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
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I Built a Self-Hosted AI Agent That Runs on a Raspberry Pi
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Same Agents, Different Minds — What 180 Configurations Proved About AI Environment Design
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Beyond the Boardroom: How Decentralized Autonomous Organizations (DAOs) are Reshaping E-commerce
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Gemma 4 Complete Guide: Architecture, Models, and Deployment in 2026
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Running OpenClaw with Gemma 4 TurboQuant on MacAir 16GB
Hi guys, We’ve implemented a one-click app for OpenClaw with Local Models built in. It includes TurboQuant caching, a large context window, and proper tool calling. It runs on mid-range devices. Free and Open source. The biggest challenge was enabling a local agentic model to run on average hardware like a Mac Mini or MacBook Air. Small models work well on these devices, but agents require more sophisticated models like QWEN or GLM. OpenClaw adds a large context to each request, which caused the MacBook Air to struggle with processing. This became possible with TurboQuant cache compression, even on 16gb memory. We found llama.cpp TurboQuant implementation by Tom Turney. However, it didn’t work properly with agentic tool calling in many cases with QWEN, so we had to patch it. Even then, the
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