Qwen3.6-35B-A3B Tool Calling Benchmark: ByteShape vs Unsloth GGUFs
Original: Qwen3.6-35B-A3B tool calling benchmark: ByteShape vs. Unsloth GGUFs, KV cache quants & long context performance
A tool-calling benchmark finds q8_0 KV cache nearly free, q4_0 worse, and long context broadly harmful.
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.
這篇貼文是一個針對 Qwen3.6-35B-A3B 的工具呼叫能力基準測試，重點不在一般 perplexity 或速度，而是模型在實際 tool calling 場景下的輸出品質。作者受到先前 r/LocalLLaMA 討論啟發，想回答三個問題：ByteShape 宣稱約 4bpw 量化能保留未量化模型 99% 以上基準分數是否可信；KV cache 量化在真實任務中是否會傷害表現；以及長上下文是否會改變結論。
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