A Reddit user on r/LocalLLaMA says qwen3.6-27b can fall into repeated tool-call loops during use. They report spending two days adjusting parameters such as temperature and top-k without resolving the issue. The post is a troubleshooting question rather than a confirmed bug report, asking whether other local model users have seen similar behavior.
A r/LocalLLaMA user shared informal impressions of JetBrains Mellum 2, focusing on local coding-style tasks and tool calls. On an AMD Radeon RX 7900 XT with llama.cpp Vulkan and 131K context, the model reportedly generated around 111 tokens/s and stayed above 100 tokens/s near full context. The author stresses this is not a scientific benchmark, but a practical workflow-oriented test.
The post benchmarks eight Qwen3.6-35B-A3B GGUF quants from ByteShape and Unsloth using llama.cpp and tool-eval-bench. It compares f16, q8_0, and q4_0 KV cache quantization under short and long-context pressure, totaling 144 runs and roughly 300 GPU-hours. The author reports no clear ByteShape versus Unsloth winner, q8_0 as close to a free lunch, q4_0 as weaker, and long context as a major tool-calling degradation factor.
On January 20, 2026, Vercel officially announced the launch of "skills" — an open AI Agent skill ecosystem — in its Changelog. As AI applications rapidly…
When building AI applications, developers often fall into the trap of "more tools equals a smarter Agent." In early versions of Vercel's AI assistants and…
This technical post from Vercel dives deep into the challenges they faced and the hands-on lessons they learned while developing and deploying AI Agents. As AI…
As large language models (LLMs) have evolved, AI applications have moved beyond simple "question-and-answer conversations" toward "AI Agents" capable of…
With the release of Qwen-3, Hugging Face's official blog published an in-depth breakdown of its chat template. Chat templates are the critical bridge…
This official Hugging Face blog post provides a detailed guide on how to use open-source large language models (LLMs) as intelligent agents within LangChain…