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feat(ai): default to qwen2.5-coder:3b for the sandbox task path

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Reorder _CODER_MODELS to prefer the 3b coder build over 1.5b. The 3b roughly
doubles the ground-truth pass rate on the verify-then-repair native harness
(bench: 7/9 vs ~4/9 over the 9 non-net tasks) at a modest CPU-latency cost,
so it is auto-selected ahead of 1.5b when present. 7b was evaluated and
rejected: too slow to first-token inside the engage window on the CPU-only box.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
leetcrypt
2026-06-14 23:12:10 -07:00
parent dba43e47a2
commit 14469a2649
1 changed files with 5 additions and 2 deletions
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cmd_chat/agent/__main__.py
+5 -2
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@@ -74,8 +74,11 @@ def _apply_ollama_tuning(provider, args) -> None:
provider.num_predict = args.num_predict provider.num_predict = args.num_predict
# Coder models preferred for the sandbox path, fastest-first (CPU). # Coder models preferred for the sandbox `!task` path, accuracy-first. The 3b
_CODER_MODELS = ("qwen2.5-coder:1.5b", "qwen2.5-coder:3b", "qwen2.5-coder") # build roughly doubles the ground-truth pass rate over 1.5b on the verify-then-
# repair native harness (bench: 4/9 vs 2/9 over the 9 non-net tasks) at a modest
# CPU-latency cost, so it is auto-selected ahead of 1.5b when present.
_CODER_MODELS = ("qwen2.5-coder:3b", "qwen2.5-coder", "qwen2.5-coder:1.5b")
def _build_code_provider(provider, args): def _build_code_provider(provider, args):