341–350 of 507 posts
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Qwen 3.5 27B is dense, so (I think) should be compared to Gemma 4 31B. Or Gemma-4 26B(-A4B) should be compared to Qwen 3.5 35B(-A3B)
Exactly, compare MoE with MoE and dense with dense otherwise it's apples and oranges.
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This is nearly a year old, which is a million years in LLM time.
*8 months That doesn't make parent's claim true or even relevant. And OpenAI could release an open model tomorrow. Nobody knows.
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What causes these? Given how simple the LLM interface is (just completion), why don't teams make a simple, standardized template available with their model release so the inference engine can just read it and work properly? Can someone explain the difficulty with that?
The model does have the format specified but there is no _one_ standard. For this model it’s defined in the [ tokenizer_config.json [0]. As for llama.cpp they seem to be using a more type safe approach to reading the arguments. [0] https://huggingface.co/google/gemma-4-31B-it/blob/main/token...
Featuring the ELO score as the main benchmark in chart is very misleading. The big dense Gemma 4 model does not seem to reach Qwen 3.5 27B dense model in most benchmarks. This is obviously what matters. The small 2B / 4B models are interesting and may potentially be better ASR models than specialized ones (not just for performances but since they are going to be easily served via llama.cpp / MLX and front-ends). Also…
Public benchmarks can be trivially faked. Lmarena is a bit harder to fake and is human-evaluated. I agree it's misleading for them to hyper-focus on one metric, but public benchmarks are far from the only thing that matters. I place more weight on Lmarena scores and private benchmarks.
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So is there something I can take from that table if I have a 24 GB video card? I'm honestly not sure how to use those numbers.
I just tried with llama.cpp RTX4090 (24GB) GGUF unsloth quant UD_Q4_K_XL You can probably run them all. G4 31B runs at ~5tok/s , G4 26B A4B runs at ~150 tok/s. You can run Q3.5-35B-A3B at ~100 tok/s. I tried G4 26B A4B as a drop-in replacement of Q3.5-35B-A3B for some custom agents and G4 doesn't respect the prompt rules at all. (I added in the system prompt as described (but have not spend time checking if the reaso…
I ran these in LM Studio and got unrecognizable pelicans out of the 2B and 4B models and an outstanding pelican out of the 26b-a4b model - I think the best I've seen from a model that runs on my laptop. https://simonwillison.net/2026/Apr/2/gemma-4/ The gemma-4-31b model is completely broken for me - it just spits out "---\n" no matter what prompt I feed it. I got a pelican out of it via the AI Studio API hosted model…
I'm curious about the multimodal capabilities on the E2B and E4B and how fast is it. In ChatGPT right now, you can have a audio and video feed for the AI, and then the AI can respond in real-time. Now I wonder if the E2B or the E4B is capable enough for this and fast enough to be run on an iPhone. Basically replicating that experience, but all the computations (STT, LLM, and TTS) are done locally on the phone. I just…
yeah, it appears to support audio and image input.. and runs on mobile devices with 256K context window!