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Coal-SQL is a post-training framework for Text-to-SQL that uses coverage-guided augmentation and failure-driven learning to improve SQL generation, achieving 64.9% execution accuracy on the BIRD benchmark with only 12.6K examples.
The article explains how to build a local decision engine using open-source LLMs and SGLang, enabling efficient scoring and probability distributions for fixed choices without full text generation, compared to systems like Jev.
A new web tool, Chat Template Playground, lets users visualize how different open-source LLMs render their chat templates, highlighting differences in prompting and tokenization.
A developer splits their AI agent's LLM calls into a cheap router model (GPT-OSS 120B) for tool-picking and a premium model (gpt-5.4) for synthesis, cutting costs by ~78% while maintaining output quality.
The article shares a performance optimization trick for llama.cpp, showing that increasing the micro-batch size (`-ub`) combined with partial CPU offloading (`--n-cpu-moe`) can drastically improve prompt processing speed for large models like gpt-oss-120b on consumer GPUs.