Mark Zuckerberg, ‘Frustrated’ by Llama 4, Assembles Meta ‘Superintelligence’ Team - retailwire.com
<a href="https://news.google.com/rss/articles/CBMib0FVX3lxTE5QVml1aWhoMEV0ZGZoVXV0Z0FUVlY3WDFaWU5MSWxmTlJuZXd5ZExIaXZCdHBRcU9xOHpETUpjSGtzblpaT3Mzb1JROHJZTVNHWW1QbjJXWUpuTDN1ajRKVktLNVZDbmpJMTBZMjlRWQ?oc=5" target="_blank">Mark Zuckerberg, ‘Frustrated’ by Llama 4, Assembles Meta ‘Superintelligence’ Team</a> <font color="#6f6f6f">retailwire.com</font>
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A Quick Note on Gemma 4 Image Settings in Llama.cpp
In my last post, I mentioned using --image-min-tokens to increase the quality of image responses from Qwen3.5 . I went to load Gemma 4 the same way, and hit an error: [58175] srv process_chun: processing image... [58175] encoding image slice... [58175] image slice encoded in 7490 ms [58175] decoding image batch 1/2, n_tokens_batch = 2048 [58175] /Users/socg/llama.cpp-b8639/src/llama-context.cpp:1597: GGML_ASSERT((cparams.causal_attn || cparams.n_ubatch > = n_tokens_all ) "non-causal attention requires n_ubatch >= n_tokens" ) failed [58175] WARNING: Using native backtrace. Set GGML_BACKTRACE_LLDB for more info. [58175] WARNING: GGML_BACKTRACE_LLDB may cause native MacOS Terminal.app to crash. [58175] See: https://github.com/ggml-org/llama.cpp/pull/17869 [58175] 0 libggml-base.0.9.11.dylib 0
v4.3
Changes ik_llama.cpp support : Add ik_llama.cpp as a new backend: new textgen-portable-ik portable builds, new --ik flag for full installs. ik_llama.cpp is a fork by the author of the imatrix quants, including support for new quant types, significantly more accurate KV cache quantization (via Hadamard KV cache rotation, enabled by default), and optimizations for MoE models and CPU inference. API: Add echo + logprobs for /v1/completions . The completions endpoint now supports the echo and logprobs parameters, returning token-level log probabilities for both prompt and generated tokens. Token IDs are also included in the output via a new top_logprobs_ids field. Further optimize my custom gradio fork, saving up to 50 ms per UI event (button click, etc). Transformers: Autodetect torch_dtype fr
Google Gemma 4: Everything Developers Need to Know
Google dropped Gemma 4 on April 2, 2026, A full generational jump in what open models can do at their parameter range and the first time in the Gemma family's history that one ships under Apache 2.0, meaning commercial use without permission-seeking. Some context: since Gemma's first generation, developers have downloaded the models over 400 million times and built more than 100,000 variants. Four Models, One Family Gemma 4 is a family of four, each aimed at a different point in the hardware spectrum. E2B : Effective 2 billion active parameters. Runs on smartphones, Raspberry Pi, Jetson Orin Nano. 128K context window. Handles images, video, and audio. Built for battery and memory efficiency. E4B : Effective 4 billion active parameters. Same hardware targets, higher reasoning quality. About
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