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fix(hwfit): keep non-LLM HF collection models out of the LLM catalog (#6731)

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Tracked HF collections also contain time-series forecasters, image
encoders and image/video generators. They were imported as "general"
LLMs and offered for vllm serve. Skip their pipeline tags; embedding,
reranker and RL-tagged LLM repos stay.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
This commit is contained in:
dxrpl
2026-10-15 01:15:08 +02:00
co-authored by Claude Opus 5.5
parent c3819e3cbf
commit 6123b70b5c
2 changed files with 47 additions and 0 deletions
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services/hwfit/hf_discovery.py
+16
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@@ -202,10 +202,26 @@ def _infer_use_case(repo_id, pipeline_tag):
return "general"
# HF collections mix in models no Cookbook LLM backend can serve (vision
# encoders, time-series forecasters, image/video generators). Image models
# have their own registry, so keep these out of the LLM catalog.
_NON_LLM_PIPELINE_TAGS = frozenset({
"time-series-forecasting",
"zero-shot-image-classification",
"image-classification",
"image-feature-extraction",
"text-to-image",
"image-to-video",
"text-to-video",
})
def _entry_from_collection_item(collection, item, source):
repo_id = item.get("id") or ""
if item.get("type") != "model" or not repo_id:
return None
if (item.get("pipeline_tag") or "").strip().lower() in _NON_LLM_PIPELINE_TAGS:
return None
repo_prefix = source.get("repo_prefix")
if repo_prefix and not repo_id.startswith(repo_prefix):
return None