b8601
<details open=""> <p>common : gpt-oss handle builtin and unsolicited tool calls (<a class="issue-link js-issue-link" data-error-text="Failed to load title" data-id="4176521984" data-permission-text="Title is private" data-url="https://github.com/ggml-org/llama.cpp/issues/21213" data-hovercard-type="pull_request" data-hovercard-url="/ggml-org/llama.cpp/pull/21213/hovercard" href="https://github.com/ggml-org/llama.cpp/pull/21213">#21213</a>)</p> </details> <p><strong>macOS/iOS:</strong></p> <ul> <li><a href="https://github.com/ggml-org/llama.cpp/releases/download/b8601/llama-b8601-bin-macos-arm64.tar.gz">macOS Apple Silicon (arm64)</a></li> <li><a href="https://github.com/ggml-org/llama.cpp/releases/download/b8601/llama-b8601-bin-macos-x64.tar.gz">macOS Intel (x64)</a></li> <li><a href="http
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Evaluation of gNB Monostatic Sensing for UAV Use Case
arXiv:2604.02205v1 Announce Type: new Abstract: 3GPP Release 19 has initiated the standardization of integrated sensing and communications (ISAC), including a channel model for monostatic sensing, evaluation scenarios, and performance assessment methodologies. These common assumptions provide an important basis for ISAC evaluation, but reproducible end-to-end studies still require a transparent sensing implementation. This paper evaluates 5G New Radio (NR) base station (gNB)-based monostatic sensing for the Unmanned Aerial Vehicle (UAV) use case using a 5G NR downlink Cyclic Prefix-Orthogonal Frequency Division Multiplexing (CP-OFDM) waveform and positioning reference signals (PRS), following 3GPP Urban Macro-Aerial Vehicle (UMa-AV) scenario assumptions. We present an end-to-end processing
v4.3.1
Changes Gemma 4 support with full tool-calling in the API and UI. 🆕 ik_llama.cpp support : Add ik_llama.cpp as a new backend through new textgen-portable-ik portable builds and a 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
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v4.3.1
Changes Gemma 4 support with full tool-calling in the API and UI. 🆕 ik_llama.cpp support : Add ik_llama.cpp as a new backend through new textgen-portable-ik portable builds and a 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
From SWE-ZERO to SWE-HERO: Execution-free to Execution-based Fine-tuning for Software Engineering Agents
arXiv:2604.01496v1 Announce Type: new Abstract: We introduce SWE-ZERO to SWE-HERO, a two-stage SFT recipe that achieves state-of-the-art results on SWE-bench by distilling open-weight frontier LLMs. Our pipeline replaces resource-heavy dependencies with an evolutionary refinement strategy: (1) SWE-ZERO utilizes large-scale, execution-free trajectories to master code semantics and repository-level reasoning, and (2) SWE-HERO applies targeted, execution-backed refinement to transition these semantic intuitions into rigorous engineering workflows. Our empirical results set a new benchmark for open-source models of comparable size. We release a dataset of 300k SWE-ZERO and 13k SWE-HERO trajectories distilled from Qwen3-Coder-480B, alongside a suite of agents based on the Qwen2.5-Coder series.
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
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