Tracked topic
…Tool calling that's baked into the architecture This model is great for practical uses One of the things that's held back local LLMs for agentic workflows is tool calling. Most…
…Now, we have smaller, more efficient models that work in tandem with better optimization tools. This ensures that if you want to get started with local LLMs , you don't always have…
…At least it actually called the tool; models this size aren't usually reliable at invoking MCP tools. And its writing was also at least coherent. LFM2.5-1.2B-Instruct It…
…For most people, that's meant accepting a smaller, quantized, or otherwise compromised version of the kind of model they could also access through a cloud API. Whether it's a smaller…
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…With 32GB, you can run heavily quantized 70B parameter models, or fit something like Qwen3.5-27B at a decent quantization. 32GB of VRAM is a lot, and it's a step…
…For smart home automation tasks, the Qwen2.5 or Qwen3 at 9B parameters hit the sweet spot. It works comfortably with the VRAM limits of a mid-range GPU with some quantization…
…You need a workstation that costs as much as a car, and even then, you're often quantizing the thing into oblivion just to fit it. That's why I've been…
…On the small stuff, it's incredibly fast, and quantized 7B and 13B models run faster than I can read the output. Even a 30B model in 4-bit quantization sits in…
…I have my suspicions that Qwen 3 Coder Next is trained on Claude Code tool calling specifically, as in other harnesses, it kept trying to make tool calls that looked like Claude…