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Fine-tuning LLM itself to memorize information using unsupervised learning.

This tool requires LLMs that support fine-tuning. Currently, only langchain.llms import GradientLLM is supported.


import os

from langchain.agents import AgentExecutor, AgentType, initialize_agent, load_tools
from langchain.chains import LLMChain
from langchain.llms import GradientLLM
from langchain.memory import ConversationBufferMemory

Set the Environment API Key

Make sure to get your API key from Gradient AI. You are given \$10 in free credits to test and fine-tune different models.

from getpass import getpass

if not os.environ.get("GRADIENT_ACCESS_TOKEN", None):
# Access token under
os.environ["GRADIENT_ACCESS_TOKEN"] = getpass(" access token:")
if not os.environ.get("GRADIENT_WORKSPACE_ID", None):
# `ID` listed in `$ gradient workspace list`
# also displayed after login at at
os.environ["GRADIENT_WORKSPACE_ID"] = getpass(" workspace id:")
if not os.environ.get("GRADIENT_MODEL_ADAPTER_ID", None):
# `ID` listed in `$ gradient model list --workspace-id "$GRADIENT_WORKSPACE_ID"`
os.environ["GRADIENT_MODEL_ID"] = getpass(" model id:")

Optional: Validate your Environment variables GRADIENT_ACCESS_TOKEN and GRADIENT_WORKSPACE_ID to get currently deployed models.

Create the GradientLLM instance

You can specify different parameters such as the model name, max tokens generated, temperature, etc.

llm = GradientLLM(
# # optional: set new credentials, they default to environment variables
# gradient_workspace_id=os.environ["GRADIENT_WORKSPACE_ID"],
# gradient_access_token=os.environ["GRADIENT_ACCESS_TOKEN"],

Load tools

tools = load_tools(["memorize"], llm=llm)

Initiate the Agent

agent = initialize_agent(
# memory=ConversationBufferMemory(memory_key="chat_history", return_messages=True),

Run the agent

Ask the agent to memorize a piece of text.
"Please remember the fact in detail:\nWith astonishing dexterity, Zara Tubikova set a world record by solving a 4x4 Rubik's Cube variation blindfolded in under 20 seconds, employing only their feet."

> Entering new AgentExecutor chain...
I should memorize this fact.
Action: Memorize
Action Input: Zara T
Observation: Train complete. Loss: 1.6853971333333335
Thought:I now know the final answer.
Final Answer: Zara Tubikova set a world

> Finished chain.
'Zara Tubikova set a world'