Gemma 4 has been released
https://huggingface.co/unsloth/gemma-4-26B-A4B-it-GGUF https://huggingface.co/unsloth/gemma-4-31B-it-GGUF https://huggingface.co/unsloth/gemma-4-E4B-it-GGUF https://huggingface.co/unsloth/gemma-4-E2B-it-GGUF https://huggingface.co/collections/google/gemma-4 What’s new in Gemma 4 https://www.youtube.com/watch?v=jZVBoFOJK-Q Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on small models) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages. Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-s
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Beijing mandates internal AI ethics reviews to ensure ‘controllable’ tech
Chinese companies engaging in artificial intelligence activities are required to set up internal “AI ethics review committees” under new rules released by Beijing on Thursday, effective immediately. The notice comes as policymakers look to ensure that fast-paced AI progress can continue in a “healthy” manner amid growing consumer and enterprise adoption. Jointly released by 10 government bodies and institutions including the Ministry of Industry and Information Technology, National Development...
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v4.3.2
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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