commit

app.py

@@ -15,11 +15,15 @@ from langchain.embeddings import HuggingFaceEmbeddings, HuggingFaceInstructEmbed
 from langchain.prompts.prompt import PromptTemplate
 from langchain import PromptTemplate, LLMChain
 from langchain.llms import HuggingFacePipeline
+from instruct_pipeline import InstructionTextGenerationPipeline
 
-from training.generate import InstructionTextGenerationPipeline, load_model_tokenizer_for_generate
+from training.generate import load_model_tokenizer_for_generate
+
+# from training.generate import InstructionTextGenerationPipeline, load_model_tokenizer_for_generate
 # from googletrans import Translator
 # translator = Translator()
 
+
 load_dotenv()
 
 embeddings_model_name = os.environ.get("EMBEDDINGS_MODEL_NAME")
@@ -52,14 +56,18 @@ retriever = db.as_retriever(search_kwargs={"k": target_source_chunks})
 # Prepare the LLM
 # callbacks = [StreamingStdOutCallbackHandler()]
 
+
+
 match model_type:
     case "dolly-v2-3b":
         model, tokenizer = load_model_tokenizer_for_generate(model_path)
-        llm = HuggingFacePipeline(
-            pipeline=InstructionTextGenerationPipeline(
-                # Return the full text, because this is what the HuggingFacePipeline expects.
-                model=model, tokenizer=tokenizer, return_full_text=True, task="text-generation", max_new_tokens=model_n_ctx))#, max_new_tokens=model_n_ctx
-                #))
+        generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer)
+        llm = HuggingFacePipeline(pipeline=generate_text)
+        # llm = HuggingFacePipeline(
+        #     pipeline=InstructionTextGenerationPipeline(
+        #         # Return the full text, because this is what the HuggingFacePipeline expects.
+        #         model=model, tokenizer=tokenizer, return_full_text=True, task="text-generation", max_new_tokens=model_n_ctx))#, max_new_tokens=model_n_ctx
+        #         #))
     # case "GPT4All":
     #     llm = GPT4All(model=model_path, n_ctx=model_n_ctx, backend='gptj', callbacks=callbacks, verbose=False)
     case _default: