merge: resolve add/add on clef-candle.yaml, keep imagePullPolicy Always

This commit is contained in:
2026-11-05 10:06:26 +08:00
+4 -10
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@@ -7,12 +7,7 @@ metadata:
app.kubernetes.io/part-of: llm-serving app.kubernetes.io/part-of: llm-serving
app: clef-candle app: clef-candle
spec: spec:
# HOLD AT 0 until candle-vllm:sm70 contains the GDN conv fix (candle-vllm PR #5). replicas: 1
# An older :sm70 zeroes the key channel band, starves the delta rule and makes
# every linear-attention layer return zero. The response is still HTTP 200 and
# well formed, so the failure is silent. Raise to 1 only after CI publishes a
# :sm70 build from the merge that carries the fix.
replicas: 0
strategy: strategy:
# One replica holds one GPU, so Recreate keeps the rollout to a single # One replica holds one GPU, so Recreate keeps the rollout to a single
# weights load instead of needing a second card for a surge pod. # weights load instead of needing a second card for a surge pod.
@@ -42,10 +37,9 @@ spec:
# and makes every linear-attention layer return zero. The symptom is # and makes every linear-attention layer return zero. The symptom is
# a well-formed /v1/systemone response with wrong answers, not an error. # a well-formed /v1/systemone response with wrong answers, not an error.
image: forgejo.riotpiao.com/riotpiao-poimen/candle-vllm:sm70 image: forgejo.riotpiao.com/riotpiao-poimen/candle-vllm:sm70
# :sm70 is a moving tag. With the default IfNotPresent a rebuilt image # :sm70 is a moving tag. Default IfNotPresent keeps a cached copy and a
# is never pulled while any node holds a cached copy of that tag, so the # rebuilt image never reaches the pod, so a merged fix looks like it did
# cluster keeps serving the old binary and the fix looks like it did not # not land. Always forces the pull on every pod start.
# land. Always forces the pull on every pod start.
imagePullPolicy: Always imagePullPolicy: Always
command: ["/usr/local/bin/candle-vllm"] command: ["/usr/local/bin/candle-vllm"]
# clef-flash is a hybrid Qwen3_5 model: 8 full-attention layers plus 24 # clef-flash is a hybrid Qwen3_5 model: 8 full-attention layers plus 24