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A new quantized AI model, Sharp-Spark-X2.5-4B-GGUF, is released with improvements for agentic coding on small GPUs and limited RAM, enhancing local coding capabilities for less privileged users.
Ai2 released Tmax-27B, a terminal-agent LLM trained with DPPO (RL) on Qwen3.6-27B, and the author provides importance-matrix-calibrated GGUF quantizations that achieve competitive performance on agentic benchmarks even at very low bit-widths, with a grafted MTP draft head for speculative decoding.