Add models using LlamaForCausalLM to _EMBEDDING_MODELS

Signed-off-by: Went-Liang <[email protected]>

vllm/model_executor/models/registry.py

@@ -100,11 +100,27 @@
     "Qwen2ForRewardModel": ("qwen2_rm", "Qwen2ForRewardModel"),
     "Qwen2ForSequenceClassification": (
         "qwen2_cls", "Qwen2ForSequenceClassification"),
+    "LlamaForCausalLM": ("llama", "LlamaForCausalLM"),
+    "Phi3ForCausalLM": ("phi3", "Phi3ForCausalLM"),
+    "DeciLMForCausalLM": ("decilm", "DeciLMForCausalLM"),
     # [Multimodal]
     "LlavaNextForConditionalGeneration": ("llava_next", "LlavaNextForConditionalGeneration"),  # noqa: E501
     "Phi3VForCausalLM": ("phi3v", "Phi3VForCausalLM"),
 }
 
+def add_embedding_models(base_models, embedding_models):
+    with_pooler_method_models = {}
+    embedding_models_name = embedding_models.keys()
+    for name, (path, arch) in base_models.items():
+        if arch in embedding_models_name:
+            with_pooler_method_models[name] = (path, arch)
+    return with_pooler_method_models
+
+_EMBEDDING_MODELS = {
+    **add_embedding_models(_TEXT_GENERATION_MODELS, _EMBEDDING_MODELS),
+    **_EMBEDDING_MODELS,
+}
+
 _MULTIMODAL_MODELS = {
     # [Decoder-only]
     "Blip2ForConditionalGeneration": ("blip2", "Blip2ForConditionalGeneration"),