Merge pull request #185 from kyusonglee/feature/v0.2.1/lite_version

put transformers under __init__ and make consistent start function for cli

Author: zhang_lu

examples/cot/run_lite.py

@@ -67,5 +67,5 @@ def _run(self, query, *args, **kwargs):
 # Initialize and start app client with workflow configuration
 config_path = CURRENT_PATH.joinpath('configs')
 cli_client = DefaultClient(interactor=workflow, config_path=config_path, workers=[SimpleInput()])
-cli_client.start_interaction()
+cli_client.start_interactor()
 

examples/general_dnc/run_lite.py

@@ -51,6 +51,6 @@ def _run(self, query, *args, **kwargs):
 # Initialize and start app client with workflow configuration
 config_path = CURRENT_PATH.joinpath('configs')
 cli_client = DefaultClient(interactor=workflow, config_path=config_path, workers=[SimpleInput()])
-cli_client.start_interaction()
+cli_client.start_interactor()
 #cli_client.start_processor_with_input({"query":"what is the price of iphone?"})
 

omagent-core/src/omagent_core/models/llms/qwen2.py

@@ -2,7 +2,6 @@
 from datetime import datetime
 from typing import Any, Dict, List
 
-from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, pipeline
 from pydantic import Field
 from .schemas import Content, Message
 from ...utils.registry import registry
@@ -32,6 +31,7 @@ class Qwen2LLM(BaseLLM):
 
     def __init__(self, **data: Any) -> None:
         super().__init__(**data)
+        from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, pipeline
         self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
         self.model = AutoModelForCausalLM.from_pretrained(self.model_name).to(self.device)        
 

omagent-core/src/omagent_core/models/llms/qwen2_vl.py

@@ -2,7 +2,6 @@
 from datetime import datetime
 from typing import Any, Dict, List
 
-from transformers import AutoTokenizer, AutoProcessor, Qwen2VLForConditionalGeneration
 from pydantic import Field
 from .schemas import Content, Message
 from ...utils.registry import registry
@@ -31,6 +30,7 @@ class Qwen2_VL(BaseLLM):
 
     def __init__(self, **data: Any) -> None:
         super().__init__(**data)
+        from transformers import AutoTokenizer, AutoProcessor, Qwen2VLForConditionalGeneration
         self.processor = AutoProcessor.from_pretrained(self.model_name,min_pixels=256 * 28 * 28, max_pixels=512 * 28 * 28)
         self.model = Qwen2VLForConditionalGeneration.from_pretrained(self.model_name, torch_dtype="auto", device_map="auto").to(self.device)