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OpenLLM-France releases Luciole-23B-Instruct-1.1, an open-source multilingual instruction-tuned language model under Apache 2.0, with smaller 8B and 1B variants also available.
This paper investigates how prompt robustness varies between objective and subjective questions in LLM evaluations, finding that sensitivity to prompt changes depends on question type, prompt change, and model.
fable-traces is a compact instruction-tuned language model based on Qwen3-4B-Instruct-2507, tuned for short conversational replies and runs on a single mid-range GPU. Released under Apache 2.0.
NousResearch teases a new model release with a 'choose your own' theme, likely an open-source instruction-tuned model.
LoopCoder-V2 is a 7B instruction-tuned code model built on the Parallel Loop Transformer (PLT), demonstrating non-monotonic test-time scaling with two loops providing the best gain-cost trade-off and significant improvements over baselines on code generation and reasoning benchmarks.
Google DeepMind releases Gemma 4 models optimized with Quantization-Aware Training (QAT) in multiple formats including GGUF, enabling high quality with reduced memory requirements.
This paper characterizes compositional literary primitives in instruction-tuned LLMs using sparse autoencoders, discovering feature classes for self, style, and affect that enable emotion steering across two architectures.
ServiceNow releases SuperApriel-15B-Instruct, a single 15B checkpoint offering 8 mixer presets that trade between 1× and 10.7× decode throughput while maintaining up to 96% quality on 32K contexts.
Google DeepMind releases Gemma 4, a family of open-weight multimodal models ranging from 2.3B to 31B parameters with support for text, image, video, and audio inputs. The models feature 256K context windows, MoE and dense architectures, enhanced reasoning capabilities, and are optimized for deployment across devices from mobile to servers.