1.7 KiB
1.7 KiB
Documentation
- Class name: LLMLlamaCPPModel
- Category: SALT/Language Toolkit/Loaders
- Output node: False
LLMLlamaCPPModel节点旨在加载和初始化LlamaCPP模型,为自然语言处理任务提供LlamaCPP功能的桥梁。它封装了定位、加载和准备模型使用的过程,包括集成嵌入模型以增强功能。
Input types
Required
- model_name
- 指定要加载的LlamaCPP模型的名称。此名称用于在预定义的可用模型集中定位模型,确保为使用初始化正确的模型。
- Comfy dtype: COMBO[STRING]
- Python dtype: str
Output types
- model
- 输出一个包含已加载LlamaCPP模型、其名称、相关嵌入模型及其名称的字典,为进一步处理或用于NLP任务做好准备。
- Comfy dtype: LLM_MODEL
- Python dtype: Dict[str, Any]
Usage tips
- Infra type:
CPU - Common nodes: unknown
Source code
class LLMLlamaCPP:
"""
@Documentation: https://docs.llamaindex.ai/en/stable/api_reference/llms/llama_cpp/
"""
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"model_name": (LOCAL_FILES, ),
},
}
RETURN_TYPES = ("LLM_MODEL", )
RETURN_NAMES = ("model", )
FUNCTION = "load_model"
CATEGORY = f"{MENU_NAME}/{SUB_MENU_NAME}/Loaders"
def load_model(self, model_name: str) -> Dict[str, Any]:
path = folder_paths.get_full_path('llm', model_name)
llm = LlamaCPP(model_path=path)
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
return ({"llm":llm, "llm_name": model_name,"embed_model": embed_model, "embed_name": "BAAI/bge-small-en-v1.5"},)