/status/1760314018575634842
Hello on behalf of the Gemma team! We are really excited to answer any questions you may have about our models. Opinions are our own and not of Google DeepMind.
Is there any truth behind this claim that folks who worked on Gemma have left Google? https://x.com/yar_vol/status/1760314018575634842
Earlier quoted context omitted.
Is there any truth behind this claim that folks who worked on Gemma have left Google? https://x.com/yar_vol/status/1760314018575634842
It seems very easy to check no? Look at the names in the paper and check where they are working now
Can someone with a Twitter account call out the tweet linked above and ask them specifically who they are referring to? Seems there is no evidence of their claim.
Hello on behalf of the Gemma team! We are really excited to answer any questions you may have about our models. Opinions are our own and not of Google DeepMind.
Is there any truth behind this claim that folks who worked on Gemma have left Google? https://x.com/yar_vol/status/1760314018575634842
# Product Management
Tris Warkentin
Ludovic Peran
# Program Management
Minh Giang
# Executive Sponsors
Clement Farabet
Oriol Vinyals
Jeff Dean
Koray Kavukcuoglu
Demis Hassabis
Zoubin Ghahramani
Douglas Eck
Joelle Barral
Fernando Pereira
Eli Collins
# Leads
Armand Joulin
Noah Fiedel
Evan Senter
# Tech Leads
Alek Andreev†
Kathleen Kenealy†Earlier quoted context omitted.
EDIT: it seems this is likely an Ollama bug, please keep that in mind for the rest of this comment :) I ran Gemma in Ollama and noticed two things. First, it is slow. Gemma got less than 40 tok/s while Llama 2 7B got over 80 tok/s. Second, it is very bad at output generation. I said "hi", and it responded this: ``` Hi, . What is up? melizing with you today! What would you like to talk about or hear from me on this fi…
I was going to try these models with Ollama. Did you use a small number of bits/quantization?
Earlier quoted context omitted.
I wonder if they have a system prompt to promote diversity in outputs that touch on race at all? I’ve seen several instances of people requesting a photo of a specific people, and it adds in more people to diversify. Not inherently bad, but it is if it forces it to provide incorrect answers like in your example.
> Not inherently bad It is, it's consistently doing something the user didn't asked to and in most cases doesn't want. In many cases the model is completely unusable.
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Hear hear! I don't understand why it has persistent mindshare, it's not even trained for chat. Meanwhile StableLM 3B runs RAG in my browser, on my iPhone, on my Pixel ..
How have you been using RAG in your browser/on your phones?
Idea is usage-based charging for non-local and a $5/month sub for syncing.
keep an eye on @jpohhhh on Twitter if you're interested
now that I got it on web, I'm hoping to at least get a PoC up soon. I've open-sourced the consitutent parts as FONNX and FLLAMA, Flutter libraries that work on all platforms. FONNX has embeddings, FLLAMA has llama.
Tried inference with the 7B model and without flash attention this is soooooo slow. With flash attention the fine-tunning requires A100 or H100. Also the inference doesn't always stop generating resulting in garbage being added to the response.
We have implementations in different ML frameworks, so I am not quite sure which one you are referring to. Would you like to file a bug at the relevant GitHub repo?
I dug a bit deeper and it seems that with transformers==4.37.0 everything works fine with other HF hosted models (like Llama) but you'll rightfully get this when trying to use Gemma:
ImportError: cannot import name 'GemmaForCausalLM' from 'transformers'
After installing transformers==4.38.0 the fine-tunning speed of Llama drops to 25% (?!?) of what used to be for a reason that I think HF should fix. Testing Gemma it seems I'm hitting a hardware limit as Gemma has a hidden size which is bigger than the available CUDA cores. This seems to make both inference & fine-tunning about 25 times slower than similarly sized Llama 7B. I guess some operations have to be broken down in multiple round trips to the GPU due to my low CUDA core count.
All in all, even if HF fixes the recently introduced slowdown, Gemma seems to be fine-tuneable in reasonable amount of time only by the lucky ones with access to A100/H100.
EDIT: I managed to hack my env to be able to run inference on Gemma with transformers==4.37.0 by keeping the necessary classes in loaded in RAM. It works about 4x faster but still very slow. And both the 7B and the 2B versions behave the same way.
EDIT2: I tried latest transformers from main branch (4.39.0.dev) and behaves the same as 4.38.0.
Earlier quoted context omitted.
More useful would be a precise characterization of the type and balance of the ideological fine tuning. They include performance benchmarks. End-users should also be aware of what thoughts are permitted in these constructs. Why omit this information?
> End-users should also be aware of what thoughts are permitted in these constructs. Why omit this information? Can you define that in a way that's actually testable? I can't, and I've been thinking about "unthinkable thoughts" for quite some time now: https://kitsunesoftware.wordpress.com/2018/06/26/unlearnable...
* List of topics that are "controversial" (models tend to evade these)
* List of arguments that are "controversial" (models wont allow you to think differently. For example, models would never say arguments that "encourage" animal cruelty)
* On average, how willing is the model to take a neutral position on a "controversial" topic (sometimes models say something along the lines of "this is on debate", but still lean heavily towards the less controversial position instead of having no position at all. For example, if you ask it what "lolicon" is, it will tell you what it is and tell you that japanese society is moving towards banning it)
edit: formatting
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…
This is actually not that unusual. Stable Diffusion's license, CreativeML Open RAIL-M, has the exact same clause: "You shall undertake reasonable efforts to use the latest version of the Model." Obviously updating the model is not very practical when you're using finetuned versions, and people still use old versions of Stable Diffusion. But it does make me fear the possibility that if they ever want to "revoke" every…