121–130 of 507 posts
How does the ecosystem work? Have things converged and standardized enough where it's "easy" (lol, with tooling) to swap out parts such as weights to fit your needs? Do you need to autogen new custom kernels to fix said things? Super cool stuff.
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We are always figuring out what parameter size makes sense. The decision is always a mix between how good we can make the models from a technical aspect, with how good they need to be to make all of you super excited to use them. And its a bit of a challenge what is an ever changing ecosystem. I'm personally curious is there a certain parameter size you're looking for?
how good they need to be to make all of you super excited to use them Isn't that more dictated by the competition you're facing from Llama and Qwent?
I personally strive to build software and models provides provides the best and most usable experience for lots of people. I did this before I joined google with open source, and my writing on "old school" generative models, and I'm lucky that I get to this at Google in the current LLM era.
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Thinking vs non-thinking. There'll be a token cost there. But still fairly remarkable!
Is there a reason we can't use thinking completions to train non-thinking? i.e. gradient descent towards what thinking would have answered?
Google might not have the best coding models (yet) but they seem to have the most intelligent and knowledgeable models of all especially Gemini 3.1 Pro is something. One more thing about Google is that they have everything that others do not: 1. Huge data, audio, video, geospatial 2. Tons of expertise. Attention all you need was born there. 3. Libraries that they wrote. 4. Their own data centers and cloud. 4. Most of…
Gemma 3 E4E runs very quick on my Samsung S26, so I am looking forward to trying Gemma 4! It is fantastic to have local alternatives to frontier models in an offline manner.
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I recently canceled my Google One subscription because getting accurate answers out of Gemini for chat is basically impossible afaict. Whether I enable thinking makes no difference: Gemini always answers me super quickly, rarely actually looks something up, and lies to me. It has a really bad unchecked hallucination problem because it prioritizes speed over accuracy and (astonishingly, to me) is way more hesitant to…
Recently I had a pretty basic question about whether there was a Factorio mod for something so decided to ask it to Gemini, it hallucinated not one but two sadly non-existing mods. Even Grok is better at search.
The other thing that kills me about Gemini is that the voice recognition is god-awful. All of the chat interfaces I use have transcriptions that include errors (which the bot usually treats unthinkingly as what I actually said, instead of acting as if we may be using a fallible voice transcription), but Gemini's is the worst by far. I often have to start conversations over because of such badly mangled transcriptions.
The accuracy problems are the biggest and most important frustrations, but I also find Gemini insufferably chummy and condescending. It often resorts to ELI5 metaphors when describing things to me where the whole metaphor is based on some tenuous link to some small factoid it thinks it remembers about my life.
The experiences it seems people get out of Gemini today seem like a waste of a frontier lab's resources tbf. If I wanted fast but lower quality I'd go to one of the many smaller providers that aren't frontier labs because lots of them are great at speed and/or efficiency. (If I wanted an AI companion, Google doesn't seem like the right choice either.)
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Lm arena is so easy to game that it's ceased to be a relevant metric over a year ago. People are not usable validators beyond "yeah that looks good to me", nobody checks if the facts are correct or not.
It's easy to game and human evaluation data has its trade-offs, but it's way easier to fake public benchmark results. I wish we had a source of high quality private benchmark results across a vast number of models like Lmarena. Having high quality human evaluation data would be a plus too.
Comparison of Gemma 4 vs. Qwen 3.5 benchmarks, consolidated from their respective Hugging Face model cards: | Model | MMLUP | GPQA | LCB | ELO | TAU2 | MMMLU | HLE-n | HLE-t | |----------------|-------|-------|-------|------|-------|-------|-------|-------| | G4 31B | 85.2% | 84.3% | 80.0% | 2150 | 76.9% | 88.4% | 19.5% | 26.5% | | G4 26B A4B | 82.6% | 82.3% | 77.1% | 1718 | 68.2% | 86.3% | 8.7% | 17.2% | | G4 E4B |…
Wild differences in ELO compared to tfa's graph: https://storage.googleapis.com/gdm-deepmind-com-prod-public/... (Comparing Q3.5-27B to G4 26B A4B and G4 31B specifically) I'd assume Q3.5-35B-A3B would performe worse than the Q3.5 deep 27B model, but the cards you pasted above, somehow show that for ELO and TAU2 it's the other way around... Very impressed by unsloth's team releasing the GGUF so quickly, if that's lik…
Hi all! I work on the Gemma team, one of many as this one was a bigger effort given it was a mainline release. Happy to answer whatever questions I can
On LM Studio I'm only seeing models/google/gemma-4-26b-a4b Where can I download the full model? I have 128GB Mac Studio
Google might not have the best coding models (yet) but they seem to have the most intelligent and knowledgeable models of all especially Gemini 3.1 Pro is something. One more thing about Google is that they have everything that others do not: 1. Huge data, audio, video, geospatial 2. Tons of expertise. Attention all you need was born there. 3. Libraries that they wrote. 4. Their own data centers and cloud. 4. Most of…
I recently canceled my Google One subscription because getting accurate answers out of Gemini for chat is basically impossible afaict. Whether I enable thinking makes no difference: Gemini always answers me super quickly, rarely actually looks something up, and lies to me. It has a really bad unchecked hallucination problem because it prioritizes speed over accuracy and (astonishingly, to me) is way more hesitant to…