Ornith-1.0-9B vs. Qwen3.5 vs. Gemma4: A Local AI Battle Royale

说明与用途

本地基准测试显示，Ornith‑1.0‑9B 在 JSON 输出简洁性上优于其父模型，但在代码修复和文件名处理上出现细微错误。

问题

Assess whether post‑training for agentic coding actually improves a model’s practical coding and tool‑calling performance on a laptop.

结果

Ornith’s fine‑tuning yields a concise JSON output and matches the base model on logic tasks, but it fails on a subtle off‑by‑one bug in a code‑fix prompt and repeats a shell‑scripting filename‑space bug present in Qwen3.5.

工作流

• Pull GGUF models via Ollama
• Configure same quantization (Q4_K_M) for all models
• Run each model through five identical prompts with temperature 0.2
• Unload a model before loading the next to avoid CPU contention
• Collect and compare outputs for correctness, speed, and token count

证据

Ornith is a 9B model post‑trained specifically for agentic coding — terminal use, tool‑calling, SWE‑Bench‑style tasks.

来源

All three correctly diagnosed the off‑by‑one (range(len(nums) - k)) excludes the final window.

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Ornith rewrote it as a rolling‑sum optimization… it returns 20 instead of 11.

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Gemma4: find /var/log -type f -size +50M -exec du -h {} + | sort -hr — handles filenames with spaces correctly.

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All three got the values right, but Ornith's answer was the tightest: {"name":"Maria Gonzalez","age":34,"city":"Austin"} in 16 tokens.

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