sleepy/llama.cpp
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convert : support sentence-transformer 5.4 config files (#22087)

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* convert : support sentence-transformer 5.4 config files

* fix: embeddinggemma

* fix: mapping

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* fix: pooling_mode

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
This commit is contained in:
Dowon
2026-04-19 16:25:39 +09:00
committed by GitHub
parent 09b4efa95f
commit 8685e7b075
1 changed files with 13 additions and 5 deletions
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convert_hf_to_gguf.py
+13 -5
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@@ -1850,20 +1850,28 @@ class TextModel(ModelBase):
with open(module_path, encoding="utf-8") as f:
modules = json.load(f)
for mod in modules:
if mod["type"] == "sentence_transformers.models.Pooling":
if mod["type"].endswith("Pooling"):
pooling_path = mod["path"]
break
mode_mapping = {
"mean": gguf.PoolingType.MEAN,
"cls": gguf.PoolingType.CLS,
"lasttoken": gguf.PoolingType.LAST,
}
# get pooling type
if pooling_path is not None:
with open(self.dir_model / pooling_path / "config.json", encoding="utf-8") as f:
pooling = json.load(f)
if pooling["pooling_mode_mean_tokens"]:
if pooling.get("pooling_mode_mean_tokens"):
pooling_type = gguf.PoolingType.MEAN
elif pooling["pooling_mode_cls_token"]:
elif pooling.get("pooling_mode_cls_token"):
pooling_type = gguf.PoolingType.CLS
elif pooling["pooling_mode_lasttoken"]:
elif pooling.get("pooling_mode_lasttoken"):
pooling_type = gguf.PoolingType.LAST
elif (pooling_mode := pooling.get("pooling_mode")) in mode_mapping:
pooling_type = mode_mapping[pooling_mode]
else:
raise NotImplementedError("Only MEAN, CLS, and LAST pooling types supported")
self.gguf_writer.add_pooling_type(pooling_type)
@@ -7180,7 +7188,7 @@ class EmbeddingGemma(Gemma3Model):
with open(modules_file, encoding="utf-8") as modules_json_file:
mods = json.load(modules_json_file)
for mod in mods:
if mod["type"] == "sentence_transformers.models.Dense":
if mod["type"].endswith("Dense"):
mod_path = mod["path"]
# check if model.safetensors file for Dense layer exists
model_tensors_file = self.dir_model / mod_path / "model.safetensors"