Create genparam.py

genparam.py

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+import streamlit as st
+from secretsload import load_stsecrets
+
+credentials = load_stsecrets()
+
+# Model Configuration
+ACTIVE_MODEL = 0   # 0 == SELECTED_MODEL_1 and PROMPT_TEMPLATE_1
+ACTIVE_INDEX = 0   # 0 == VECTOR_INDEX_1 
+
+TYPE = "chat" # so that it uses the chat history
+SELECTED_MODEL_1 = "meta-llama/llama-3-1-70b-instruct"
+SELECTED_MODEL_2 = "mistralai/mistral-large"
+# Pick a model_id, you can find them here https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-api-model-ids.html?context=wx&audience=wdp or switch between the two options up there by adjusting the ACTIVE_MODEL variable between 0 (1) and 1 (2).
+VERIFY = False
+
+# Prompt Configuration
+PROMPT_TEMPLATE_1 = "llama3-instruct (llama-3, 3.1 & 3.2) - system"
+PROMPT_TEMPLATE_2 = "mistral & mixtral v2 tokenizer - system segmented"
+# <pick prompt template from model_family_syntax below> For example "llama3-instruct (llama-3 & 3.1) - user" if you don't use a system prompt.
+BAKE_IN_PROMPT_SYNTAX = True
+
+# Bot Names
+BOT_1_NAME = "PATH-er B."
+BOT_2_NAME = "MOD-ther S."
+BOT_3_NAME = "SYS-ter V."
+
+# Bot Prompts
+
+BOT_1_PROMPT = str(st.secrets["system_prompt_1"])
+BOT_2_PROMPT = str(st.secrets["system_prompt_2"])
+BOT_3_PROMPT = str(st.secrets["system_prompt_3"])
+
+# Vector Indexes
+VECTOR_INDEX_1 = str(st.secrets["vector_index_id_1"])
+VECTOR_INDEX_2 = str(st.secrets["vector_index_id_2"])
+
+# Generation Parameters
+DECODING_METHOD = "greedy" # greedy or sample
+MAX_NEW_TOKENS = 850
+MIN_NEW_TOKENS = 1
+REPETITION_PENALTY = 1.0
+STOP_SEQUENCES = ["<|end_of_text|>","</s>"] # This one is set up for llama models, if you use mistral </s> is the preferred stop_sequence
+
+# Additional Parameters - Only active if you pick sampling in decoding method
+TEMPERATURE = 0.7
+TOP_P = 1.0
+TOP_K = 50
+
+DISPLAY_CHAT_HISTORY = 1  # 0 to not display chat history, 1 to display chat history
+TOKEN_CAPTURE_ENABLED = 0  # Set to 1 to enable token capture preview in the side_bar, 0 to disable