merge: resolve add/add on clef-candle.yaml, keep imagePullPolicy Always
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@@ -7,12 +7,7 @@ metadata:
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app.kubernetes.io/part-of: llm-serving
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app.kubernetes.io/part-of: llm-serving
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app: clef-candle
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app: clef-candle
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spec:
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spec:
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# HOLD AT 0 until candle-vllm:sm70 contains the GDN conv fix (candle-vllm PR #5).
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replicas: 1
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# An older :sm70 zeroes the key channel band, starves the delta rule and makes
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# every linear-attention layer return zero. The response is still HTTP 200 and
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# well formed, so the failure is silent. Raise to 1 only after CI publishes a
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# :sm70 build from the merge that carries the fix.
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replicas: 0
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strategy:
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strategy:
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# One replica holds one GPU, so Recreate keeps the rollout to a single
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# One replica holds one GPU, so Recreate keeps the rollout to a single
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# weights load instead of needing a second card for a surge pod.
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# weights load instead of needing a second card for a surge pod.
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@@ -42,10 +37,9 @@ spec:
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# and makes every linear-attention layer return zero. The symptom is
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# and makes every linear-attention layer return zero. The symptom is
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# a well-formed /v1/systemone response with wrong answers, not an error.
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# a well-formed /v1/systemone response with wrong answers, not an error.
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image: forgejo.riotpiao.com/riotpiao-poimen/candle-vllm:sm70
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image: forgejo.riotpiao.com/riotpiao-poimen/candle-vllm:sm70
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# :sm70 is a moving tag. With the default IfNotPresent a rebuilt image
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# :sm70 is a moving tag. Default IfNotPresent keeps a cached copy and a
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# is never pulled while any node holds a cached copy of that tag, so the
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# rebuilt image never reaches the pod, so a merged fix looks like it did
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# cluster keeps serving the old binary and the fix looks like it did not
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# not land. Always forces the pull on every pod start.
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# land. Always forces the pull on every pod start.
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imagePullPolicy: Always
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imagePullPolicy: Always
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command: ["/usr/local/bin/candle-vllm"]
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command: ["/usr/local/bin/candle-vllm"]
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# clef-flash is a hybrid Qwen3_5 model: 8 full-attention layers plus 24
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# clef-flash is a hybrid Qwen3_5 model: 8 full-attention layers plus 24
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