201–210 of 421 posts
Am I missing something or are the Ollama versions of this ( https://ollama.com/library/gemma4/tags ) text-only for now?
What's Google's business case for releasing open models? Don't get me wrong, I am grateful and appreciative of these releases. I'm trying to understand how it fits into their bigger picture as a for profit company? Are they not helping competitors build on the novel technology they have developed? Is it simply goodwill and/or marketing? Or am I missing something strategic?
Plus every open model undermines their local competition by furthering open research and reduces moats, especially since Gemini as a frontier model isn't really competitive with GPT nor Claude for most applications.
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There is demand for US open models.
I sincerely wonder why. Chinese censorship is only really relevant if you're doing anti China stuff, which is to say never, while the Western kind of model censorship ( a combination of copyrights and general fairness ) are something everyone's had to work around at least once, even if just for writing an interesting story.
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This is one https://post.bot/
What model is it using?
I expect it to be something like https://huggingface.co/OuteAI/Llama-OuteTTS-1.0-1B-GGUF
I ran the Q4 quant (used with llama.cpp) though my "minesweeper" vibe-coding benchmark: https://senko.net/vibecode-bench/2026/minesweeper-gamma-4-12... The result is decent, but it had a few bizzare/trivial syntax errors I had to fix manually: it would do an extra closing bracket or paren a few times, and wanted to separate function definitions with comma. Not sure what that was about, but otherwise the output run ju…
For 16GB laptops, Qwen 3.5 9B is the undisputed champ.
Gemma 4 31B is the top dog at small model coding, but is dense so it needs ~48GB unified RAM for full context. If you want decent coding on a laptop you need a lot of RAM. But this shouldn't be surprising, dev machines have always needed lots of resources.
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I haven't tried this model yet, but I can run Gemma 31B w/ the MTP drafter in pure CPU at about 10tok/s so this should run at about 20-30tok/s on a decent CPU, it'll probably run at >50tok/s on any Mac that can fit it, and lots of people have a gaming GPU with enough VRAM. In terms of access to hardware being a gate, it's one you can hop pretty easily.
Could you outline how you are running the MTP drafters? I've tried LM Studio but no dice there. I'm probably missing something but I think llama.cpp and Ollama can't do it yet either?
https://github.com/ggml-org/llama.cpp/pull/23398
Please don't use Ollama, it's a bad actor in the OSS community.
I ran the Q4 quant (used with llama.cpp) though my "minesweeper" vibe-coding benchmark: https://senko.net/vibecode-bench/2026/minesweeper-gamma-4-12... The result is decent, but it had a few bizzare/trivial syntax errors I had to fix manually: it would do an extra closing bracket or paren a few times, and wanted to separate function definitions with comma. Not sure what that was about, but otherwise the output run ju…
> It roughly compares with GPT-4.1 (!!), released 14 months ago I think the mayor win for coding was reasoning. That's why such a small model can match GPT-4.1 in coding, but I suspect that GPT-4.1 still wins in general world knowledge due to bigger size.
Encyclopedic knowledge matters relatively little in perspective, given the expectable future developments: even the more knowledgeable of us will use that knowledge for reasoning and intuition (and we will have absorbed the intellectual keys during our training), but under our professional hat we should in theory be ready to go "I stand corrected" and "more precisely" with the actual data at hand.
I.e.: for the encyclopedic knowledge needed, the /understander/ will have a RAG subsystem and a corpus of knowledge to inquire upon processing queries.
(Corroboration: we can't delirate, and neither can the machine...)
The big story here is the encoder-free part, which I still don't fully understand. > Vision: We replaced Gemma 4’s vision encoder with a lightweight embedding module consisting of a single matrix multiplication, positional embedding and normalizations. That's technically encoding, just without using a dedicated model for it like SigLIP? The Developer's Guide elaborates, it's still a 35M layer which I am curious is ro…
This is just early fusion basically. FAIR did this 2 years ago now: https://arxiv.org/abs/2405.09818 I've been waiting for something like this to be released since then. The annoying thing is that chameleon was multi-modal out based on the same principles, but this model is just inputs... (I'm curious how they did pre-training without having multi-modal outputs as well. I wonder if they just chopped them off rather t…
Standard approach for training MM-LLMs is we train the encoder first, there are O(2-10B) good images on the internet, so encoder needs to see each image O(10-100) times, that is O(100T) tokens, which is more than the entire pre-training budget for most runs. That is the reason we train the encoder separately (smaller model, 2B active vs 30B or 200B active LLM); there is nothing magical about training the encoder and LLM together, it is just more token-efficient to train the image modality first.
Just tried this out. Jesus Christ. Google does some things so well.