4 ms·

Is there something that will allow me to run this locally?! This is exactly what I want to do but no clue how to pipe my data into llama. Any pointers will be h

Is there something that will allow me to run this locally?! This is exactly what I want to do but no clue how to pipe my data into llama. Any pointers will be highly appreciated!

TrapLord_Rhodo 4y ago
Locally? It's not really possible to run the model locally, but finetuning on directories is possible. Here's the code: It's runs on three different directorys to give me three different 'answers' using an excel sheet to pull the q's from. def excelGPT(self, dir, excel_file, sheet): #my GPT Key os.environ['OPENAI_API_KEY'] = 'sk- #Working Directory for training # root = root_folder1 = documents1 = SimpleDirectoryReader(root_folder1).load_data() index1 = GPTSimpleVectorIndex(documents1) root_folder2 = documents2 = SimpleDirectoryReader(root_folder2).load_data() index2 = GPTSimpleVectorIndex(documents2) root_folder3 = documents3 = SimpleDirectoryReader(root_folder3).load_data() index3 = GPTSimpleVectorIndex(documents3) file_name = dir + excel_file df = pd.read_excel(file_name, sheet_name=sheet) GSA_answer_array = [] basic_answer_array = [] QA_answer_array = [] df_series = df.iloc[:,0] for i,x in enumerate(df_series): print("This is the index ", i) print(x) GSA_response = index1.query(x) basic_response = index2.query(x) QA_response = index3.query(x) GSA_answer_array.append(str(GSA_response)) basic_answer_array.append(str(basic_response)) QA_answer_array.append(str(QA_response)) self.zip_to_docv2(dir, "Gippie_Response.docx", df_series, GSA_answer_array, basic_answer_array, QA_answer_array)
- syntaxing 4y ago
  This is awesome! Thank you for the code!
ukuina 4y ago
This, so much this! There are so many projects that claim to do this, but end up piping data to OpenAI. Can someone who has managed to get this set up locally send some pointers our way?

Is there something that will allow me to run this locally?! This is exactly what I want to do but no clue how to pipe my data into llama. Any pointers will be highly appreciated!

TrapLord_Rhodo 4y ago

Locally? It's not really possible to run the model locally, but finetuning on directories is possible. Here's the code: It's runs on three different directorys to give me three different 'answers' using an excel sheet to pull the q's from. def excelGPT(self, dir, excel_file, sheet): #my GPT Key os.environ['OPENAI_API_KEY'] = 'sk- #Working Directory for training # root = root_folder1 = documents1 = SimpleDirectoryReader(root_folder1).load_data() index1 = GPTSimpleVectorIndex(documents1) root_folder2 = documents2 = SimpleDirectoryReader(root_folder2).load_data() index2 = GPTSimpleVectorIndex(documents2) root_folder3 = documents3 = SimpleDirectoryReader(root_folder3).load_data() index3 = GPTSimpleVectorIndex(documents3) file_name = dir + excel_file df = pd.read_excel(file_name, sheet_name=sheet) GSA_answer_array = [] basic_answer_array = [] QA_answer_array = [] df_series = df.iloc[:,0] for i,x in enumerate(df_series): print("This is the index ", i) print(x) GSA_response = index1.query(x) basic_response = index2.query(x) QA_response = index3.query(x) GSA_answer_array.append(str(GSA_response)) basic_answer_array.append(str(basic_response)) QA_answer_array.append(str(QA_response)) self.zip_to_docv2(dir, "Gippie_Response.docx", df_series, GSA_answer_array, basic_answer_array, QA_answer_array)

syntaxing 4y ago
This is awesome! Thank you for the code!

ukuina 4y ago

This, so much this! There are so many projects that claim to do this, but end up piping data to OpenAI. Can someone who has managed to get this set up locally send some pointers our way?